User Engagement in an Online Digital Health Intervention to Promote Problem Solving
Bibliographic record
Abstract
Digital health interventions (DHIs) can facilitate positive health outcomes. User engagement (UE) plays an important role in DHI efficacy. Yet, DHIs vary in functionality, design and intended outcomes, underscoring the importance of incremental, user-centred design to understand engagement in specific settings. This study explores the relationship between user engagement and DHI implementation in three design iterations, or rounds, of a unique, multi-week asynchronous intervention that leverages online discussion and problem-solving therapy (PST). The intervention seeks to engage older adults to improve problem solving skills relating to the intervention focus, health aging (two rounds) and Lewy Body Dementias (LBD) caregiving (one round). The PST component drew upon personas, a common user-centered design method, in a novel way. Exit interviews were conducted at the end of each round to understand participants' experiences. Using thematic analysis, we identified factors that contributed to social engagement ('engaging with others') and learning engagement ('engaging with content') with the DHI. The findings demonstrate how iterative changes in the design and delivery of a DHI can contribute to user engagement, increasing the likelihood of knowledge acquisition and developing problem-solving skills as part of health self-management.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".