A randomised, controlled trial of the effects of a mobile telehealth intervention on clinical and patient-reported outcomes in people with poorly controlled diabetes
Bibliographic record
Abstract
Objective The objective of this research is to determine the effects of mobile telehealth (MTH) on glycosylated haemoglobin (HbA1c) and other clinical and patient-reported outcomes in insulin-requiring people with diabetes. Methods A nine-month randomised, controlled trial compared standard care to standard care supplemented with MTH (self-monitoring, mobile-phone data transmissions, graphical and nurse-initiated feedback, and educational calls). Clinical (HbA1c, blood pressure, daily insulin dose, diabetes outpatient appointments (DOAs)) and questionnaire data (health-related quality of life, depression, anxiety) were collected. Mean group changes over time were compared using hierarchical linear models and Mann-Whitney tests. Results Eighty-one participants with a baseline HbA1c of 8.98% ± 1.82 were randomised to the intervention ( n = 45) and standard care ( n = 36). The Group by Time effect revealed MTH did not significantly influence HbA1c ( p = 0.228), but p values were borderline significant for blood pressure ( p = 0.054) and mental-health related quality of life ( p = 0.057). Examination of effect sizes and 95% confidence intervals for mean group differences at nine months supported the existence of a protective effect of MTH on mental health-related quality of life as well as depression. None of the other measured outcomes were found to be affected by the MTH intervention. Conclusions Findings from this study must be interpreted with caution given the small sample size, but they do not support the widespread adoption of MTH to achieve clinically significant changes in HbA1c. MTH may, however, have positive effects on blood pressure and protective effects on some aspects of mental health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".