Cross-Cutting mHealth Behavior Change Techniques to Support Treatment Adherence and Self-Management of Complex Medical Conditions: Systematic Review

Background: 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 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 behavior change techniques 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. Objective: To identify cross-cutting mHealth-based behavior change techniques to incorporate in 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. Methods: 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. Methodological characteristics and behavior change techniques in each study were extracted using a standard data collection form. Results: 122 studies were reviewed; the majority involved people with type 2 diabetes (n=28/122, 23%), asthma (n=27/122, 22%), and type 1 diabetes (n=19/122, 16%). mHealth interventions rated as having a positive outcome on adherence/self-management used more behavior change techniques (M=4.95, SD=2.56) compared to interventions with no impact on outcomes (M=3.57, SD=1.95) or used >1 outcome measure or analytic approach (M=3.90, SD=1.93; P=.02). The following behavior change techniques 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/cues were associated with positive outcomes (34/45, 76%). In adolescent/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 taking medicine, prompts/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. Conclusions: To support adherence and self-management in people with complex medical conditions, mHealth tools should purposefully incorporate effective and developmentally appropriate behavior change techniques. A cross-cutting approach to behavior change technique selection could accelerate the development of much needed mHealth interventions for target populations, though mHealth intervention developers should continue to consider the unique needs of the target population when designing these tools. Clinical Trial: PROSPERO International prospective register of systematic reviews CRD42021224407; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=224407