Behavioral Lifestyles and Survival: A Meta-Analysis

Promoting physical activity during retirement age with psychological components: multilevel meta-analysis

Background: Introduction: Physical activity is essential in preventing and treating age-related chronic diseases and mortality. Retirement is a key period to promote health behaviours, as individuals restructure their routines. Thus, we aimed to identify effective components and behaviour change techniques (BCTs) in interventions promoting physical activity in retirement-age individuals. Methods: We conducted a meta-analysis. Included studies were randomised controlled trials that (p)targeted retirement-age adults (50-70 years), (i)applied BCTs, (c)had any comparator, and (o)promoted physical activity. Screening, full-text review, and data extraction were conducted independently by at least two reviewers. A multilevel random effects model with three effect sizes was fitted, and meta-regressions tested several moderators. Results: 67 studies (N = 12,147) were included. High risk of bias related to larger effects, so these studies were excluded from the main analyses. While individual effects were often non-significant, the overall pooled effect was small but statistically significant. Predictors varied across effect sizes and included action planning, motivational interviewing, and prompts/cues. Email and website delivery were associated with smaller effect sizes. Conclusions: The effectiveness of lifestyle interventions is heterogeneous and presented small effects; implementing action planning, motivational interviewing, and prompts could improve the effectiveness. However, many BCTs that are not frequently used remain unexplored.

Analysis of Metabolic and Quality-of-Life Factors in Patients With Cancer for a New Approach to Classifying Walking Habits: Secondary Analysis of a Randomized Controlled Trial

BACKGROUND

As the number of people diagnosed with cancer continues to increase, self-management has become crucial for patients recovering from cancer surgery or undergoing chemotherapy. Technology has emerged as a key tool in supporting self-management, particularly through interventions that promote physical activity, which is important for improving health outcomes and quality of life for patients with cancer. Despite the growing availability of digital tools that facilitate physical activity tracking, high-level evidence of their long-term effectiveness remains limited.

OBJECTIVE

This study aimed to investigate the effect of long-term physical activity on patients with cancer by categorizing them into active and inactive groups based on step count time-series data using the mobile health intervention, the Walkon app (Swallaby Co, Ltd.).

METHODS

Patients with cancer who had previously used the Walkon app in a previous randomized controlled trial were chosen for this study. Walking step count data were acquired from the app users. Biometric measurements, including BMI, waist circumference, blood sugar levels, and body composition, along with quality of life (QOL) questionnaire responses (European Quality of Life 5 Dimensions 5 Level version and Health-related Quality of Life Instrument with 8 Items), were collected during both the baseline and 6-month follow-up at an outpatient clinic. To analyze step count patterns over time, the concept of sample entropy was used for patient clustering, distinguishing between the active walking group (AWG) and the inactive walking group (IWG). Statistical analysis was performed using the Shapiro-Wilk test for normality, with paired t tests for parametric data, Wilcoxon signed-rank tests for nonparametric data, and chi-square tests for categorical variables.

RESULTS

The proposed method effectively categorized the AWG (n=137) and IWG (n=75) based on step count trends, revealing significant differences in daily (4223 vs 5355), weekly (13,887 vs 40,247), and monthly (60,178 vs 174,405) step counts. Higher physical activity levels were observed in patients with breast cancer and younger individuals. In terms of biometric measurements, only waist circumference (P=.01) and visceral fat (P=.002) demonstrated a significant improvement exclusively within the AWG. Regarding QOL measurements, aspects such as energy (P=.01), work (P<.003), depression (P=.02), memory (P=.01), and happiness (P=.05) displayed significant improvements solely in the AWG.

CONCLUSIONS

This study introduces a novel methodology for categorizing patients with cancer based on physical activity using step count data. Although significant improvements were noted in the AWG, particularly in QOL and specific physical metrics, differences in 6-month change between the AWG and IWG were statistically insignificant. These findings highlight the potential of digital interventions in improving outcomes for patients with cancer, contributing valuable insights into cancer care and self-management.

TRIAL REGISTRATION

Clinical Research Information Service by Korea Centers for Diseases Control and Prevention, Republic of Korea KCT0005447; https://tinyurl.com/3zc7zvzz.

Blind spots in health perception: the underestimated role of social connection for health outcomes

BACKGROUND

Robust evidence indicates that having few or poor-quality social connections is associated with poorer physical health outcomes and risk for earlier death (Snyder-Mackler N, Science 368, 2020; Vila J, Front Psychol 12:717164, 2021).

AIM

This study sought to determine whether recent attention on social connection and loneliness brought on by the COVID-19 pandemic may influence risk perception and whether these perceptions were heightened among those who are lonely.

METHODS

Two waves of online survey data were collected. The first included data from 1,486 English-speaking respondents in the US, UK, and Australia, and a second sample of 999 nationally representative US adults, with a final sample of 2392 respondents from the US and UK.

RESULTS

Perceptions of risk have remained consistent, underestimating the influence of social factors on health outcomes and longevity, even among respondents who reported moderate-to-severe levels of loneliness.

CONCLUSIONS

Despite heightened awareness and discourse during the COVID-19 pandemic, public perception in the US and UK continues to significantly underestimate the impact of social factors on physical health and mortality. This underestimation persists regardless of individual loneliness levels, underscoring the need for enhanced public education and policy efforts to recognize social connection as a crucial determinant of health outcomes.

Impact of associated factors and adherence to Mediterranean diet on insomnia among Arab men living in Jordan

The detrimental health and economic burdens associated with insomnia have led to investigations examining its prevalence and potential predictors to utilize this information in insomnia prevention/treatment. Ethnicity, gender, and lifestyle habits are among the important predictors associated with insomnia. The aim of this study was to examine the prevalence of insomnia and potential predictors of insomnia among Arab men living in Jordan, including Mediterranean diet (MD) adherence. The inclusion criteria of this cross-sectional, correlational study were: (1) being a male; (2) aged 18 and older; and (3) able to read, write, and speak the Arabic language. Online questionnaires were used to assess participants' sociodemographic information, adherence to the MD, physical activity, and insomnia. Results demonstrated that there was a high prevalence of insomnia (53.9%) among the study's participants (N = 529). Moreover, in the unadjusted model, the study revealed that being unemployed (OR = 1.90, p = 0.018), categorized as dehydrated (OR = 1.20, p = 0.020), overweight (OR = 1.30, p = 0.035), sleeping less than 6 hours/day (OR = 2.10, p < 0.001), and having high adherence to the MD (OR = 1.60, p = 0.010) were predictors of insomnia. Meanwhile, the adjusted multivariate logistic regression model indicated that being married (OR = 1.20, p = 0.020) played a significant protective role against developing insomnia among Arab men. While high adherence to MD (OR = 1.642, p = 0.028) and sleeping less than 6 hours/day (OR = 2.760, p < 0.001) were relative risk factors for experiencing insomnia. The high prevalence of insomnia in this population and the identification of possible predictors associated with insomnia, and the unexpected relationship between adherence to the MD and insomnia warrant further in-depth investigations that may help promote the understanding of the factors associated with insomnia and facilitate the development of interventions to target it.

Climbing the longevity pyramid: overview of evidence-driven healthcare prevention strategies for human longevity

Longevity medicine is an emerging and iterative healthcare discipline focusing on early detection, preventive measures, and personalized approaches that aim to extend healthy lifespan and promote healthy aging. This comprehensive review introduces the innovative concept of the "Longevity Pyramid." This conceptual framework delineates progressive intervention levels, providing a structured approach to understanding the diverse strategies available in longevity medicine. At the base of the Longevity Pyramid lies the level of prevention, emphasizing early detection strategies and advanced diagnostics or timely identification of potential health issues. Moving upwards, the next step involves lifestyle modifications, health-promoting behaviors, and proactive measures to delay the onset of age-related conditions. The Longevity Pyramid further explores the vast range of personalized interventions, highlighting the importance of tailoring medical approaches based on genetic predispositions, lifestyle factors, and unique health profiles, thereby optimizing interventions for maximal efficacy. These interventions aim to extend lifespan and reduce the impact and severity of age-related conditions, ensuring that additional years are characterized by vitality and wellbeing. By outlining these progressive levels of intervention, this review offers valuable insights into the evolving field of longevity medicine. This structured framework guides researchers and practitioners toward a nuanced strategic approach to advancing the science and practice of healthy aging.

Identification of a "Blue Zone" in the Netherlands: A Genetic, Personal, Sociocultural, and Environmental Profile

BACKGROUND AND OBJECTIVES

"Blue Zones" (BZs) are regions with exceptionally high numbers of longevous inhabitants. Several factors have been suggested to promote longevity in BZs, but the evidence generally does not meet scientific quality criteria. We aimed to characterize a municipality as a "relative BZ," satisfying 3 criteria: compared to other municipalities, more exceptionally longevous inhabitants, a higher life expectancy, and a more stable population.

RESEARCH DESIGN AND METHODS

The population-based Longitudinal Aging Study Amsterdam has been ongoing since 1992 in 11 municipalities across the Netherlands with 3- or 4-yearly measurement waves. Using all available waves, we included 39 genetic, personal, sociocultural, and environmental characteristics.

RESULTS

One municipality satisfied the 3 BZ criteria. In comparison with participants in other municipalities in the same province and other provinces in the Netherlands, BZ-participants more often had a polygenic risk score linked to longevity, smoked less, consumed less alcohol and more fruit, biked more minutes, did more often paid work, practiced singing more often, attached higher importance to religion, and lived in a more walkable and livable environment. In contrast, BZ-participants had a slower walking speed, more depressive symptoms, felt less purpose in life, had a larger waist circumference, walked and did sports less often, consumed less vegetables, and exchanged less instrumental support. Other indicators of their physical and mental health and social connectedness did not substantially differ from non-BZ-participants.

DISCUSSION AND IMPLICATIONS

Rather than clues to healthy aging, our findings suggest factors conducive to longevity regardless of impaired health.

Life-Course Pathways to Exceptional Longevity: Evidence From the Lothian Birth Cohort of 1921

BACKGROUND

Longevity, a hallmark of successful aging, is a multifactorial trait with influences from birth onwards. However, limited evidence exists on the pathways linking diverse life-course exposures to longevity, especially within a single cohort.

METHODS

We investigated associations between life-course factors and longevity among community-dwelling adults aged 79 (N = 547) from the Lothian Birth Cohort 1921 with a mortality follow-up of 24 years. Cox proportional hazards and structural equation (path) models were used to explore how factors from early life (social class, childhood intelligence quotient [IQ], education), midlife (social class), and later life (health, lifestyle, psychosocial well-being), as well as sex, personality, and apolipoprotein E e4 status, influence survival time in days.

RESULTS

During follow-up (1999-2023), 538 participants (98%) died (mean age of death = 89.3 years) and 9 survived (mean age = 101.6 years). Factors associated with lower mortality risk in the multivariable Cox model were higher cognitive function (hazard ratio [HR] = 0.72; 95% confidence interval [CI]: 0.59-0.88), better physical function (HR = 0.61; 95% CI: 0.44-0.85), and greater physical activity (HR = 0.81; 95% CI: 0.71-0.92), while history of cancer was associated with higher mortality risk (HR = 1.84; 95% CI: 1.22-2.77). The life-course path model identified the same direct predictors, with additional contributions from female sex and nonsmoking status, to greater longevity. Early- and midlife factors (IQ, education, social class), and emotional stability, conscientiousness, and female sex, were indirectly and positively associated with survival trajectories via multiple dimensions of adult health.

CONCLUSIONS

In understanding why people live to very old ages it is necessary to consider factors from throughout the life course, and to include demographic, psychosocial, and health variables.

What factors preventing the older adults in China from living longer: a machine learning study

BACKGROUND

The fact that most older people do not live long means that they do not have more time to pursue self-actualization and contribute value to society. Although there are many studies on the longevity of the elderly, the limitations of traditional statistics lack the good ability to study together the important influencing factors and build a simple and effective prediction model.

METHODS

Based on the the data of Chinese Longitudinal Healthy Longevity Survey (CLHLS), 2008-2018 cohort and 2014-2018 cohort were selected and 16 features were filtered and integrated. Five machine learning algorithms, Elastic-Net Regression (ENR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and eXtreme Gradient Boosting (XGBoost), were used to develop models and assessed by internal validation with CLHLS 2008-2018 cohort and temporal validation with CLHLS 2014-2018 cohort. Besides, the best performing model was explained and according to the variable importance results, simpler models would be developed.

RESULTS

The results showed that the model developed by XGBoost algorithm had the best performance with AUC of 0.788 in internal validation and 0.806 in temporal validation. Instrumental activity of daily living (IADL), leisure activity, marital status, sex, activity of daily living (ADL), cognitive function, overall plant-based diet index (PDI) and psychological resilience, 8 features were more important in the model. Finally, with these 8 features simpler models were developed, it was found that the model performance did not decrease in both internal and temporal validation.

CONCLUSIONS

The study indicated that the importance of these 8 factors for predicting the death of elderly people in China and built a simple machine learning model with good predictive performance. It can inspire future key research directions to promote longevity of the elderly, as well as in practical life to make the elderly healthy longevity, or timely end-of-life care for the elderly, and can use predictive model to aid decision-making.

Association between dopamine receptor D2 Taq IA gene polymorphism (rs1800497) and personality traits

Objective

This study aimed to find a potential association between the DRD2 Taq1A gene polymorphism (rs1800497 C32806T) and personality traits.

Methods

In all, 249 youths were recruited for this study. The Short-form Revised Eysenck Personality Questionnaire was administered to assess personality traits. The participants were genotyped for the DRD2 Taq1A polymorphism using the polymerase chain reaction-restriction fragment length polymorphism method. Statistical analysis was carried out to find a possible association between the genotypes and aspects of personality traits assessed.

Results

The frequencies of the A1 and A2 alleles in our sampled population were 215 (43.2%) and 283 (56.8%), while the frequencies of A1A1, A1A2, and A2A2 were 67 (26.9%), 81 (32.5%), and 101 (40.6%), respectively. The study population was not in Hardy-Weinberg equilibrium (χ2 = 17.64, p < 0.001). The A2 allele was significantly associated with extraversion. Although this allele was also associated with neuroticism, psychoticism, and lie, the association was not significant.

Conclusion

The A2 allele of the DRD2 Taq1A polymorphism was found to be more associated with extraversion, as measured by the Short-form Revised Eysenck Personality Questionnaire.

Enhancing understanding of healthy aging based on time-varying dependencies among multidimensional health, life satisfaction, and health behaviors of older adults aged 60 years and over

BACKGROUND

Healthy aging is a process of not only achieving good health but also increasing the life satisfaction of older adults aged 60 years and over, in which health behaviors play an important role. There is a lack of research on the time-varying dependencies between health, life satisfaction, and health behaviors, impeding a deeper understanding of healthy aging.

PURPOSE

To develop an integrated framework for modeling the interrelationships among the components of healthy aging between multiple time slices.

METHODS

Based on the Chinese Longitudinal Healthy Living Survey (CLHLS) data in the three waves of 2011/2012, 2014, and 2017/2018, Bayesian network and dynamic Bayesian network are jointly employed to study the relationships among the components of healthy aging within one time slice, as well as to explore the time-varying dependencies among the components between time slices.

RESULTS

The results of structure learning reveal the direction of effects between different dimensions of health, with mental health and social health affecting physical health and self-rated health affecting both physical and mental health. In addition, health behaviors are found to affect mental health and social health, while self-rated health can influence life satisfaction. The parameters learned from the data show the magnitude and direction of concurrent effects, one-period lagged effects and two-period lagged effects between the factors, which find that the time-varying dependencies vary but are generally positive, long-term, and accumulative over time. In addition, the results of autoregressive effects show the positive predictive effects of health and life satisfaction.

CONCLUSIONS

It confirms the influence pathway from health behaviors to multidimensional health to life satisfaction, and the time-varying dependencies among the components of healthy aging, which facilitates a deeper understanding of healthy aging. Combining the results of autoregressive effects and descriptive statistics, it further indicates that healthy aging is a comprehensive result arising from interactions of multiple factors. Policymakers should guide older adults aged 60 years and over to adopt healthier behaviors and ensure the long-term sustainability and continuity of policies.

Factors associated with poor health literacy in older adults: A systematic review

OBJECTIVES

To identify factors associated with poor health literacy in older adults.

METHODS

A systematic literature review was conducted, employing the descriptors "Aged" and "Health Literacy".

PROSPERO - CRD

42022350140.

RESULTS

Out of 23,500 articles screened, 176 were selected. Several factors associated with poor health literacy in older adults were identified, such as sociodemographic (e.g., advanced age, low educational level, non-white population, and others), social (e.g., poor family/social support, loneliness, social isolation, few social activities, and others), economic (e.g., lower income and/or lower socioeconomic status) and health aspects (e.g., poor health, chronic conditions, mental health challenges, hospitalizations, frailty, physical inactivity, cognitive impairment, and others).

CONCLUSIONS

The factors associated with poor health literacy in older adults identified in this review could contribute to future research, support interventions to improve health literacy, and assist professionals in planning educational activities and public policies.

Effects of leisure activities and general health on the survival of older people: a cohort study in China

Objective

This study aimed to examine the influence of physical and cognitive leisure activities on the survival of older adults in China, while also exploring the potential mediating and moderating effects of general health.

Methods

This study utilized the China Longitudinal Healthy Longevity Survey (CLHLS) datasets spanning from 2008 to 2018, and 10,347 eligible participants were included. The primary study outcome was all-cause mortality, and independent variables included physical leisure activities (PLA), cognitive leisure activities (CLA), and self-rated general health. Three sets of covariates were adjusted, including socio-demographic characteristics, health behaviors, and health status.

Results

The longest survival time was the older people participating in PLA & CLA (mean = 50.31 months), while those participating in neither exhibited the lowest (mean = 29.60 months). Significant differences in survival status were observed in different types of leisure activities participation (Log-rank test, Chi-square = 576.80, p < 0.001). Cox regression indicated that PLA (HR = 0.705, 95% CI: 0.651-0.764), CLA (HR = 0.872, 95% CI: 0.816-0.933), and the both PLA & CLA (HR = 0.700, 95% CI: 0.656-0.747) were protective factors for the survival. Additionally, general health significantly moderated the relationship between PLA and reduced mortality risk (Coefficient = -0.089, p = 0.042). While CLA indirectly influenced the survival through general health (Coefficient = -0.023, p < 0.001). For the older people participating in PLA and CLA, general health played mediating (Coefficient = -0.031, p < 0.001) and moderating (Coefficient = -0.026, p = 0.013) role in the relationship between leisure activities and survival.

Conclusion

Leisure activities and self-rated general health were important predictors of survival of the older adults, and general health exhibited a mediator and moderator in the relationship between leisure activities and survival status.

Predictors of the Level of Physical Activity in Physically Active Older People

There has not been much study of risk profiles in older people according to different levels of practice in physical activity. For this reason, the aim of this research was to evaluate whether the elements that influence the quality of life and factors such as gender and education can predict the level of physical activity in the physically active elderly population. The Fernández−Ballesteros quality of life questionnaire and the WHO International Physical Activity Questionnaire were applied to a sample of 397 people with a mean age of 69.65 years (SD = 4.71). The results revealed the following predictive factors of practicing a low level of physical activity (p < 0.05): being a woman; having a low educational level; and low scores in activity and leisure and in functional skills. In conclusion, gender, education, functional skills, activity and leisure, and health are elements of quality of life that predict the level of physical activity performed by the elderly, where it is necessary to use leisure activities (visiting friends, playing games, running errands) as an indirect way to increase participation in physical activity.