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  • Am J Lifestyle Med
  • v.12(6); Nov-Dec 2018

Lifestyle Medicine: The Health Promoting Power of Daily Habits and Practices

There is no longer any serious doubt that daily habits and actions profoundly affect both short-term and long-term health and quality of life. This concept is supported by literally thousands of research articles and incorporated in multiple evidence-based guidelines for the prevention and/or treatment of chronic metabolic diseases. The study of how habits and actions affect both prevention and treatment of diseases has coalesced around the concept of “lifestyle medicine.” The purpose of this review is to provide an up-to-date summary of many of the modalities fundamental to lifestyle medicine, including physical activity, proper nutrition, weight management, and cigarette smoking cessation. This review will also focus specifically on how these modalities are employed both in the prevention and treatment of chronic diseases including coronary heart disease, diabetes, obesity, and cancer. The review concludes with a Call to Action challenging the medical community to embrace the modalities of lifestyle medicine in the daily practice of medicine.

‘The strength of the scientific literature supporting the health impact of daily habits and actions is underscored by their incorporation into virtually every evidence-based clinical guideline . . .’

An overwhelming body of scientific and medical literature supports the concept that daily habits and actions exert an enormous impact on short-term and long-term health and quality of life. 1 This influence may be either positive or negative. Thousands of studies provide evidence that regular physical activity, maintenance of a healthy body weight, not smoking cigarettes, and following sound nutritional and other health promoting practices all profoundly influence health. The strength of the scientific literature supporting the health impact of daily habits and actions is underscored by their incorporation into virtually every evidence-based clinical guideline stressing the prevention and treatment of metabolically related diseases. 2 - 18 A sampling of the guidelines and consensus statements from various prestigious medical organizations is found in Table 1 . All of these statements emphasize lifestyle habits and practices as key components in the prevention and treatment of disease.

Sampling of Guidelines That Incorporate Lifestyle Recommendations for the Threat or Prevention of Chronic Disease.

Despite the widespread recognition of the important role of lifestyle measures and practices as a key component of the treatment of metabolic diseases, scant progress has been made in improving the habits and actions of the American population. For example, in the Strategic Plan for 2020 released by the American Heart Association, it was stated that only 5% of the adult population of the United States practice all of the positive lifestyle measures known to significantly reduce the risk of developing cardiovascular disease (CVD). 14

The power of positive lifestyle decisions and actions is underscored by multiple randomized controlled trials and a variety of cohort studies. For example, the Nurses’ Health Study demonstrated that 80% of all heart disease and over 91% of all diabetes in women could be eliminated if they would adopt a cluster of positive lifestyle practices including maintenance of a healthy body weight (body mass index [BMI] of 19-25 kg/m 2 ); regular physical activity (30 minutes or more on most days); not smoking cigarettes; and following a few, simple nutritional practices such as increasing whole grains and consuming more fruits and vegetables. 19 The US Health Professionals Study showed similar, dramatic reductions in risk of chronic disease in men from these same positive behaviors. 20 In fact, if individuals adopted only one of these positive behaviors, their risk of developing coronary artery disease (CAD) could be cut in half.

For decades physicians have emphasized the importance of practicing “evidence-based medicine.” Yet when it comes to incorporating the vast amount of evidence supporting positive health outcomes from lifestyle practices and habits, the medical community has been relatively slow to respond. This, despite the fact that virtually every physician would agree with the premise that regular physical activity, weight management, sound nutrition, and not smoking all result in significant health benefits.

The purpose of the current review is to provide a summary of the literature underscoring the benefits of positive health promoting habits and to present some strategies and guidelines for implementing these actions within the practice of medicine and issue a call for increased emphasis on lifestyle medicine among physicians.

What Is Lifestyle Medicine?

I had the privilege of editing the first, multi-author, academic textbook in lifestyle medicine. 21 In fact, this textbook, published in 1999, coined the term lifestyle medicine , which we defined as “the discipline of studying how daily habits and practices impact both on the prevention and treatment of disease, often in conjunction with pharmaceutical or surgical therapy, to provide an important adjunct to overall health.”

While there have been a number of different constructs concerning these disciplines and many investigators have made important contributions to components of lifestyle medicine such as nutrition, physical activity, weight management, smoking cessation, and so on, it is clear that the field is now going to coalesce around the term lifestyle medicine . For example, the American Heart Association changed the name of one its Councils from the “Council on Nutrition, Physical Activity and Metabolism” to the “Council on Lifestyle and Cardiometabolic Health” in 2013. 22 In addition, both the American College of Preventive Medicine and the American Academy of Family Practice have established working groups and educational tracks in the area of lifestyle medicine. Circulation , a major academic journal from the American Heart Association, published a series of multiple articles titled “Recent Advances in Preventive Cardiology and Lifestyle Medicine.” Representatives from a variety of organizations including the American Academy of Pediatrics, the American College of Sports Medicine, the Academy of Nutrition and Dietetics, the American Academy of Family Practice, and the American College of Preventive Medicine sent representatives to a working group that established the first summary of competencies physicians should possess to practice lifestyle medicine, which was published in the Journal of the American Medical Association . 23

Importantly, a new health care organization has been formed called the “American College of Lifestyle Medicine” (ACLM), which is devoted to providing a professional home for individuals who wish to emphasize lifestyle medicine in their practices. 24 This organization has doubled its membership each year for the past 5 years. ACLM has also spawned initiatives to develop curricula and encourage education of medical students in the area of lifestyle medicine. ACLM has also supported the development of Lifestyle Medicine Student Interest Groups at medical schools and has developed criteria for lifestyle medicine certification. 25 The goal of this organization is ultimately to establish certification boards in lifestyle medicine. Lifestyle medicine has also become an international movement with the development of the Lifestyle Medicine Global Alliance. 26

In addition, a peer-reviewed academic journal has been established, the American Journal of Lifestyle Medicine , 27 to provide a forum for individuals interested in exchanging academic information in this growing field.

There are multiple reasons why the term lifestyle medicine seems particularly appropriate for this discipline. First, the field is focused on lifestyle and its relationship to health. Second, it is clearly medicine based on the wide range and large volume of evidence supporting the health benefits of daily habits and actions.

The Power of Lifestyle Habits and Practices to Promote Good Health

Multiple daily practices have a profound impact on both long-term and short-term health and quality of life. This review will focus on 5 key aspects of lifestyle habits and practices: regular physical activity, proper nutrition, weight management, avoiding tobacco products, and stress reduction/mental health. This initial section will focus on general considerations related to each of these lifestyle habits and practices. In the subsequent section, this information will be applied to specific diseases or conditions.

Physical Activity

Physical activity is a vitally important component to overall health and both prevention and treatment of various diseases. Regular physical activity has been specifically demonstrated to reduce risk of CVD, type 2 diabetes, the metabolic syndrome, obesity, and certain types of cancer. 18 The important role of physical activity in these conditions has been underscored by its prominent role in evidence-based guidelines and consensus statements from virtually every organization that deals with chronic disease. In addition, there is strong evidence that regular physical activity is important for brain health and cognition as well as reduction in anxiety and depression and amelioration of stress. 16

The recently released 2018 Physical Activity Guidelines Advisory Committee Scientific Report emphasizes that increased physical activity carries multiple individual and public health benefits. 18 This report also catalogs that physical activity contributes powerfully to improved quality of life by improving sleep, general feelings of well-being, and daily functioning. The report emphasizes that some of the benefits of physical activity occur immediately and most of the benefits become even more significant with ongoing and regular performance of moderate to vigorous physical activity.

In addition, physical activity has been shown to prevent or minimize excessive weight gain in adults as well as reducing the risk of both excess body weight and adiposity in children. 28 Physical activity decreases the likelihood that women will gain excessive weight during pregnancy, making them less likely to develop gestational diabetes. 29 Physical activity may also decrease the likelihood of postpartum depression.

Physical activity has also been demonstrated to lower the risk of dementia and improve other aspects of cognitive functioning. Importantly, as the population in the United States continues to grow older regular physical activity has been shown to decrease the likelihood of falls 30 and fall-related injuries and assist in the preservation of lean body mass. 31

Other conditions that regular physical activity improves are osteoarthritis and hypertension. 18 All in all, the multiple benefits of regular physical activity make it one of the key considerations that should be recommended to all children and adults as part of a comprehensive lifestyle medicine approach to health and well-being.

Numerous studies have shown that physicians’ own physical activity behavior predicts the likelihood that they will recommend physical activity to their patients. Unfortunately, it has been estimated that less than 40% of physicians regularly counsel their patients on the importance of increasing physical activity. The low level of prescription among physicians, as well as the recognized benefits of regular physical activity in health, stimulated the American College of Sports Medicine to launch the “Exercise is Medicine®” (EIM) initiative. This initiative is designed to encourage primary care providers and health providers to design treatment plans that include physical activity or to refer patients to evidence-based exercise programs with qualified exercise professionals. EIM also encourages health care providers to assess and record physical activity as a vital sign during patients’ visits. The initiative further recommends concluding each visit with an exercise “prescription.” 32

Nutrition plays a key role in lifestyle habits and practices that affect virtually every chronic disease. There is strong evidence for a role of nutrition in CVD, diabetes, obesity, and cancer, among many other conditions. 33 Dietary guidelines and consensus statements from a variety of organizations have recognized the key role for nutrition, both in the prevention and treatment of chronic disease. 4 , 6 , 8 These consensus statements are very similar to each other in nature and consistently recommend a dietary pattern higher in fruits and vegetables, whole grains (particularly, high fiber), nonfat dairy, seafood, legumes and nuts. 34 Guidelines further recommend that those who consume alcohol (among adults), do so in moderation. The guidelines are also consistent in recommending diets that are lower in red and processed meats, refined grains, sugar sweetened foods, and saturated and trans fats. All the guidelines also emphasize the importance of balancing calories and also regular physical activity as strategies for maintaining a healthy weight and, thereby, further reducing the risk of chronic diseases.

Dietary guidance over the past 2 decades has moved from specific foods and nutrients to a greater emphasis on dietary patterns. The emphasis has also shifted to the critical aspect of providing practical advice for implementing recommendations. 9 This latter emphasis recognizes that despite consistent guidelines and nutrition recommendations for many decades, a distinct minority of Americans are following these guidelines. For example, in the area of hypertension <20% of individuals with high blood pressure follow the DASH Diet. 35 It is also estimated that <30% of adults in the United States consume the recommended daily number of fruits and vegetables. 34

The 2015-2020 Dietary Guidelines for Americans focused on integrating available science and systematic reviews, scientific research, and food pattern modeling on current intake of the US population to develop the “Healthy U.S. Style Eating Pattern.” 8 This approach allowed blending recommendations for an overall diet including constituent foods, beverages, nutrients, and health outcomes, while allowing for flexibility in amounts of food from all food groups to establish healthy eating patterns that meet nutrient needs and accommodated limitations for saturated fats, added sugars, and sodium. This approach also allowed for the potential nutritional areas of public health concern. Utilizing this approach to Dietary Guidelines for Americans 2015-2020 indicated the following:

Within the body of evidence higher intakes of vegetables and fruits consistently have been identified as characteristic of healthy eating patterns: whole grains have been identified as well, although slightly less consistently. Other characteristics of healthy eating patterns have been identified with less consistency including fat free or low fat dairy, seafood, legumes and nuts. Lower intakes of meats including processed meats, poultry, sugar sweetened foods (particularly beverages), and refined grains have also been identified as characteristics of healthy eating patterns.

Despite the multiple known benefits of proper nutrition, most physicians feel they have inadequate education in this area. In one survey, 22% of polled physicians received no nutrition education in medical school, and 35% polled said that nutrition education came in a single lecture or a section of a single lecture. 36 The situation does not improve during medical residency. More than 70% of residents surveyed felt they received minimal or no education on nutrition during medical residency. In the United States, 67% of physicians indicate they have nutritional counseling sessions for patients. However, this education was largely focused on the ill effects of high sodium, sugars, and fried foods. It is noteworthy that only 21% of patients feel they received effective communication in the area of nutrition from their physician. 36

Issues related to healthy nutrition permeate virtually every condition where lifestyle medicine plays a role and will be treated in detail under each specific condition.

Weight Management

In many ways overweight and obesity represent quintessential lifestyle diseases. These conditions serve as significant public health problems in the United States and other countries throughout the world. 37 In the United States, the prevalence of overweight (BMI ≥ 19-25 kg/m 2 ) has been estimated at approximately 70%, while obesity (BMI ≥ 30 kg/m 2 ) is estimated at 36%, and severe obesity (BMI ≥ 35 kg/m 2 ) at 16%. 38 These rates are significant since even small amounts of excess body weight have been associated with many chronic diseases including CVD, diabetes, some forms of cancer, 39 muscular skeletal disorders, arthritis, 40 and many others. The cornerstone of obesity treatment relies on lifestyle measures that contribute to balancing energy to prevent weight gain or creating an energy deficit to achieve weight loss. These lifestyle factors including both physical activity and nutrition are cornerstone modalities to achieve these results.

Tobacco Products

Overwhelming evidence exists from multiple sources that cigarette smoking significantly increases the risk of multiple chronic diseases including heart disease and stroke, diabetes, and cancer. Early in the 20th century in the United States, cigarette smoking was more prevalent in men than women. 41 However, women have rapidly caught up with men. The health risks of smoking in women are the equivalent of men. Substantial benefits in the reduction of risk of both CVD and cancer accrue in individuals who stop smoking cigarettes. These benefits occur over a very brief period of time. 42

After years of significant decreases in cigarette smoking, the prevalence of cigarette smoking has appeared to level off in recent years with approximately 15% of individuals currently smoking cigarettes. 43

It should be noted that secondhand smoke also increases the risk of multiple chronic diseases, since secondhand smoke contains numerous carcinogens and may linger, particularly in indoor air environments, for a number of hours after cigarettes have been smoked. 44

Stress, Anxiety, and Depression

Stress is endemic in the modern, fast-paced world. It has been estimated that up to one third of the adult population in the United States experiences enough stress in their daily lives to have an adverse impact on their home or work performance. Anxiety and depression are also very common. Lifestyle measures, such a regular physical activity, have been demonstrated to provide effective amelioration of many aspects of all three of these conditions. 45 , 46

Interestingly, in the past decade positive psychology has also emerged as a significant component of lifestyle medicine. 47 This field has demonstrated that positive approaches to psychological issues such as gratitude, forgiveness, and other strategies can play a very important role in stress reduction and amelioration of both anxiety and depression.

Obtaining adequate amounts of sleep has also been demonstrated to be an effective strategy in all these conditions, which proved so troublesome to many individuals. 48

Lifestyle Medicine Approaches in the Treatment and Prevention of Chronic Diseases

Lifestyle medicine modalities have been demonstrated in multiple studies to play an important role in both the treatment and prevention of many chronic diseases and conditions. This section will explore some of the most common diseases or conditions where lifestyle modalities have been studied.

Cardiovascular Disease

Daily lifestyle practices and habits profoundly affect the likelihood of developing CVD. Many of these same practices and habits also play a role in treating CVD. 1 - 4 , 9 , 14 - 16

Numerous studies have demonstrated that regular physical activity, not smoking cigarettes, weight management, and positive nutritional practices all profoundly affect both CVD itself and also risk factors for CVD. 49 , 50 Numerous epidemiologic studies have shown that positive lifestyle decisions such as engaging in at least 30 minutes of physical activity on most days; not smoking cigarettes; consuming a diet of more fish, whole grains, fruits, and vegetables; and maintaining a healthy body weight can reduce the incidence of CHD by over 80% and diabetes by over 90% in both men and women. 19 , 20

Between 1980 and 2000, mortality rates from CHD fell by over 40%. 51 CVD, nonetheless, remains the leading cause of worldwide mortality, and in the United States, it results in over 37% of annual mortality. 51 Approximately half of the reduction in CVD deaths since 1980 can be attributed to reduction in major lifestyle risk factors such as increasing physical activity, smoking cessation, and better control of blood pressure and cholesterol. Unfortunately, increases in obesity and diabetes have moved in the opposite direction and could jeopardize the gains achieved in other lifestyle risk factors, unless these negative trends can be reversed. 51

In the past decade a number of important initiatives have been undertaken and comprehensive summaries published linking overall life strategies to reductions in cardiovascular risks. In 2012, the American Heart Association (AHA) released its National Goals for Cardiovascular Health Promotion and Disease Reductions for 2020 and beyond. 14 This important document also introduced the concept of “primordial prevention” (preventing risk factors from occurring in the first place) into the cardiology lexicon as well as introducing the concept of “ideal cardiovascular health.” Daily lifestyle measures were central to both these new concepts. In 2013, the American Heart Association and American College of Cardiology (ACC) jointly issued Guidelines for Lifestyle Management to Reduce Cardiovascular Risk, 52 which also emphasized lifestyle measures to reduce the risk of CVD or assist in its treatment if already present.

Unfortunately, a distinct minority of Americans are following the recommendations from the AHA to achieve “ideal” cardiovascular health. Ideal cardiovascular health was defined as achieving appropriate levels of physical activity, consuming a healthy diet score, maintaining a total blood cholesterol of <200 mg/dL, maintaining a blood pressure of <120/<80 mm Hg, and a fasting blood glucose of <100 mg/dL (the cholesterol, blood pressure, and glucose parameters were all defined as “untreated” values). In the AHA document, it was noted that less than 5% of adults in the United States fulfill all 7 criteria for achieving ideal cardiovascular health. 14

Metrics for Cardiovascular Health

Overweight and obesity represent significant risk factors for cardiovascular disease. Guidelines developed by a joint effort from The Obesity Society (TOS), AHA, and ACC 53 were designed to help physicians manage obesity more effectively. Key recommendations include enrolling overweight or obese patients in comprehensive lifestyle interventions for weight loss delivery and programs for 6 months or longer.

Increased levels of moderate or vigorous intensity physical activity have been repeatedly shown to lower the risk for cardiovascular disease. Compared with those who are physically active, the risk of coronary heart disease (CHD) in sedentary individuals is 150% to 240% higher. 54 Unfortunately, only about 25% of Americans engage in enough regular physical activity to meet minimum standards of the Centers for Disease Control and Prevention of at least 150 minutes/week of moderate intensity aerobic exercise or at least 75 minutes of vigorous exercise and muscle strengthening activities at least 2 days/week. 18

The greatest reduction in risk for CHD appears to result from those engaging in even modest amounts of physical activity compared with the most physically inactive. Even relatively small amounts of increase in physical activity could potentially result in a significant decrease in CHD for a large portion of the American population. Both the 2008 Physical Activity Guidelines for Americans and the 2018 55 Physical Activity Guidelines Advisory Committee Scientific Report 18 recommend similar levels of physical activity as an important component of the reduction of risk for CHD.

There is no question that diet plays a significant role in overall strategies for cardiovascular risk reduction. 56 , 57 This fact is recognized by numerous scientific statements and documents from the American Heart Association including the AHA 2020 Strategic Plan 14 as well as the AHA/ACC Guidelines for Lifestyle Management 52 and the 2006 AHA Nutrition Guidelines. 56 All these recommendations are similar and include consumption of increased fruits and vegetables, consuming at least 2 fish meals/week (preferably oily fish), consuming fiber-rich grains, and restricting sodium to <1500 mg/day and sugar sweetened beverages ≤450 kcal (36 ounces) per week. The AHA Dietary Guidelines recommend plant-based diets such as the DASH Diet 35 (Dietary Approach to Stop Hypertension), as well as the Mediterranean-style diets. 58 Definitive evidence-based guidelines for overall dietary health are summarized in the Dietary Guidelines for Americans 2015-2020 Report. 8

Smoking and Use of Tobacco Products

Overwhelming evidence from multiple sources demonstrates that cigarette smoking significantly increases the risk of both heart disease and stroke. This evidence has been ably summarized elsewhere 59 and is incorporated as a recommendation for every AHA document including the 2020 Strategic Plan. The good news is that risk of CHD and stroke diminish rapidly once smoking cessation occurs. It should also be noted that secondhand smoke also increases the risk of CHD. It has been estimated that 1 nonsmoker dies from secondhand smoke exposure to every 8 smokers who die from smoking. 60

Hypertension

Hypertension represents a significant risk factor for CHD and is the leading risk factor for stroke. The recently released 2017 Guidelines for the Prevention and Detection, Evaluation and Management for High Blood Pressure in Adults from the AHA and the ACC defines normal blood pressure as <120 mm Hg/<80 mm Hg, and hypertension as systolic >120 mm Hg and diastolic high blood pressure as >80 mm Hg. 3 The new criteria are found on Table 2 . Using these criteria, more than 40% of the adult population in the United States has high blood pressure. Recommendations for treating high blood pressure, particularly in the lowest categories, involve a number of lifestyle medicine modalities such as increased physical activity, weight loss (if necessary), and improved nutrition including a salt reduction to <1500 mg/day. 3

2017 Blood Pressure Guidelines From the American Heart Association and American College of Cardiology.

Dyslipidemias

In 2013, the ACC and AHA issued “Guidelines for the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Disease in Adults.” 6 These guidelines recommend an increased use of statin medications to reduce atherosclerotic cardiovascular disease (ASCVD) both in primary and secondary prevention and recommended the discontinuation of the use of specific low-density lipoprotein and high-density lipoprotein treatment targets. Within these guidelines for treating blood lipids, it was acknowledged that lifestyle is the foundation of ASCVD risk reduction efforts. This includes adhering to a heart healthy diet, regular exercise, avoidance of tobacco, and maintenance of a healthy body weight.

Diabetes and Pre-Diabetes

Dramatic increases in diabetes have occurred around the world in the past 2 decades. Lifestyle medicine modalities to prevent or treat diabetes focus on nutrition therapy, physical activity, education, counseling, and support given the great importance given to the metabolic basis of the vast majority of individuals who have either pre-diabetes or diabetes. 61 The International Diabetes Federation estimates that 387 million adults in the world live with type 1 or type 2 diabetes. Tragically, almost half of these individuals do not know they have these diseases. It is estimated that the number of individuals living with diabetes will increase to 392 million people by 2035.

In the United States in 2011-2012, the estimated prevalence of diabetes was 12% to 14%. 62 There is a higher prevalence in adults who are non-Hispanic-Black, non-Hispanic-Asian, or Hispanic. The proportion of people who have undiagnosed diabetes has decreased between 3.1% and 5.2% during this period of time. The prevalence of pre-diabetes has been reported to be between 37% and 38% of the overall US population. Consequently, 49% to 52% of the US population has either diabetes or pre-diabetes. 63

Pre-Diabetes

Multiple lifestyle interventions play critically important roles in preventing pre-diabetes from turning into diabetes. The strongest evidence comes from the Diabetes Prevention Program (DPP), which demonstrated that an intensive lifestyle intervention in individuals with pre-diabetes could reduce the incidence of type 2 diabetes by 58% over 3 years. 64 Other studies that have supported the importance of lifestyle intervention for diabetes prevention include the Da Qing Study, where 43% reduction in conversion from pre-diabetes to diabetes occurred at 20 years, 65 and the Finnish Diabetes Prevention Study, which showed also a 43% reduction in conversion of pre-diabetes to diabetes at 7 years and a 34% reduction at 10 years. 66

The 2 major goals of the DPP in the lifestyle intervention arm were to achieve a minimum of 7% weight loss and 150 minutes of physical activity/week at a moderate intensity such as brisk walking. These goals were selected based on previous literature suggesting that these were both feasible and could influence the development of diabetes. Both these goals were largely met in the DPP.

The nutrition plan in DPP focused on reducing calorie intake in order to achieve weight loss if needed. The recommended diet was consistent with both Mediterranean and DASH eating patterns. Conversely, sugar sweetened beverages and red meats were minimized since they are associated with the increased risk of type 2 diabetes. 63 The 150 minutes/week of moderate intensity physical activity was achieved largely through brisk walking, which also contributed to beneficial effects in individuals with pre-diabetes.

Education and support in the DPP was provided with an individual model of treatment rather than a group-based approach including a 16-session core curriculum completed in the first 24 weeks including sections on lowering calories, increasing physical activity, self-monitoring, maintaining healthy lifestyle behaviors, and psychological, social, and motivational challenges. 63

Recent evidence has also suggested that breaking up sedentary time (such as screen time) further decreases the risk of pre-diabetes being converted to diabetes.

Lifestyle modalities are a cornerstone for diabetes care. These modalities include medical nutrition therapy (MNT), physical activity, smoking cessation, counseling, psychosocial care, and diabetes self-management education support. 66

There are many different ways of achieving the nutritional goals. Individuals with diabetes should be referred for individualized MNT. MNT promotes healthful eating patterns emphasizing a variety of nutrient-dense foods at appropriate levels with the goal of achieving and maintaining healthy body weight; maintaining individual glycemic, blood pressure, and lipid goals; and delaying or preventing complications of diabetes. There is not one ideal percentage of calories from carbohydrate, protein, and fat for all people with diabetes. A variety of eating patterns are acceptable for the management of diabetes including the Mediterranean, DASH, and other plant-based diets. All of these have been shown to achieve benefits for people with diabetes. 67

Weight management, if necessary, and reduction of weight are important particularly for overweight and obese people with diabetes. Weight loss can be attained in lifestyle programs that achieve 500 to 750 kcal daily reduction for both men and women adjusted to the individual based on body weight. For many obese individuals with type 2 diabetes, weight loss >5% is necessary in order to achieve beneficial outcomes for glycemic control, lipids, and blood pressure, while sustained weight loss of >7% is optimal.

Regular physical activity is also vitally important for the management of diabetes. People with diabetes should be encouraged to perform both aerobic and resistance exercise regularly. 68 Aerobic activity bouts should ideally be at least 10 minutes, with a goal of 30 minutes/day or more on most days of the week. Recent evidence supports the concept that individuals with diabetes should be encouraged to reduce time spent being sedentary in activities such as working at a computer, watching TV, and so on, or breaking up sedentary activities by briefly standing, walking, or performing light physical activity. Research trials have demonstrated strong evidence for A1C lowering value of exercise in individuals with type 2 diabetes. The ADA Consensus Report indicates that prior to starting an exercise program medical providers should perform a careful history to assess cardiovascular risk factors and be aware of atypical presentation of CAD in patients with diabetes. Health care providers should customize exercise regiments to individuals’ needs. 67

In many ways obesity represents the quintessential lifestyle disease. 34 Obesity is the result of energy imbalance, since energy expenditure and energy intake are key factors in the energy balance equation. 61 Thus, both nutritional and physical activity components of lifestyle intervention are critically important to both short-term weight loss and also long-term maintenance of healthy body weight.

It is currently estimated that 78 million individuals in the United States are obese. This represents 36% of the population. 37 More than 70% of individuals in the United States are overweight (BMI ≥ 25 kg/m 2 ), including obese (BMI ≥ 30 kg/m 2 ) and severely obese (BMI ≥ 35 kg/m 2 ). While it may seem simple that either decreased caloric intake or increased physical activity may contribute to weight loss, in fact the process is complicated. As emphasized in the Consensus Statement on Obesity from the American Society of Nutrition, metabolism consists of multiple factors including percent body fat, other issues related to metabolism, and a host of environmental factors. 69

At any time, approximately 50% to 70% of obese Americans are actively trying to lose weight. Sustained weight loss of as little as 5% to 10% is considered clinically significant, since it reduces risk factors for a variety of chronic diseases such as diabetes and heart disease. Both the Diabetes Prevention Program and the Look AHEAD Trial 70 showed that weight loss of 7% in obese individuals resulted in significant improvement in risk factors for both heart disease and diabetes. Nutrition represents a cornerstone of treatment for overweight and obesity. 71 Dietary treatments for disease have been called MNT. This therapeutic approach has been used in a variety of medical conditions, but there is particularly strong proof that MNT improves waist circumference, waist-to-hip ratio, fasting blood sugar, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and blood pressure.

Typical nutritional interventions for weight loss in obese individuals involve sustaining an average daily caloric deficit of 500 kcal. Energy recommendations also include that intake should not be <1200 calories/day for male or female adults in order to maintain adequate nutrient intake.

A variety of evidence-based diets have been demonstrated to assist in healthy weight loss. These include the Mediterranean diet, the DASH diet, and the Healthy U.S. Eating Style Pattern. It has also been demonstrated that macronutrient composition of a weight loss plan (eg, low fat vs low carb, etc) do not achieve different results in studies lasting longer than one year.

The Weight Loss Guidelines jointly issued by the US Preventive Services Task Force, the American Heart Association, the American College of Cardiology, the Obesity Society, and the Academy of Nutrition and Dietetics all recommend a multidisciplinary team approach to managing obesity. 53 These approaches include physical activity counseling, MNT, as well as a structured approach to behavioral change utilizing problem solving and goal setting as well as self-monitoring. Most evidence suggests that effective weight loss programs should last at least 6 months and have a minimum of 24 counseling sessions.

There is a prevalent misconception that maintenance of weight loss is virtually impossible. In both the Diabetes Prevention Program and the Look AHEAD Trial, however, individuals who completed the initial 16-week program and then were followed on a monthly basis for the next 3 to 4 years were able to maintain 90% of the weight that they initially lost. The National Weight Control Registry, which is a registry of over 10 000 individuals who have lost at least 50 pounds and kept it off for at least 1 year, also demonstrated that key components of lifestyle measures such as regular attention to monitoring nutritional intake as well as regular physical activity (on average 60 minutes/day) were key components of how these individuals were able to maintain initial weight loss. 72

It has been argued that physical activity alone is not a powerful tool for initial weight loss. However, abundant evidence supports the concept that regular physical activity is a key component of long-term maintenance of weight loss. Regular physical activity also plays an important role in preservation of lean body mass, which is a key component of maintaining adequate metabolism to support maintenance of weight loss. 28 As already indicated, regular physical activity also conveys a host of health enhancing benefits in addition to its role in weight loss and weight management.

Lifestyle measures play a critically important role both in the prevention of cancer and treatment of individuals who have already established cancer. Moreover, lifestyle measures play a very important role in the ongoing health of cancer survivors. These facts are underscored by the joint statement issued by the American Cancer Society, the American Diabetes Association, and the American Heart Association on preventing cancer, CVD, and diabetes. 15

Cancer is a generic term that represents more than 100 diseases, each of which has a different etiology. Nonetheless, lifestyle measures can play a critically important role in virtually every form of cancer. In 2016, an estimated 1 685 210 new cases of cancer were diagnosed in the United States and 595 690 people died from the disease. 73 Worldwide it has been estimated that the number of new cancers could rise by as much as 70% over the next 2 decades. Approximately 70% of deaths from cancer will occur in low- and middle-income countries. 74

Cancer is no longer viewed as an inevitable consequence of aging. In fact, only 5% to 10% of cancers can be classified as familial. Thus, most cancers are associated with multiple environmental factors including lifestyle issues. For example, the importance of nutrition was emphasized more than 35 years ago by Dahl and Petro. They estimated that approximately 35% (10% to 70%) of all cancers in the United States could be attributable to dietary factors. 75 In 2007, the World Cancer Research Fund and American Institute for Cancer Research (WCRF/AICR) evaluated 7000 studies and concluded that diet and physical activity were major determents of cancer risk. 76 Thus, on a global scale, 3 to 4 million cancer cases could be prevented each year from more positive lifestyle habits and actions. 76

The relationship of obesity to cancer is also very strong. 77 This relationship is based not only on hormonal changes associated with obesity but also a variety of other physiologic mechanisms. Adipocytes, which compose the predominant cell in body fat, have been historically thought to be simply passive storage vessels. It is now clear, however, that adipocytes secrete a variety of metabolically active substances that promote inflammation, insulin resistance, and a variety of other factors, all of which may promote cancer cell growth. The AICR and IARC (International Agency for Research on Cancer) have concluded that there is sufficient evidence to link 13 human malignancies to excess body fatness. 78 Excess body fatness is now the second leading preventable cause of cancer, behind only cigarette smoking. 79 The AICR also reported in 2017, that, unfortunately, only 50% of Americans are aware that obesity promotes cancer growth, so there is an important educational issue to combat, as well. 80

Individuals who are overweight or obese should follow standard cancer screening guidelines. Intentional weight loss lowers cancer risk and improves survival. Individuals with cancer should avoid excess weight gain or, if already overweight or obese, should attempt to lose weight to improve prognosis. The typical program for safe and effective weight loss involves both regular physical activity and caloric restriction. These programs may need to be modified given the unique aspects of each cancer.

General nutrition guidelines for cancer prevention and treatment are very similar to those for healthy eating, in general. However, some modifications may be necessary to protect against certain cancers or treat various side effects of cancer therapy, such as excessive weight loss. In general, lifestyle nutrition measures for cancer prevention involve increasing the consumption of foods that have been shown to decrease the cancer risk, which include whole grains, vegetables, fruits, and legumes. In addition, individuals should decrease consumption of foods associated with increased cancer risk such as processed meat (including ham and bacon), red meat such as beef, pork and lamb, and decrease alcoholic beverages and salt preserved foods. Individuals should eat a healthy diet rather than relying on supplements to protect against cancer.

Physical activity also plays a key role in the association of lifestyle risk to cancer. 81 , 82 Although specific biologic mechanisms linking physical activity to cancer reduction remain unknown, there is growing evidence supporting the role of physical inactivity in various cancer diagnoses. According to the World Cancer Research Fund International, 20% of cancer cases in the United States could be prevented through physical activity, weight control, and consumption of a healthy diet. 83 In addition, a pooled analysis of 12 prospective cohort studies involving 1.4 million participants in the United States and Europe demonstrated an association between higher levels of leisure time physical activity and risk reduction of 13 different cancer types. 84

Among those cancers linked to inactivity, colon, breast, and endometrial cancers are the most studied. 85 The link between physical activity and breast cancer may be through reducing levels of sex hormones and increasing concentrations of sex hormone binding globulin proteins. 86 The relationship between exercise and decreased endometrial cancer risks may have similar mechanisms. 87 The relationship between physical activity and decreased colon cancer risk may be due to immune function modulation reduction in intestinal transit time, hyperinsulinemia, and inflammation. 88 Despite these postulated underlying factors, the biological link between physical activity and reduced colon cancer risk is not well understood.

There are multiple physical activity guidelines that not only have been demonstrated to reduce the risk of cancer, but may also be employed as a treatment tool for cancer populations. Safety is the key consideration in physical activity for cancer survivors. Guidelines for physical activity and cancer have been issued both by the American Cancer Society 89 and the AICR. 90 While a detailed explanation of these guidelines is beyond the scope of this review, the interested reader is referred to these guidelines for more specific detail.

Dementia/Cognition

Maintaining cognitive function is vital to maintaining quality of life, functional independence, and is an important component of the aging process. As life expectancy continues to increase in developed countries, the number of individuals over the age of 65 will undoubtedly increase dramatically over the next 15 to 20 years. 16 It has been estimated that there are currently 47 million people with dementia worldwide and this is projected to increase to 75 million individuals in 2030 and 131 million individuals by 2050. 16

There is a strong linkage between brain health and cardiovascular health. This central fact is underscored by the Presidential Advisory from the AHA and American Stroke Association (ASA) on “Defining Optimal Brain Health in Adults.” 16

Lifestyle measures play a central role in the recommendations from the AHA/ASA for maintaining healthy cognition throughout a lifetime. Modifiable risk factors that may compromise brain health are also associated with poor cardiovascular health such as uncontrolled hypertension, diabetes mellitus, obesity, physical inactivity, smoking, and depression. Each of these conditions has been shown to be potentially ameliorated, at least to some degree, by positive lifestyle measures. For this reason, the AHA and ASA have identified 7 metrics to define optimal brain health including nonsmoking, physical activity at goal levels, a healthy diet consistent with current guideline levels, and a body mass index of <25 kg/m 2 . 16 In addition, the AHA and ASA recommend 3 ideal health factors including an untreated blood pressure of <120/<80 mm Hg, untreated cholesterol <200 mg/dL, and fasting blood glucose of <100 mg/dL.

Virtually all of these factors are affected by positive lifestyle decisions, making cognition and reducing the risk of dementia strongly linked to lifestyle factors. Furthermore, it is important to stress that while many of the manifestations of the spectrum ranging from diminished cognition to dementia occur in individuals in their 50s, 60s, and beyond, paying attention to these risk factors should occur throughout a lifetime, thus enhancing the importance of lifestyle measures in maintaining positive brain health.

A variety of dietary habits have also been shown to decrease the risk of cognitive decline and risk of dementia. These include Mediterranean style diets (MST) and the Dietary Approach to Stop Hypertension (DASH) diet. 91 A combination of MST and DASH (the so-called MIND diet) has also been observed to be associated with decreased risk of dementia with aging. All of these diets are plant based as their principle sources of energy and involve a high intake of grains and cereals, fruits, vegetables, legumes, nuts, olive oil, and fish as fat sources. In addition, a recent study demonstrated that consumption of cocoa, both in individuals over the age of 60 with maintained cognition and also mild cognitive impairment, may improve levels of cognition. 92 , 93 A number of studies have shown that regular physical activity is associated with improved cognition. 16

Anxiety, Depression, and Stress Reduction

Anxiety, depression, and stress are all endemic in the modern, fast-paced world. Lifestyle interventions have been demonstrated to play an effective role in ameliorating all three of these conditions.

Within all of the mental health disorders, anxiety is the most common. 94 The overall prevalence of anxiety disorders has been reported at more than 30%. Regular physical activity has been demonstrated in multiple studies to lower anxiety levels. While the state of anxiety has been shown to be reduced immediately after performing a single bout of exercise, the anxiolytic effect of treating the trait of anxiety appears to require a training period of at least 10 weeks. The exact level of physical activity has not been determined. However, most studies employ the general guidelines of 30 minutes of moderate intensity physical activity/session.

Depression is also quite common with a lifetime prevalence of significant depression of approximately 10% in the US population. 95 Even in the absence of significant depressive disorder, symptoms of depression can negatively influence health and quality of life. Physical activity has been repeatedly shown to decrease symptoms of depression. Typical levels of physical activity employed once again have involved at least 30 minutes of moderate intensity physical activity performed on a regular basis.

Stress is endemic in our modern world. 96 While exact prevalence figures for stress are difficult or impossible to determine, most people experience at least moderate stress in their daily lives. It has been estimated that up to one third of individuals experience enough stress in their daily life to decrease their performance at either work or home. While a certain level of stress may be protective, excessive stress may be harmful through a variety of physiologic and psychological effects.

A variety of approaches to ameliorate stress have been studied and found effective. These include the relaxation response and other mind-body therapies. Certainly, these mind-body therapies play an important role in the delivery of lifestyle medicine. One other aspect of modern psychological therapy that has gained increased prominence in the past decade is positive psychology involving modalities and concepts such as gratitude and forgiveness, which may help reduce stress.

Lifestyle Medicine and Pediatrics

A detailed discussion of lifestyle medicine in the pediatric population is beyond the scope of this review. However, it should be noted that many of the conditions that manifest themselves in adulthood have their roots in childhood. In particular, there has been a dramatic increase in the prevalence of overweight and obesity in children 97 and a corresponding increase in the prevalence of type 2 diabetes. Dyslipidemia 98 and hypertension 99 have also increased in prevalence in the pediatric population.

There is emerging evidence that many of the conditions now increasing in prevalence in children may actually begin in utero. 100 The same types of lifestyle measures that are applicable both for the prevention and treatment of chronic disease in adults are also very relevant to children. Good information on physical activity in children can be found in the recently revised Physical Activity Guidelines for Americans. 18 Nutritional guidance may also be found in the 2015-2020 Dietary Guidelines for Americans. 8 Since many of the lifestyle medicine modalities employed in adults are highly relevant to families, issues related to physical activity, nutrition, and weight management should be addressed in the family setting.

Conclusions

There is no longer any serious doubt that daily habits and practices profoundly affect the short-term and long-term health and quality of life. Increased physical activity, proper nutrition, weight management, avoidance of tobacco, and stress reduction are all key modalities that can lower the risk of chronic disease and improve quality of life. Despite the overwhelming evidence that these practices have a profound impact on health, the medical community has been slow to respond in addressing these modalities and in encouraging patients to make positive lifestyle changes. This represents a significant missed opportunity since more 75% of Americans see a primary care doctor every year. Employing the principles of lifestyle medicine in the daily practice of medicine represents a substantial opportunity to increase the value of proposition in medicine by improving outcomes for patients, while controlling costs. 101 The time has come to employ the vast body of evidence in lifestyle medicine and encourage positive lifestyle medicine not only for our patients but also in our own lives.

Declaration of Conflicting Interests: The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr Rippe is the editor in chief, American Journal of Lifestyle Medicine , and editor of Lifestyle Medicine (CRC Press). He is also founder and director, Rippe Lifestyle Institute, a research organization that has conducted multiple studies in physical activity, nutrition, and weight management.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval: Not applicable, because this article does not contain any studies with human or animal subjects.

Informed Consent: Not applicable, because this article does not contain any studies with human or animal subjects.

Trial Registration: Not applicable, because this article does not contain any clinical trials.

  • Research article
  • Open access
  • Published: 29 September 2022

A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

  • Pauline Hautekiet   ORCID: orcid.org/0000-0003-3805-3004 1 , 2 ,
  • Nelly D. Saenen 1 , 2 ,
  • Dries S. Martens 2 ,
  • Margot Debay 2 ,
  • Johan Van der Heyden 3 ,
  • Tim S. Nawrot 2 , 4 &
  • Eva M. De Clercq 1  

BMC Medicine volume  20 , Article number:  328 ( 2022 ) Cite this article

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Studies often evaluate mental health and well-being in association with individual health behaviours although evaluating multiple health behaviours that co-occur in real life may reveal important insights into the overall association. Also, the underlying pathways of how lifestyle might affect our health are still under debate. Here, we studied the mediation of different health behaviours or lifestyle factors on mental health and its effect on core markers of ageing: telomere length (TL) and mitochondrial DNA content (mtDNAc).

In this study, 6054 adults from the 2018 Belgian Health Interview Survey (BHIS) were included. Mental health and well-being outcomes included psychological and severe psychological distress, vitality, life satisfaction, self-perceived health, depressive and generalised anxiety disorder and suicidal ideation. A lifestyle score integrating diet, physical activity, smoking status, alcohol consumption and BMI was created and validated. On a subset of 739 participants, leucocyte TL and mtDNAc were assessed using qPCR. Generalised linear mixed models were used while adjusting for a priori chosen covariates.

The average age (SD) of the study population was 49.9 (17.5) years, and 48.8% were men. A one-point increment in the lifestyle score was associated with lower odds (ranging from 0.56 to 0.74) for all studied mental health outcomes and with a 1.74% (95% CI: 0.11, 3.40%) longer TL and 4.07% (95% CI: 2.01, 6.17%) higher mtDNAc. Psychological distress and suicidal ideation were associated with a lower mtDNAc of − 4.62% (95% CI: − 8.85, − 0.20%) and − 7.83% (95% CI: − 14.77, − 0.34%), respectively. No associations were found between mental health and TL.

Conclusions

In this large-scale study, we showed the positive association between a healthy lifestyle and both biological ageing and different dimensions of mental health and well-being. We also indicated that living a healthy lifestyle contributes to more favourable biological ageing.

Peer Review reports

According to the World Health Organization (WHO), a healthy lifestyle is defined as “a way of living that lowers the risk of being seriously ill or dying early” [ 1 ]. Public health authorities emphasise the importance of a healthy lifestyle, but despite this, many individuals worldwide still live an unhealthy lifestyle [ 2 ]. In Europe, 26% of adults smoke [ 3 ], nearly half (46%) never exercise [ 4 ], 8.4% drink alcohol on a daily basis [ 5 ] and over half (51%) are overweight [ 5 ]. These unhealthy behaviours have been associated with adverse health outcomes like cardiovascular diseases [ 6 , 7 , 8 ], respiratory diseases [ 9 ], musculoskeletal diseases [ 10 ] and, to a lesser extent, mental disorders [ 11 , 12 ].

Even though the association between lifestyle and health outcomes has been extensively investigated, biological mechanisms explaining these observed associations are not yet fully understood. One potential mechanism that can be suggested is biological ageing. Both telomere length (TL) and mitochondrial DNA content (mtDNAc) are known biomarkers of ageing. Telomeres are the end caps of chromosomes and consist of multiple TTAGGG sequence repeats. They protect chromosomes from degradation and shorten with every cell division because of the “end-replication problem” [ 13 ]. Mitochondria are crucial to the cell as they are responsible for apoptosis, the control of cytosolic calcium levels and cell signalling [ 14 ]. Living a healthy lifestyle can be linked with healthy ageing as both TL and mtDNAc have been associated with health behaviours like obesity [ 15 ], diet [ 16 ], smoking [ 17 ] and alcohol abuse [ 18 ]. Furthermore, as biomarkers of ageing, both TL and mtDNAc have been associated with age-related diseases like Parkinson’s disease [ 19 ], coronary heart disease [ 20 ], atherosclerosis [ 21 ] and early mortality [ 22 ]. Also, early mortality and higher risks for the aforementioned age-related diseases are observed in psychiatric illnesses, and it is suggested that advanced biological ageing underlies these observations [ 23 ].

Multiple studies evaluated individual health behaviours, but research on the combination of these health behaviours is limited. As they often co-occur and may cause synergistic effects, assessing them in combination with each other rather than independently might better reflect the real-life situation [ 24 , 25 ]. Therefore, in a general adult population, we combined five commonly studied health behaviours including diet, smoking status, alcohol consumption, BMI and physical activity into one healthy lifestyle score to evaluate its association with mental health and well-being and biological ageing. Furthermore, we evaluated the association between the markers of biological ageing and mental health and well-being. We hypothesise that individuals living a healthy lifestyle have a better mental health status, a longer TL and a higher mtDNAc and that these biomarkers are positively associated with mental health and well-being.

Study population

In 2018, 11611 Belgian residents participated in the 2018 Belgian Health Interview Survey (BHIS). The sampling frame of the BHIS was the Belgian National Register, and participants were selected based on a multistage stratified sampling design including a geographical stratification and a selection of municipalities within provinces, of households within municipalities and of respondents within households [ 26 ]. The study population for this cross-sectional study included 6054 BHIS participants (see flowchart in Additional file 1 : Fig. S1) [ 27 , 28 , 29 , 30 , 31 ]. Minors (< 18 years) and participants not eligible to complete the mental health modules (participants who participated through a proxy respondent, i.e. a person of confidence filled out the survey) were excluded ( n  = 2172 and n  = 846, respectively). Furthermore, of the 8593 eligible participants, those with missing information to create the mental health indicators, the lifestyle score or the covariates used in this study were excluded ( n  = 1642, 788 and 109, respectively).

For the first time in 2018, a subset of 1184 BHIS participants contributed to the 2018 Belgian Health Examination Survey (BELHES). All BHIS participants were invited to participate except for minors (< 18 years), BHIS participants who participated through a proxy respondent and residents of the German Community of Belgium, the latter representing 1% of the Belgian population. Participants were recruited on a voluntary basis until the regional quotas were reached (450, 300 and 350 in respectively Flanders, Brussels Capital Region and Wallonia). These participants underwent a health examination, including anthropological measurements and completed an additional questionnaire. Also, blood and urine samples were collected. Of the 6054 included BHIS participants, 909 participated in the BELHES. Participants for whom we could not calculate both TL and mtDNAc were excluded ( n  = 170). More specifically, participants were excluded because they did not provide a blood sample ( n  = 91) or because they did not provide permission for DNA research ( n  = 32). Twenty samples were excluded from DNA extraction because either total blood volume was too low ( n  = 7), samples were clothed ( n  = 1) or tubes were broken due to freezing conditions ( n  = 12). Twenty-seven samples were excluded because they did not meet the biomarker quality control criteria (high technical variation in qPCR triplicates). This was not met for 3 TL samples, 20 mtDNAc samples and 4 samples for both biomarkers. For this subset, we ended up with a final number of 739 participants. Further in this paper, we refer to “the BHIS subset” for the BHIS participants ( n  = 6054) and the “BELHES subset” for the BELHES participants ( n  = 739).

As part of the BELHES, this project was approved by the Medical Ethics Committee of the University Hospital Ghent (registration number B670201834895). The project was carried out in line with the recommendations of the Belgian Privacy Commission. All participants have signed a consent form that was approved by the Medical Ethics Committee.

Health interview survey

The BHIS is a comprehensive survey which aims to gain insight into the health status of the Belgian population. The questions on the different dimensions of mental health and well-being were based on international standardised and validated questionnaires [ 32 ], and this resulted in eight mental health outcomes that were used in this study. Detailed information on each indicator score and its use is addressed in Additional file 1 : Table. S1. Firstly, the General Health Questionnaire (GHQ-12) provides the prevalence of psychological and severe psychological distress in the population [ 27 ]. On the total GHQ score, cut-off points of + 2 and + 4 were used to identify respectively psychological and severe psychological distress.

Secondly, we used two indicators for the positive dimensions of mental health: vitality and life satisfaction. Four questions of the short form health survey (SF-36) indicate the participant’s vital energy level [ 28 , 33 ]. We used a cut-off point to identify participants with an optimal vitality score, which is a score equal to or above one standard deviation above the mean, as used in previous studies [ 34 , 35 ]. Life satisfaction was measured by the Cantril Scale, which ranges from 0 to 10 [ 29 ]. A cut-off point of + 6 was used to indicate participants with high or medium life satisfaction versus low life satisfaction.

Thirdly, the question “How is your health in general? Is it very good, good, fair, bad or very bad?” was used to assess self-perceived health, also known as self-rated health. Based on WHO recommendations [ 36 ], the answer categories were dichotomised into “good to very good self-perceived health” and “very bad to fair self-perceived health”.

Fourthly, depressive and generalised anxiety disorders were defined using respectively the Patient Health Questionnaire (PHQ-9) and the Generalised Anxiety Disorder Questionnaire (GAD-7). We identified individuals who suffer from major depressive syndrome or any other type of depressive syndrome according to the criteria of the PHQ-9 [ 37 ]. A cut-off point of + 10 on the total sum of the GAD-7 score was used to indicate generalised anxiety disorder [ 31 ]. Additionally, a dichotomous question on suicidal ideation was used: “Have you ever seriously thought of ending your life?”; “If yes, did you have such thoughts in the past 12 months?”. Finally, the BHIS also includes personal, socio-economic and lifestyle information. The standardised Cronbach’s alpha coefficients for the PHQ-9, GHQ-12, GAD-7 and questions on vitality of the SF-36 ranged between 0.80 and 0.90.

Healthy lifestyle score

We developed a healthy lifestyle score based on five different health behaviours: body mass index (BMI), smoking status, physical activity, alcohol consumption and diet (Table 1 ). These health behaviours were defined as much as possible according to the existing guidelines for healthy living issued by the Belgian Superior Health Council [ 38 ] and the World Health Organisation [ 39 , 40 , 41 ]. Firstly, BMI was calculated as a person’s self-reported weight in kilogrammes divided by the square of the person’s self-reported height in metres (kg/m 2 ). BMI was classified into four categories: underweight (BMI < 18.5 kg/m 2 ), normal weight (BMI 18.5–24.9 kg/m 2 ), overweight (BMI 25.0–29.9 kg/m 2 ) and obese (BMI ≥ 30.0 kg/m 2 ). Due to a J-shaped association of BMI with the overall mortality and multiple specific causes of death, obesity and underweight were both classified as least healthy [ 42 ]. BMI was scored as follows: obese and underweight = 0, overweight = 1 and normal weight = 2.

Secondly, smoking status was divided into four categories. Participants were categorised as regular smokers if they smoked a minimum of 4 days per week or if they quit smoking less than 1 month before participation (= 0). Occasional smokers were defined as smoking more than once per month up to 3 days per week (= 1). Participants were classified as former smokers if they quit smoking at least 1 month before the questionnaire or if they smoked less than once a month (= 2). The final category included people who never smoked (= 3).

Thirdly, physical activity was assessed by the question: “What describes best your leisure time activities during the last year?”. Four categories were established and scored as follows: sedentary activities (= 0), light activities less than 4 h/week (= 1), light activities more than 4 h/week or recreational sport less than 4 h/week (= 2) and recreational sport more than 4 h or intense training (= 3). Fourthly, information on the number of alcoholic drinks per week was used to categorise alcohol consumption. The different categories were set from high to low alcohol consumption: 22 drinks or more/week (= 0), 15–21 drinks/week (= 1), 8–14 drinks/week (= 2), 1–7 drinks/week (= 3)and less than 1 drink/week (= 4).

Finally, in line with the research by Benetou et al., a diet score was calculated using the frequency of consuming fruit, vegetables, snacks and sodas [ 43 ]. For fruit as well as vegetable consumption, the frequency was scored as follows: never (= 0), < 1/week (= 1), 1–3/week (= 2), 4–6/week (= 3) and ≥ 1/day (= 4). The frequency of consuming snacks and sodas was scored as follows: never (= 4), < 1/week (= 3), 1–3/week (= 2), 4–6/week (= 1) and ≥ 1/day (= 0). The diet score was then divided into tertiles, in line with the research by Benetou et al. [ 43 ]. A diet score of 0–9 points was classified as the least healthy behaviour (= 0). A diet score ranging from 10 to 12 made up the middle category (= 1), and a score from 13 to 16 was classified as the healthiest behaviour (= 2).

All five previously described health behaviours were combined into one healthy lifestyle score (Table 1 ). The sum of the scores obtained for each health behaviour indicated the absolute lifestyle score. To calculate the relative lifestyle score, each absolute scored health behaviour was given equal weight by recalculating its maximum absolute score to a relative score of 1. The relative lifestyle scores were then summed up to achieve a final continuous lifestyle score, ranging from 0 to 5, with a higher score representing a healthier lifestyle.

Telomere length and mitochondrial DNA content assay

Blood samples were collected during the BELHES and centrifuged for 15 min at 3000 rpm before storage at − 80 °C. After extracting the buffy coat from the blood sample, DNA was isolated using the QIAgen Mini Kit (Qiagen, N.V.V Venlo, The Netherlands). The purity and quantity of the sample were measured with a NanoDrop spectrophotometer (ND-2000; Thermo Fisher Scientific, Wilmington, DE, USA). DNA integrity was assessed by agarose gel electrophoresis. To ensure a uniform DNA input of 6 ng for each qPCR reaction, samples were diluted and checked using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, Europe).

Relative TL and mtDNAc were measured in triplicate using a previously described quantitative real-time PCR (qPCR) assay with minor modifications [ 44 , 45 ]. All reactions were performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in a 384-well format. Used telomere, mtDNAc and single copy-gene reaction mixtures and PCR cycles are given in Additional file 1 : Text. S1. Reaction efficiency was assessed on each plate by using a 6-point serial dilution of pooled DNA. Efficiencies ranged from 90 to 100% for single-copy gene runs, 100 to 110% for telomere runs and 95 to 105% for mitochondrial DNA runs. Six inter-run calibrators (IRCs) were used to account for inter-run variability. Also, non-template controls were used in each run. Raw data were processed and normalised to the reference gene using the qBase plus software (Biogazelle, Zwijnaarde, Belgium), taking into account the run-to-run differences.

Leucocyte telomere length was expressed as the ratio of telomere copy number to single-copy gene number (T/S) relative to the mean T/S ratio of the entire study population. Leucocyte mtDNAc was expressed as the ratio of mtDNA copy number to single-copy gene number (M/S) relative to the mean M/S ratio of the entire study population. The reliability of our assay was assessed by calculating the interclass correlation coefficient (ICC) of the triplicate measures (T/S and M/S ratios and T, M and S separately) as proposed by the Telomere Research Network, using RStudio version 1.1.463 (RStudio PBC, Boston, MA, USA). The intra-plate ICCs of T/S ratios, TL runs, M/S ratios, mtDNAc runs and single-copy runs were respectively 0.804 ( p  < 0.0001), 0.907 ( p  < 0.0001), 0.815 ( p  < 0.0001), 0.916 ( p  < 0.0001) and 0.781 ( p  < 0.0001). Based on the IRCs, the inter-plate ICC was 0.714 ( p  < 0.0001) for TL and 0.762 ( p  < 0.0001) for mtDNAc.

Statistical analysis

Statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). We performed a log(10) transformation of the TL and mtDNAc data to reduce skewness and to better approximate a normal distribution. Three analyses were done: (1) In the BHIS subset ( n  = 6054), we evaluated the association between the lifestyle score and the mental health and well-being outcomes (separately). These results are presented as the odds ratio (95% CI) of having a mental health condition or disorder for a one-point increment in the lifestyle score. (2) In the BELHES subset ( n  = 739), we evaluated the association between the lifestyle score and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) for a one-point increment in the lifestyle score. (3) In the BELHES subset ( n  = 739), we evaluated the association between the mental health and well-being outcomes and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) when having a mental health condition or disorder compared with the healthy group.

For all three analyses, we performed multivariable linear mixed models (GLIMMIX; unstructured covariance matrix) taking into account a priori selected covariates including age (continuous), sex (male, female), region (Flanders, Brussels Capital Region, Wallonia), highest educational level of the household (up to lower secondary, higher secondary, college or university), country of birth (Belgium, EU, non-EU) and household type (single, one parent with child, couple without child, couple with child, others). To capture the non-linear effect of age, we included a quadratic term when the result of the analysis showed that both the linear and quadratic terms had a p -value < 0.1. For the two analyses on TL and mtDNAc, we additionally adjusted for the date of participation in the BELHES. As multiple members of one household participated, we added household numbers in the random statement.

Bivariate analyses evaluating the associations between the characteristics and TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health and well-being are evaluated based on the same model. The chi-squared tests (categorical data) and t -tests (continuous data) were used to evaluate the characteristics of included and excluded participants. The lifestyle score was validated by creating a ROC curve and calculating the area under the curve (AUC) of the adjusted association between the lifestyle score and self-perceived health. Adjustments were made for age, sex, region, highest educational level of the household, country of birth and household type.

In a sensitivity analysis, to evaluate the robustness of our findings, we additionally adjusted our main models separately for perceived quality of social support (poor, moderate, strong) and chronic disease (suffering from any chronic disease or condition: yes, no). The third model, evaluating the biomarkers with the mental health outcomes, was also additionally adjusted for the lifestyle score.

Population characteristics

The characteristics of the BHIS and BELHES subset are presented in Table 2 . In the BHIS subset, 48.8% of the participants were men. The average age (SD) was 49.9 (17.5) years, and most participants were born in Belgium (79.5%). The highest educational level in the household was most often college or university degree (53.3%), and the most common household composition was couple with child(ren) (37.7%). The proportion of participants in different regions of Belgium, i.e. Flanders, Brussels Capital Region and Wallonia, was respectively 41.1%, 23.3% and 35.6%. For the BELHES subset, we found similar results except for region and education. We noticed more participants from Flanders and more participants with a high educational level in the household. The mean (SD) relative TL and mtDNAc were respectively 1.04 (0.23) and 1.03 (0.24). TL and mtDNAc were positively correlated (Spearman’s correlation = 0.21, p  < 0.0001).

We compared (1) the characteristics of the 6054 eligible BHIS participants that were included in the BHIS subset with the 2539 eligible participants that were excluded from the BHIS subset (Additional file 1 : Table S2) and (2) the 739 participants from the BHIS subset that were included in the BELHES subset with the 5315 participants that were excluded from the BELHES subset (Additional file 1 : Table S3). Except for sex and nationality in the latter, all other covariates showed differences between the included and excluded groups. On the other hand, population data from 2018 indicates that the average age (SD) of the adult Belgian population was 49.5 (18.9) with a distribution over Flanders, Brussels Capital Region and Wallonia of respectively 58.2%, 10.2% and 31.6% and that 48.7% were men. The distribution of our sample according to age and sex thus largely corresponds to the age and sex distribution of the adult Belgian population figures. The large difference in the regional distribution is due to the oversampling of the Brussels Capital Region in the BHIS.

Bivariate associations evaluating the characteristics with TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health are presented in Additional file 1 : Table S4. Briefly, men had a − 6.41% (95% CI: − 9.10 to − 3.65%, p  < 0.0001) shorter TL, a − 8.03% (95% CI: − 11.00 to − 4.96%, p  < 0.0001) lower mtDNAc, lower odds of psychological distress (OR = 0.59, 95% CI: 0.53 to 0.66, p  < 0.0001) and a lifestyle score of − 0.28 (95% CI: − 0.32 to − 0.24, p  < 0.0001) points less compared with women. Furthermore, a 1-year increment in age was associated with a − 0.64% (− 0.73 to − 0.55%, p  < 0.0001) shorter TL and a − 0.19% (95% CI: − 0.31 to − 0.08%, p  = 0.00074) lower mtDNAc.

Mental health prevalence and lifestyle characteristics

Within the BHIS subset, 32.3% and 18.0% of the participants had respectively psychological and severe psychological distress. 86.7% had suboptimal vitality, 12.0% indicated low life satisfaction and 22.0% had very bad to fair self-perceived health. The prevalence of depressive and generalised anxiety disorders was respectively 9.0% and 10.8%, respectively. 4.4% of the participants indicated to have had suicidal thoughts in the past 12 months. Similar results were found for the BELHES subset (Table 3 ).

Within the BHIS subset, the average lifestyle score (SD) was 3.1 (0.9) (Table 4 ). A histogram of the lifestyle score is shown in Additional file 1 : Fig. S2. 16.6% were regular smokers, and 4.9% reported 22 alcoholic drinks per week or more. 29.7% reported that their main leisure time included mainly sedentary activities, and 18.6% were underweight or obese. 29.2% were classified as having an unhealthy diet score. The participants of the BELHES subset were slightly more active, but no other dissimilarities were found (Table 4 ). The ROC curve shows an area under the curve (AUC) of 0.74, indicating a 74% predictive accuracy for the lifestyle score as a self-perceived health predictor (Additional file 1 : Fig. S3).

Healthy lifestyle and mental health and well-being

Living a healthier lifestyle, indicated by having a higher lifestyle score, was associated with lower odds of all mental health and well-being outcomes (Table 5 ). After adjustment, a one-point increment in the lifestyle score was associated with lower odds of psychological (OR = 0.74, 95% CI: 0.69, 0.79) and severe psychological distress (OR = 0.69, 95% CI: 0.64, 0.75). Similarly, for the same increment, the odds of suboptimal vitality, low life satisfaction and very bad to fair self-perceived health were respectively 0.62 (95% CI: 0.56, 0.68), 0.62 (95% CI: 0.56, 0.68) and 0.56 (95% CI: 0.52, 0.61). Finally, the odds of having depressive disorder, generalised anxiety disorder or suicidal ideation were respectively 0.57 (95% CI: 0.51, 0.63), 0.63 (95% CI: 0.57, 0.69) and 0.63 (95% CI: 0.55, 0.72) for a one-point increment in the lifestyle score.

The biomarkers of ageing

After adjustment, living a healthy lifestyle was positively associated with both TL and mtDNAc (Table 6 ). A one-point increment in the lifestyle score was associated with a 1.74 (95% CI: 0.11, 3.40%, p  = 0.037) higher TL and a 4.07 (95% CI: 2.01, 6.17%, p  = 0.00012) higher mtDNAc.

People suffering from severe psychological distress had a − 4.62% (95% CI: − 8.85, − 0.20%, p  = 0.041) lower mtDNAc compared with those who did not suffer from severe psychological distress. Similarly, people with suicidal ideation had a − 7.83% (95% CI: − 14.77, − 0.34%, p  = 0.041) lower mtDNAc compared with those without suicidal ideation. No associations were found for the other mental health and well-being outcomes, and no associations were found between mental health and TL (Table 6 ).

Sensitivity analysis

Additional adjustment of the main analyses for perceived quality of social support, chronic disease or lifestyle score (in the association between the mental health outcomes and the biomarkers of ageing) did not strongly change the effect of our observations (Additional file 1 : Tables S5-S7). However, we noticed that most of the associations between severe psychological distress or suicidal ideation and mtDNAc showed marginally significant results.

In this study, we evaluated the associations between eight mental health and well-being outcomes, a healthy lifestyle score and 2 biomarkers of biological ageing: telomere length and mitochondrial DNA content. Having a healthy lifestyle was positively associated with all mental health and well-being indicators and the markers of biological ageing. Furthermore, having had suicidal ideation or suffering from severe psychological distress was associated with a lower mtDNAc. However, no association was found between mental health and TL.

In the first part of this research, we evaluated the association between lifestyle and mental health and well-being and showed that living a healthy lifestyle was positively associated with better mental health and well-being outcomes. Similar trends were found in previous studies for each of the health behaviours separately [ 11 , 12 , 46 , 47 , 48 ]. Although evaluating these health behaviours separately provides valuable information, assessing them in combination with each other rather than independently might better reflect the real-life situation as they often co-occur and may exert a synergistic effect on each other [ 24 , 25 , 49 ]. For example, 68% of the adults in England engaged in two or more unhealthy behaviours [ 25 ]. Especially, smoking status and alcohol consumption co-occurred, but half of the studies in the review by Noble et al. indicated clustering of all included health behaviours [ 24 ].

To date, the number of studies evaluating the combination of multiple health behaviours and mental health and well-being in adults is limited, and most of them use a different methodology to assess this association [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Firstly, differences are found between the included health behaviours. Most studies included the four “SNAP” risk factors, i.e. smoking, poor nutrition, excess alcohol consumption and physical inactivity. Other health behaviours that were sometimes included were BMI/obesity, sleep duration/quality and psychological distress [ 50 , 53 , 54 , 56 ]. Secondly, differences are found in the scoring of the health behaviours and the use of the lifestyle score. Whereas in this study the health behaviours were scored categorically, studies often dichotomised the health behaviours and/or the final lifestyle score [ 50 , 52 , 53 , 56 ]. Also, two studies performed clustering [ 54 , 55 ]. Health behaviours can cluster together at both ends of the risk spectrum, but less is known about the middle categories. This is avoided by using the cluster method where participants are clustered based on similar behaviours. On the other hand, a lifestyle score can be of better use and more easily be interpreted when aiming to compare healthy versus unhealthy lifestyles, as was the case for this study.

Despite these different methods, all previously mentioned studies show similar results. Together with our findings, which also support these results, this provides clear evidence that an unhealthy lifestyle is associated with poor mental health and well-being outcomes. Important to notice is that, like our research, most studies in this field have a cross-sectional design and are therefore not able to assume causality. Therefore, mental health might be the cause or the consequence of an unhealthy lifestyle. Further prospective and longitudinal studies are warranted to confirm the direction of the association.

Healthy lifestyle and biomarkers of ageing

How lifestyle affects our health is not yet fully understood. One possible pathway is through oxidative stress and biological ageing. An unhealthy lifestyle has been associated with an increase in oxidative stress [ 57 , 58 , 59 ], and in turn, higher concentrations of oxidative stress are known to negatively affect TL and mtDNAc [ 60 ]. In this study, we showed that living a healthy lifestyle was associated with a longer TL and a higher mtDNAc. Our results showed a stronger association of lifestyle with mtDNAc compared with TL. TL is strongly determined by TL at birth [ 61 ]. On the other hand, mtDNAc might be more variable in shorter time periods. Although mtDNAc and TL were strongly correlated, this could explain why lifestyle is more strongly associated with mtDNAc. However, we can only speculate about this, and further research is necessary to confirm our results.

Similar as for the association with mental health, in previous studies, the biomarkers have been associated with health behaviours separately rather than combined [ 62 , 63 , 64 , 65 ]. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc. Our results are in line with our expectations. As TL and mtDNAc are known to be correlated [ 60 ], we would expect similar trends for both biomarkers. In the case of TL, few studies included a combined lifestyle score in association with this biomarker. Consistent with our results, in a study population of 1661 men, the sum score of a healthier lifestyle was correlated with a longer TL [ 66 ]. Similar results were found by Sun et al. where a combination of healthy lifestyles in a female study population was associated with a longer TL compared with the least healthy group [ 67 ]. Also, improvement in lifestyle has been associated with TL maintenance in the elderly at risk for dementia [ 68 ], and a lifestyle intervention programme was positively associated with leucocyte telomere length in children and adolescents [ 69 ]. These results suggest that on a biological level, a healthy lifestyle is associated with healthy ageing. Within this context, a study on adults aged 60 and older showed that maintaining a normal weight, not smoking and performing regular physical activity were associated with slower development of disability and a reduction in mortality [ 70 ]. Similarly, midlife lifestyle factors like non-smoking, higher levels of physical activity, non-obesity and good social support have been associated with successful ageing, 22 years later [ 71 ].

Mental health and well-being and biomarkers of ageing

Finally, we evaluated the association between the biomarkers of ageing and the mental health and well-being outcomes. The hypothesis that biological ageing is associated with mental health has been supported by observations showing that chronically stressed or psychiatrically ill persons have a higher risk for age-related diseases like dementia, diabetes and hypertension [ 23 , 72 , 73 ]. Important to notice is that, like our research, the majority of studies on this topic have a cross-sectional design and therefore are unable to identify causality. Therefore, it is currently unknown whether psychological diseases accelerate biological ageing or whether biological ageing precedes the onset of these diseases [ 74 ].

Our results showed a lower mtDNAc for individuals with suicidal ideation or severe psychological distress but not for any of the other mental health outcomes. Evidence on the association between mtDNAc and mental health is inconsistent. Women above 60 years old with depression had a significantly lower mtDNAc compared with the control group [ 75 ]. Furthermore, individuals with a low mtDNAc had poorer outcomes in terms of self-rated health [ 76 ]. In contrast, Otsuka et al. showed a higher peripheral blood mtDNAc in suicide completers [ 77 ], and studies on major depressive syndrome [ 78 ] and self-rated health [ 79 ] showed the same trend. Finally, Vyas et al. showed no significant association between mtDNAc and depression status in mid-life and older adults [ 80 ]. These differences might be due to the differences in study population and methods. For example, the two studies indicating lower mtDNAc in association with poor mental health both had an elderly study population, and one study population consisted of psychiatrically ill patients. Next to that, differences were found in the type of samples, mtDNAc assays and questionnaires or diagnostics. The inconsistency of these studies and our results calls for further research on this association and for standardisation of methods within studies to enable clear comparisons.

As for TL, we did not find an association with any of the mental health and well-being outcomes. Previous studies in adults showed a lower TL in association with current but not remitted anxiety disorder [ 81 ], depressive [ 82 ] and major depressive disorder [ 73 , 83 ], childhood trauma [ 84 ] suicide [ 77 , 85 ], depressive symptoms in younger adults [ 86 ] and acculturative stress and postpartum depression in Latinx women [ 87 ]. Also, in a meta-analysis, psychiatric disorders overall were associated with a shorter leucocyte TL [ 88 ]. However, other studies failed to demonstrate an association between TL and mental health outcomes like major depressive disorder [ 89 ], late-life depression [ 90 ] and anxiety disorder [ 91 ]. Again, this could be due to a different method to assess the mental health outcomes, a different study design, uncontrolled confounding factors and the type of telomere assay. For example, a meta-analysis showed stronger associations with depression when using southern blot or FISH assay compared with qPCR to measure telomere length [ 92 ].

Strengths and limitations

An important strength of this study is the use of a validated lifestyle score that can easily be reproduced and used for other research on lifestyle. Secondly, we were able to use a large sample size for our analyses in the BHIS subset. Thirdly, by assessing multiple dimensions of mental health and well-being, we were able to give a comprehensive overview of the mental health status. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc.

Our results should however be interpreted with consideration for some limitations. As mentioned before, the study has a cross-sectional design, and therefore, we cannot assume causality. Secondly, for the lifestyle score, we used self-reported data, which might not always represent the actual situation. For example, BMI values tend to be underestimated due to the overestimation of height and underestimation of weight [ 93 ], and also, smoking behaviour is often underestimated [ 94 ]. Also, equal weights were used for each of the health behaviours as no objective information was available on which weight should be given to a specific health behaviour. Thirdly, there is a distinct time lag between the completion of the BHIS questionnaire and the collection of the BELHES samples. The mean (SD) number of days is 52 (35). This is less than the period for suicidal ideation, assessed over the 12 previous months, but there might be a more limited overlap with the period for assessment of the other mental health variables, such as vitality and psychological distress, assessed over the last few weeks, and depressive and generalised anxiety disorders, assessed over the last 2 weeks. Fourthly, due to a non-response bias, the lowest socio-economic classes are less represented in our study population. This will not affect our dose–response associations but might affect the generalisability of our findings to the overall population. Finally, we do not have data on blood cell counts, which has been associated with mtDNAc [ 95 ].

In this large-scale study, we showed that living a healthy lifestyle was positively associated with mental health and well-being and, on a biological level, with a higher TL and mtDNAc, indicating healthy ageing. Furthermore, individuals with suicidal ideation or suffering from severe psychological distress had a lower mtDNAc. Our findings suggest that implementing strategies to incorporate healthy lifestyle changes in the public’s daily life could be beneficial for public health, and might offset the negative impact of environmental stressors. However, further studies are necessary to confirm our results and especially prospective and longitudinal studies are essential to determine causality of the associations.

Availability of data and materials

The dataset used for this study is available through a request to the Health Committee of the Data Protection Authority.

Abbreviations

Area under the curve

Body mass index

Confidence intervals

Generalised Anxiety Disorder Questionnaire

General Health Questionnaire

Inter-run calibrator

  • Mitochondrial DNA content

Patient Health Questionnaire

Relative operating characteristic curve

Short Form Health Survey

  • Telomere length

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Acknowledgements

We are grateful to all BHIS and BELHES participants for contributing to this study.

The HuBiHIS project is financed by Sciensano (PJ) N°: 1179–101. Dries Martens is a postdoctoral fellow of the Research Foundation—Flanders (FWO 12X9620N).

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Additional file 1: text s1..

TL, mtDNAc and single copy-gene reaction mixture and PCR cycling conditions. Table S1. The mental health indicators with their scores and uses. Table S2. Comparison of the characteristics of the 6,054 eligible BHIS participants that were included in the BHIS subset compared to the 1,838 eligible participants that were excluded from the BHIS subset. Table S3. Comparison of the characteristics of the 739 participants from the BHIS subset that were included in the BELHES subset compared to the 5,315 participants that were excluded from the BELHES subset. Table S4. Bivariate associations between the characteristics and telomere length (TL), mitochondrial DNA content (mtDNAc), the lifestyle score or psychological distress. Table S5. Results of the sensitivity analysis of the association between lifestyle and mental health. Table S6. Results of the sensitivity analysis of the association between lifestyle and the biomarkers of ageing. Table S7. Results of the sensitivity analysis of the association between mental health and the biomarkers of ageing. Fig. S1. Exclusion criteria. The BHIS subset consisted of 6,055 BHIS participants and the BELHES subset consisted of 739 BELHES participants. Fig. S2. Histogram of the lifestyle score. Fig. S3. Validation of the lifestyle score. ROC curve for the lifestyle score as a predictor for good to very good self-perceived health. The model was adjusted for age, sex, region, highest educational level in the household, household composition and country of birth.

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Healthy lifestyle is linked to gains in disease-free life expectancy in China

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A healthy lifestyle is associated with longer total life expectancy and a larger proportion of remaining years lived without a major noncommunicable disease in the Chinese population. Public health initiatives that promote healthy lifestyles may have a role in realizing the Healthy China 2030 strategic plan.

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GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392 , 1789–1858 (2018). This paper reports that the prevalence and disease burden of noncommunicable diseases in the Chinese population was heavy.

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Healthy Living Guide 2021/2022

A digest on healthy eating and healthy living.

Cover image of the Healthy Living Guide downloadable PDF

Over the course of 2021, many of us continued to adapt to a “new normal,” characterized by a return to some pre-pandemic activities mixed with hobbies or habits that have emerged since 2020’s lockdowns. On the topic of food and eating, according to one U.S. consumer survey the year marked a decrease in certain behaviors that had changed abruptly during 2020. For example, fewer Americans reported that they were “snacking more” (18% in 2021 vs. 32% in 2020) or “eating more in general” (11% in 2021 vs. 20% in 2020). However, consumers also signaled a decrease in cooking at home (47% in 2021 vs. 60% in 2020); while other survey findings underscored significant disparities in food security. Beyond food, the COVID-19 pandemic continues to generate a wide range of unique and individual impacts, and the emergence of new disease variants is a sobering reminder of the urgency for increased vaccination globally, especially in low- and lower-middle-income countries.

As we all continue to navigate the twists and turns of this pandemic, we once again invite you to do what you can to incorporate healthy behaviors into your daily life. This year’s edition revisits the core themes of eating well, being active, and getting enough sleep with selected research highlights, as well as a closer look at some popular nutrition and lifestyle topics. We hope that you find it useful, and we wish you a very healthy and fulfilling 2022.

Download a copy of the Healthy Living Guide (PDF) featuring printable tip sheets and summaries, or access many of the full online articles through the links below. 

Key features this issue:

  • A blueprint for building healthy meals
  • Exploring aquatic foods
  • Are anti-nutrients harmful? 
  • Navigating information on food packages
  • The science of cravings
  • Anti-inflammatory diet review
  • Clearing up confusion on soy and health
  • Spotlight on collagen
  • Yoga for exercise
  • High-intensity interval training
  • Workout supplement review
  • Updates on sleep

Plus: Test your healthy living knowledge

Hint: the answers can be found throughout last year’s Healthy Living Guide. Access the full edition here if you haven’t checked it out!

Healthy living

  • Climate Solutions
  • Commencement 2024

Living a Healthy Life

What is the secret to living a healthy life? Harvard experts explore the decisions we can make every day to ensure that we are prioritizing our health and wellbeing.

What we eat

How does our relationship with food impact our overall health? Harvard experts are researching the ways that food helps and hinders our wellness.

Carrots and strawberries

Science and research can help us make better choices when it comes to the foods we eat. Research suggests that some foods, like avocados and olive oil , provide benefits to our minds and bodies.

Scientists study everything from the relationship between late-night eating and weight gain and whether drinking coffee is good for you to how diets differ based on race and gender and how your diet and oral health are related.

Explore the evolution of the human metabolism and what makes a meal healthy .

How we move

Why is exercise important and how can we make sure we are active enough? Harvard experts are working to better understand how we can benefit from an active lifestyle.

Humans have deep-rooted instincts to avoid unnecessary physical activity, because until recently it was beneficial to avoid it.” Dan Lieberman Professor of biological science Learn more about evolution and exercise

Balancing diet and exercise

The best balance is both a good diet and an active lifestyle, Professor I-Min Lee says.

Top 5 exercises

No matter your age or fitness level, these activities are some of the best you can do to help get in shape.

What we feel

How are our mental health and physical health linked? Harvard experts study the ways we can best take care of ourselves holistically.

Students form a circle outdoors around a large lawn

We're all human

Intellectual growth and academic achievement should not come at the expense of our health. Harvard recently launched a new collection of mental health and wellbeing resources for students, faculty, and staff.

  • Harvard Medical School

Is a mobile app as good as a therapist?

  • Brigham and Women’s Hospital

Daytime eating and mental health

  • Harvard Law School

Student group focuses on wellbeing

  • Harvard Chan School

Spirituality may lead to better health outcomes

  • Harvard Graduate School of Education

Making time for mindfulness in the classroom

How we find joy.

What things in our world make us happier and healthier? Harvard experts are exploring how quality of life impacts both our mental and physical health.

An 80-year study investigates how to live a healthy and happy life

Learn about opportunities in your neighborhood and which greenspaces are easily accessible to you.” Heather Eliassen Professor of nutrition and epidemiology Read more about how spending time outdoors can improve your health

Music and mood

New analysis finds that music boosts our mood and wellbeing, and may even help during treatments for certain health conditions.

Healthy buildings

What makes a place “healthy” is complex, layered, and sometimes even contradictory, but our wellbeing depends on the places around us.

How we rest

How are sleep and wellness entwined? Researchers at Harvard study the different aspects of how sleep relates to our health.

Learn tips for how to get enough rest

Our brain while we sleep

Scientists believe that sleep plays a role in how we learn and form long-term memories.

Excessive napping

A study of older adults found that excessive daytime napping may signal an elevated risk of Alzheimer’s disease.

Sleepy students

Researchers have found evidence that school start times impact non-cognitive factors like academic performance, attendance, and dropout rates.

What we moderate

What habits should we avoid to ensure that we are prioritizing healthy living? Many of the dangers to our health and wellbeing are being investigated by experts at Harvard.

Four glasses of beer

Risky behaviors can negatively impact our health, cause health complications, and even decrease our lifespan.

Scientists at Harvard and beyond are investigating the dangers of vaping , evaluating the effectiveness of medical marijuana , and researching the paths to addiction .

Recent research has revealed that consuming less sodium and more potassium can decrease the risk of cardiovascular disease.   Researchers have also linked sleep deprivation and alcohol consumption to the rise of cancer in those under 50 .

How we live longer

What habits can we adopt to lengthen our lives? Harvard experts are studying the choices we can make that may help increase our longevity.

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May 29, 2024

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Live long, stay healthy: Study reveals important health markers

by Rasmus Cloes, Leibniz Institute for Prevention Research and Epidemiology

health

In a recent study, the Leibniz Institute for Prevention Research and Epidemiology—BIPS has made significant progress in identifying health markers that are crucial for a long and healthy life. Led by Prof. Dr. Krasimira Aleksandrova and in close collaboration with the German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), the research provides valuable insights for healthy aging.

In the study published in the journal Age and Ageing , Aleksandrova and her team analyzed specific combinations of molecular markers reflecting various biological processes as possible indicators of healthy aging. The focus was particularly on identifying specific combinations of blood biomarkers that can help distinguish people who are reaching older age in good health versus those who develop chronic diseases, such as diabetes, coronary heart disease and cancer.

"Our results show that people who reach old age and remain free of chronic diseases are those who have maintained optimal levels of specific combinations of metabolic analytes linked with insulin sensitivity and inflammation throughout late phases of their life," explains Aleksandrova. This may indicate a common protective mechanism that reduces the risk of age-related diseases.

By understanding these markers and their complex interrelation, we can better assess what preventive measures need to be taken to avoid chronic diseases and improve quality of life at old age.

Methodology of the study

For the study, data was collected from a large group of older adults who were part of the EPIC-Potsdam study (EPIC: European Prospective Investigation into Cancer and Nutrition). This comprised 27,548 participants aged between 34 and 65 years who were recruited between 1994 and 1998 in Potsdam and the surrounding area.

At the beginning of the study, all participants underwent comprehensive anthropometric measurements and provided data on their lifestyle and diet. In addition, blood samples were taken from 26,437 participants. This group was followed for several years, with information on new chronic diseases being collected every 2–3 years.

A randomly selected subgroup of 2,500 people was formed for the current study. From this group, participants who already suffered from certain diseases or whose diagnoses were unclear were excluded, leaving 2,296 participants.

Of these participants, the concentrations of 13 specific blood biomarkers were quantified using established laboratory assays and protocols. These markers included molecules that reflected sugar and fat metabolism, liver and kidney function, insulin sensitivity and inflammation.

Data analysis and results

Using innovative statistical modeling, the research team was able to isolate several combinations of molecules that characterized groups of individuals in relation to healthy aging. The study defined healthy aging as reaching age 70 without developing any chronic disease, such as diabetes, coronary heart disease , or cancer.

The analysis revealed that individuals that maintained high concentrations of high-density lipoprotein cholesterol, known as "the good cholesterol," the fat hormone adiponectin and insulin-like growth factor binding proteins-2 along with low triglyceride levels had a higher likelihood of living without chronic disease at old age compared to their counterparts. These findings underline the need to understand the complex pathways reflected by these biomarkers that underscore protective mechanisms leading to healthy aging.

"Our results show how important it is to study combinations of multiple biomarkers rather than looking at individual molecules in isolation," explains Aleksandrova. She adds, "Our study reverses the focus from individual disease outcomes to a composite healthy aging outcome.

"Rather than trying to focus on single molecules and single pathologies, we are interested to understand the complex biological pathways that promote healthy longevity. This paradigm shift is further reflected in the activities of the Leibniz Research Network Resilient aging with the participation of our institute.

"Importantly, the study also showed that the beneficial biomarker profiles could be influenced by individual behaviors, such as keeping healthy weight, non-smoking and eating a balanced diet—especially avoiding highly processed and red meats and including high abundance of various fruits and vegetables.

"Further studies involving a broader range of biomarkers are needed to better understand the biological pathways that contribute to maintaining health in old age. This may eventually lead to proposing panels of blood biomarkers that can be used for improved prevention and health monitoring."

The study highlights what we already know about the importance of active and healthy lifestyle and proposes that biomarkers can be better used as tools to guide individuals and medical professionals towards health monitoring and chronic disease prevention.

Considering how biomarkers can be influenced by our lifestyle choices, here are 5 tips for healthy aging

  • Follow a balanced diet : Along with assuring many fresh fruits and vegetables and limited intakes of processed foods, adding healthy fats to your diet can help increase levels of HDL cholesterol (HDL-C). For example, foods such as avocados, nuts and fatty fish (such as salmon and mackerel) are known to increase HDL-C levels.
  • Keep physically active: Regular exercise helps improve metabolic health and can increase levels of adiponectin, which in turn reduces inflammation and improves insulin resistance. Activities such as walking, running, cycling or swimming are recommended.
  • Maintain a healthy body weight: Maintaining a healthy body weight and reducing body fat is important to maintain low triglyceride levels and promote overall metabolic health. This can be achieved through a combination of a healthy diet and regular physical activity.
  • Avoid smoking: Smoking has negative effects on lipid profiles and overall health. Staying non-smoking or quitting smoking can help improve levels of HDL-C and other important biomarkers.
  • Take steps to manage stress and anxiety: Chronic stress can have negative effects on inflammation and metabolism. Simple practices such as getting enough sleep, walking and techniques such as meditation, yoga and mindfulness can help to reduce stress levels and therefore promote overall health.

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Changing Your Habits for Better Health

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What stage of change are you in?

Contemplation: are you thinking of making changes, preparation: have you made up your mind, action: have you started to make changes, maintenance: have you created a new routine, clinical trials.

Are you thinking about being more active? Have you been trying to cut back on less healthy foods? Are you starting to eat better and move more but having a hard time sticking with these changes?

Old habits die hard. Changing your habits is a process that involves several stages. Sometimes it takes a while before changes become new habits. And, you may face roadblocks along the way.

Adopting new, healthier habits may protect you from serious health problems like obesity and diabetes . New habits, like healthy eating and regular physical activity, may also help you manage your weight and have more energy. After a while, if you stick with these changes, they may become part of your daily routine.

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The information below outlines four stages you may go through when changing your health habits or behavior. You will also find tips to help you improve your eating, physical activity habits, and overall health. The four stages of changing a health behavior are

  • contemplation
  • preparation
  • maintenance

Contemplation: “I’m thinking about it.”

In this first stage, you are thinking about change and becoming motivated to get started.

You might be in this stage if you

  • have been considering change but are not quite ready to start
  • believe that your health, energy level, or overall well-being will improve if you develop new habits
  • are not sure how you will overcome the roadblocks that may keep you from starting to change

Preparation: “I have made up my mind to take action.”

In this next stage, you are making plans and thinking of specific ideas that will work for you.

  • have decided that you are going to change and are ready to take action
  • have set some specific goals that you would like to meet
  • are getting ready to put your plan into action

Action: “I have started to make changes.”

In this third stage, you are acting on your plan and making the changes you set out to achieve.

  • have been making eating, physical activity, and other behavior changes in the last 6 months or so
  • are adjusting to how it feels to eat healthier, be more active, and make other changes such as getting more sleep or reducing screen time
  • have been trying to overcome things that sometimes block your success

Maintenance: “I have a new routine.”

In this final stage, you have become used to your changes and have kept them up for more than 6 months.

You might be in this stage if

  • your changes have become a normal part of your routine
  • you have found creative ways to stick with your routine
  • you have had slip-ups and setbacks but have been able to get past them and make progress

Did you find your stage of change? Read on for ideas about what you can do next.

Making the leap from thinking about change to taking action can be hard and may take time. Asking yourself about the pros (benefits) and cons (things that get in the way) of changing your habits may be helpful. How would life be better if you made some changes?

Think about how the benefits of healthy eating or regular physical activity might relate to your overall health. For example, suppose your blood glucose, also called blood sugar, is a bit high and you have a parent, brother, or sister who has type 2 diabetes . This means you also may develop type 2 diabetes. You may find that it is easier to be physically active and eat healthy knowing that it may help control blood glucose and protect you from a serious disease.

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You may learn more about the benefits of changing your eating and physical activity habits from a health care professional. This knowledge may help you take action.

Look at the lists of pros and cons below. Find the items you believe are true for you. Think about factors that are important to you.

Healthy Eating

Physical activity.

If you are in the preparation stage, you are about to take action. To get started, look at your list of pros and cons. How can you make a plan and act on it?

The chart below lists common roadblocks you may face and possible solutions to overcome roadblocks as you begin to change your habits. Think about these things as you make your plan.

Once you have made up your mind to change your habits, make a plan and set goals for taking action. Here are some ideas for making your plan:

  • learn more about healthy eating and food portions
  • learn more about being physically active
  • healthy foods that you like or may need to eat more of—or more often
  • foods you love that you may need to eat less often
  • things you could do to be more physically active
  • fun activities you like and could do more often, such as dancing

After making your plan, start setting goals for putting your plan into action. Start with small changes. For example, “I’m going to walk for 10 minutes, three times a week.” What is the one step you can take right away?

You are making real changes to your lifestyle, which is fantastic! To stick with your new habits

  • review your plan
  • look at the goals you set and how well you are meeting them
  • overcome roadblocks by planning ahead for setbacks
  • reward yourself for your hard work

Track your progress

  • Tracking your progress helps you spot your strengths, find areas where you can improve, and stay on course. Record not only what you did, but how you felt while doing it—your feelings can play a role in making your new habits stick.
  • Recording your progress may help you stay focused and catch setbacks in meeting your goals. Remember that a setback does not mean you have failed. All of us experience setbacks. The key is to get back on track as soon as you can.
  • You can track your progress with online tools such as the NIH Body Weight Planner . The NIH Body Weight Planner lets you tailor your calorie and physical activity plans to reach your personal goals within a specific time period.

Overcome roadblocks

  • Remind yourself why you want to be healthier. Perhaps you want the energy to play with your nieces and nephews or to be able to carry your own grocery bags. Recall your reasons for making changes when slip-ups occur. Decide to take the first step to get back on track.
  • Problem-solve to “outsmart” roadblocks. For example, plan to walk indoors, such as at a mall, on days when bad weather keeps you from walking outside.
  • Ask a friend or family member for help when you need it, and always try to plan ahead. For example, if you know that you will not have time to be physically active after work, go walking with a coworker at lunch or start your day with an exercise video.

Reward yourself

  • After reaching a goal or milestone, allow for a nonfood reward such as new workout gear or a new workout device. Also consider posting a message on social media to share your success with friends and family.
  • Choose rewards carefully. Although you should be proud of your progress, keep in mind that a high-calorie treat or a day off from your activity routine are not the best rewards to keep you healthy.
  • Pat yourself on the back. When negative thoughts creep in, remind yourself how much good you are doing for your health by moving more and eating healthier.

Make your future a healthy one. Remember that eating healthy, getting regular physical activity, and other healthy habits are lifelong behaviors, not one-time events. Always keep an eye on your efforts and seek ways to deal with the planned and unplanned changes in life.

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Now that healthy eating and regular physical activity are part of your routine, keep things interesting, avoid slip-ups, and find ways to cope with what life throws at you.

Add variety and stay motivated

  • Mix up your routine with new physical activities and goals, physical activity buddies, foods, recipes, and rewards.

Deal with unexpected setbacks

  • Plan ahead to avoid setbacks. For example, find other ways to be active in case of bad weather, injury, or other issues that arise. Think of ways to eat healthy when traveling or dining out, like packing healthy snacks while on the road or sharing an entrée with a friend in a restaurant.
  • If you do have a setback, don’t give up. Setbacks happen to everyone. Regroup and focus on meeting your goals again as soon as you can.

Challenge yourself!

  • Revisit your goals and think of ways to expand them. For example, if you are comfortable walking 5 days a week, consider adding strength training twice a week. If you have limited your saturated fat intake by eating less fried foods, try cutting back on added sugars, too. Small changes can lead to healthy habits worth keeping.

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and other components of the National Institutes of Health (NIH) conduct and support research into many diseases and conditions.

What are clinical trials, and are they right for you?

Clinical trials are part of clinical research and at the heart of all medical advances. Clinical trials look at new ways to prevent, detect, or treat disease. Researchers also use clinical trials to look at other aspects of care, such as improving the quality of life for people with chronic illnesses. Find out if clinical trials are right for you.

What clinical trials are open?

Clinical trials that are currently open and are recruiting can be viewed at www.ClinicalTrials.gov .

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

The NIDDK would like to thank: Dr. Carla Miller, Associate Professor, Ohio State University

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Fast Facts: Health and Economic Costs of Chronic Conditions

At a glance.

Chronic diseases account for most illness, disability, and deaths in the United States and are the leading drivers of health care costs.

a stethoscope on a pile of hundred dollar bills

The impact of chronic diseases in America

Ninety percent of the nation's $4.5 trillion in annual health care expenditures are for people with chronic and mental health conditions. 1 2 Interventions to prevent and manage these diseases have significant health and economic benefits .

Heart disease and stroke

Nothing kills more Americans than heart disease and stroke . More than 934,500 Americans die of heart disease or stroke every year—that's more than 1 in 4 deaths. 3 These diseases take an economic toll, as well, costing our health care system $251 billion per year and causing $156 billion in lost productivity on the job. Costs from cardiovascular diseases are projected to top $1 trillion by 2035. 3

See the health and economic benefits of high blood pressure interventions .

Each year in the United States, 1.7 million people are diagnosed with cancer , and more than 600,000 die from it, making it the second leading cause of death. The cost of cancer care continues to rise and is expected to reach more than $240 billion by 2030. 4

See the health and economic benefits of interventions for breast cancer , cervical cancer , colorectal cancer , and skin cancer .

More than 38 million Americans have diabetes , and another 98 million adults in the United States have prediabetes, which puts them at risk for type 2 diabetes. Diabetes can cause serious complications, including heart disease, kidney failure, and blindness. In 2022, the total estimated cost of diagnosed diabetes was $413 billion in medical costs and lost productivity. 5

See the health and economic benefits of diabetes interventions .

Obesity affects 20% of children and 42% of adults, putting them at risk of chronic diseases such as type 2 diabetes, heart disease, and some cancers. Over 25% of young people aged 17 to 24 are too heavy to join the U.S. military. Obesity costs the U.S. health care system nearly $173 billion a year. 6

Arthritis affects 53.2 million adults in the United States, which is about 1 in 5 adults. 7 It is a leading cause of work disability in the United States, one of the most common chronic conditions, and a leading cause of chronic pain. Arthritis costs appear to be increasing and were estimated at over $600 billion in 2019. 8 9

Alzheimer's disease

Alzheimer's disease , a type of dementia, is an irreversible, progressive brain disease that affects nearly 7 million Americans, including 1 in 9 adults aged 65 and older. Two-thirds of these older adults (4.1 million) are women. Deaths due to Alzheimer's disease more than doubled between 2000 and 2019, increasing 145%. The cost of caring for people with Alzheimer's and other dementias was an estimated $345 billion in 2023, with projected increases to nearly $1 trillion (in today's dollars) by 2050. 10

In the United States, about 3 million adults and about half a million children and teens younger than 18 have active epilepsy —meaning that they have been diagnosed by a doctor, had a recent seizure, or both. Adults with epilepsy report worse mental health, more cognitive impairment, and barriers in social participation compared to adults without epilepsy. In 2019, total health care costs (epilepsy-attributable and other health-related costs) for noninstitutionalized people with epilepsy was $13.4 billion, of which $5.4 billion were directly attributable to epilepsy. 11

Tooth decay

Cavities (also called tooth decay) are one of the most common chronic diseases in the United States. One in six children aged 6 to 11 years and 1 in 4 adults have untreated cavities. Untreated cavities can cause pain and infections that may lead to problems eating, speaking, and learning. On average, 34 million school hours are lost each year because of unplanned (emergency) dental care, and almost $46 billion is lost in productivity due to dental disease. 12 13

See the health and economic benefits of oral disease interventions .

Risk Factors

Cigarette smoking.

Cigarette smoking is the leading cause of preventable death and disease in the United States. More than 16 million Americans have at least one disease caused by smoking. This amounts to more than $240 billion in health care spending that could be reduced every year if we could prevent young people from starting to smoke and help every person who smokes quit. 14

See the health and economic benefits of tobacco use interventions .

Physical inactivity

Not getting enough physical activity comes with high health and financial costs. It can lead to heart disease, type 2 diabetes, some cancers, and obesity. 15 Physical inactivity also costs the nation $117 billion a year for related health care. 16

Excessive alcohol use

Excessive alcohol use is responsible for 140,000 deaths in the United States each year, including 1 in 5 deaths among adults aged 20 to 49 years. 17 18 Binge drinking is responsible for over 40% these deaths. 17 In 2010, excessive alcohol use cost the U.S. economy $249 billion, or $2.05 a drink, and $2 of every $5 of these costs were paid by the public. 19 Three-quarters of these costs were due to binge drinking.

  • Buttorff C, Ruder T, Bauman M. Multiple Chronic Conditions in the United States . Rand Corp.; 2017.
  • National health expenditure data: historical. Center for Medicare & Medicaid Services. Updated December 13, 2023. Accessed February 6, 2024. https://www.cms.gov/data-research/statistics-trends-and-reports/national-health-expenditure-data/historical
  • Centers for Disease Control and Prevention, National Center for Health Statistics. Multiple Cause of Death 1999–2019 on CDC WONDER Online Database website. http://wonder.cdc.gov/mcd-icd10.html . Accessed February 1, 2023.
  • Mariotto AB, Enewold L, Zhao J, Zeruto CA, Yabroff KR. Medical care costs associated with cancer survivorship in the United States. Cancer Epidemiol Biomarkers Prev. 2020;29:1304–1312.
  • Parker ED, Lin J, Mahoney T, et al. Economic costs of diabetes in the U.S. in 2022. Diabetes Care. 2023. doi: 10.2337/dci23-0085
  • Ward ZJ, Bleich SN, Long MW, Gortmaker SL. Association of body mass index with health care expenditures in the United States by age and sex. PLoS One . 2021;16(3):e0247307.
  • Fallon EA, Boring MA, Foster AL, et al. Prevalence of diagnosed arthritis—United States, 2019–2021. MMWR Morb Mortal Wkly Rep . 2023;72:1101–1107.
  • Murphy LB, Cisternas MG, Pasta DJ, Helmick CG, Yelin EH. Medical expenditures and earnings losses among US adults with arthritis in 2013. Arthritis Care Res (Hoboken) . 2018;70(6):869–876.
  • Lo J, Chan L, Flynn S. A systematic review of the incidence, prevalence, costs, and activity and work limitations of amputation, osteoarthritis, rheumatoid arthritis, back pain, multiple sclerosis, spinal cord injury, stroke, and traumatic brain injury in the United States: a 2019 Update. Arch Phys Med Rehabil . 2021;102(1):115–131.
  • Alzheimer's Association. 2023 Alzheimer's disease facts and figures. Alzheimers Dement . 2023;19(4).
  • Moura LMVR, Karakis I, Zack MM, Tian N, Kobau R, Howard D. Drivers of US health care spending for persons with seizures and/or epilepsies, 2010-2018. Epilepsia . 2022;63(8):2144–2154.
  • Righolt AJ, Jevdjevic M, Marcenes W Listl S. Global-, regional-, and country-level economic impacts of dental diseases. J Dent Res. 2018;97(5):501–507.
  • Naavaal S, Kelekar U. Hours lost due to planned and unplanned dental visits among US adults. Health Behav Policy Rev. 2018;5(2):66–73.
  • Xu X, Shrestha SS, Trivers KF, Neff L, Armour BS, King BA. U.S. healthcare spending attributable to cigarette smoking in 2014. Prev Med . 2021;150:106529.
  • U.S. Department of Health and Human Services. Step It Up! The Surgeon General's Call to Action to Promote Walking and Walkable Communities . Office of the Surgeon General; 2015.
  • Carlson SA, Fulton JE, Pratt M, Yang Z, Adams EK. Inadequate physical activity and health care expenditures in the United States . Prog Cardiovasc Dis . 2015;57:315–323.
  • Centers for Disease Control and Prevention (CDC). Alcohol-Related Disease Impact (ARDI).
  • Esser MB, Leung G, Sherk A, et al. Estimated deaths attributable to excessive alcohol use among US adults aged 20 to 64 years, 2015 to 2019. JAMA Netw Open . 2022;5(11):e2239485. doi: 10.1001/jamanetworkopen.2022.39485
  • Sacks JJ, Gonzales KR, Bouchery EE, Tomedi LE, Brewer RD. 2010 national and state costs of excessive alcohol consumption. Am J Prev Med 2015;49(5):e73–e79.

Chronic Disease

Prevalence, costs, risks, prevention, and management of chronic diseases in the United States

For Everyone

Public health.

This paper is in the following e-collection/theme issue:

Published on 31.5.2024 in Vol 26 (2024)

Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review

Authors of this article:

Author Orcid Image

  • Sharon Guardado 1 , MSc   ; 
  • Maria Karampela 1 , PhD   ; 
  • Minna Isomursu 1 , PhD   ; 
  • Casandra Grundstrom 2 , PhD  

1 Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland

2 Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

Corresponding Author:

Sharon Guardado, MSc

Faculty of Information Technology and Electrical Engineering

University of Oulu

Pentti Kaiteran katu 1

Oulu, 90570

Phone: 358 504388396

Email: [email protected]

Background: Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients’ behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context.

Objective: This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them.

Methods: A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses.

Results: The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients’ devices. PGHD about patients’ behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies.

Conclusions: PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce.

International Registered Report Identifier (IRRID): RR2-10.2196/39389

Introduction

The term “mobile health” (mHealth) has been in use for nearly 2 decades to refer to the application of mobile technologies in delivering health services and collecting data pertinent to disease diagnosis, prevention, and management [ 1 , 2 ]. In the last decade, the scope of mHealth has expanded to include consumer-grade devices, such as smartphones, wearable, sensors, and other quasi-medical devices, while it increasingly targets specific health conditions, in addition to wellness [ 2 , 3 ]. Whereas medical-grade mobile devices require clinical evidence for certification, often requiring years to bring a device to the market [ 4 ], consumer-grade mobile devices evolving at a rapid pace, and open numerous possibilities through their capacity for ubiquitous data collection [ 5 ]. mHealth solutions have become integral to many people’s lives, serving as tools for tracking health and well-being. Research has found that mHealth solutions can benefit individuals in general by fostering moderate increases in physical activity [ 6 ] or by being a convenient tool for self-management of health issues [ 7 ]. For individuals with chronic diseases, mHealth solutions have been particularly effective in offering support for condition management, goal setting, and enhancing overall satisfaction [ 7 , 8 ]. In addition to supporting people’s efforts to manage their health, mHealth solutions also enable the collection of electronic patient-generated health data (PGHD), which can be used in the clinical context. PGHD refer to health-related data created, recorded, and gathered by and from patients outside of the clinical settings [ 9 , 10 ]. PGHD encompasses a broad range of data types from both passive and active sensing [ 1 , 11 ]. Passive data collection usually involves sensors that are connected to a mobile device that may be worn or embedded, limiting the patient’s participation to wearing, carrying, or activating the device [ 12 ]. Active data collection requires patients to manually enter information or interact with an external device such as a peak flowmeter, glucometer, or thermometer to generate information. These data are “patient-generated” since the patient has actively participated in collecting and recording [ 12 ]. It has been hypothesized that through both passively and actively collected PGHD, health care professionals (HCPs) could gain insights into patients’ activities, lifestyle, and physical condition to inform care decisions and personalize care approaches [ 13 ].

In countries with medium or high levels of digitalization, more than 56% of people appear willing to share their personal health data, even if the purpose of sharing them is not directly related to the improvement of their health [ 14 ]. Similarly, 46.3% of individuals who owned a wearable medical device indicated having shared data with a health provider in 2019 [ 15 ]. With mHealth solutions becoming increasingly accessible, it can be expected that more people may be interested in sharing their health data with HCPs if they believe that it could help them improve health care. However, a recent study found that although providers of mHealth solutions for chronic condition self-management encourage data sharing with HCPs, few solutions are designed to facilitate HCPs’ review of these data [ 4 ]. This issue, in combination with already known challenges such as interoperability, data privacy issues, data validity, and the added burden of reviewing [ 9 , 16 ], makes the use of PGHD in the clinic an unrealistic possibility for many HCPs.

HCPs might have different approaches and goals when deciding to ponder PGHD collected through nonmedical mobile devices. According to Nittas et al [ 17 ], when integrating PGHD into the care process, HCPs can take the supporter or the reviewer role. In the supporter role, they limit themselves to motivating patients to use mHealth, whereas in the reviewer role, HCPs assess PGHD to complement medical data. Taking the reviewer role implies an active stance, and though some might value PGHD’s contribution to care, this type of data may still be a new and unfamiliar source of information for some HCPs [ 18 ]. For PGHD for mobile devices to be feasible as a complementary tool in the clinical setting, their use should benefit both patients and HCPs. Though the adoption of mHealth solutions by patients supports their well-being and enables the availability of PGHD, such availability does not automatically equate to usefulness for HCPs. Despite the acceptance and adoption of mHealth solutions by HCPs being one of the most influential factors regarding the success of those solutions [ 19 , 20 ], there has not been significant research on the role HCPs are expected to take in the use of mHealth solutions [ 4 , 17 ] or on the concrete experiences and motivators of those willing to review PGHD.

The main objective of our review is to systematically analyze existing scientific literature to identify what types of PGHD and in what health conditions HCPs have been using PGHD from consumer-grade mobile devices, as well as further context information for their motivations to use these types of data as a complementary tool in the clinic.

To attain these objectives the proposed research questions for our review are as follows: (1) In what health conditions have PGHD from consumer-grade mobile been a suitable tool for HCP? (2) What types of PGHD have HCPs found useful in the care of chronic conditions? (3) What are the main motivations behind HCPs’ decision to review PGHD from consumer-grade devices?

Study Design

A systematic literature review (SLR) was selected as our main research method to comprehensively synthesize evidence and prior research on HCPs’ experiences reviewing PGHD from consumer-grade mobile devices to address our research questions. We wanted to follow a transparent and systematic method to inform further research on this topic. We adopted methodologies from the Guidelines for Performing Systematic Literature Reviews in Software Engineering [ 21 ] and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement) [ 22 , 23 ], both of which provide reliable methodologies to perform SLRs in the fields of computer science and medicine, respectively ( Multimedia Appendix 1 [ 24 ]). We deemed it pertinent to combine methodological traditions from both computer science and medicine, as our research topic combines technical and care viewpoints and is interdisciplinary by nature [ 24 ].

To perform this SLR, we adhered to a systematic review protocol that was prepared before starting the searches and screening process. The protocol has been published elsewhere [ 25 ] and provides an ample description of the methods used in the search strategy and the inclusion and exclusion criteria used. The review adhered closely to the original protocol, with no significant deviations.

Search Strategy

Eligible studies were identified through comprehensive literature searches we conducted in bibliographical databases on health and biomedicine and information technology domains. The searched databases included PubMed, ACM Digital Library (including the ACM Guide to Computing Literature), IEEE Xplore, and Scopus. The searches were carried out in May 2022.

To ensure the identification of relevant papers, we constructed the search strategy in an iterative way [ 25 ]. The search string used for each database is available in Multimedia Appendix 2 . Based on the specific objectives of our review, and after conducting a pilot search in PubMed, we determined that the literature search should be constructed around 3 specific key topics: “patient-generated health data,” “health personnel,” and “mobile technologies.” We used the corresponding Medical Subject Headings (MeSH) and their possible variants to construct the final search query. Once the query had been tested, it was validated by a research librarian from the University of Oulu. After completing the electronic searches, we performed a supplementary hand search of the citations found within other SLRs and scoping reviews that were retrieved during the literature searches.

Eligibility Criteria

The defined eligibility criteria ( Table 1 ) aimed to include original papers that reported on the use of PGHD created via consumer-grade mHealth solutions by HCPs. PGHD reported in the studies should have been collected outside of the clinical setting, through either the patients’ use of mobile health apps or the wearable devices such as smartwatches, smart rings, fitness trackers, and similar wearable trackers; studies reporting on PGHD collected by HCPs during appointments or inside the clinical settings were excluded. Studies were limited to those involving consumer-grade devices to focus on, excluding those solely focusing on PGHD from medical-grade devices. The included papers report on the experiences of HCPs who have experience using PGHD in their clinical practice, as part of a stand-alone mHealth solution, by personal initiative, or for any other reasons. We excluded papers that focus solely on the perceptions or perspectives of HCPs as potential users of PGHD. Eligible publications were restricted to those accepted in peer-reviewed journals and conference proceedings written in the English language.

a PGHD: patient-generated health data.

b HCP: health care professional.

Though current consumer-grade mobile and wearable technologies started to become more accessible in the first half of the last decade, their impact on the health care scene started to become evident only years later. In 2013, it was acknowledged that only a few studies had assessed the impact of mobile apps in the health context, and all those studies referred to apps that had been created only for research purposes and were not available to the public at that time [ 26 ]. Therefore, we limited our search to papers with publication dates starting in 2013. Our search criteria did not delimit aspects such as the medical profile or specialties of the HCPs participating in the studies, or the health conditions treated, as we aimed to ascertain whether PGHD usage by HCPs would be more prevalent in the treatment of certain medical conditions or certain medical fields.

Selection Process

After the electronic search, the resulting papers were imported into Covidence (Veritas Health Innovation) for screening. The screening process was divided into 2 stages carried out independently by 2 researchers (SG and MK) with computer science backgrounds and previous research experience with mHealth and PGHD. Initially, the screening was limited to titles and abstracts. Before starting this stage, the reviewers completed a joint exercise to validate the review methodology and ensure that the inclusion and exclusion criteria were correctly understood. The disagreements that arose during the initial stage were all discussed and resolved between the 2 reviewers before starting the second screening stage. The second screening round included the review of the full text of all the preliminarily included papers.

Data Extraction

The relevant information of the included papers was collected using a structured data extraction form constructed in Covidence ( Multimedia Appendix 3 ). The most relevant data extracted for each paper included the professions of the participants; health conditions treated; mobile technologies used; the type of PGHD collected; and the channels used for visualization. In addition, to understand what motivated HCPs to review PGHD quotes related to their motivations and conclusions related to the topic were extracted from each study.

The data extraction task was completed by the 2 original reviewers (SG and MK) and 2 additional reviewers (CG and MI), all of whom have previous research experience with the topic of this review. Each paper was randomly assigned to be examined by 2 of the reviewers. Each reviewer performed the data extraction independently. Upon completion, the data extracted by both reviewers were compared. Discrepancies were resolved through discussion between the reviewers and a final consensus was reached in all cases.

Data Analysis and Synthesis

Quantitative and qualitative studies were included in this review. Due to the significant heterogeneity observed in the studies’ design, types of health conditions, types of PGHD, and types of mHealth solutions, methods such as meta-analysis or meta-synthesis were not deemed the most appropriate approach for the data synthesis. The extracted data were summarized using a combination of descriptive and narrative syntheses [ 27 ]. The descriptive analysis was conducted to summarize data from the different studies. This involved classifying the studies based on the type of mobile technologies used, health conditions treated, and the types of PGHD reviewed. This approach arranged the studies into more homogenous subgroups, which aided in synthesizing different types of data. The data related to the motivations of HCPs were examined using a thematic analysis, from which different categories were derived. For the narrative synthesis, similarities, and differences between the findings of different studies were identified. The analysis and synthesis comprised three major steps: (1) organization of the included studies, (2) descriptive analysis of the findings within studies, and (3) a narrative synthesis aiming at exploring interconnections between the studies.

Quality Assessment

The quality of the included studies was assessed in parallel to the data extraction process. From the checklists, the quality of studies proposed by Kitchenham and Charters [ 21 ] were assessed. As the included studies were both qualitative and quantitative, we selected the questions that were most appropriate for our specific research questions that were present in both the qualitative and quantitative checklists.

Upon assessment, all reviewers agreed that the included studies had credible findings; proper data collection methods; clear and coherent reporting; and clear links between data, interpretation, and conclusions ( Figure 1 ).

research topic about healthy lifestyle

The single topic that produced some uncertainty during the quality assessment was the lack of clarity on whether some of the selected studies had explored enough diversity of perspective and context. This can likely be attributed to the fact that almost all included studies were performed in developed countries, predominantly in the United States or Europe, which is a typical setting for digital health studies. Hence, the findings of these investigations will provide the most accurate depiction of the state of health care systems in developed countries.

Ethical Considerations

The Ethics Committee of Human Sciences of the University of Oulu guidelines state that as no human or animal subjects were involved in the study, no separate ethics statement is required. However, the general ethical guidelines from the Finnish National Board on Research Integrity [ 28 ] guided the ethics of the study.

Our search across electronic databases and supplementary hand searches identified 1696 papers. Covidence automatically removed 374 duplicates. We screened 1322 titles and abstracts, resulting in 86 papers for full-text screening. Following the completion of this second screening stage, 18 papers met all the inclusion criteria. However, upon closer examination, it was observed that 2 pairs of papers ([ 29 , 30 ] and [ 31 , 32 ]) had similar authors and identical samples and methodologies. Each pair was merged into a single study for analysis, resulting in the final inclusion of 16 studies for our SLR ( Figure 2 ).

During full-text screening, papers were primarily excluded for focusing exclusively on PGHD from medical-grade devices (31/68, 46%); evaluating the usability of specific mHealth solutions, rather than PGHD use (27/68, 39%); lacking data collection from HCPs (9/68, 13%); and discussing potential rather than actual use of PGHD (1/68, 1%).

research topic about healthy lifestyle

Characteristics of the Included Studies

We included studies spanning 2015-2021. Notably, more than two-thirds of the papers (11/16, 69%) were published in 2019 or later, indicating a growing interest in the topic both before and during the COVID-19 pandemic. The predominant location was North America (11/16, 69%), specifically the United States and Canada; within Europe (3/16, 19%), Sweden and the United Kingdom were the primary locations; and 1 study was conducted in Asia and 1 in a multicountry setting. The authors used diverse methodologies for data collection, with interviews (8/16, 50%) and mixed methods (4/16, 25%) being the most common. More comprehensive insights into the specific study designs and data collection methods are available in Table 2 . A complete summary of the included studies can be found in Multimedia Appendix 4 [ 29 , 30 , 32 - 46 ].

a HCP: health care professional.

b PGHD: patient-generated health data.

Medical Profiles and Specialties

Although some of the studies examined data collected from various stakeholders such as patients, researchers, hospital managers, or solution providers, our focus centered on data collected from HCPs. Collectively, the studies in our review had 355 HCPs as participants. Among the represented professions, physicians accounted for the largest number of participants, present in 81% (13/16) of the studies. While approximately half of those studies referred to physicians using a general term, the other half provided clear information about the medical specialties of the physicians. Nurses were the second most represented profession, participating in 62% (10/16) of the studies. Physiotherapists were the third most represented, participating in 38% (6/16) of the studies. Other health professions present were psychologists (3/16, 19%) and surgeons, dietitians, health coaches, and assistant practitioners, each mentioned in 12% (2/16) of the studies ( Table 2 ).

All the studies reported the medical specialties where PGHD was being used. Those specialties included geriatrics, anesthesiology, orthopedic surgery, gastroenterology, dietetics and nutrition, behavioral and clinical psychology, psychiatry, obstetrics and gynecology, infertility, endocrinology, internal medicine, family medicine, rehabilitation, pediatric nephrology, otorhinolaryngology, and audiology.

Health Conditions Treated

The studies examined a wide range of health conditions, classified according to the WHO International Classification of Diseases , Eleventh Revision ( ICD-11 ), into categories such as endocrine, nutritional, or metabolic diseases; mental, behavioral, or neurodevelopmental disorders; diseases of the nervous, circulatory respiratory, and digestive systems, and diseases of the musculoskeletal system or connective tissue. In addition, some studies reported the use of PGHD for other types of medical tasks including perioperative care and care of older adults.

The most cited health conditions for which PGHD from mobile devices were reviewed by HCPs were diabetes and obesity, each mentioned in at least 3 studies. A quarter of the studies did not address a specific health condition. In those cases, the contextual information provided was limited to medical specialties or professions ( Table 2 ).

Types of mHealth Solutions

Among the 16 included studies, 5 mentioned specific mHealth solutions patients had been using to self-manage their health condition. The remaining studies mentioned commercial mHealth solutions in general. In half of the studies, HCPs reported using PGHD derived from a combination of diverse mHealth solutions, which included 1 or multiple mobile health apps and wearable devices. The remaining half of the studies addressed the experience of HCPs using PGHD exclusively generated through mobile health apps installed in patients’ smartphones (4/16, 25%) or captured from wearable devices (4/16, 25%).

Types of PGHD

Various classifications of PGHD have been proposed in terms of purpose (self-use, behavior change, clinical use, and research), management of a condition (eg, diabetes, hypertension), data type (physiological, behavioral, or environmental), mode of data capture (using sensors, external devices, implanted devices, patient portals, web-based surveys, and manual entry), and whether the process is active, passive, or mixed [ 12 ]. In this study, we focused on classifying PGHD based on data types.

Physiological data were reviewed in all studies. In 7 of 16 studies, at least 3 different types of physiological data were collected. Weight was the most frequently mentioned physiological data, reported in 44% (7/16) of the studies, followed by mood (6/16, 38%) and vital signs (5/16, 31%). Other less commonly reviewed types of data were pain, blood glucose level, and other symptoms ( Table 3 ).

Behavioral data constituted the most used category of PGHD. More than 80% (13/16) of the studies indicated that HCPs had reviewed some form of behavioral data, although always in combination with physiological data. Physical activity seems to be the most reviewed type of PGHD produced by consumer-grade devices, with 75% of the studies reporting its use, followed by food intake (9/16, 56%), sleep quality or quantity (8/16, 50%), and medication adherence (6/16, 38%).

Only 12% (2/16) of the studies reported the use of environmental data, which were primarily collected through passive sensing, using wearables, whereas physiological and behavioral types of data were reported to be collected through either passive or active sensing or by a combination of both. For instance, certain types of PGHD, such as sleep, physical activity, or sedentariness, were collected through active sensing in some studies and through passive sensing in others.

Access to PGHD

Diverse channels for PGHD access were presented. Notably, 19% (3/16) of the papers did not describe the precise channels HCPs used to access PGHD. Dashboards or solution portals were used in 56% (9/16) of the studies. The second most common channel was the patient’s mobile device (5/16, 31%). In a few studies, HCPs accessed PGHD through integration with the electronic health record (EHR; 2/16, 12%), by email (2/16, 12%), or from patients’ verbal summaries of data from their mobile devices (1/16, 6%).

Motivation for Reviewing PGHD

Although not all studies cited the reasons behind HCPs’ willingness to review PGHD from consumer-grade devices, motivation for reviewing them centered into 3 main categories: benefits for the patient, supporting their clinical roles, and strengthening the patient-HCP relationship ( Figure 3 ). Key motivations that showed how PGHD supported HCPs included topics such as accessing additional data types, identifying health patterns, and reducing data collection workload.

research topic about healthy lifestyle

Principal Findings

Our review underlines a growing interest in understanding the experiences of HCPs who are using PGHD in the clinic. We aimed to identify how PGHD from consumer-grade mobile devices have been used to assist them in clinical practice. HCPs, who were primarily physicians and nurses, shared their experience on the topic. The health conditions for which HCPs most resorted to PGHD were diabetes and obesity. We found that physiological data, such as weight, mood, and vital signs, and behavioral data, such as physical activity, food intake, and sleep quality, have been frequently used. HCPs had access to PGHD through different channels, such as web portals provided by the mHealth solutions or through integration with the EHR.

Previous reviews have explored the role of PGHD in facilitating prevention and health promotion [ 17 ], their use in clinical practice [ 18 ], and their effect on patient-clinician relationships [ 47 ]. However, those studies have concentrated on PGHD from medical-grade devices, which tend to be more accurate and more accepted in the medical community. PGHD created through consumer-grade mHealth solutions, although praised for their potential to transform health care, have typically not been deemed reliable or accurate enough for the clinical context [ 3 , 48 , 49 ]. Despite concerns over PGHD accuracy and reliability, HCPs recognized that their clinical value outweighs the absence of data [ 40 ]. This value comes with a caveat, as recent studies indicate that PGHD must be curated by HCPs to ascribe actionable clinical value, but even then, they can be treated as supplementary to data collected through clinically recognized standards such as through laboratory tests [ 50 , 51 ]. PGHD from consumer-grade solutions have been used by HCPs in the treatment of a wide variety of health conditions, although it seems common only in the care of diabetes, cardiovascular diseases, and obesity ( Table 2 ).

The most frequently used types of data (physical activity, food intake, sleep quantity, and weight) are highly associated with lifestyle health risks, implying that access to lifestyle-related data can provide valuable insights into the control of lifestyle-related diseases. Furthermore, patients having these conditions are more willing to share PGHD, therefore, fostering HCPs’ familiarity with those types of data [ 15 ]. Our findings reveal that PGHD’s use in clinical practice remains relatively scarce [ 29 , 36 , 42 ], pointing out a gap between their potential and their current use. This finding is in line with a recent study suggesting that in comparison with the expectations of policies related to the European Health Data Space, the prompting and reviewing of PGHD from consumer-grade devices seem still relatively rare [ 50 ]. It is plausible that these types of PGHD have been used by HCPs in practice, but research on the practicalities of this phenomenon has only increased in the last 5 years.

HCPs indicated that PGHD are useful in the identification of patterns, to support certain diagnoses, and for certain types of monitoring. For example, lifestyle diseases [ 36 ], irritable bowel syndrome [ 42 ], or infertility [ 37 ] requires long-term management or presents a high level of uncertainty. In these cases, PGHD can provide longitudinal insights into patients’ health between clinic visits or even before they start treatment, saving time in identifying patterns. It is worth noting that, although HCPs in those studies acknowledged the value of PGHD, they also indicated engaging with PGHD infrequently and only with a few specific data types, in comparison with the substantial amount of data some patients want to share. For patients with chronic diseases, knowing that HCPs are reviewing their PGHD can be a comfortable way to know that they are being monitored and can provide data at the right time to facilitate decision-making and early intervention [ 41 , 52 ].

Multiple types of data were collected in all the studies, which signifies that as more data are collected, the need for analytical strategies that can support HCPs in reviewing and analyzing the potential relationships between different categories of data will be higher. Most existing mHealth solutions for self-monitoring lack standardized formats and mechanisms for patients to control and share PGHD [ 40 , 45 ]. Support for HCPs’ data access and use requires standardization and, in some cases, EHR integration [ 44 , 45 ].

Limitations

We limited our inclusion to papers written in English. However, this approach may have excluded relevant papers from developing regions where English is not the primary language for scientific dissemination but where the interest and potential for mHealth solutions and PGHD are growing. Similarly, a gap in the current body of research regarding these topics in developing regions is highlighted, since all studies came from countries with high economic and digitalization levels.

PGHD is a relatively recent definition, and some relevant papers published prior to its official designation as a MeSH term may have employed alternative terminology to describe the same concept of PGHD used in our study.

The shift toward digital health solutions the COVID-19 pandemic potentiated may have modified HCPs’ perceptions of PGHD use. However, no studies explicitly examining this relationship were identified in our prior searches or a later search. Therefore, future research could explore whether the shift toward digital health has catalyzed the adoption of consumer-grade technologies and PGHD in clinical settings.

Conclusions

Despite skepticism regarding the reliability and accuracy of PGHD and the multiple challenges that they convey, our study highlights a noticeable shift toward recognizing their practical value in health care, particularly in managing chronic conditions such as diabetes, obesity, and cardiovascular diseases. Yet, their impact in supporting the clinical practice is not clear from the literature. Many HCPs in the study, predominantly physicians and nurses, showed interest in using PGHD in the clinical workflows, albeit with a cautious approach that considers them as supplementary to traditional clinical data only. While they acknowledged the benefit of reviewing PGHD for the patient-HCP relationship, it was also noted that only certain types of PGHD are truly deemed useful and even then, they are not regularly used by HCPs. The findings call for continued research and innovation in mHealth, with a focus on enhancing the reliability, usability, and clinical relevance of PGHD, which in return can foster a culture of trust and collaboration between patients and HCPs.

Acknowledgments

We would like to acknowledge the More Stamina Project research group for supporting the development of this work. We acknowledge the use of ChatGPT version 4 and Grammarly to identify improvements in the organization of our text and to improve the writing style in the Introduction and Discussion sections.

Data Availability

This literature review synthesizes findings from peer-reviewed journal papers and conference papers. Given the nature of this review, it does not generate new primary data; instead, it compiled and analyzed existing publications on the use of PGHD from mobile technologies by HCPs. The reviewed papers are all available in public scientific databases. The data extraction form and the extracted data are available in the Multimedia Appendix section. These resources aim to ensure the reproducibility of our methods and facilitate future research in this area.

Conflicts of Interest

None declared.

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

Search strategies for all searched databases.

Data extraction form.

Summary of the included studies.

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Abbreviations

Edited by A Mavragani; submitted 26.05.23; peer-reviewed by CM Chu, P Dunn, A Brigden, C Baxter; comments to author 08.02.24; revised version received 05.04.24; accepted 11.04.24; published 31.05.24.

©Sharon Guardado, Maria Karampela, Minna Isomursu, Casandra Grundstrom. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.05.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

ScienceDaily

Intervention reduces stress and feelings of burden of family caregivers of older adults with dementia

Caregivers of older adults living with dementia experienced a 15% drop in stress after a 9-week online peer support program.

According to the U.S. Centers for Disease Control and Prevention, 80% of those living with dementia receive informal care from family members or friends. This equates to 16 million family caregivers in the U.S. However, caring for family members with dementia is often associated with increased caregiver burden (which includes emotional, physical, and financial strain), stress, and worse physical health for the caregiver.

A recent study published in the Journal of Applied Gerontology, led by George Mason University researchers, found that a 9-week online stress management intervention program for family caregivers reduced burden scores by 15% for 97 family caregivers of older adults living with dementia. The Stress-Busting Program for Family Caregivers TM, intervention was specifically designed to help family caregivers with managing their own stress when caring for older adults living with dementia or a chronic illness

"In this study, we found evidence of a range in average caregiver burden levels based on the dementia severity category of care recipients. The findings show that an online Zoom intervention in a peer group setting can be beneficial for family caregivers of older adults with mild, moderate, or severe dementia," said Catherine Tompkins, principal investigator, professor of social work, and associate dean of faculty and staff affairs in the College of Public Health.

The intervention provided family caregivers with education and strategies to manage stress when caring for someone living with dementia. Examples of self-care techniques included breathing and meditation; troubleshooting behaviors associated with dementia; and peer-to-peer support within a virtual group setting.

"Reducing caregiver burden and managing stress are critical to the well-being of families. These findings show that effective stress management interventions for family caregivers can be facilitated through online peer groups," said Gilbert Gimm, first author and associate professor of health administration and policy.

"Mason Caregivers Aiming for Resilience, Empowerment, and Support Study: Assessing Family Caregiver Burden Post-Intervention" was published online in April 2024. Co-authors include George Mason Associate Professor Megumi Inoue, Professor Emily Ihara, Mason CARES Project Manager Shannon Layman, and Master of Social Work alumna graduate Harveen Pantleay. This study was supported by a grant (#2021048) from the Retirement Research Foundation (RRF).

The study is part of a larger project, entitled Mason CARES (Caregivers Aiming for Resilience, Empowerment, and Support), that implemented and assessed interventions for family caregivers.

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Materials provided by George Mason University . Original written by Mary Cunningham. Note: Content may be edited for style and length.

Journal Reference :

  • McAtee RE, Spradley L, Tobey L, Thomasson W, Azhar G, Mercado C. Caregiver Burden: Caregiving Workshops Have a Positive Impact on Those Caring for Individuals With Dementia in Arkansas . Journal of Patient Experience , 2021;8 [ abstract ]

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Bacteriology and the UN Sustainable Development Goals 2030

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The United Nations Sustainable Development Goals (SDGs) represent an ambitious blueprint encompassing 17 overarching goals and 169 targets, all aimed at enhancing life on Earth. The primary objectives include eradicating poverty, safeguarding the planet, ensuring prosperity for all, and addressing the most pressing global economic, societal, and environmental challenges. Bacteriology plays a pivotal role in expediting progress towards achieving these SDGs. A comprehensive understanding of bacterial behavior and interactions is essential for tackling widespread issues like infectious diseases, food security, and environmental sustainability. The application of bacteriological research enables the development of innovative solutions, particularly in areas such as improved sanitation, water quality, and disease prevention. Moreover, advancements in microbial technology present sustainable alternatives in agriculture, waste management, and energy production. Harnessing the potential of bacteriology not only fosters transformative interventions but also significantly contributes to the realization of the SDGs. Through these efforts, we can pave the way for a healthier, more equitable, and sustainable world for future generations. This research topic aims to collect various articles on the areas above. We will accept all article types permissible in the Frontiers in Bacteriology submission guidelines.

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