Nurse‐led remote digital support for adults with chronic conditions: A systematic synthesis without meta‐analysis
Bibliographic record
Abstract
AIM: The systematic review aims to synthesize the literature examining the effectiveness of nurse-led remote digital support on health outcomes in adults with chronic conditions. BACKGROUND: Adults with chronic diseases have increased rates of mortality and morbidity and use health care resources at a higher intensity than those without chronic conditions-placing strain on the patient, their caregivers and health systems. Nurse-led digital health disease self-management interventions have potential to improve outcomes for patients with chronic conditions by facilitating care in environments other that the hospital setting. DESIGN AND METHODS: We searched PubMed/MEDLINE, Embase, PsycINFO and Cochrane Central databases from inception to 7 December 2022. We included randomized controlled trials assessing the impact of nurse-led remote digital support interventions compared to usual care on health-related outcomes in adults with chronic illness. The Cochrane risk-of-bias tool was used to assess bias in studies. Outcomes were organized into four categories: self-management, clinical outcomes, health care resource use and satisfaction with care. Results are presented narratively based on statistical significance. RESULTS: Forty-four papers pertaining to 40 unique studies were included. Interventions most targeted diabetes (n = 11) and cardiovascular disease (n = 8). Websites (n = 10) and mobile applications (n = 10) were the most used digital modalities. Nurses supported patients either in response to incoming patient health data (n = 14), virtual appointment (n = 8), virtual health education (n = 5) or through a combination of these approaches (n = 13). Positive impacts of nurse-led digital chronic disease support were identified in each outcome category. Mobile applications were the most effective digital modality. CONCLUSION AND RELEVANCE TO CLINICAL PRACTICE: Results show that nurse-led remote digital support interventions significantly improve self-management capacity, clinical health outcomes, health care resource use and satisfaction with care. Such interventions have potential to support overall health for adults with chronic conditions in their home environments.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".