Digital health interventions to improve recovery for intensive care unit survivors: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Recovery models of care for intensive care unit (ICU) survivors are limited by availability, accessibility, and efficacy. Digital health interventions represent an alternative mode of service delivery. The primary aim of this systematic review was to describe implementation factors (Reach, Effectiveness, Adoption, Implementation, and Maintenance) for digital health interventions for ICU survivors. The secondary aim was to describe any effect on patient-reported health outcomes. DATA SOURCES: A systematic search of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excertpa Medica Database (EMBASE), Cumulative Index of Nursing and Allied Health Literature (CINAHL), and Cochrane Central Register of Systematic Reviews (CENTRAL) databases was undertaken in March 2023. STUDY SELECTION: Two independent reviewers screened abstracts and full texts against eligibility criteria. Studies of adult survivors with any post-ICU discharge care, delivered via a digital mode, were included. Studies were excluded if published before 1990 or not in English. DATA EXTRACTION: Quantitative data were extracted using predefined data fields. Risk of bias was assessed using the Newcastle-Ottawa Scale and Cochrane Risk of Bias Tool 2.0. Implementation factors were reported according to the Reach, Effectiveness, Adoption, Implementation and Maintenance framework. DATA SYNTHESIS: A total of 6482 studies were screened. Ten studies, with 686 participants, were included. Implementation factors were reported in all studies. Acceptability (reported in six studies) was high, with high satisfaction and usability scores, defined a priori by investigators. Eight studies reported intervention adherence rates between 46% and 100%. Nine studies report final outcome measurement retention rates up to 12 months, between 52% and 100%. Five studies included the primary outcome as the difference in a patient-reported health outcome. Appraisal of efficacy and digital health literacy was limited due to substantial methodological variation and a lack of reporting in included studies. There was some risk of bias in 50% of studies. CONCLUSIONS: Digital health interventions can be successfully implemented for critical care survivors and have varying intervention adherence and retention rate success. To broaden reach, future research should include cultural diversity and investigate digital health access, literacy, and cost-effectiveness. INTERNATIONAL PROSPECTIVE REGISTER OF SYSTEMATIC REVIEWS REGISTRATION: CRD42022348252.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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 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".