Examining usage behavior of a goal-supporting mHealth app in primary care among patients with multiple chronic conditions: A qualitative study (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Although mobile health (mHealth) applications are increasingly being used to support patients with multiple chronic conditions (multimorbidity), the majority of mHealth apps experience low interaction and eventual abandonment. To tackle this engagement issue, it is important to understand social-behavioral factors that impact patients’ usage behavior when developing a mHealth program. </sec> <sec> <title>OBJECTIVE</title> This study aims to explore the social and behavioral factors contributing to the patients’ usage behavior of a mHealth app called the electronic Patient Reported Outcome (ePRO). The ePRO app supports goal-oriented care delivery in interdisciplinary primary care models. </sec> <sec> <title>METHODS</title> A descriptive qualitative study was used to analyze interview data collected for a larger mixed-method pragmatic trial. The original 15-month trial was conducted in six primary care teams across Ontario between 2018 and 2019. For this analysis, patients were classified as long-term or short-term users based on their length of usage of the ePRO app during the trial. Bandura’s Social Cognitive Theory (SCT) was used to categorize social-behavioral factors that contributed to patients' decisions to continue/discontinue the app. </sec> <sec> <title>RESULTS</title> The patient-provider relationship emerged as a key factor that shaped patients’ experiences with the app and subsequent decisions to continue using the app. Other factors that contributed to the patients’ decisions to continue using the app were: personal and social circumstances, perceived usefulness, patients’ prior experience in goal-related behaviors, and confidence in one’s capability to achieve goals and/or use technology. There was an overlap of experience between long-term and short-term app users but in general, long-term users perceived the app to be more useful and their goals to be more meaningful than short-term users. This observation was complicated by the fact that patient health-related goals are dynamic and changed over time. </sec> <sec> <title>CONCLUSIONS</title> Multimorbid patients’ usage behavior of a goal-supporting mHealth is shaped by an array of socio-behavioral factors that can evolve. To tackle this dynamism, there should be an emphasis on creating adaptable health technologies that are easily customizable by patients and able to respond to their changing contexts and needs. </sec> <sec> <title>CLINICALTRIAL</title> ClinicalTrials.gov NCT02917954; https://clinicaltrials.gov/ct2/show/NCT02917954 </sec>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".