M100. LOOSELEAF: DEVELOPING A MOBILE-BASED APPLICATION TO MONITOR DAILY CANNABIS USAGE IN YOUTH AT CLINICAL HIGH-RISK OF PSYCHOSIS: APP DEVELOPMENT AND USABILITY TESTING
Bibliographic record
Abstract
Abstract Background Youth at clinical high risk for psychosis (CHR) often use cannabis, which can have a negative impact on their attenuated psychotic symptoms (APS). Our overall goal is to develop an app that will monitor cannabis use and its impact on APS. Objectives: (1) To describe the development of a mobile-based application named LooseLeaf (LL) to monitor daily cannabis use of individuals at CHR through participatory design; and (2) To test initial usability, discover and fix technical issues, and ensure correct data transmission of LL. Methods Two two-hour focus groups were run with CHR participants, age 12–30. Opinions of participants on (i) application content, (ii) graphic design, and (iii) user experience of the different features (i.e., home screen, inventory, questions, feedback, and calculator) were gathered from the first focus group. Based on the comments from the first focus group, a usable prototype of the application was created and was shown to the second focus group. The second focus group provided further feedback on the user experience of each feature, and finalized the application’s name and logo. The focus groups were audio recorded and transcribed verbatim for analysis. Following Braun and Clarke’s guidelines, data obtained from the focus groups was qualitatively analyzed with thematic analysis to identify patterns in responses. The application was refined accordingly. Then, six healthy controls and two CHR participants used LL for one week to test its effectiveness in monitoring cannabis use. On days that participants used cannabis they answered LL’ questions about how much cannabis they used, how they used, their subjective emotional experience, and what their social and environmental context was during and after using cannabis. When they did not use cannabis, LL asked questions about their subjective emotional experience and how they felt about not using cannabis. LL included a bug-report feature that participants were encouraged to use when they encountered problems. Qualitative data about LL was gathered through the 23-item Mobile Application Rating Scale (MARS) covering questions about engagement, functionality, aesthetics, information provided, and subjective quality of LL. Descriptive statistics were calculated for the quantitative data from MARS. Results Participants favored a minimal and neutral design, buttons with icons, and color-coding of the emotions. Participants named the application “LooseLeaf” and helped to refine its features. The final design of the application consisted of 11 questions about cannabis consumption and feelings associated with it (i.e., euphoria, anxiety, and psychosis-like experiences). Over the one-week usability testing period, LL had an 85.7% response rate. The bug-report feature was used 13 times by seven participants to flag technical issues and provide suggestions to improve user experience of LL. The App received a good overall score on the MARS. LL’s functionality, aesthetics, information, and safety rated high. Few customization options, lack of willingness to pay for applications in general, and technical issues resulted in lower engagement and subjective quality scores. LL’s perceived impact score was good. Discussion The application’s development process was based on the feedback of CHR youth. This provided important information on the design and content needed to build a user-centric mobile application. LL demonstrated initial usability, an effective bug-report feature, and some technical issues and problems with data transmission. The MARS, interviews, and bug-reports provided effective feedback for refining LL for the next phase of development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".