Division of General Pediatrics, Department of Pediatrics, Johns Hopkins School of Medicine, Baltimore, MD, United States.
Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.
JMIR Mhealth Uhealth. 2024 May 1;12:e49024. doi: 10.2196/49024.
Mobile health (mHealth) interventions have immense potential to support disease self-management for people with complex medical conditions following treatment regimens that involve taking medicine and other self-management activities. However, there is no consensus on what discrete behavior change techniques (BCTs) should be used in an effective adherence and self-management-promoting mHealth solution for any chronic illness. Reviewing the extant literature to identify effective, cross-cutting BCTs in mHealth interventions for adherence and self-management promotion could help accelerate the development, evaluation, and dissemination of behavior change interventions with potential generalizability across complex medical conditions.
This study aimed to identify cross-cutting, mHealth-based BCTs to incorporate into effective mHealth adherence and self-management interventions for people with complex medical conditions, by systematically reviewing the literature across chronic medical conditions with similar adherence and self-management demands.
A registered systematic review was conducted to identify published evaluations of mHealth adherence and self-management interventions for chronic medical conditions with complex adherence and self-management demands. The methodological characteristics and BCTs in each study were extracted using a standard data collection form.
A total of 122 studies were reviewed; the majority involved people with type 2 diabetes (28/122, 23%), asthma (27/122, 22%), and type 1 diabetes (19/122, 16%). mHealth interventions rated as having a positive outcome on adherence and self-management used more BCTs (mean 4.95, SD 2.56) than interventions with no impact on outcomes (mean 3.57, SD 1.95) or those that used >1 outcome measure or analytic approach (mean 3.90, SD 1.93; P=.02). The following BCTs were associated with positive outcomes: self-monitoring outcomes of behavior (39/59, 66%), feedback on outcomes of behavior (34/59, 58%), self-monitoring of behavior (34/59, 58%), feedback on behavior (29/59, 49%), credible source (24/59, 41%), and goal setting (behavior; 14/59, 24%). In adult-only samples, prompts and cues were associated with positive outcomes (34/45, 76%). In adolescent and young adult samples, information about health consequences (1/4, 25%), problem-solving (1/4, 25%), and material reward (behavior; 2/4, 50%) were associated with positive outcomes. In interventions explicitly targeting medicine taking, prompts and cues (25/33, 76%) and credible source (13/33, 39%) were associated with positive outcomes. In interventions focused on self-management and other adherence targets, instruction on how to perform the behavior (8/26, 31%), goal setting (behavior; 8/26, 31%), and action planning (5/26, 19%) were associated with positive outcomes.
To support adherence and self-management in people with complex medical conditions, mHealth tools should purposefully incorporate effective and developmentally appropriate BCTs. A cross-cutting approach to BCT selection could accelerate the development of much-needed mHealth interventions for target populations, although mHealth intervention developers should continue to consider the unique needs of the target population when designing these tools.
移动健康（mHealth）干预措施具有巨大的潜力，可以支持接受需要服药和其他自我管理活动的复杂医疗条件治疗方案的患者进行疾病自我管理。然而，对于任何慢性疾病，用于促进坚持治疗和自我管理的 mHealth 解决方案中应使用哪些离散的行为改变技术（BCT），目前尚无共识。对 mHealth 干预措施中促进坚持治疗和自我管理的现有文献进行综述，以确定有效的、跨领域的 BCT，可以帮助加快具有潜在普遍性的行为改变干预措施的开发、评估和传播。
本研究旨在通过系统综述具有相似坚持治疗和自我管理需求的各种慢性疾病的文献，确定可纳入有效的 mHealth 坚持治疗和自我管理干预措施的跨领域、基于 mHealth 的 BCT。
对 mHealth 坚持治疗和自我管理干预措施的文献进行了注册系统综述，以评估慢性疾病的疗效。使用标准数据收集表提取每项研究的方法学特征和 BCT。
共审查了 122 项研究；大多数研究涉及 2 型糖尿病（28/122，23%）、哮喘（27/122，22%）和 1 型糖尿病（19/122，16%）患者。在坚持治疗和自我管理方面有积极结果的 mHealth 干预措施使用了更多的 BCT（平均 4.95，SD 2.56），而对结果没有影响的干预措施（平均 3.57，SD 1.95）或使用了 >1 种结果测量或分析方法的干预措施（平均 3.90，SD 1.93；P=.02）。以下 BCT 与积极结果相关：行为结果的自我监测（39/59，66%）、行为结果的反馈（34/59，58%）、行为的自我监测（34/59，58%）、行为的反馈（29/59，49%）、可信来源（24/59，41%）和目标设定（行为；14/59，24%）。在仅为成年人的样本中，提示和线索与积极结果相关（34/45，76%）。在青少年和年轻成人样本中，健康后果的信息（1/4，25%）、问题解决（1/4，25%）和物质奖励（行为；2/4，50%）与积极结果相关。在专门针对服药的干预措施中，提示和线索（25/33，76%）和可信来源（13/33，39%）与积极结果相关。在专注于自我管理和其他坚持治疗目标的干预措施中，如何执行行为的指导（8/26，31%）、目标设定（行为；8/26，31%）和行动计划（5/26，19%）与积极结果相关。
为了支持复杂医疗条件患者的坚持治疗和自我管理，mHealth 工具应明确纳入有效的、适合发展阶段的 BCT。BCT 选择的跨领域方法可以加速针对目标人群的急需 mHealth 干预措施的开发，尽管 mHealth 干预措施开发者在设计这些工具时仍应继续考虑目标人群的独特需求。
