A Tailored mHealth App for Improving Health and Well-Being Behavioral Transformation in UK Police Workers: Usability Testing via a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: When considering the policing environment of 2022, many roles previously in the domain of warranted officers (police officer) are now performed by nonwarranted police staff equivalents. These police staff roles have expanded rapidly into other areas such as investigations, custody, and contact management, which were traditionally seen as police officer functions and put staff under some of the same stresses as police officers. A UK police force requested help in investigating technologies that could be used to improve health and well-being for both officers and staff. OBJECTIVE: The aim of this study was to create a health and well-being app for police officers and staff, which considered the unique requirements of the users throughout the designing, building, prototyping, and testing stages. METHODS: This study involved quantitative approaches (demographic web-based survey questions and the System Usability Scale) and qualitative approaches (open web-based survey questions and semistructured interviews). Unsupervised usability testing of a prototype app was undertaken by members (N=48) of the commissioning client using their smartphones. After completing a preregistration application for screening purposes, participants downloaded a trial version of the app. Then, they completed a web-based questionnaire after testing the app for 10 days. A subsample of participants (9/48, 19%) was interviewed. Deductive thematic analysis was undertaken to identify key themes and subthemes. RESULTS: Data collected during usability testing concerned the 6 domains of the app-food and diet, activity, fluid intake, sleep, good mental health, and financial well-being-and informed the creation of improved design during prototyping. Some usability and design issues and suggestions for improvements were also addressed and implemented-including shift management and catch-up cards-during this cycle of development. CONCLUSIONS: This study highlights the importance of coparticipation with officers and staff across the entire development cycle, to coproduce a human-centered design methodology to enable the development of a considered and user-centered solution. It demonstrates the need for producing a multifunctional tool rather than focusing purely on an individual element for this user group. It also highlights how linking and being able to track optional, personalized elements of health data against one another, cross-referenced to individual shift patterns, might help to inform and provide users with a chance for reflection and therefore influence behavior change.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".