Assessment of a Mobile App by Adolescents and Young Adults With Cystic Fibrosis: Pilot Evaluation
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF) continues to be the most common life-limiting chronic pulmonary disease in adolescents and young adults. Treatment of CF demands a high treatment time investment to slow the progression of lung function decline, the most important contributor to morbidity and mortality. Adherence is challenging in CF due to the high treatment burden and the lack of immediate health consequences in case of nonadherence. Lung function decline is particularly pronounced in the transition phase between 12 and 24 years of age. The improvement of self-management and self-responsibility and independence from parents and desire for normalcy are conflicting aspects for many adolescents with CF, which influence adherence to the time-consuming pulmonary therapy. Mobile health (mHealth) care apps could help to support self-management and independence and thereby reconcile seemingly conflicting goals to improve adherence, quality of life, and ultimately CF life expectancy. OBJECTIVE: This study aimed to (1) assess user behavior and satisfaction among adolescents and young adults with CF over an observation period of three months using an mHealth app; (2) identify areas of improvement for this mHealth app; and (3) compare overall and disease-specific satisfaction, lung function, and anthropometry before and after using the mHealth app. METHODS: A total of 27 adolescents and young adults with CF (age range 12-24 years, mean age 16 years, SD 3 years; 14 females, 11 males) used a free mHealth app for three months of whom 25 provided questionnaire data for analysis at the end of the study. Data collection was carried out using questionnaires on usage characteristics and life satisfaction, and standardized assessment of lung function and anthropometry. RESULTS: The use of the reminder function for medication declined from 70% (15/21) of the participants at week 4 to 65% (13/20) at week 8 of the observation period. At the end of the study, only 17% (4/23) of the participants wanted to continue using the app. Nevertheless, 56% (14/25) of participants saw the mobile app as a support for everyday life. Potential improvements targeting hedonistic qualities were identified to improve mHealth app adherence. Comparisons of satisfaction with different life aspects hinted at improvements or stabilization for the subitem respiration and the subitem lack of handicap by CF, suggesting that app use might stabilize certain CF-specific aspects of the weighted satisfaction with life. Lung function and anthropometry were not affected consistently. CONCLUSIONS: Most of the patients did not want to continue using the app after the study period. Only a few CF-specific aspects of weighted life satisfaction were possibly stabilized by the mHealth app; clinical parameters were not affected. Adaptation of the functions to adolescent-specific needs could improve the long-term use and thus positively affect the disease course.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".