Home telehealth for diabetes management: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: It is estimated that more than 180 million people worldwide have diabetes. Health-care providers can remotely deliver health services to this patient population using information and communication technology, also known as home telehealth. Home telehealth may be classified into two subtypes: home telemonitoring (HTM) and telephone support (TS). The research objective was to systematically review the literature and perform meta-analyses to assess the potential benefits of home telehealth compared with usual care (UC) for patients with diabetes. METHODS: An electronic literature search was conducted to identify studies on home telehealth and patients with diabetes that were published between 1998 and 2008 using Medline, Medline In-Process & Other Non-Indexed Citations, BIOSIS Previews and EMBASE. RESULTS: Twenty-six studies (n = 5069 patients) on home telehealth for diabetes were selected. Twenty-one studies evaluated HTM and 5 randomized controlled trials assessed TS. HTM had a positive effect on glycaemic control [as measured by lower glycated haemoglobin level] compared with UC (weighted mean difference =-0.21; 95% confidence interval -0.35 to -0.08), but the results were mixed for TS. Study results indicated that home telehealth helps to reduce the number of patients hospitalized, hospitalizations and bed days of care. Home telehealth was similar or favourable to UC across studies for quality-of-life and patient satisfaction outcomes. CONCLUSIONS: In general, home telehealth had a positive impact on the use of numerous health services and glycaemic control. More studies of higher methodological quality are required to give more precise insights into the potential clinical effectiveness of home telehealth interventions.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".