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The American Cancer Society (ACS) publishes the Diet and Physical Activity Guideline to serve as a foundation for its communication, policy, and commu-nity strategies and, ultimately, to affect dietary and physical activity patterns among Americans. This guideline is developed by a national panel of experts in cancer re-search, prevention, epidemiology, public health, and policy, and reflects the most current scientific evidence related to dietary and activity patterns and cancer risk. The ACS guideline focuses on recommendations for individual choices regarding diet and physical activity patterns, but those choices occur within a community context that either facilitates or creates barriers to healthy behaviors. Therefore, this com-mittee presents recommendations for community action to accompany the 4 recom-mendations for individual choices to reduce cancer risk. These recommendations for community action recognize that a supportive social and physical environment is indispensable if individuals at all levels of society are to have genuine opportunities to choose healthy behaviors. This 2020 ACS guideline is consistent with guidelines from the American Heart Association and the American Diabetes Association for the prevention of coronary heart disease and diabetes as well as for general health promotion, as defined by the 2015 to 2020 Dietary Guidelines for Americans and the 2018 Physical Activity Guidelines for Americans.

Unhealthy behaviors, such as physical inactivity, sedentary lifestyle, and unhealthful eating, remain highly prevalent, posing formidable challenges in efforts to improve cardiovascular health. While traditional interventions to promote healthy lifestyles are both costly and effective, wearable trackers, especially Fitbit devices, can provide a low-cost alternative that may effectively help large numbers of individuals become more physically fit and thereby maintain a good health status. The objectives of this meta-analysis are (1) to assess the effectiveness of interventions that incorporate a Fitbit device for healthy lifestyle outcomes (eg, steps, moderate-to-vigorous physical activity, and weight) and (2) to identify which additional intervention components or study characteristics are the most effective at improving healthy lifestyle outcomes. A systematic review was conducted, searching the following databases from 2007 to 2019: MEDLINE, EMBASE, CINAHL, and CENTRAL (Cochrane). Studies were included if (1) they were randomized controlled trials, (2) the intervention involved the use of a Fitbit device, and (3) the reported outcomes were related to healthy lifestyles. The main outcome measures were related to physical activity, sedentary behavior, and weight. All the studies were assessed for risk of bias using Cochrane criteria. A random-effects meta-analysis was conducted to estimate the treatment effect of interventions that included a Fitbit device compared with a control group. We also conducted subgroup analysis and fuzzy-set qualitative comparative analysis (fsQCA) to further disentangle the effects of intervention components. Our final sample comprised 41 articles reporting the results of 37 studies. For Fitbit-based interventions, we found a statistically significant increase in daily step count (mean difference [MD] 950.54, 95% CI 475.89-1425.18; P<.001) and moderate-to-vigorous physical activity (MD 6.16, 95% CI 2.80-9.51; P<.001), a significant decrease in weight (MD -1.48, 95% CI -2.81 to -0.14; P=.03), and a nonsignificant decrease in objectively assessed and self-reported sedentary behavior (MD -10.62, 95% CI -35.50 to 14.27; P=.40 and standardized MD -0.11, 95% CI -0.48 to 0.26; P=.56, respectively). In general, the included studies were at low risk for bias, except for performance bias. Subgroup analysis and fsQCA demonstrated that, in addition to the effects of the Fitbit devices, setting activity goals was the most important intervention component. The use of Fitbit devices in interventions has the potential to promote healthy lifestyles in terms of physical activity and weight. Fitbit devices may be useful to health professionals for patient monitoring and support. PROSPERO International Prospective Register of Systematic Reviews CRD42019145450; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019145450.

AbstractObjectiveTo examine how a healthy lifestyle is related to life expectancy that is free from major chronic diseases.DesignProspective cohort study.Setting and participantsThe Nurses’ Health Study (1980-2014; n=73 196) and the Health Professionals Follow-Up Study (1986-2014; n=38 366).Main exposuresFive low risk lifestyle factors: never smoking, body mass index 18.5-24.9, moderate to vigorous physical activity (≥30 minutes/day), moderate alcohol intake (women: 5-15 g/day; men 5-30 g/day), and a higher diet quality score (upper 40%).Main outcomeLife expectancy free of diabetes, cardiovascular diseases, and cancer.ResultsThe life expectancy free of diabetes, cardiovascular diseases, and cancer at age 50 was 23.7 years (95% confidence interval 22.6 to 24.7) for women who adopted no low risk lifestyle factors, in contrast to 34.4 years (33.1 to 35.5) for women who adopted four or five low risk factors. At age 50, the life expectancy free of any of these chronic diseases was 23.5 (22.3 to 24.7) years among men who adopted no low risk lifestyle factors and 31.1 (29.5 to 32.5) years in men who adopted four or five low risk lifestyle factors. For current male smokers who smoked heavily (≥15 cigarettes/day) or obese men and women (body mass index ≥30), their disease-free life expectancies accounted for the lowest proportion (≤75%) of total life expectancy at age 50.ConclusionAdherence to a healthy lifestyle at mid-life is associated with a longer life expectancy free of major chronic diseases.

Background Cancer poses a huge disease burden, which could be reduced by adopting healthy lifestyles mainly composed of healthy diet, body weight, physical activity, limited alcohol consumption, and avoidance of smoking. However, no systematic review has summarised the relations of combined lifestyle factors with cancer morbidity and mortality. Methods EMBASE and PubMed were searched up to April 2019. Cohort studies investigating the association of combined lifestyle factors with risks of incident cancer and cancer mortality were selected. Summary hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using random-effects models. Heterogeneity and publication bias tests were conducted. Results The HRs (95% CIs) comparing individuals with the healthiest versus the least healthy lifestyles were 0.71 (0.66–0.76; 16 studies with 1.9 million participants) for incident cancer and 0.48 (0.42–0.54; 30 studies with 1.8 million participants) for cancer mortality. Adopting the healthiest lifestyles was also associated with 17 to 58% lower risks of bladder, breast, colon, endometrial, oesophageal, kidney, liver, lung, rectal, and gastric cancer. The relations were largely consistent and significant among participants with different characteristics in the subgroup analyses. Conclusions Adopting healthy lifestyles is associated with substantial risk reduction in cancer morbidity and mortality, and thus should be given priority for cancer prevention.

How long one lives, how many years of life are spent in good and poor health, and how the population's state of health and leading causes of disability change over time all have implications for policy, planning, and provision of services. We comparatively assessed the patterns and trends of healthy life expectancy (HALE), which quantifies the number of years of life expected to be lived in good health, and the complementary measure of disability-adjusted life-years (DALYs), a composite measure of disease burden capturing both premature mortality and prevalence and severity of ill health, for 359 diseases and injuries for 195 countries and territories over the past 28 years. We used data for age-specific mortality rates, years of life lost (YLLs) due to premature mortality, and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to calculate HALE and DALYs from 1990 to 2017. We calculated HALE using age-specific mortality rates and YLDs per capita for each location, age, sex, and year. We calculated DALYs for 359 causes as the sum of YLLs and YLDs. We assessed how observed HALE and DALYs differed by country and sex from expected trends based on Socio-demographic Index (SDI). We also analysed HALE by decomposing years of life gained into years spent in good health and in poor health, between 1990 and 2017, and extra years lived by females compared with males. Globally, from 1990 to 2017, life expectancy at birth increased by 7·4 years (95% uncertainty interval 7·1–7·8), from 65·6 years (65·3–65·8) in 1990 to 73·0 years (72·7–73·3) in 2017. The increase in years of life varied from 5·1 years (5·0–5·3) in high SDI countries to 12·0 years (11·3–12·8) in low SDI countries. Of the additional years of life expected at birth, 26·3% (20·1–33·1) were expected to be spent in poor health in high SDI countries compared with 11·7% (8·8–15·1) in low-middle SDI countries. HALE at birth increased by 6·3 years (5·9–6·7), from 57·0 years (54·6–59·1) in 1990 to 63·3 years (60·5–65·7) in 2017. The increase varied from 3·8 years (3·4–4·1) in high SDI countries to 10·5 years (9·8–11·2) in low SDI countries. Even larger variations in HALE than these were observed between countries, ranging from 1·0 year (0·4–1·7) in Saint Vincent and the Grenadines (62·4 years [59·9–64·7] in 1990 to 63·5 years [60·9–65·8] in 2017) to 23·7 years (21·9–25·6) in Eritrea (30·7 years [28·9–32·2] in 1990 to 54·4 years [51·5–57·1] in 2017). In most countries, the increase in HALE was smaller than the increase in overall life expectancy, indicating more years lived in poor health. In 180 of 195 countries and territories, females were expected to live longer than males in 2017, with extra years lived varying from 1·4 years (0·6–2·3) in Algeria to 11·9 years (10·9–12·9) in Ukraine. Of the extra years gained, the proportion spent in poor health varied largely across countries, with less than 20% of additional years spent in poor health in Bosnia and Herzegovina, Burundi, and Slovakia, whereas in Bahrain all the extra years were spent in poor health. In 2017, the highest estimate of HALE at birth was in Singapore for both females (75·8 years [72·4–78·7]) and males (72·6 years [69·8–75·0]) and the lowest estimates were in Central African Republic (47·0 years [43·7–50·2] for females and 42·8 years [40·1–45·6] for males). Globally, in 2017, the five leading causes of DALYs were neonatal disorders, ischaemic heart disease, stroke, lower respiratory infections, and chronic obstructive pulmonary disease. Between 1990 and 2017, age-standardised DALY rates decreased by 41·3% (38·8–43·5) for communicable diseases and by 49·8% (47·9–51·6) for neonatal disorders. For non-communicable diseases, global DALYs increased by 40·1% (36·8–43·0), although age-standardised DALY rates decreased by 18·1% (16·0–20·2). With increasing life expectancy in most countries, the question of whether the additional years of life gained are spent in good health or poor health has been increasingly relevant because of the potential policy implications, such as health-care provisions and extending retirement ages. In some locations, a large proportion of those additional years are spent in poor health. Large inequalities in HALE and disease burden exist across countries in different SDI quintiles and between sexes. The burden of disabling conditions has serious implications for health system planning and health-related expenditures. Despite the progress made in reducing the burden of communicable diseases and neonatal disorders in low SDI countries, the speed of this progress could be increased by scaling up proven interventions. The global trends among non-communicable diseases indicate that more effort is needed to maximise HALE, such as risk prevention and attention to upstream determinants of health. Bill & Melinda Gates Foundation.

Weight gain accompanied by an increased tendency for central fat distribution is common among women in midlife. These changes are a result of aging, decreasing estrogen levels after menopause, and other unique influences in menopausal women that interfere with the adoption of healthy lifestyle measures. Central obesity, in particular, results in several adverse metabolic consequences, including dysglycemia, dyslipidemia, hypertension, and cardiovascular disease. Given that cardiovascular disease is the leading cause of death in postmenopausal women, the importance of weight management in midlife cannot be overemphasized. In addition, weight gain in midlife contributes to other health risks including cancer, arthritis, mood disorders, and sexual dysfunction. It is imperative that primary care physicians screen midlife women for overweight/obesity and offer appropriate advice and referral. In addition to counseling regarding lifestyle change, behavioral modification, and psychological support, it is important to address the unique barriers to adoption of healthy lifestyle measures in postmenopausal women, including the presence of vasomotor symptoms, mood disorders, and sleep disturbance. When indicated, menopausal hormone therapy should be considered to manage bothersome symptoms. Despite its favorable influence on body fat distribution, menopausal hormone therapy cannot be recommended as a treatment for central obesity in midlife women.

Background Although previous studies have revealed many factors related to mild cognitive impairment (MCI) reversion, information about reversible factors related MCI reversion is limited, impeding the development of intervention strategies. The aim of the present study was to examine whether reversible factors such as lifestyle activities are associated with MCI reversion in elderly individuals using the National Center for Geriatrics and Gerontology—Study of Geriatric Syndromes database. A total of 396 community-living older adults (age ≥ 65 years) participated in the study. They were classified as reverters or non-reverters from mild cognitive impairment to normal cognition. We assessed lifestyle activities, potential confounding factors of cognitive decline, and reversion of mild cognitive impairment. Results In a completed data set of 396 participants, 202 participants (51.0%) reverted from MCI to normal cognition. The reversion rate in participants for whom we imputed data was 34.3%. In the imputed group, a logistic regression model showed that the odds ratios (ORs) for reversion were significantly higher in participants who drove a car (OR 1.50, 95% confidence interval (CI) 1.41–1.60), used a map to travel to unfamiliar places (OR 1.12, 95% CI 1.06–1.18), read books or newspapers (OR 1.54, 95% CI 1.37–1.73), took cultural classes (OR 1.10, 95% CI 1.04–1.15), attended meetings in the community (OR 1.22, 95% CI 1.16–1.28), participated in hobbies or sports activities (OR 1.09, 95% CI 1.03–1.16), and engaged in fieldwork or gardening (OR 1.14, 95% CI 1.08–1.21). The imputed sample showed that non-reverters were more likely to discontinue fieldwork or gardening (11.0% vs. 6.1%) than reverters during the follow-up period. Conclusions Specific lifestyle activities may play important roles in MCI reversion in older adults. The longitudinal data indicate that it is reasonable to recommend that individuals continue to engage in fieldwork or gardening to increase their chance of recovery from MCI.

Embodied conversational agents (ECAs) are animated computer characters that simulate face-to-face counseling. Owing to their capacity to establish and maintain an empathic relationship, they are deemed to be a promising tool for starting and maintaining a healthy lifestyle. This review aimed to identify the current practices in designing and evaluating ECAs for coaching people in a healthy lifestyle and provide an overview of their efficacy (on behavioral, knowledge, and motivational parameters) and use (on usability, usage, and user satisfaction parameters). We used the Arksey and O'Malley framework to conduct a scoping review. PsycINFO, Medical Literature Analysis and Retrieval System Online, and Scopus were searched with a combination of terms related to ECA and lifestyle. Initially, 1789 unique studies were identified; 20 studies were included. Most often, ECAs targeted physical activity (n=16) and had the appearance of a middle-aged African American woman (n=13). Multiple behavior change techniques (median=3) and theories or principles (median=3) were applied, but their interpretation and application were usually not reported. ECAs seemed to be designed for the end user rather than with the end user. Stakeholders were usually not involved. A total of 7 out of 15 studies reported better efficacy outcomes for the intervention group, and 5 out of 8 studies reported better use-related outcomes, as compared with the control group. ECAs are a promising tool for persuasive communication in the health domain. This review provided valuable insights into the current developmental processes, and it recommends the use of human-centered, stakeholder-inclusive design approaches, along with reporting on the design activities in a systematic and comprehensive manner. The gaps in knowledge were identified on the working mechanisms of intervention components and the right timing and frequency of coaching.

In 2011 over 1.7 million people were hospitalized because of a fragility fracture, and direct costs associated with osteoporosis treatment exceeded 70 billion dollars in the United States. Failure to reach and maintain optimal peak bone mass during adulthood is a critical factor in determining fragility fracture risk later in life. Physical activity is a widely accessible, low cost, and highly modifiable contributor to bone health. Exercise is especially effective during adolescence, a time period when nearly 50% of peak adult bone mass is gained. Here, we review the evidence linking exercise and physical activity to bone health in women. Bone structure and quality will be discussed, especially in the context of clinical diagnosis of osteoporosis. We review the mechanisms governing bone metabolism in the context of physical activity and exercise. Questions such as, when during life is exercise most effective, and what specific types of exercises improve bone health, are addressed. Finally, we discuss some emerging areas of research on this topic, and summarize areas of need and opportunity.

To determine the association between combined lifestyle factors, including healthy diet, moderate alcohol consumption, non-smoking, physical activity, and optimal weight, and cardiovascular disease (CVD) risk among younger and older adults. We conducted a literature search using PubMed, EMBASE, Cochrane Library, and EBSCO databases up to November 30, 2019 and performed dose–response analysis, subgroup analysis and meta-regression with odds ratios and 95% confidence intervals (CIs). Twenty cohort studies involving 1,090,261 participants with 46,288 cardiovascular events and mean follow-up duration of 12.33 years were included. Compared with the group with the lowest number of healthy lifestyle factors, the group with the highest number had lower CVD risk [pooled hazard ratio, 0.37 (95% CI 0.31–0.43)]. With age as an effect modifier, the lifetime risk of CVD was 0.31 (95% CI 0.24–0.41) at age 37.1–49.9 years, 0.36 (95% CI 0.30–0.45) at age 50.0–59.9 years and 0.49 (95% CI 0.38–0.63) at age 60.0–72.9 years. The hazard ratio of CVD significantly increased from 37.1 to 72.9 years of age [slope in multivariate meta-regression: 0.01 (95% CI < 0.001–0.03; p  = 0.042)]. Younger adults have more cardiovascular benefits from combined healthy lifestyle factors.