Comparing a clinician-assisted and app-supported behavioral activation intervention to promote brain health and well-being in frontline care
Bibliographic record
Abstract
BACKGROUND: Positive psychiatry offers an unique approach to promote brain health and well-being in aging populations. Minimal interventions through behavioral activation to promote wellness are increasingly available using self-guided apps, yet little is known about the effectiveness of app technology or the difference between clinician-supported behavioral activation versus self-guided app methodologies. OBJECTIVES: Investigate the difference in users and outcomes between two methods of the Fountain of Health (FoH) positive psychiatry intervention for behavioral activation to promote brain health and well-being: (1) clinician-assisted and (2) independent app use for behavioral self-management. DESIGN AND SETTING: As part of a larger knowledge translation intervention in positive psychiatry, two specific methods of a behavioral activation intervention were retrospectively compared. PARTICIPANTS: Two subsets of patients were compared; 254 clinician-assisted patients; 333 independent app users. INTERVENTION: A minimal positive psychiatry intervention in frontline care using the FoH health and behavior change clinical tools. MEASUREMENTS: Main outcomes were changes in psychological (health and resilience, well-being scores) and behavioral indices (goal attainment, items of goal SMART-ness). User profiles (age, sex and completion rates) were also compared. RESULTS: Clinician-assisted patients were more likely to be male, older, and have lower health and resilience scores at baseline. Clinician-assisted patients had notably higher completion rates (99.2% vs. 10.8%). Psychological outcomes (improved health and resilience, and well-being) were similar regardless of intervention method for those who completed the intervention. Behavioral outcomes revealed clinician-assisted patients set goals that better adhered to key goal-setting items. CONCLUSIONS: Clinician-patient relationships appear to be an important factor for intervention completion and behavioral outcomes, while further exploration of best practices for intervention completion using health apps in clinical practice is needed. A preliminary goal-setting methodology for effective behavioral activation, to promote brain health and wellness, is given.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".