Digital health interventions in reducing loneliness and improving mental health in older adults - a literature review
Bibliographic record
Abstract
Introduction: Loneliness remains a concerning health issue, especially among the older population. It leads to numerous negative psychological and physiological negative health outcomes. Digital health interventions have presented themselves as promising strategies for mitigating loneliness and improving mental health in this demographic group. Aim of the study: This review aims to evaluate existing literature on select digital health interventions in mitigating loneliness and improving mental well-being in the older population. We aimed to present strengths, limitations and obstacles to adoption of these types of interventions, while providing insights into areas of research which could be more thoroughly explored. Materials and methods: To write this article, databases such as Scopus, PubMed and Google Scholar were searched using the key terms to find relevant information. Studies published between 2009 and 2025 were included. Conclusions: Digital health interventions, including mHealth apps, virtual reality systems (VR), AI (artificial intelligence) chatbots, companion robots, and video communication platforms have shown potential of mitigating loneliness among older adults. However, the evidence is mixed due to differences in methods of measurement, short duration of follow-up research and obstacles to adoption such as digital literacy, technical barriers and ethical considerations. To fully determine their effectiveness, future studies should implement standardized measurement of outcomes, explore personalized interventions and mitigate the barriers to adoption. Possible risks of overreliance, personal data safety, and discouragement of human relations need to be addressed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| 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 | 0.000 |
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".