Usability and Emotions of Mental Health Assessment Tools: Comparing Mobile App and Paper-and-Pencil Modalities
Bibliographic record
Abstract
Users’ experiences in mental health assessment are multifaceted, including their emotional experiences. Yet, studies of mobile apps for psychiatric assessment have centered on diagnostic accuracy and perceived usability, with little consideration of the impact of user emotional experiences. In this study, we focused on users’ perceived usability and emotions and compared the user experience of a paper-and-pencil and an app-based collection of mental health screening questionnaires: EarlyDetect. The System Usability Scale (SUS) and modality-directed emotion questionnaires were administered using paper-and-pencil or iPad. Modality was assigned pseudo-randomly on patients’ first visit at a referral-based mental health clinic. We found that patients assigned to the iPad app reported a significantly higher SUS score than patients assigned to paper-and-pencil, qualified by a modality-by-gender interaction where modality effects were significant for men but not for women. Moreover, enjoyment was positively linked to perceived usability, whereas boredom, frustration, and anxiety were negatively linked to usability. Our findings illustrate the added value of studying user experience applied to psychiatric assessments, where both emotions and gender-specific user experience should be taken into consideration. We further discuss the implications for psychiatric assessments via app versus traditional data collection.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".