Exploring the General Acceptability and User Experience of a Digital Therapeutic for Cognitive Training in a Singaporean Older Adult Population: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Singapore's large aging population poses significant challenges for the health care system in managing cognitive decline, underscoring the importance of identifying and implementing effective interventions. Cognitive training delivered remotely as a digital therapeutic (DTx) may serve as a scalable and accessible approach to overcoming these challenges. While previous studies indicate the potential of cognitive training as a promising solution for managing cognitive decline, understanding the attitudes and experiences of older adults toward using such DTx platforms remains relatively unexplored. OBJECTIVE: This study aimed to characterize the general acceptability and user experience of CURATE.DTx, a multitasking-based DTx platform that challenges the cognitive domains of attention, problem-solving, and executive function in the Singaporean older adult population. METHODS: A total of 15 older adult participants (mean age 66.1, SD 3.5 years) were recruited for a 90-minute in-person session. This session included a 30-minute playtest of CURATE.DTx, followed by a 60-minute semistructured interview to understand their overall attitudes, experience, motivation, and views of the intervention. Interviews were audio-recorded and transcribed verbatim, then analyzed using an inductive approach. Thematic analysis was used to identify emerging patterns and insights. RESULTS: A total of 3 main themes, and their respective subthemes, emerged from the interviews: comprehension, with subthemes of instruction and task comprehension; acceptability, with subthemes of tablet usability, engagement and enjoyment, and attitude and perceived benefits; and facilitators to adoption, with subthemes of framing and aesthetics, motivation recommendations and the role of medical professionals. Our findings revealed that participants encountered some challenges with understanding certain elements of CURATE.DTx. Nevertheless, they were still highly engaged with it, finding the challenge to be enjoyable. Participants also showed a strong awareness of the importance of cognitive training and expressed a keen interest in using CURATE.DTx for this purpose, especially if recommended by medical professionals. CONCLUSIONS: Given the positive engagement and feedback obtained from Singaporean older adults on CURATE.DTx, this study can serve as a basis for future platform iterations and strategies that should be considered during implementation. Future studies should continue implementing an iterative codesign approach to ensure the broader applicability and effectiveness of interventions tailored to this demographic.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".