Health Research Institute, University of Canberra, Bruce, ACT, 2617, Australia.
Research Institute for Sports and Exercise (UCRISE), Faculty of Health, University of Canberra, Bruce, ACT, 2617, Australia.
Int J Behav Nutr Phys Act. 2022 Jul 7;19(1):81. doi: 10.1186/s12966-022-01319-8.
Smartphone apps are increasingly used to deliver physical activity and sedentary behaviour interventions for people with cardiovascular disease. However, the active components of these interventions which aim to change behaviours are unclear.
To identify behaviour change techniques used in smartphone app interventions for improving physical activity and sedentary behaviour in people with cardiovascular disease. Secondly, to investigate the association of the identified techniques on improving these behaviours.
Six databases (Medline, CINAHL Plus, Cochrane Library, SCOPUS, Sports Discus, EMBASE) were searched from 2007 to October 2020. Eligible studies used a smartphone app intervention for people with cardiovascular disease and reported a physical activity and/or sedentary behaviour outcome. The behaviour change techniques used within the apps for physical activity and/or sedentary behaviour were coded using the Behaviour Change Technique Taxonomy (v1). The association of behaviour change techniques on physical activity outcomes were explored through meta-regression.
Forty behaviour change techniques were identified across the 19 included app-based interventions. Only two studies reported the behaviour change techniques used to target sedentary behaviour change. The most frequently used techniques for sedentary behaviour and physical activity were habit reversal and self-monitoring of behaviour respectively. In univariable analyses, action planning (β =0.42, 90%CrI 0.07-0.78) and graded tasks (β =0.33, 90%CrI -0.04-0.67) each had medium positive associations with increasing physical activity. Participants in interventions that used either self-monitoring outcome(s) of behaviour (i.e. outcomes other than physical activity) (β = - 0.47, 90%CrI -0.79--0.16), biofeedback (β = - 0.47, 90%CrI -0.81--0.15) and information about health consequences (β = - 0.42, 90%CrI -0.74--0.07) as behaviour change techniques, appeared to do less physical activity. In the multivariable model, these predictors were not clearly removed from zero.
The behaviour change techniques action planning and graded tasks are good candidates for causal testing in future experimental smartphone app designs.
智能手机应用程序越来越多地被用于为心血管疾病患者提供身体活动和久坐行为干预。然而，这些旨在改变行为的干预措施的积极组成部分尚不清楚。
确定智能手机应用程序干预措施中用于改善心血管疾病患者身体活动和久坐行为的行为改变技术。其次，研究所确定的技术对改善这些行为的关联。
从 2007 年到 2020 年 10 月，在 6 个数据库（Medline、CINAHL Plus、Cochrane 图书馆、SCOPUS、Sports Discus、EMBASE）中进行了搜索。合格的研究使用了智能手机应用程序干预措施，针对心血管疾病患者，并报告了身体活动和/或久坐行为结果。使用行为改变技术分类（v1）对应用程序中用于身体活动和/或久坐行为的行为改变技术进行编码。通过元回归探索行为改变技术对身体活动结果的关联。
在 19 项基于应用程序的干预措施中，共确定了 40 种行为改变技术。只有两项研究报告了用于针对久坐行为改变的行为改变技术。用于久坐行为和身体活动的最常用技术分别是习惯逆转和行为自我监测。在单变量分析中，行动计划（β=0.42，90%置信区间 0.07-0.78）和分级任务（β=0.33，90%置信区间 -0.04-0.67）都与增加身体活动呈中等正相关。在使用自我监测行为结果（即除身体活动以外的结果）（β=-0.47，90%置信区间 -0.79--0.16）、生物反馈（β=-0.47，90%置信区间 -0.81--0.15）和有关健康后果的信息（β=-0.42，90%置信区间 -0.74--0.07）作为行为改变技术的干预措施的参与者，似乎进行的身体活动较少。在多变量模型中，这些预测因素并未从 0 中明显删除。
行动计划和分级任务这两种行为改变技术是未来智能手机应用程序实验设计中因果检验的良好候选者。
