Active remote monitoring of long-term conditions with mobile devices: a systematic review of cost-effectiveness analyses
Bibliographic record
Abstract
This study aimed to identify and appraise published cost-effectiveness analyses of mobile device-based active remote monitoring technologies for long-term conditions. A systematic literature review (PROSPERO: CRD42023406364) identified studies from Medline and Embase (2008 until November 2024). Interventions required frequent patient-reported responses to questions about their condition on a mobile device (smartphone or tablet). Seven cost-effectiveness analyses were identified for six long-term conditions: rheumatoid arthritis; schizophrenia; older adults with complex conditions; cancer; multiple sclerosis; inflammatory bowel disease. Interventions facilitated early intervention to prevent condition worsening (n = 4); self-management (n = 2); and patient-initiated care (n = 1). Intervention costs were estimated by top-down costing (n = 2); bottom-up micro-costing (n = 3) and assumptions (n = 2). Mobile device-based active remote monitoring was cost-effective in six of the seven studies with a high degree of decision uncertainty. The results will help decision-makers, intervention developers and analysts to guide resource allocation, product development and study designs for future mobile device-based monitoring interventions, respectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".