Effects of Choice Set Sizes and Moderations of Anxiety and State Emotions on Mental Health Self-Care Uptake, Engagement, and User Experience: Experimental Study
Bibliographic record
Abstract
Background: Digital mental health platforms often consist of many different forms of self-care exercises. To our knowledge, whether the number of choices presented to the users affects their uptake and experiences and poses negative consequences (ie, not choosing any exercises, choice dissatisfaction) for users, especially those experiencing anxiety and depressive symptoms or unpleasant state emotions, has not been empirically investigated. Objective: This study investigated the impact of choice set size on practice decisions, completion, satisfaction, and subjective experiences, as well as potential moderators including depression and anxiety symptoms, state emotions, and motivational and decisional attributes on these choice outcomes. Methods: Participants were recruited through university mass email and social media, and 652 participants were included in our analyses. Participants completed questions regarding anxiety and depressive symptoms, state emotions, and other psychological attributes. Then, they were randomly assigned to 1-choice, 4-choice, and 16-choice conditions, in which they may choose a self-care activity to practice or decide not to practice. Finally, they completed questions regarding completion, satisfaction, engagement, attitude, and perceived improvement in psychological state. Results: Presenting multiple choices resulted in a higher likelihood of practice (odds ratio 3.12, 95% CI 2.08 to 4.67 and 3.83, 95% CI 2.55 to 5.76; P<.001) and better decision satisfaction (16-choice vs 1-choice: d=0.36, 95% CI 0.17 to 0.56, P<.001; 4-choice vs 1-choice: d=0.24, 95% CI 0.05 to 0.43, P=.03) compared with presenting with a single choice. Tentative evidence indicates anxiety symptoms and state emotions were meaningful moderators. Specifically, for individuals with more anxiety symptoms and intense negative emotions, presenting a larger choice set (16 choices) resulted in more positive chosen exercise satisfaction, better attitudes toward chosen activity, and higher perceived improvement in mental health state after the activity, when compared with presenting with smaller choice sets (anxiety: β=-0.38, 95% CI -0.69 to -0.06 to -0.51, 95% CI -0.84 to -0.18; state emotions: β=-0.31, 95% CI -0.66 to 0.03 to -0.60, 95% CI -0.92 to -0.28). No evidence was found for the moderating effect of motivational and decisional attributes. Conclusions: The moderation results were contradictory to prior research and our expectation that a larger choice set may result in worse outcomes than a smaller choice set for people who were experiencing higher levels of psychological distress. We speculated that a possible reason for these findings may be that people with more anxiety symptoms and unpleasant emotions may have a stronger need to reduce these uncomfortable symptoms and emotions, and when presented with more choices on self-care activities, there may be a higher possibility that these self-care activities can address their distress.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".