An Overview of Reviews on Telemedicine and Telehealth in Dementia Care: Mixed Methods Synthesis
Bibliographic record
Abstract
Background: Population aging has intensified the global burden of dementia, creating significant challenges for patients, caregivers, and health care systems. While traditional in-person dementia care faces barriers, digital health technologies offer promising solutions to enhance accessibility, efficiency, and patient-centered care. However, evidence on applicability, safety, and effectiveness in dementia care remains fragmented, underscoring systematic evaluation. Objective: This study aims to assess the effectiveness, applicability, safety, and cost-efficiency of telemedicine technologies in dementia care, providing a comprehensive summary of evidence spanning clinical, psychological, socioeconomic, and operational impacts for persons living with dementia and their caregivers and assess alignment with the World Health Organization (WHO) Age-friendly Cities and Communities' Framework and Dementia Inclusive Society Framework. Methods: An overview of systematic and scoping reviews was conducted following a search in 5 databases (MEDLINE, Embase, Scopus, Epistemonikos, and Cochrane Database of Systematic Reviews), with a gray literature search on February 20, 2024. Eligible studies reported predefined outcomes related to telemedicine interventions for integrated dementia care, including effects on mental health, quality of life, physical activity, hospitalization, financial costs, safety, social isolation, and motor function. Screening and data extraction were performed by 10 reviewers. The findings were synthesized using the Thematic Analysis in Meta-Evidence (TAME) methodology, combining thematic and lexical analyses with single-proportion meta-analysis for comprehensive qualitative-quantitative synthesis. The methodological quality was assessed using the AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews), with GRADE-CERQual (Confidence in the Evidence from Reviews of Qualitative Research) for outcomes' confidence in evidence. Results: Ninety-one reviews provided evidence on the impact of telemedicine in dementia care. The most frequently reported outcomes were the effects of remote interventions on psychiatric and psychological well-being, particularly depression and anxiety (relative frequency of occurrence [RFO]=65%, 95% CI 54-75, moderate certainty of evidence). Fifty-seven studies highlighted the positive impact of telemedicine and telehealth on satisfaction and quality of life for persons living with dementia, caregivers, and health care providers (RFO=63%, 95% CI 52-73, moderate certainty of evidence). Remote technology-related interventions for reducing falls and managing behavioral symptoms were also frequently reported (RFO=33% 95% CI 23-44], moderate certainty of evidence). These interventions showed effectiveness in alleviating social isolation and loneliness (RFO=31%, 95% CI 22-41, moderate certainty of evidence). The methodological quality of the included reviews varied significantly, with the majority rated as low or critically low quality. Conclusions: Telemedicine and telehealth have been demonstrated to be effective and valuable tools in dementia care, offering significant benefits across psychological well-being, quality of life, and social impacts for persons living with dementia and their caregivers. This supports the adoption and implementation of telemedicine in dementia care, aligning with the strategies outlined in the United Nations Decade of Healthy Ageing (2021-2030).
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 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 | 0.046 | 0.136 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".