Evaluating a mental health support mobile app for adults with type 1 diabetes living in rural and remote communities: The <scp>REACHOUT</scp> pilot study
Bibliographic record
Abstract
AIMS: To evaluate a mobile app that delivers mental health support to adults with type 1 diabetes (T1D) living in rural and remote communities using the Reach, Effectiveness, Adoption, Intervention fidelity, Maintenance (RE-AIM) framework. METHODS: This study recruited 46 adults to participate in a 6-month intervention using REACHOUT, a mobile app that delivers peer-led mental health support (one-on-one, group-based texting and face-to-face virtual). Baseline and 6-month assessments measured diabetes distress (DD), depressive symptoms and perceived support (from family/friends, health care team and peers) along with other RE-AIM metrics. RESULTS: Calculations for reach and adoption found that 3% of eligible adults enrolled in REACHOUT and 55% of diabetes education centres participated in recruitment efforts. Maintenance metrics revealed 56% and 24% of peer supporters and participants, respectively, became peer supporters for a subsequent randomized controlled trial of REACHOUT. Post-intervention reductions were observed for overall distress (p = 0.007), powerlessness (p = 0.009), management distress (p = 0.001), social perception distress (p = 0.023), eating distress (p = 0.032) and depressive symptoms (p = 0.009); and elevations in support from family/friends and peers. After adjusting for sex and age, only support-related improvements persisted. When analysing women and men groups separately, women reported lower levels of overall distress, three distress subscales, and higher levels of family/friends and peer support whereas men did not. CONCLUSIONS: While reach was relatively low, metrics for adoption and maintenance are promising. Improvements in distress were observed for the total sample, but these changes were reduced when controlling for sex and age, with significance maintained only for women. Digital health-enabled peer support may be instrumental in the delivery of mental health support to geographically isolated communities.
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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 | 0.003 | 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".