Waki Kayo, Enomoto Syunpei, Yamauchi Toshimasa, Nangaku Masaomi, Ohe Kazuhiko
Department of Biomedical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan, 81 03-38122111.
Department of Diabetes and Metabolic Diseases, The University of Tokyo, Tokyo, Japan.
JMIR Form Res. 2025 Mar 28;9:e60221. doi: 10.2196/60221.
A 12-week pilot of the StepAdd mobile health (mHealth) behavior change intervention based on social cognitive theory (SCT) saw an 86.7% increase in mean daily step counts among patients with type 2 diabetes. Due to the lack of exploration of theoretical implications in mHealth intervention studies, there is a need to understand the mechanism underlying the behavioral change to inform the future design of digital therapeutics.
This study aimed to examine the SCT drivers underlying the mean increase in exercise among Japanese patients with type 2 diabetes who participated in the StepAdd intervention.
This is a post hoc analysis of data collected in the single-arm pilot study of the 32 patients who completed the StepAdd intervention. The StepAdd app uses self-mastery and coping strategies to increase self-efficacy and thus increase walking. Self-mastery was measured by the goal completion (GC) rate, which is the percentage of days in which patients met these adapting goals. The use of coping strategies was measured by the strategy implementation (SI) rate, which is the percentage of days in which patients applied their selected coping strategies. We assessed correlations between GC, SI, and self-efficacy to increase walking via linear regression and analyzed relationships via structural equation modeling.
We found statistically significant support for the SCT approach, including a correlation coefficient (ρ) of 0.649 between step increase and GC rate (P<.001); a ρ of 0.497 between the coping SI rate and self-efficacy increase (P=.004); a ρ of 0.446 between GC rate and self-mastery increase (P=.01); and a ρ of 0.355 between self-regulation increase and step increase (P=.046), giving us insight into why the behavior intervention succeeded. We also found significant correlations between self-efficacy for barriers and self-efficacy for task-specific behavior (ρ=0.358; P=.04), as well as self-regulation and self-efficacy for task-specific behavior (ρ=0.583; P<.001). However, a cross-lagged panel modeling analysis found no significant evidence that changes in self-efficacy preceded behavior changes in line with SCT.
Self-mastery and coping strategies contributed to the walking behavior change in StepAdd, supporting the SCT model of behavior change. Future research is needed to better understand the causal pathways proposed by SCT.
一项基于社会认知理论（SCT）的为期12周的StepAdd移动健康（mHealth）行为改变干预试点研究发现，2型糖尿病患者的平均每日步数增加了86.7%。由于mHealth干预研究缺乏对理论影响的探索，因此有必要了解行为改变背后的机制，以为未来数字疗法的设计提供参考。
本研究旨在探讨参与StepAdd干预的日本2型糖尿病患者运动平均增加背后的SCT驱动因素。
这是一项对32名完成StepAdd干预的患者进行的单臂试点研究中收集的数据的事后分析。StepAdd应用程序使用自我掌控和应对策略来提高自我效能，从而增加步行量。自我掌控通过目标完成（GC）率来衡量，即患者达到这些适应性目标的天数百分比。应对策略的使用通过策略实施（SI）率来衡量，即患者应用其选定应对策略的天数百分比。我们通过线性回归评估了GC、SI和增加步行的自我效能之间的相关性，并通过结构方程模型分析了它们之间的关系。
我们发现SCT方法在统计学上得到了显著支持，包括步数增加与GC率之间的相关系数（ρ）为0.649（P<.001）；应对SI率与自我效能增加之间的ρ为0.497（P=.004）；GC率与自我掌控增加之间的ρ为0.446（P=.01）；自我调节增加与步数增加之间的ρ为0.355（P=.046），这让我们了解了行为干预成功的原因。我们还发现障碍自我效能与特定任务行为自我效能之间存在显著相关性（ρ=0.358；P=.04），以及自我调节与特定任务行为自我效能之间存在显著相关性（ρ=0.583；P<.001）。然而，交叉滞后面板模型分析没有发现显著证据表明自我效能的变化先于符合SCT的行为变化。
自我掌控和应对策略促成了StepAdd中步行行为的改变，支持了行为改变的SCT模型。未来需要开展更多研究，以更好地理解SCT提出的因果路径。
