International Journal of Behavioral Nutrition and Physical Activity最新文献

Background: Stepping has been associated with reduced risk for type 2 diabetes (T2D), but existing prospective studies focus largely on average stepping volume (steps per day or week) and ignore important differences in how stepping is accumulated. Here, we examined independent associations of stepping volume and within and between day variability, with incident T2D.

Methods: Participants (n = 4594, 40-75y) without preexisting T2D from The Maastricht Study wore an activPAL3 accelerometer (6-7 days). Prospective associations of stepping volume (steps/day) with incident T2D were assessed using Cox proportional hazards models with restricted cubic splines, adjusted for age, sex, BMI, education, smoking, CVD, sedentary time and diet. Four indices of between-day (i-iii below) and within-day (iv below) stepping pattern were modelled alongside total steps/day. These were: (i) proportion of steps accumulated on the 2 most active days (%Active-2days), (ii) between-day step count variability (BDV) and (iii) inter-daily step count stability (IS), (iv) within-day variability in stepping (WDV) (variability in steps/hour). Higher values in %Active-2days, BDV and WDV indicate greater variation in stepping between or within days. Higher IS values indicate greater uniformity in hourly stepping pattern between days.

Results: Over 30,336 person-years of follow-up (mean 6.6y), 178 incident cases of T2D were recorded. A non-linear (p = 0.04) 'L-shaped' association was observed between stepping volume and T2D risk, with steeper risk reduction earlier in the steps/day distribution. Relative to accumulating ≤ 5000 steps/day, adjusted hazard ratios (95% CI) were 0.57 (0.34, 0.96) for 5000-7500 steps/day, 0.60 (0.65-0.94) for 7501-10,000 steps/day, 0.48 (0.25, 0.89) for 10,001-12,500 steps/day and 0.68 (0.37, 1.24) for > 12,501 steps/day. Higher %Active-2days, BDV, and lower IS, (cumulatively describing a stepping pattern which is variable between days and within days), were linearly associated with T2D risk independent of stepping volume. HRs per SD increase were: %Active-2days 0.70 (0.65, 0.97), BDV 0.69 (0.54, 0.89) and IS 1.32 (1.08, 1.63).

Conclusions: Substantial reductions in T2D risk can be achieved by accumulating more steps during the day. Further, accumulating steps in a pattern possibly reflecting periodic larger doses of stepping may provide additional reductions in T2D risk. Future research regarding volume and optimum patterns of stepping could form the basis of the next generation of public health guidance and interventions to improve health through movement.

Background: Urban parks may promote physical activity (PA); however, little is known about whether renovated urban parks contribute to overall PA similarly across diverse sociodemographic groups. In this cross-sectional study, we examined associations between park-based activities and overall PA among adults living near recently renovated parks in low-income New York City (NYC) neighborhoods, with particular attention to differences by race/ethnicity and sex.

Methods: A total of 1336 adult survey respondents who reported past-month use of a renovated park within 0.5 miles of their residence were included. Surveys captured past-month participation in activities at the renovated park (walking, exercising, taking children to the playground/park, relaxing, socializing, volunteering) and self-reported PA level during visits (sitting, light, moderate-to-vigorous). The outcome, overall past-week PA, was measured in metabolic equivalent of task (MET)-minutes using the International Physical Activity Questionnaire and log-transformed prior to analysis. Linear regression models estimated associations of park-based activities and park-based PA levels with log(MET-minutes) of overall PA in the full sample and stratified by race/ethnicity and sex.

Results: In the full sample, park-based activities explained 5.4% of the variability in overall PA of past-month renovated park users; self-reported park-based PA level explained 5.2% of the variability. Compared to past-month park users who did not engage in these activities, those who reported exercising at the study park had 47% higher overall PA and those who reported walking had 33% higher overall PA, while those who reported volunteering had 26% lower overall PA, after confounder adjustment. Activities at renovated parks explained more variability in overall PA among males (6.3%) compared with females (5.1%), and among minority groups (Latino/as = 6.5%, Blacks = 6.8%, other race/ethnicity = 11.2%) compared with Whites (4.3%).

Conclusions: Among adults with past-month renovated park use in low-income NYC neighborhoods, park-based activities explained a significant proportion of the variability in overall PA, with stronger contributions among minorities and males. These findings highlight the importance of considering sociodemographic differences when assessing the role of renovated urban parks in promoting PA and suggest that investments in high-quality green spaces may be particularly impactful for minority groups facing disproportionate barriers to PA.

Background: Economic evaluations of complex public health interventions are becoming increasingly important. This presents health economists with challenges of adapting methodologies originally designed for healthcare to other contexts, such as education. This study presents an economic evaluation of the UK School Food Standards (SFS), with a particular focus on the methodological challenges involved.

Methods: The economic evaluation was conducted alongside an observational study comparing the SFS-mandated secondary schools to non-mandated schools in the Midlands (UK). Costs of food provision and SFS implementation were collected directly from schools and supplemented by secondary data on schools' catering expenditure. The outcomes included dietary intake, dental health, health-related quality of life (HRQoL), and educational performance, collected from pupils and secondary data. The analysis comprised a micro-costing, cost-consequence, and an exploratory cost-utility analysis, from school and societal perspectives.

Results: Data were collected from 36 schools and 2,543 pupils. We found mandated schools spent less on food provision compared to non-mandated schools, and pupils attending mandated schools had marginally better HRQoL, dental health, and slightly worse nutritional intake. Mandated schools performed worse according to the educational outcomes. There were large amounts of missing cost data despite repeated data collection attempts, and the results of the cost-utility analysis were uncertain.

Discussion: We found no clear evidence on the cost-effectiveness of the SFS in secondary schools, likely due to substantial variation in implementation and compliance across both mandated and non-mandated schools, as well as multiple challenges, including the COVID-19 pandemic, difficulties in collecting cost data from schools, and the complexity of the study context. This study highlights the challenges of primary cost data collection for evaluating complex interventions and the need to balance data accuracy with the resources required. As economic evaluations of school-based interventions become more common, there is a growing need to refine methods for such evaluations.

Background: Continuous glucose monitoring (CGM) is increasingly being used within precision health interventions to motivate behaviour change. However, there is considerable variability and complexity in the design of behaviour change interventions that incorporate CGM-based biofeedback, making it challenging to disentangle the intervention components that are driving intervention effects. The objective of this review is to identify the behaviour change techniques and mechanisms of action commonly implemented alongside CGM-based biofeedback.

Methods: We conducted secondary analyses of a scoping review to identify health behaviour interventions (RCTs) that provided CGM-based biofeedback to promote behaviour change in adults. Two researchers applied the 93-item Behaviour Change Techniques (BCT) Taxonomy (v1) to independently code intervention content in all trial arms (i.e., intervention and comparison arms) dependent upon their targeted behaviour of CGM use, glucometer use, diet, physical activity, or medication adherence. BCTs were analysed individually and according to their corresponding category. We performed univariate linear regression analyses to examine whether the presence of individual BCTs and target behaviours influenced pre-post changes in HbA1c within CGM-based intervention arms.

Results: Thirty-one RCTs comprising 35 intervention arms and 29 comparison arms were included. Theory was reported in 4 studies (13%), most commonly Self-Efficacy Theory. Mechanisms of action (MoAs) were specified in 5 studies (16%), typically targeting beliefs about capabilities. We identified 40 (of 93 possible) unique BCTs, with intervention arms employing an average of 7.1 BCTs (SD: 4.8) compared to 5.3 BCTs (SD: 4.3) in comparison arms. The most frequently implemented BCT categories in CGM-based biofeedback interventions were 'Feedback and monitoring' (n = 35/35, 100%), 'Shaping knowledge' (n = 28/35, 80%), and 'Social support' (n = 22/35, 63%). Commonly used BCTs supporting CGM use and promoting dietary and physical activity changes included 'Biofeedback' (n = 35/35; 100%), 'Instruction on how to perform the behaviour' (n = 19/35; 54%), and 'Credible source' (n = 14/35; 40%). Univariate linear regressions did not identify any individual BCTs or targeted behaviours that significantly moderated HbA1c outcomes.

Conclusions: RCTs using CGM to change behaviour in adult populations include a range of BCTs, focusing predominantly on BCTs that support the implementation of CGM itself. Future research should examine whether BCTs operate through distinct MoAs when supporting CGM uptake and use versus when promoting broader health behaviour change in conjunction with CGM-based biofeedback.

Background: Physical activity (PA) is essential for physical and mental health, yet sustaining long-term PA engagement remains a challenge. Booster strategies-follow-up contacts delivered after the end of interventions-have been proposed as a strategy to support PA maintenance, but their effectiveness remains unclear. The primary objective of this systematic review and meta-analysis was to classify the boosters used in PA interventions depending on their type and number. The secondary objective was to explore the efficacy of boosters in supporting participants' PA maintenance.

Methods: A systematic search was conducted across seven databases, up to February 2025. Randomised controlled trials were included if they incorporated boosters and reported PA outcomes. Risk of bias was assessed using the RoB 2 tool. Meta-analysis examined changes in moderate-to-vigorous physical activity (MVPA) from baseline to the last available follow-up, and moderation analysis explored the effects of booster type, number of boosters administered, and follow-up duration on changes in MVPA. Studies not suitable for meta-analysis were synthesised narratively.

Results: Forty studies were included in the systematic review. The most common types of boosters used were phone calls and text messages, which were employed either alone or in combination with other types. 16 studies provided data for inclusion in the meta-analysis. There was conclusive evidence that including a booster in the intervention led to sustained increases in PA levels at follow-up. The estimated added effect of the booster over the intervention alone was a 6% increase. There was also conclusive evidence of increased MVPA for interventions with more boosters, and interventions that used remote and mixed-format delivery (vs in-person only) boosters. Finally, results showed conclusive evidence of increased MVPA for interventions that assessed MVPA using self-reported measures.

Conclusions: Our findings suggest a trend indicating that boosters may support the maintenance of PA. Higher number of boosters and delivery through remote or mixed formats showed promising trends. Future research should also explore optimal booster numbers and formats to clarify their role in sustaining PA.

Registration: PROSPERO (CRD42024510018); Protocol also available on Open Science Framework (OSF): https://osf.io/6abkw/?view_only=915375148520427db3dca76d2c32934d .

Background: The directionality of longitudinal associations between children's food fussiness and parental feeding behaviors remains contested. This study aimed to assess the dynamic relationship between children's food fussiness and feeding behaviors.

Methods: To disentangle these effects, this study employed cross-lagged panel models (CLPMs) and random-intercept cross-lagged panel models (RI-CLPMs) using longitudinal data from 588 Chinese children (Mean age = 3.7 years, SD = 0.3, 51.7% boys) across three waves over two years. CLPMs capture between-person associations, while RI-CLPMs isolate within-person dynamics over time. Within-person effects represent how temporary deviations predict subsequent changes beyond stable traits, whereas between-person effects reflect enduring cross-family differences.

Results: Analyses revealed distinct patterns depending on the feeding behavior and model type: for restrictions, the CLPM showed parent-driven effects (restrictions at 3.7 years→ fussiness at 4.8 years, β = -0.104, p = 0.003), whereas the RI-CLPM identified child-driven effects (fussiness at 4.8 years → restrictions at 5.7 years, β = 0.179, p = 0.033). Both models consistently revealed child-driven effects for pressure to eat (CLPM: β = 0.151, p = 0.002; RI-CLPM: β = 0.218, p = 0.013). Food as a reward showed bidirectionality in CLPM (reward at 4.8 years → fussiness at 5.7 years: β = 0.112, p < 0.001; fussiness at 4.8 years→ reward at 5.7 years: β = 0.144, p = 0.005) but no significant cross-lagged paths in the RI-CLPM. Notably, the multi-group analysis revealed no moderating effect of child sex.

Conclusions: After accounting for stable between-person differences, RI-CLPM findings reveal that child food fussiness prospectively drives increases in parental use of restriction and pressure to eat at the within-person level. This suggests that these specific feeding behaviors may function more as reactive responses to children's eating behaviors than as caregiver-initiated strategies.