Objective Evidence backing the effectiveness of mobile health technology is growing, and behavior change communication applications (apps) are fast becoming a useful platform for behavioral health programs. However, data to support the cost-effectiveness of these interventions are limited. Suggestions for overcoming the low output of economic data include addressing the methodological challenges for conducting cost-effectiveness analysis of behavior change app programs. This study is a systematic review of cost-effectiveness analyses of behavior change communication apps and a documentation of the reported challenges for investigating their cost-effectiveness. Materials and methods Four academic databases: Medline (Ovid), CINAHL, EMBASE and Google Scholar, were searched. Eligibility criteria included original articles that use a cost-effectiveness evaluation method, published between 2008 and 2018, and in the English language. Results Out of the 60 potentially eligible studies, 6 used cost-effectiveness analysis method and met the inclusion criteria. Conclusion The evidence to support the cost-effectiveness of behavior change communication apps is insufficient, with all studies reporting significant study challenges for estimating program costs and outcomes. The main challenges included limited or lack of cost data, inappropriate cost measures, difficulty with identifying and quantifying app effectiveness, representing app effects as Quality-adjusted Life Years, and aggregating cost and effects into a single quantitative measure like Incremental Cost Effectiveness Ratio. These challenges highlight the need for comprehensive economic evaluation methods that balance app data quality issues with practical concerns. This would likely improve the usefulness of cost-effectiveness data for decisions on adoption, implementation, scalability, sustainability, and the benefits of broader healthcare investments.
Statin Cost-Effectiveness in Patients with Previous Coronary Heart Disease: A Systematic Review of the Cost-Effectiveness Analysis Derived from Single Randomised Clinical Trials
