Internet Devices and Desires: A Review of Randomized Controlled Trials of Interactive, Internet-mediated, In-home, Chronic Disease Monitoring Programs
Bibliographic record
Abstract
Abstract:Background and Objectives: The advent of the Internet has made in-home monitoring a possibility for patients suffering from chronic disease, although few studies have examined this phenomena across different disease states. The goal of this review is to identify and evaluate studies where randomized control trials were used to evaluate Internet-mediated home monitoring systems designed to manage and support patients with chronic diseases.Methods and Results: We reviewed 454 abstracts of articles describing computerbased health interventions and read forty-three articles in depth. Seventeen articles met inclusion criteria and were selected for this review. Only completed randomized, controlled trials that reported physiological health outcomes of the intervention were included. Other results reviewed included the populations studied, the short and long term effectiveness of the interventions, costs and technology-related issues and health care provider communication. Internet-mediated home monitoring interventions appear to have some benefit for specific chronic diseases in specific circumstances. Few studies documented cost savings; none of those that did used consistent measures. Studies seldom addressed the challenges of introducing sophisticated interactive-monitoring systems into patients’ homes, the reasons for attrition from trials, or the effects of the intervention on the work of care providers or interprofessional practices.Conclusions: The interventions reviewed showed potential to enhance chronic disease management in some cases. However, the short duration of the studies made it difficult to generalize the results to wider home care settings or predict the effectiveness of such systems over the long and complicated courses of chronic diseases. Thus, despite hopes for significant cost and labour savings, Internet-mediated systems for monitoring chronic diseases in patients’ homes will likely complement rather than replace usual care.
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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 | 0.035 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".