Social Gaming to Decrease Loneliness in Older Adults: Recruitment Challenges and Attrition Analysis in a Digital Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Digital mental health interventions could sustainably and scalably prevent and reduce loneliness in older adults. We designed an app containing 29 text-based games and a questionnaire-administering chatbot to stimulate intergenerational contact. OBJECTIVE: This study aims to evaluate the feasibility of a social gaming app in reducing loneliness among older adults by evaluating recruitment strategies, data collection procedures, and gameplay activity. METHODS: This mixed methods study recruited participants via newsletters, articles, and a social media campaign. We used semistructured interviews and descriptive analysis of questionnaire answers and game data to assess feasibility. Key measures included recruitment reach and efficiency, participant demographics, in-app activity, and app usability and engagement feedback. RESULTS: The social media campaign reached 192,641 potential participants, resulting in 1363 game downloads. A total of 155 participants (aged 65 years and older: n=34, 21.9% and aged less than 65 years: n=121, 78.1%) provided informed consent, yielding a conversion rate of 0.08%. The recruitment campaign focusing on distanced playful interaction had a significantly (P<.001) higher click-through rate (1.98%) than a campaign focusing on research participation (click-through rate=0.51%; P<.001). The overall conversion rate from advertisement exposure to research participation was 0.08%. Participants had a mean age of 48 (SD 16) years. The 65 years and older group averaged 70 (SD 5) years, while the less 65 years group averaged 42 (SD 12) years. Additionally, 45.2% (57/126) reported at least moderate levels of loneliness at baseline. Of the initial 554 players, 91 (16.4%) remained active after the first week, and 32 (5.8%) remained active for more than 90 days. Active participants tended to interact with those within their own age group, as indicated by a Pearson correlation of r=0.31 between the ages of the message sender and receiver. Interviews with 12 (48%) participants (aged 65-79 years; female: n=12, 83%) revealed barriers such as excessive chatbot questions and a mismatch between the target group and app design focus. Questionnaire completion rates dropped from 46% at baseline to 10% at follow-up. CONCLUSIONS: These findings underscore the challenges of recruitment and retention for older adults in a fully digital social gaming intervention. Effective recruitment strategies and targeted app design are crucial for engagement. Based on these insights, future interventions should focus on simplified interfaces, clear guidance for gameplay, and addressing the specific needs and preferences of older adults, thereby enhancing the effectiveness of digital mental health interventions.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".