Effective Engagement of Adolescent Asthma Patients With Mobile Health–Supporting Medication Adherence
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps have the potential to support patients' medication use and are therefore increasingly used. Apps with broad functionality are suggested to be more effective; however, not much is known about the actual use of different functionalities and the effective engagement. OBJECTIVE: The aim of this study was to explore the use and the effective engagement of adolescents (aged 12 to 18 years) with the Adolescent Adherence Patient Tool (ADAPT). METHODS: The ADAPT intervention consisted of an app for patients, which was connected to a management system for their pharmacist. The aim of the ADAPT intervention was to improve medication adherence and, therefore, the app contained multiple functionalities: questionnaires to monitor symptoms and adherence, medication reminders, short movies, pharmacist chat, and peer chat. For this study, data of the ADAPT study and a cluster randomized controlled trial were used. Adolescents with asthma had 6 months' access to the ADAPT intervention, and all app usage was securely registered in a log file. RESULTS: In total, 86 adolescents (mean age 15.0, SD 2.0 years) used the ADAPT app 17 times (range 1-113) per person. Females used the app more often than males (P=.01) and for a longer period of time (P=.03). On average, 3 different functionalities were used, and 13% of the adolescents used all functionalities of the app. The questionnaires to monitor symptoms and adherence were used by most adolescents. The total app use did not affect adherence; however, activity in the pharmacist chat positively affected medication adherence (P=.03), in particular, if patients sent messages to their pharmacist (P=.01). CONCLUSIONS: mHealth apps for adolescents with asthma should contain different functionalities to serve the diverging needs and preferences of individual patients. Suggested key functionalities to promote use and effectiveness in adolescents with asthma are questionnaires to monitor symptoms and a health care provider chat.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".