College of Health Solutions, Arizona State University, 425 North 5th Street, MC9020, Phoenix, AZ, 85004, USA.
Department of Psychology, Clemson University, Clemson, SC, USA.
Int J Behav Nutr Phys Act. 2023 Aug 15;20(1):97. doi: 10.1186/s12966-023-01494-2.
Ecological models suggest that interventions targeting specific behaviors are most effective when supported by the environment. This study prospectively examined the interactions between neighborhood walkability and an mHealth intervention in a large-scale, adequately powered trial to increase moderate-to-vigorous physical activity (MVPA).
Healthy, insufficiently active adults (N = 512) were recruited purposefully from census block groups ranked on walkability (high/low) and socioeconomic status (SES, high/low). Participants were block-randomized in groups of four to WalkIT Arizona, a 12-month, 2 × 2 factorial trial evaluating adaptive versus static goal setting and immediate versus delayed financial reinforcement delivered via text messages. Participants wore ActiGraph GT9X accelerometers daily for one year. After recruitment, a walkability index was calculated uniquely for every participant using a 500-m street network buffer. Generalized linear mixed-effects hurdle models tested for interactions between walkability, intervention components, and phase (baseline vs. intervention) on: (1) likelihood of any (versus no) MVPA and (2) daily MVPA minutes, after adjusting for accelerometer wear time, neighborhood SES, and calendar month. Neighborhood walkability was probed at 5th, 25th, 50th, 75th, and 95th percentiles to explore the full range of effects.
Adaptive goal setting was more effective in increasing the likelihood of any MVPA and daily MVPA minutes, especially in lower walkable neighborhoods, while the magnitude of intervention effect declined as walkability increased. Immediate reinforcement showed a greater increase in any and daily MVPA compared to delayed reinforcement, especially relatively greater in higher walkable neighborhoods.
Results partially supported the synergy hypotheses between neighborhood walkability and PA interventions and suggest the potential of tailoring interventions to individuals' neighborhood characteristics.
Preregistered at clinicaltrials.gov (NCT02717663).
生态模型表明，当针对特定行为的干预措施得到环境的支持时，它们最有效。本研究前瞻性地检查了在一项大规模、充分有力的试验中，邻里可步行性与移动健康干预措施之间的相互作用，以增加中等至剧烈体力活动（MVPA）。
从按可步行性（高/低）和社会经济地位（SES，高/低）排名的普查街区组有目的地招募健康但活动不足的成年人（N=512）。参与者按四组进行街区随机分组，参加为期 12 个月的 2×2 析因试验，评估自适应与静态目标设定以及通过短信即时与延迟提供的财务奖励。参与者佩戴 ActiGraph GT9X 加速度计一年。招募后，为每个参与者使用 500 米街道网络缓冲区计算独特的可步行性指数。广义线性混合效应障碍模型测试了可步行性、干预措施组成部分和阶段（基线与干预）之间的相互作用，包括：（1）任何（而非无）MVPA 的可能性和（2）调整加速度计佩戴时间、邻里 SES 和日历月后，每日 MVPA 分钟。在第 5、25、50、75 和 95 百分位探测邻里可步行性，以探索全部效果范围。
自适应目标设定在增加任何 MVPA 和每日 MVPA 分钟的可能性方面更有效，尤其是在可步行性较低的社区，而干预效果的幅度随着可步行性的增加而下降。即时奖励比延迟奖励显示出对任何和每日 MVPA 的更大增加，尤其是在可步行性较高的社区相对更大。
结果部分支持了邻里可步行性与 PA 干预之间的协同作用假设，并表明有可能根据个体的邻里特征调整干预措施。
在 clinicaltrials.gov 上预先注册（NCT02717663）。
