Cost-effectiveness of Digital Tools for Behavior Change Interventions Among People With Chronic Diseases: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Chronic diseases, including cardiovascular diseases, diabetes, chronic obstructive pulmonary disease, and cerebrovascular diseases, contribute to the most significant disease burden worldwide, negatively impacting patients and their family members. People with chronic diseases have common modifiable behavioral risk factors, including smoking, alcohol overconsumption, and unhealthy diets. Digital-based interventions for promoting and sustaining behavioral changes have flourished in recent years, although evidence of the cost-effectiveness of such interventions remains inconclusive. OBJECTIVE: In this study, we aimed to investigate the cost-effectiveness of digital health interventions for behavioral changes among people with chronic diseases. METHODS: This systematic review evaluated published studies focused on the economic evaluation of digital tools for behavioral change among adults with chronic diseases. We followed the Population, Intervention, Comparator, and Outcomes framework to retrieve relevant publications from 4 databases: PubMed, CINAHL, Scopus, and Web of Science. We used the Joanna Briggs Institute's criteria for economic evaluation and randomized controlled trials to assess the risk of bias in the studies. Two researchers independently screened, assessed the quality, and extracted data from the studies selected for the review. RESULTS: In total, 20 studies published between 2003 and 2021 fulfilled our inclusion criteria. All the studies were conducted in high-income countries. These studies used telephones, SMS text messaging, mobile health apps, and websites as digital tools for behavior change communication. Most digital tools for interventions focused on diet and nutrition (17/20, 85%) and physical activity (16/20, 80%), and a few focused on smoking and tobacco control (8/20, 40%), alcohol reduction (6/20, 30%), and reduction of salt intake (3/20, 15%). Most studies (17/20, 85%) used the health care payer perspective for economic analysis, and only 15% (3/20) used the societal perspective. Only 45% (9/20) of studies conducted a full economic evaluation. Most studies (7/20, 35%) based on full economic evaluation and 30% (6/20) of studies based on partial economic evaluation found digital health interventions to be cost-effective and cost-saving. Most studies had short follow-ups and failed to include proper indicators for economic evaluation, such as quality-adjusted life-years, disability-adjusted life-years, lack of discounting, and sensitivity analysis. CONCLUSIONS: Digital health interventions for behavioral change among people with chronic diseases are cost-effective in high-income settings and can therefore be scaled up. Similar evidence from low- and middle-income countries based on properly designed studies for cost-effectiveness evaluation is urgently required. A full economic evaluation is needed to provide robust evidence for the cost-effectiveness of digital health interventions and their potential for scaling up in a wider population. Future studies should follow the National Institute for Health and Clinical Excellence recommendations to take a societal perspective, apply discounting, address parameter uncertainty, and apply a lifelong time horizon.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".