Do Stairs Inhibit Seniors Who Live on Upper Floors From Going Out?

Age-friendly neighbourhood environment, functional abilities and life satisfaction: A longitudinal analysis of older adults in urban China

The construction of age-friendly neighbourhoods is a priority for practice and policy to promote active ageing and increase life satisfaction for older adults. However, there has been a paucity of longitudinal evidence on the interaction effects of age-friendly neighbourhood environment and functional abilities on life satisfaction among older adults in urban China. This study makes the first attempt to examine the effect of person-environment fit on life satisfaction by the accumulative effects of age-friendliness of the baseline neighbourhood environment (measured within the WHO Age-Friendly City framework) on functional abilities and life satisfaction trajectories and the long-term effects of functioning changes on life satisfaction, drawing from four waves of longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS) and latent growth curve modelling. Results indicated that the age-friendliness of transportation was positively associated with functional abilities, and housing was the primary factor that shaped life satisfaction at the baseline. Better maintenance of functional abilities could promote life satisfaction over time. For older adults living in recently built neighbourhoods, social environment factors such as associations and facilities for social participation were more important in functional abilities, and communication and information were more important in life satisfaction. For those living in neighbourhoods built before 2000, physical environment factors such as transportation were essential in functional abilities, and housing was more essential in life satisfaction; health-related facilities and services at baseline exerted a cumulative effect on maintaining functional abilities in the long run. Our findings inform policymakers about how to effectively allocate public resources to enhance older adults' life satisfaction in the Chinese urban context.

Exploring outdoor activity limitation (OAL) factors among older adults using interpretable machine learning

BACKGROUND

The occurrence of outdoor activity limitation (OAL) among older adults is influenced by multidimensional and confounding factors associated with aging.

AIM

The aim of this study was to apply interpretable machine learning (ML) to develop models for multidimensional aging constraints on OAL and identify the most predictive constraints and dimensions across multidimensional aging data.

METHODS

This study involved 6794 community-dwelling participants older than 65 from the National Health and Aging Trends Study (NHATS). Predictors included related to six dimensions: sociodemographics, health condition, physical capacity, neurological manifestation, daily living habits and abilities, and environmental conditions. Multidimensional interpretable machine learning models were assembled for model construction and analysis.

RESULTS

The multidimensional model demonstrated the best predictive performance (AUC: 0.918) compared to the six sub-dimensional models. Among the six dimensions, physical capacity had the most remarkable prediction (AUC: physical capacity: 0.895, daily habits and abilities: 0.828, physical health: 0.826, neurological performance: 0.789, sociodemographic: 0.773, and environment condition: 0.623). The top-ranked predictors were SPPB score, lifting ability, leg strength, free kneeling, laundry mode, self-rated health, age, attitude toward outdoor recreation, standing time on one foot with eyes open, and fear of falling.

DISCUSSION

Reversible and variable factors, which are higher in the set of high-contribution constraints, should be prioritized as the main contributing group in terms of interventions.

CONCLUSION

The integration of potentially reversible factors, such as neurological performance in addition to physical function into ML models, yields a more accurate assessment of OAL risk, which provides insights for targeted, sequential interventions for older adults with OAL.

The effect of living environmental factors on cardiovascular diseases in Chinese adults: results from a cross-sectional and longitudinal study

AIMS

This study aimed to investigate the association between multiple living environmental factors and cardiovascular diseases (CVDs).

METHODS AND RESULTS

This study was conducted on the China Health and Retirement Longitudinal Study (CHARLS), with 12 489 subjects in the cross-sectional study and 7932 subjects in the 7-year follow-up. Living environmental factors included ambient fine particulate matter (PM2.5), indoor fuel use, tap water use, and residence type. Logistic regression and Cox proportional hazard regression models were applied to explore the association between living environmental risk factors and CVD events in a cross-sectional and a cohort analysis, respectively. Compared with subjects in the low-risk groups, those in the middle-risk (odd ratio [OR], 95% confidence interval [CI]: 1.203, 0.943-1.534) and high-risk groups (OR, 95% CI: 1.616, 1.259-2.074) showed increased risks of CVD prevalence when considering the combined effects of their living environment. During the follow-up, similar associations were observed (hazard ratio [HR], 1.541, 95% CI [1.142-2.080] for the high-risk group; HR 1.296, 95% CI [0.968-1.736] for the middle-risk group); P for trend = 0.003).

CONCLUSION

An overall poor living environmental quality is a potential risk factor for CVD. Future studies should focus more on the effects of exposure to multiple factors.

Association between living environmental quality and risk of arthritis in middle-aged and older adults: a national study in China

Background

The association between combined environmental factors and the risk of arthritis is still scarcely studied. The present study performed cross-sectional and cohort studies to explore the association between risk score of living environment quality and the risk of arthritis in middle-aged and older adults in China.

Methods

The study was based on China Health and Retirement Longitudinal Study (CHARLS), and it recruited 17,218 participants in the cross-sectional study and 11,242 participants in the seven-year follow-up study. The living environment quality was measured by household fuel types, household water sources, room temperature, residence types, and ambient concentration of PM2.5. Logistic regression and Cox proportional hazard regression models were utilized to examine the association between the living environment quality and the risk of arthritis. Competing risk models and stratified analyses were applied to further verify our results.

Results

Compared with individuals in the suitable environment group, people who lived in moderate (OR:1.28, 95%CI: 1.14-1.43) and unfavorable environments (OR:1.49, 95%CI:1.31-1.70) showed higher risks of arthritis when considering the multiple living environmental factors (P for trend <0.001) in the cross-sectional analysis. In the follow-up study, similar results (P for trend = 0.021), moderate environment group (HR:1.26, 95%CI:1.01-1.56) and unfavorable environment group (HR: 1.36, 95%CI: 1.07-1.74), were founded.

Conclusion

Inferior living environment might promote the development of arthritis. It is necessary for the public, especially old people, to improve the living environment, which may be the key to the primary prevention of arthritis.

Older Adults' Frequency of Going Outside during the COVID-19 Pandemic: Associations with Physical Distancing, Health Status, and Fall Risk Factors

Research shows significant health benefits of going outside in late life. Using the 2019 and 2020 National Health and Aging Trend Study and its 2020 COVID-19 supplemental survey (N = 3,857, age 70+), we examined changes in the past-month frequency of going outside one's home/building during the COVID-19 pandemic in 2020 compared to the same time in 2019. We found that 57.0% reported no change in the frequency of going outside, 32.0% went out less frequently, and 11.0% went out more frequently. Logistic regression models showed that decreased frequency was associated with higher frequency of going outside in 2019, avoidance of contact with those outside their household (AOR = 1.51, 95% CI = 1.10, 2.06), dementia diagnosis, mobility device use, self-rated health (AOR = .85, 95% CI = .75-.97), and being 90+ years of age, female, non-Hispanic Black or Hispanic, divorced/separated. Older adults are likely to benefit from going outside more often when they can safely do so.

The Impact of Wearable Technologies in Health Research: Scoping Review

BACKGROUND

Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research.

OBJECTIVE

In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research.

METHODS

We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing.

RESULTS

We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations-wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%).

CONCLUSIONS

Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

Built Environment and Self-Rated Health: Comparing Young, Middle-Aged, and Older People in Chengdu, China

OBJECTIVES

This article explores how the building-scale built environment is associated with self-rated health, examining differences in this association among younger, middle-aged, and older age groups. Features examined included building type, building condition, and sidewalk presence in front of dwellings.

BACKGROUND

Understanding how the relationships between built environments and health vary across age groups helps to build a healthy environment for all. However, most studies have concentrated on the neighborhood or indoor environment, rather than whole buildings, and few have compared age groups.

METHODS

This study analyzed survey data from 1,019 adults living in 40 neighborhoods in Chengdu, China, recruited through a clustered random sampling approach. It used a Bayesian logistic mixed-effects model with interaction terms between age-group indicators and other variables.

RESULTS

Significant differences exist in the relationships of self-rated health with some environmental and other indicators among age groups. For older people, living in multi-floor buildings, having a household smoker, and undertaking fewer hours of weekly exercise were associated with lower odds of reporting good, very good, or excellent health. These relationships were not identified among middle-aged and younger people. More education was associated with higher odds of reporting better health among older and middle-aged groups.

CONCLUSIONS

Older people experience more health-related challenges compared to middle-aged and younger people. However, among the examined built environment factors, building type was the only significant factor related to self-rated health among older people. To promote health among older people, this study recommends adding elevators in the multi-floor buildings.