Interventions for reducing loneliness: An umbrella review of intervention studies
Bibliographic record
Abstract
Loneliness is a common phenomenon associated with several negative health outcomes. Current knowledge regarding interventions for reducing loneliness in randomised controlled trials (RCTs) is conflicting. The aim of the present work is to provide an overview of interventions to reduce loneliness, using an umbrella review of previously published systematic reviews and meta-analyses. We searched major databases from database inception to 31 March 2020 for RCTs comparing active versus non-active interventions for reducing loneliness. For each intervention, random-effects summary effect size and 95% confidence intervals (CIs) were calculated. For significant outcomes (p-value < 0.05), the GRADE (Grading of Recommendations Assessment, Development and Evaluation) tool was used, grading the evidence from very low to high. From 211 studies initially evaluated, seven meta-analyses for seven different types of interventions were included (median number of RCTs: 8; median number of participants: 600). Three interventions were statistically significant for reducing loneliness, that is, meditation/mindfulness, social cognitive training and social support. When applying GRADE criteria, meditation/mindfulness (mean difference, MD = -6.03; 95% CI: -9.33 to -2.73; very low strength of the evidence), social cognitive training (8 RCTs; SMD = -0.49; 95% CI: -0.84 to -0.13; very low strength of the evidence) and social support (9 RCTs; SMD = -0.13; 95% CI: -0.25 to -0.01; low strength of the evidence) significantly decreased the perception of loneliness. In conclusion, three intervention types may be utilised for reducing loneliness, but they are supported by a low/very low certainty of evidence indicating the need for future large-scale RCTs to further investigate the efficacy of interventions for reducing loneliness.
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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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".