A Digital Gaming Intervention to Strengthen the Social Networks of Older Dutch Adults: Mixed Methods Process Evaluation of a Digitally Conducted Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Digital loneliness interventions for older adults are promising, yet conclusive evidence is lacking due to a lack of randomized controlled trials (RCTs) and difficulties with recruitment. Process evaluation of performed RCTs is essential to inform future interventions. Still, it is rarely carried out, resulting in an overly optimistic view of the impact of eHealth interventions on loneliness in older adults and options to conduct such research entirely remotely. OBJECTIVE: We describe a mixed methods process evaluation of a digitally conducted RCT assessing the effectiveness of a mobile social gaming app to facilitate meaningful social interactions in older adults. METHODS: We analyzed the questionnaire and game data of the RCT participants to evaluate recruitment and onboarding, intervention adherence, and intervention acceptability. The RCT participants were allocated either to the main group of older adults (aged 65 years or older) or the side group (aged between 18 and 64 years). The side group used networking to play with the older adults. We also conducted 6 post-RCT evaluation interviews and 1 focus group with a total of 4 RCT participants and 5 welfare organization representatives that aided in RCT recruitment. RESULTS: In total, 371 people aged 18 years or older signed up for the RCT, of which 64% (238/371) were aged 65 years or older. Of the total sample, 20% (76/371) installed the app and signed informed consent, showing a large dropout during onboarding. The high number of questions was a relevant barrier for participants. Both questionnaire and gameplay adherence were low. Participants indicated that the games elicited contact and a feeling of togetherness and proposed challenging and competitive games with increasing difficulty levels. They suggested focusing on enjoying the games rather than administering questionnaires. CONCLUSIONS: Conducting a remote digital trial of a social gaming intervention for older adults is a great challenge. Remote recruitment and informed consent acquisition may often not result in sufficient participation. Personal engagement with fellow participants and researchers might be essential for adherence and enjoyment. Future digital gaming interventions should start with small-scale studies with in-person contact, repeated instructions, and fewer questionnaires.
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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 | 0.069 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".