Mining User Reviews From Hypertension Management Mobile Health Apps to Explore Factors Influencing User Satisfaction and Their Asymmetry: Comparative Study
Notice bibliographique
Résumé
BACKGROUND: Hypertension significantly impacts the well-being and health of individuals globally. Hypertension management apps (HMAs) have been shown to assist patients in controlling blood pressure (BP), with their efficacy validated in clinical trials. However, the utilization of HMAs continues to be suboptimal. Presently, there is a dearth of real-world research based on big data and exploratory mining that compares Chinese and American HMAs. OBJECTIVE: This study aims to systematically gather HMAs and their user reviews from both China and the United States. Subsequently, using data mining techniques, the study aims to compare the user experience, satisfaction levels, influencing factors, and asymmetry between Chinese and American users of HMAs. In addition, the study seeks to assess the disparities in satisfaction and its determinants while delving into the asymmetry of these factors. METHODS: The study sourced HMAs and user reviews from 10 prominent Chinese and American app stores globally. Using the latent Dirichlet allocation (LDA) topic model, the research identified various topics within user reviews. Subsequently, the Tobit model was used to investigate the impact and distinctions of each topic on user satisfaction. The Wald test was applied to analyze differences in effects across various factors. RESULTS: We examined a total of 261 HMAs along with their associated user reviews, amounting to 116,686 reviews in total. In terms of quantity and overall satisfaction levels, Chinese HMAs (n=91) and corresponding reviews (n=16,561) were notably fewer compared with their American counterparts (n=220 HMAs and n=100,125 reviews). The overall satisfaction rate among HMA users was 75.22% (87,773/116,686), with Chinese HMAs demonstrating a higher satisfaction rate (13,866/16,561, 83.73%) compared with that for American HMAs (73,907/100,125, 73.81%). Chinese users primarily focus on reliability (2165/16,561, 13.07%) and measurement accuracy (2091/16,561, 12.63%) when considering HMAs, whereas American users prioritize BP tracking (17,285/100,125, 17.26%) and data synchronization (12,837/100,125, 12.82%). Seven factors (easy to use: P<.001; measurement accuracy: P<.001; compatibility: P<.001; cost: P<.001; heart rate detection function: P=.02; blood pressure tracking function: P<.001; and interface design: P=.01) significantly influenced the positive deviation (PD) of Chinese HMA user satisfaction, while 8 factors (easy to use: P<.001; reliability: P<.001; measurement accuracy: P<.001; compatibility: P<.001; cost: P<.001; interface design: P<.001; real-time: P<.001; and data privacy: P=.001) affected the negative deviation (ND). Notably, BP tracking had the greatest effect on PD (β=.354, P<.001), while cost had the most significant impact on ND (β=3.703, P<.001). All 12 factors (easy to use: P<.001; blood pressure tracking function: P<.001; data synchronization: P<.001; blood pressure management effect: P<.001; heart rate detection function: P<.001; data sharing: P<.001; reliability: P<.001; compatibility: P<.001; interface design: P<.001; advertisement distribution: P<.001; measurement accuracy: P<.001; and cost: P<.001) significantly influenced the PD and ND of American HMA user satisfaction. Notably, BP tracking had the greatest effect on PD (β=0.312, P<.001), while data synchronization had the most significant impact on ND (β=2.662, P<.001). In addition, the influencing factors of PD and ND in user satisfaction of HMA in China and the United States are different. CONCLUSIONS: User satisfaction factors varied significantly between different countries, showing considerable asymmetry. For Chinese HMA users, ease of use and interface design emerged as motivational factors, while factors such as cost, measurement accuracy, and compatibility primarily contributed to user dissatisfaction. For American HMA users, motivational factors were ease of use, BP tracking, BP management effect, interface design, measurement accuracy, and cost. Moreover, users expect features such as data sharing, synchronization, software reliability, compatibility, heart rate detection, and nonintrusive advertisement distribution. Tailored experience plans should be devised for different user groups in various countries to address these diverse preferences and requirements.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».