Vicomtech Foundation, Basque Research and Technology Alliance, Donostia-San Sebastián, Spain.
Multimedia and Computer Vision Group, Universidad del Valle, Cali, Colombia.
JMIR Mhealth Uhealth. 2022 Sep 9;10(9):e33247. doi: 10.2196/33247.
The popularization of mobile health (mHealth) apps for public health or medical care purposes has transformed human life substantially, improving lifestyle behaviors and chronic condition management.
This review aimed to identify behavior change techniques (BCTs) commonly used in mHealth, assess their effectiveness based on the evidence reported in interventions and reviews to highlight the most appropriate techniques to design an optimal strategy to improve adherence to data reporting, and provide recommendations for future interventions and research.
We performed a systematic review of studies published between 2010 and 2021 in relevant scientific databases to identify and analyze mHealth interventions using BCTs that evaluated their effectiveness in terms of user adherence. Search terms included a mix of general (eg, data, information, and adherence), computer science (eg, mHealth and BCTs), and medicine (eg, personalized medicine) terms.
This systematic review included 24 studies and revealed that the most frequently used BCTs in the studies were feedback and monitoring (n=20), goals and planning (n=14), associations (n=14), shaping knowledge (n=12), and personalization (n=7). However, we found mixed effectiveness of the techniques in mHealth outcomes, having more effective than ineffective outcomes in the evaluation of apps implementing techniques from the feedback and monitoring, goals and planning, associations, and personalization categories, but we could not infer causality with the results and suggest that there is still a need to improve the use of these and many common BCTs for better outcomes.
Personalization, associations, and goals and planning techniques were the most used BCTs in effective trials regarding adherence to mHealth apps. However, they are not necessarily the most effective since there are studies that use these techniques and do not report significant results in the proposed objectives; there is a notable overlap of BCTs within implemented app components, suggesting a need to better understand best practices for applying (a combination of) such techniques and to obtain details on the specific BCTs used in mHealth interventions. Future research should focus on studies with longer follow-up periods to determine the effectiveness of mHealth interventions on behavior change to overcome the limited evidence in the current literature, which has mostly small-sized and single-arm experiments with a short follow-up period.
移动健康 (mHealth) 应用程序在公共卫生或医疗保健方面的普及极大地改变了人类生活，改善了生活方式行为和慢性病管理。
本综述旨在确定 mHealth 中常用的行为改变技术 (BCT)，根据干预措施和综述报告的证据评估其有效性，以突出最适当的技术来设计最佳策略以提高数据报告的依从性，并为未来的干预措施和研究提供建议。
我们对 2010 年至 2021 年期间在相关科学数据库中发表的研究进行了系统综述，以识别和分析使用 BCT 的 mHealth 干预措施，并评估其在用户依从性方面的有效性。搜索词包括一般（例如数据、信息和依从性）、计算机科学（例如 mHealth 和 BCT）和医学（例如个性化医疗）术语的混合。
本系统综述共纳入 24 项研究，结果显示，研究中使用最多的 BCT 是反馈和监测 (n=20)、目标和计划 (n=14)、关联 (n=14)、塑造知识 (n=12) 和个性化 (n=7)。然而，我们发现这些技术在 mHealth 结果中的有效性存在差异，在评估实施反馈和监测、目标和计划、关联和个性化类别的技术的应用程序时，有效结果多于无效结果，但我们不能根据结果推断因果关系，并建议仍有必要改进这些技术和许多常见 BCT 的使用，以获得更好的结果。
在关于 mHealth 应用程序依从性的有效试验中，个性化、关联和目标与计划技术是使用最多的 BCT。然而，它们不一定是最有效的，因为有些研究使用这些技术，但在提出的目标中没有报告显著的结果；实施的应用程序组件内存在 BCT 的明显重叠，这表明需要更好地了解应用（组合）此类技术的最佳实践，并获取 mHealth 干预措施中使用的特定 BCT 的详细信息。未来的研究应侧重于随访时间较长的研究，以确定 mHealth 干预措施对行为改变的有效性，以克服当前文献中有限的证据，当前文献主要是小型单臂实验，随访时间短。
