Evaluating User Preferences, Comprehension, and Trust in Apps for Environmental Health Hazards: Qualitative Case Study
Bibliographic record
Abstract
BACKGROUND: Climate change is projected to increase environmental health hazard risks through fire-related air pollution and increased airborne pollen levels. To protect vulnerable populations, it is imperative that evidence-based and accessible interventions are available. The environmental health app, AirRater, was developed in 2015 in Australia to provide information on multiple atmospheric health hazards in near real time. The app allows users to view local environmental conditions, and input and track their personal symptoms to enable behaviors that protect health in response to environmental hazards. OBJECTIVE: This study aimed to develop insights into users' perceptions of engagement, comprehension, and trust in AirRater to inform the future development of environmental health apps. Specifically, this study explored which AirRater features users engaged with, what additional features or functionality needs users felt they required, users' self-perception of understanding app information, and their level of trust in the information provided. METHODS: A total of 42 adult AirRater users were recruited from 3 locations in Australia to participate in semistructured interviews to capture location- or context-specific experiences. Participants were notified of the recruitment opportunity through multiple avenues including newsletter articles and social media. Informed consent was obtained before participation, and the participants were remunerated for their time and perspectives. A preinterview questionnaire collected data including age range, any preexisting conditions, and location (postcode). All participant data were deidentified. Interviews were recorded, transcribed, and analyzed using thematic analysis in NVivo 12 (QSR International). RESULTS: Participants discussed app features and functionality, as well as their understanding of, and trust in, the information provided by the app. Most (26/42, 62%) participants used and valued visual environmental hazard features, especially maps, location settings, and hazard alerts. Most (33/42, 78%) found information in the app easy to understand and support their needs, irrespective of their self-reported literacy levels. Many (21/42, 50%) users reported that they did not question the accuracy of the data presented in the app. Suggested enhancements include the provision of meteorological information (eg, wind speed or direction, air pressure, UV rating, and humidity), functionality enhancements (eg, forecasting, additional alerts, and the inclusion of health advice), and clarification of existing information (eg, symptom triggers), including the capacity to download personal summary data for a specified period. CONCLUSIONS: Participants' perspectives can inform the future development of environmental health apps. Specifically, participants' insights support the identification of key elements for the optimal development of environmental health app design, including streamlining, capacity for users to customize, use of real time data, visual cues, credibility, and accuracy of data. The results also suggest that, in the future, iterative collaboration between developers, environmental agencies, and users will likely promote better functional design, user trust in the data, and ultimately better population health outcomes.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".