Effects of Telemedicine on Informal Caregivers of Patients in Palliative Care: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Telemedicine technology is a rapidly developing field that shows immense potential for improving medical services. In palliative care, informal caregivers assume the primary responsibility in patient care and often face challenges such as increased physical and mental stress and declining health. In such cases, telemedicine interventions can provide support and improve their health outcomes. However, research findings regarding the use of telemedicine among informal caregivers are controversial, and the efficacy of telemedicine remains unclear. Objective: This study aimed to evaluate the impacts of telemedicine on the burden, anxiety, depression, and quality of life of informal caregivers of patients in palliative care. Methods: A systematic literature search was conducted using the PubMed, Embase, Web of Science, CENTRAL, PsycINFO, CINAHL Plus with Full Text, CBM, CNKI, WanFang, and VIP databases to identify relevant randomized controlled trials published from inception to March 2023. Two authors independently screened the studies and extracted the relevant information. The methodological quality of the included studies was assessed using the Cochrane risk-of-bias tool. Intervention effects were estimated and sensitivity analysis was conducted using Review Manager 5.4, whereas 95% prediction intervals (PIs) were calculated using R (version 4.3.2) and RStudio. Results: A total of 9 randomized controlled trials were included in this study. The meta-analysis indicated that telemedicine has reduced the caregiving burden (standardized mean differences [SMD] -0.49, 95% CI -0.72 to -0.27; P<.001; 95% PI -0.86 to -0.13) and anxiety (SMD -0.23, 95% CI -0.40 to -0.06; P=.009; 95% PI -0.98 to 0.39) of informal caregivers; however, it did not affect depression (SMD -0.21, 95% CI -0.47 to 0.05; P=.11; 95% PI -0.94 to 0.51) or quality of life (SMD 0.35, 95% CI -0.20 to 0.89; P=.21; 95% PI -2.15 to 2.85). Conclusions: Although telemedicine can alleviate the caregiving burden and anxiety of informal caregivers, it does not significantly reduce depression or improve their quality of life. Further high-quality, large-sample studies are needed to validate the effects of telemedicine. Furthermore, personalized intervention programs based on theoretical foundations are required to support caregivers.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".