Evaluating the Effectiveness of Digital Social Robots in Reducing Loneliness Among Community-Dwelling Older Adults in Japan: Randomized Controlled Trial and Qualitative Analysis
Bibliographic record
Abstract
Background: Most studies on interventions using social robots to reduce loneliness have been conducted in facilities in Western countries. Objective: This study evaluated the effectiveness of digital social robot interventions in reducing loneliness among community-dwelling older Japanese adults using a randomized controlled trial and qualitative analysis. Methods: Individuals aged ≥65 years who lived alone in Tokyo and neighboring areas and experienced loneliness were recruited. In total, 73 eligible participants were randomly assigned to either an intervention or a control group. The 4-week intervention involved a humanoid social communication robot (BOCCO emo), which facilitated conversations with human operators and family members and reminded participants of daily tasks. The primary outcome was loneliness, with mental health (psychological well-being, depression, and self-rated health), the frequency of laughter in daily life, health competence, and interpersonal relationships (social network and generalized trust) as secondary outcomes. Participants were evaluated at baseline and follow-up using a self-administered questionnaire. In the follow-up survey, participants in the intervention group provided open-ended responses regarding their experiences using the social robot. Results: In total, 68 participants completed both the baseline and follow-up surveys (34 in each group). The average age of the participants was 82.3 (SD 6.5) years, and 64 (N=68, 94%) participants were women. A linear mixed-effects model with a random intercept indicated that loneliness decreased more in the intervention group than in the control group (difference-in-difference -3.1, 95% CI -5.9 to -0.4). Psychological well-being also improved in the intervention group (difference-in-difference 1.9, 95% CI 0.1 to 3.7). We identified 4 categories through content analysis: emotional support and psychological connection, lifestyle assistance, enrichment of social interaction, and cognitive and mental stimulation. Conclusions: Social robots can reduce loneliness among community-dwelling older adults in non-Western societies. Information and communication technology appears to be an effective approach for alleviating loneliness and enhancing well-being among older adults in community settings.
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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 | 0.011 | 0.002 |
| 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".