Effectiveness of Smartphone-Based Cognitive Behavioral Therapy Among Patients With Major Depression: Systematic Review of Health Implications
Bibliographic record
Abstract
BACKGROUND: Depression is often associated with rapid changes in mood and quality of life that persist for a period of 2 weeks. Despite medical innovations, there are problems in the provision of care. Long waiting times for treatment and high recurrence rates of depression cause enormous costs for health care systems. At the same time, comprehensive limitations in physical, psychological, and social dimensions are observed for patients with depression, which significantly reduce their quality of life. In addition to patient-specific limitations, undersupply and inappropriate health care have been determined. For this reason, new forms of care are discussed. Smartphone-based therapy is considered to have great potential due to its reach and easy accessibility. Low socioeconomic groups, which are always difficult to reach for public health interventions, can now be accessed due to the high dispersion of smartphones. There is still little information about the impact and mechanisms of smartphone-based therapy on depression. In a systematic literature review, the health implications of smartphone-based therapy were presented in comparison with standard care. OBJECTIVE: The objective of this review was to identify and summarize the existing evidence regarding smartphone-based cognitive behavioral therapy for patients with depression and to present the health implications of smartphone-based cognitive behavioral therapy of considered endpoints. METHODS: A systematic literature review was conducted to identify relevant studies by means of inclusion and exclusion criteria. For this purpose, the PubMed and Psyndex databases were systematically searched using a search syntax. The endpoints of depressive symptoms, depression-related anxiety, self-efficacy or self-esteem, and quality of life were analyzed. Identified studies were evaluated for study quality and risk of bias. After applying the inclusion and exclusion criteria, 8 studies were identified. RESULTS: The studies examined in this review reported contradictory results regarding the investigated endpoints. In addition, due to clinical and methodological heterogeneity, it was difficult to derive evident results. All included studies reported effects on depressive symptoms. The other investigated endpoints were only reported by isolated studies. Only 50% (4/8) of the studies reported effects on depression-related anxiety, self-efficacy or self-esteem, and quality of life. CONCLUSIONS: No clear implications of smartphone-based cognitive behavioral therapy could be established. Evidence for the treatment of depression using smartphone-based cognitive behavioral therapy is limited. Additional research projects are needed to demonstrate the effects of smartphone-based cognitive behavioral therapy in the context of evidence-based medicine and to enable its translation into standard care. Participatory technology development might help to address current problems in mobile health intervention studies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".