The Effect of Telehealth on Quality of Life and Psychological Outcomes Over a 12-Month Period in a Diabetes Cohort Within the Whole Systems Demonstrator Cluster Randomized Trial
Bibliographic record
Abstract
BACKGROUND: Much is written about the promise of telehealth and there is great enthusiasm about its potential. However, many studies of telehealth do not meet orthodox quality standards and there are few studies examining quality of life in diabetes as an outcome. OBJECTIVE: To assess the impact of home-based telehealth (remote monitoring of physiological, symptom and self-care behavior data for long-term conditions) on generic and disease-specific health-related quality of life, anxiety, and depressive symptoms over 12 months in patients with diabetes. Remote monitoring provides the potential to improve quality of life, through the reassurance it provides patients. METHODS: The study focused on participant-reported outcomes of patients with diabetes within the Whole Systems Demonstrator (WSD) Telehealth Questionnaire Study, nested within a pragmatic cluster-randomized trial of telehealth (the WSD Telehealth Trial), held across 3 regions of England. Telehealth was compared with usual-care, with general practice as the unit of randomization. Participant-reported outcome measures (Short-Form 12, EuroQual-5D, Diabetes Health Profile scales, Brief State-Trait Anxiety Inventory, and Centre for Epidemiological Studies Depression Scale) were collected at baseline, short-term (4 months) and long-term (12months) follow-ups. Intention-to-treat analyses testing treatment effectiveness, were conducted using multilevel models controlling for practice clustering and a range of covariates. Analyses assumed participants received their allocated treatment and were conducted for participants who completed the baseline plus at least one follow-up assessment (n=317). RESULTS: Primary analyses showed differences between telehealth and usual care were small and only reached significance for 1 scale (diabetes health profile-disinhibited eating, P=.006). The magnitude of differences between trial arms did not reach the trial-defined minimal clinically important difference of 0.3 standard deviations for most outcomes. Effect sizes (Hedge's g) ranged from 0.015 to 0.143 for Generic quality of life (QoL) measures and 0.018 to 0.394 for disease specific measures. CONCLUSIONS: Second generation home-based telehealth as implemented in the WSD evaluation was not effective in the subsample of people with diabetes. Overall, telehealth did not improve or have a deleterious effect quality of life or psychological outcomes for patients with diabetes over a 12-month period.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".