Takumi Abe

Background: Physical activity, sedentary behaviour, and sleep are three behaviours which occupy the 24-h cycle and interact to influence a multitude of health outcomes. Different terms are currently used to describe these behaviours collectively (e.g., movement behaviours or physical behaviours ). The lack of clear and consistent terminology makes it hard to compare, interpret, and build on research findings. As a first step towards developing consensus on what terminology to use for discussing the 24-h cycle of physical activity, sedentary behaviour, and sleep, we conducted a scoping review to identify the breadth of terminology used to describe this phenomenon. Methods: All searches occurred between September 18th – 20th 2024. We searched nine databases: Medline, EMBASE, CINAHL, PsycInfo, Web of Science, Scopus, ERIC, Education Source, and Compendex from inception to September 18th 2024. The Journal of Activity, Sedentary and Sleep Behaviors and the International Network of Time-Use Epidemiologists (INTUE) publication lists were manually searched on September 19th – 20th 2024. Peer-reviewed articles were included if they 1) were published in English, Spanish, Portuguese, French, Chinese, Korean, or Japanese; 2) focused on humans; and 3) describe physical activity, sedentary behaviour, and sleep in an integrated manner. We identified different terminologies used for collectively discussing physical activity, sedentary behaviour, and sleep across the 24-h cycle. Results: Our search identified 10,749 articles of which 979 articles met our inclusion criteria. Across all articles, 199 different terms were used to describe physical activity, sedentary behaviour, and sleep collectively. The term movement behaviours was used in 673 articles, 24-h movement behaviours was used in 457 articles, physical behaviours was used in 58 articles, 24-h activity cycle was used in 36 articles, and time-use behaviours was used in 31 articles. Conclusions: The two most popular terms for describing the collective interaction of physical activity, sedentary behaviour, and sleep are movement behaviours and 24-h movement behaviours . However, there is not yet an internationally recognized and agreed-upon term for holistically describing the 24-h cycle of physical activity, sedentary behaviour, and sleep. Developing a homogeneous and widely accepted set of terminology and definitions is thus critical if we hope to ensure clarity, enhance reproducibility, and enable the translation of future research into coherent guidelines, policies, and practices for these behaviours.

Background: Research investigating whether walking promotion programs work equally at different levels of walkability needs to be scaled up. This study examined the associations of neighbourhood walkability attributes with adult participants’ withdrawal from an incentive-based walking promotion program, and changes in step counts among non-withdrawing participants. Methods: We used data from 30,530 participants aged 40–74 years enrolled in an incentive walking program in Yokohama City, Japan, in which uploading step-count data was required to enter a raffle for rewards. The follow-up period was up to 48 months. Withdrawal was defined as not uploading step counts during the last six months of the follow-up. Neighbourhood walkability attributes included population density, intersection density, destination diversity, and their composite measure (the Japan Postcode-level Walkability Index). We fitted multilevel Cox proportional hazards models to examine the associations between walkability attributes and program withdrawal, and mixed-effects models to examine the interaction between walkability attributes and time on step counts. Results: During the follow-up period (median: 28 months), 62% of participants withdrew from the program. Compared to those living in the lowest intersection density areas, those in areas with the highest, the second-highest, and the third-highest intersection density had 7% (95% CI: 0.88, 0.98), 6% (0.89, 0.99), and 7% (0.89, 0.98) lower hazard ratios of withdrawal from the program, respectively. The third-highest population density areas showed a 5% (1.00, 1.10) higher hazard ratio of withdrawal, relative to the lowest. Step counts, which exceeded 7,000 steps/day during the study period, declined between the first and second years but slightly increased from the second to third year. There were no statistically significant interactions between the neighbourhood walkability attributes and time in relation to changes in step counts. Conclusion: Living in areas with higher street connectivity was associated with a higher retention in the program, whereas the program showed almost comparable effectiveness regarding changes in step counts for continuing participants living in areas with different levels of walkability. These findings suggest the need for initiatives to support adherence to walking-incentive programs, particularly among those who live in areas with less connected streets.

Background : Employment among older adults has been increasing; however, its association with daily activities remains unclear. This study examined the associations between working condition categories and nonoccupational physical activity among older workers in Japan. Methods : This cross-sectional study used the baseline data from the Wako Cohort Study conducted in 2023. Of the 6429 older respondents, we analyzed 1689 older workers (age range: 65–91). They were categorized by their paid work hours (full time: ≥20 h/wk, part time: Results : Compared with part-time work involving occupational physical activity, full-time work with and without occupational physical activity showed significantly lower odds of engaging in leisure-time physical activity (odds ratio: 0.62; 95% CI, 0.42–0.91 and 0.54; 95% CI, 0.35–0.84, respectively). Full- and part-time work hours without occupational physical activity had a lower odds ratio of engaging in household physical activity (odds ratio: 0.35; 95% CI, 0.22–0.56 and 0.31; 95% CI, 0.19–0.50, respectively). In addition, they were associated with lower levels of total nonoccupational physical activity (mean ratio: 0.83; 95% CI, 0.71–0.98 and 0.72; 95% CI, 0.61–0.86, respectively). Conclusion : Working condition categories can reflect the physical activity characteristics of older workers outside their work hours.

This study examined the cross-sectional and longitudinal associations between neighborhood socioeconomic disadvantage and frailty in middle-to-older-aged adults. Data were obtained from a cohort study conducted in Brisbane, Australia, with 3966 participants (mean age 58.2 years; female 57.6%) included in cross-sectional analysis and 2846 in longitudinal analysis (3-year follow-up). A frailty index (FI), a composite score consisting of 32 items, was used continuously (range 0–1) and categorized into frail (FI > 0.35) and non-frail. Neighborhood disadvantage was measured using the Index of Relative Socioeconomic Disadvantage, an area-level composite index, and was categorized into tertiles. Two-level mixed-effects linear and logistic regression models were fitted, accounting for area-level clustering through random intercepts for areas and adjusting for demographic variables and individual-level socioeconomic status. In the cross-sectional analysis, 11% of participants were frail. Compared to residents of low disadvantage neighborhoods, those in middle and high disadvantage neighborhoods had higher levels of frailty and greater odds of being frail (OR = 1.54 [95% CI 1.13, 2.09] and OR = 2.86 [2.11, 3.88], respectively). In the longitudinal analysis, residing in middle and high disadvantage neighborhoods was significantly associated with increases in FI scores, relative to low disadvantage neighborhoods. Incident frailty was observed in 5% of participants at the follow-up. Residing in high disadvantage neighborhoods showed significantly higher odds of incident frailty (OR = 1.75 [1.08, 2.86]). Mid-to-older-aged residents of disadvantaged neighborhoods are more likely to be frail and to become frail over 3 years, compared to those in advantaged neighborhoods. Community-level interventions to reduce frailty should target disadvantage neighborhoods.

Aim: This study aimed to examine the relationships between levels of competence and impaired physical and cognitive functions in older adults.Methods: We used a data set of the Integrated Longitudinal Studies on Aging in Japan for 2017 including 5475 community‐dwelling older adults. Levels of competence were assessed using the Japan Science and Technology Agency Index of Competence (JST‐IC). Grip strength (low grip strength: Geriatr Gerontol Int 2024; 24: 352–358.

Aim: Muscle mass and strength correlate with cognitive function; however, it remains unclear whether dynapenia (i.e., muscle weakness with preserved muscle mass) is relevant. This study aimed to explore whether dynapenia is associated with global cognitive function in community‐dwelling older Japanese adults.Methods: This cross‐sectional study used data from the Integrated Research Initiative for Living Well with Dementia Cohort Study, which pooled data from five community‐based geriatric cohorts. Dynapenia was defined as muscle weakness without muscle mass loss according to the Asian Working Group for Sarcopenia criteria. Cognitive function was assessed using the Mini‐Mental State Examination (MMSE). An ordered logistic regression analysis was conducted with dynapenia as the exposure and with cognitive decline stages, defined as an MMSE score of 27–30 for normal cognition, 24–26 for possible cognitive decline, and Geriatr Gerontol Int 2024; 24: 123–129.

Aim: To examine the effects of employment engagement, classified by frailty and working status, on the incidence of disability in urban community‐dwelling older adults.Methods: We used data from 6386 initially nondisabled residents aged 65–84 years from Ota City, Tokyo, Japan, in 2016. The observation duration was 3.6 years. Self‐administered questionnaires applied the Cox proportional hazard model by assessing the existence of frailty through Check‐List 15 (with a score ≥4 indicating the state of frailty), controlling for age, sex, living situation, education level, equivalent income, chronic conditions, body mass index, instrumental activities of daily living, self‐rated health, drinking and smoking status, and social activities. We evaluated the predictive value of working status (full‐time, part‐time, temporary, or nonworker) at baseline for cause‐specific disability (dementia‐type vs. non–dementia‐type) incidence, identified using the long‐term care insurance system's nationally unified database.Results: Of the 6386 participants, 806 (63/1134 full‐time workers; 58/1001 part‐time workers; 61/547 temporary workers; 624/3704 nonworkers) presented with disabilities during the 3.6‐year‐long duration. Adjustments for conventional covariates showed that nonfrail full‐time and part‐time workers, as well as frail full‐time workers, had significantly lower risks of all‐cause disability incidence. Furthermore, nonfrail and frail full‐time workers had significantly lower risks of dementia‐type and nondementia‐type disabilities, respectively.Conclusions: The incidence of disability in older adults was influenced by working and frailty status. Engaging in full‐time work thus prevents disabilities in older adults, regardless of their frailty status. Meanwhile, nonfrail older adults are able to avoid disabilities even when engaging in part‐time work. Geriatr Gerontol Int 2023; 23: 855–863.