Investigating the behavioural effects of a mobile-phone based home telehealth intervention in people with insulin-requiring diabetes: Results of a randomized controlled trial with patient interviews
Bibliographic record
Abstract
Introduction Evidence supporting home telehealth effects on clinical outcomes in diabetes is available, yet mechanisms of action for these improvements remain poorly understood. Behavioural change is one plausible explanation. This study investigated the behavioural effects of a mobile-phone based home telehealth (MTH) intervention in people with diabetes. It was hypothesized that MTH would improve self-efficacy, illness beliefs, and diabetes self-care. Methods A randomized controlled trial compared standard care to standard care supplemented with MTH (self-monitoring, data transmission, graphical and nurse-initiated feedback, educational calls). Self-report measures of self-efficacy, illness beliefs, and self-care were repeated at baseline, three months, and nine months. MTH effects were based on the group by time interactions in hierarchical linear models and effect sizes with 95% confidence intervals (CIs). Interviews with MTH participants explored the perceived effects of MTH on diabetes self-management. Results Eighty-one participants were randomized to the intervention ( n = 45) and standard care ( n = 36). Significant group by time effects were observed for five out of seven self-efficacy subscales. Effect sizes were large, particularly at nine months. Interaction effects for illness beliefs and self-care were non-significant, but effect sizes and confidence intervals suggested MTH may positively affect diet and exercise. In interviews, MTH was associated with increased awareness, motivation, and a greater sense of security. Improved self-monitoring and diet were reported by some participants. Discussion MTH empowers people with diabetes to manage their condition and may influence self-care. Future MTH research would benefit from investigating behavioural mechanisms and determining patient profiles predictive of greater behavioural effectiveness.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".