Journal of physical activity & health最新文献

Background: Seasonality profoundly impacts older adults in rural communities, influencing daily routines, physical activity (PA), and functionality. Effective interventions require a nuanced understanding of these variations particularly for those living alone.

Methods: This longitudinal study examined 58 older adults (mean age 80.22 y) living alone in a rural community across summer and winter. Variables assessed included sociodemographics, lifestyle factors, weekly social contacts, a composite index of agricultural activity, independence in daily activities, functional status, and weather conditions. PA was measured using an ActiGraph GT9X accelerometer. Paired t tests and K-means cluster analysis were applied, followed by discriminant analysis and cluster evolution monitoring.

Results: Significant seasonal differences were observed in lifestyle, weekly contacts, agricultural activity, and PA. Engagement in agricultural activities correlated with higher PA levels during summer and lower sedentary behavior in both seasons. Cluster analysis identified 3 distinct groups each season, with unique levels of PA, functionality, and behaviors. The discriminant analysis highlighted seasonal patterns, while the bipartite graph analysis showed subject reaggregation in winter, illustrating cluster composition shifts.

Conclusion: This study demonstrates that older adults living alone in rural areas are vulnerable to seasonal variations, with higher PA, more frequent social contacts, and greater agricultural engagement in summer than in winter. Cluster analysis revealed 3 groups of older adults each season, underscoring the necessity for customized interventions that consider behavioral patterns, specific needs, and seasonal changes. The significance of suitable interventions to improve social interactions and prevent functional decline is emphasized.

Background: Characterizing interindividual differences in physical activity within schools and understanding potential drivers of these differences may support the development of improved strategies to increase in-school physical activity.

Methods: Anonymized physical activity data from a school-specific wrist-worn device were obtained from 17,043 pupils and 2555 staff from 165 primary schools in England. Additional school information included UK government school data, area-level deprivation, and estimated playground area. Descriptive statistics were calculated, and data visualizations were used to explore within- and between-school differences. Multilevel models were used to explore the associations between children's in-school physical activity and school variables, with pupils nested within schools, adjusted for individual characteristics.

Results: There was large variation in physical activity between schools. Some schools accumulated twice the amount of physical activity within the school day compared with other schools, with physical activity ranging from 4253 to 8356 steps per school day or 16 to 39 minutes of moderate to vigorous physical activity per school day. School-level data, such as playground space and government school ratings, did not explain between-school variance. Although boys were generally more active than girls in most schools, some schools had more equitable physical activity, and in a small number of schools (∼5%), girls were more active than boys.

Conclusions: There is considerable heterogeneity in physical activity levels between English primary schools. Technology-enabled surveillance from wearable devices can be used to identify schools needing greater support and facilitate learning from those with higher and more equitable physical activity among pupils.

Background: Evidence on the long-term implementation and sustainability of whole-school physical activity programs remains limited. The Creating Active Schools (CAS) program incorporated the CAS Framework to support schools to focus on organizational and cultural change for physical activity. This study evaluates the medium-term implementation of CAS, after 2 academic years.

Methods: A qualitative descriptive approach was employed. After 2 years of implementation, 35 participants from 30 Bradford schools, including school staff, CAS Champions, and Bradford CAS locality leads took part in semistructured focus groups. Thematic analysis followed a codebook method, combining inductive, data-driven insights with deductive themes based on McKay et al's implementation evaluation roadmap, and aligned to the Consolidated Framework Implementation Research.

Results: The program increased the reach compared with the first year, with more staff buying into CAS and gaining confidence as advocates of physical activity. Schools shifted from creating new initiatives to embedding and sustaining previous efforts, and some schools required repeated doses to reinstate CAS as a priority where there had been high staff turnover or superficial initial buy-in. Core components of CAS that mitigated negative influences from the wider educational system, included peer-to-peer learning, and inter and intraknowledge exchange arising from the communities of practice. Collectively, these factors contributed to the combined agency within the school to implement CAS.

Conclusions: The medium-term implementation of whole-school physical activity programs is contingent on understanding the broader educational context and system influences. This study underscores the importance of communities of practice and supportive structures in sustaining school-based physical activity initiatives.

Background: Exercise reduces falls in older people, and the benefits are stronger with greater exercise adherence. This review summarized adherence and behavior change techniques in fall prevention supervised exercise interventions and investigated the association between adherence, participants' characteristics, and intervention components.

Methods: A secondary analysis of trials from the previous 2019 Cochrane Review and its associated updates, investigating fall prevention exercises in community-dwelling people aged 60+ years. We dichotomized the interventions based on adherence, using the threshold of ≥75% of the sessions provided, or ≥75% of the participants attended ≥75% of sessions. Logistic regression examined the associations between adherence and participant characteristics, and intervention components.

Results: One hundred and two studies investigated 136 supervised exercise interventions, of which 116 interventions (85%) reported adherence. The median proportion of exercise sessions attended was 78% (range: 38%-100%). Adherence was associated with exercise frequency with the odds of adherence increased by 167% (95% CI, 12%-536%) for interventions conducted ≥2 times/week, compared to those <2 times/week; 72% higher (95% CI, 12%-85%) for supervised programs without home exercise component, compared to those with 4% more for 1 week less in exercise intervention conducted in trials (2%-7%), and 33% higher (95% CI, 11%-50%) for each behavior change technique not used.

Conclusion: Higher adherence was observed in interventions that were shorter, did not have home exercise components, had more weekly sessions (≥2 times/wk), and used fewer behavior change techniques. Clinicians should consider these factors to optimize adherence to supervised programs.

Background: The 24-hour movement (physical activity-sedentary behavior-sleep) paradigm can promote an ability-focused approach to changing movement behaviors in people diagnosed with cancer. This scoping review aimed to explore how the 24-hour movement paradigm has been applied in people diagnosed with cancer, examining behavior measurement methods and associations between 24-hour movement behaviors and health variables.

Methods: A systematic search was conducted on the August 8, 2024. MEDLINE, Embase, and Cumulative Index of Nursing and Allied Health were searched. There were 5 steps: identifying the research question; identifying studies; study selection; charting the data; collating, summarizing, and reporting results. Study design, sample demographics, movement behavior measurement, analytical approach, and study outcomes were extracted. The review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews framework.

Results: Of 88 records identified, 7 studies met inclusion criteria. All studies were cross-sectional with movement behaviors as the exposures. One study was conducted in children. Four studies used device-based measures for all behaviors, 2 used device-based measures of daytime behaviors with self-reported sleep, and 1 study used self-report for all behaviors. All studies used isotemporal substitution modeling. One study applied a compositional data approach. Outcomes included anthropometrics, quality of life, cognitive function, and bone health. Reallocating time into moderate to vigorous physical activity was associated with the most consistent health benefits.

Conclusions: Limited studies have applied the 24-hour movement paradigm in this population. Most used devices for measurement. While current evidence is constrained by cross-sectional designs and limited generalizability, results support the positive impact of reallocating time from other behaviors into physical activity.

Background: Mobile health (mHealth) interventions offer scalable opportunities to promote healthy movement behaviors, but their real-world effectiveness and implementation remain unclear. This review aims to evaluate scaled-up mHealth interventions targeting physical activity, sedentary behavior, and sleep at a population level and in real-world settings using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework.

Methods: Systematic searches were conducted across 5 databases (PubMed, Embase, Scopus, Web of Science, and PsycINFO). Studies were included if they targeted an entire population or population group, used mHealth, promoted at least one movement behavior, and reported RE-AIM-related outcomes (RE-AIM) either implicitly or explicitly. Study and intervention characteristics alongside RE-AIM-related indicators were extracted and synthesized narratively.

Results: From 9385 records, 43 studies reporting on 24 interventions were included. All were nonrandomized studies and targeted physical activity, with 21.0% also addressing sedentary behavior and 4.8% including sleep. Median reporting across RE-AIM indicators was 6.5 out of 14, with Reach most commonly reported (3 out of 4 indicators), followed by Implementation (2 out of 4), Maintenance (1 out of 2), Effectiveness (1 out of 2), and Adoption (0 out of 2). Representativeness was often assessed by age and sex, but rarely by socioeconomic status or ethnicity. Most interventions had positive effects on movement behavior, and 9.5% assessed cost-effectiveness.

Conclusions: While mHealth interventions show promise for promoting movement behaviors, gaps in evaluation and reporting persist, limiting our understanding of their real-world impact. Future research should prioritize rigorous evaluations and comprehensive reporting to strengthen the case for scalable, sustainable mHealth interventions.

Background: Wearable sensors recording acceleration provide a powerful tool for analysis of physical activity (PA). Continuous, high-rate data acquisition over extended periods gives highly resolved measurement of movement intensity. While increased complexity of PA analytics allows for deeper insight, it brings a challenge to statistical testing, where commonly used approaches require a single defining metric for PA per participant.

Methods: We adapt an econometric measure to obtain a statistical test metric for movement intensity-the intensity inequality index, I≠. This is a "Gini coefficient for movement" that quantifies the inequality in distribution of time spent across a range of activity intensity values. The I≠ metric is calculated using a graphical method on plots of cumulative time versus cumulative intensity level. Hypothesis testing of I≠ is performed on 24-hour activity traces of 58 children, aged 7-11 years, to assess statistical differences in PA between typically developing children and those suspected of having developmental coordination disorder.

Results: The I≠ test metric provided high statistical confidence with low sample numbers: P < .05 for n ≥ 30. When differentiating between groups, I≠ halved the sample size required for a statistical power of 80% at α = .05, in comparison to the alternative metrics of intensity gradient or log ratio of minutes at low and moderate to high intensity.

Conclusions: The inequality index provides a metric that is based on the accumulated time-counts across an activity intensity distribution. This integrative description of the distribution makes it a powerful statistical metric for PA.

Background: Sexual and gender minority adults experience health inequities, including poorer health and reduced participation in health-promoting behaviors. This systematic review aims to (1) examine adherence to physical activity, sedentary behavior, and sleep guidelines within these communities and (2) compare participation and guideline attainment with cisgender and heterosexual adults.

Methods: Studies published before January 2024 were included if they reported physical activity, sedentary behavior, and/or sleep outcomes for gender and/or sexual minority adults. Proportional meta-analyses were performed using random effects models.

Results: Ninety-five studies were included; 79% were rated "fair" quality. Most studies were cross-sectional (76%), conducted in North America (76%), and focused on sexual orientation (80%) and physical activity (77%). Sexual minority women had higher odds of meeting aerobic physical activity guidelines compared with heterosexual women (odd ratios 1.18; 95% CI, 1.02-1.37), with no significant differences identified between sexual minority and heterosexual men (1.03; 0.86-1.23). Gender minority adults had lower odds of participating in aerobic physical activity compared with cisgender adults (0.63; 0.45-0.88). Sexual minority women, but not men, had lower odds of meeting sleep guidelines than their heterosexual counterparts (0.85; 0.74-0.97). Sedentary behavior data were limited, with no differences detected.

Conclusions: Movement behaviors varied within sexual and gender minority groups, highlighting the limitation of considering the "LGBTQIA+ community" a homogenous cohort. Tailored approaches for improving movement behaviors are warranted, particularly for gender minority adults. Further research is needed for sedentary behavior, sleep, and more nuanced categorizations of gender and sexuality using representative samples.

Background: The World Health Organization and many national health bodies have released physical activity and sedentary behavior guidelines; however, there are inconsistencies across jurisdictions, which may partly be due to variation in guideline development processes. This study aimed to develop international consensus on the methodological standards for the development of future physical activity and sedentary behavior guidelines.

Methods: We conducted a modified Delphi study. Experts in physical activity and/or guideline development rated a series of statements on stakeholder involvement, the types of evidence and study designs considered, and the utilization of formal approaches in guideline development. Consensus was defined as group agreement of ≥80%.

Results: Twenty-three participants from 8 countries reached consensus that (1) different stages of the guidelines development process require the involvement of different stakeholders; (2) previous study-level synthesized evidence must be included in evidence reviews, and individual studies can be included if published after the most recent review or where review evidence is unavailable; (3) parallel randomized controlled studies must be included in review processes (83.3% agreement), with observational cohort studies marginally missing the agreement criterion (79.2% agreement), while predictive modeling, crossover trials, nonrandomized trials and case control studies can be included; and (4) formal approaches must be utilized to assess the quality of individual primary studies, the reporting and quality of systematic reviews, and the overall process for grading evidence.

Conclusions: The findings provide a set of methodological standards to improve consistency and rigor in the development of future physical activity and sedentary behavior guidelines.