Background Mobile health applications are being increasingly used for people’s health management. The different uses of mobile health applications lead to different health outcomes. Although active usage of mobile health applications is shown to be linked to the effectiveness of mobile health services, the factors that influence people’s active usage of mobile health applications are not well studied. Objective This paper aims to examine the antecedents of active usage of mobile health applications. Methods Grounded on the 3-factor theory, we proposed 10 attributes of mobile health applications that influence the active usage of mobile health applications through consumers’ satisfaction and dissatisfaction. We classified these 10 attributes into 3 categories (ie, excitement attributes, performance attributes, and basic attributes). Using the survey method, 494 valid responses were collected and analyzed using structural equation modeling. Results Our analysis results revealed that both consumer satisfaction (β=0.351, t=6.299, P<.001) and dissatisfaction (β=–0.251, t=5.119, P<.001) significantly influenced active usage. With regard to the effect of attributes, excitement attributes (β=0.525, t=12.861, P<.001) and performance attributes (β=0.297, t=6.508, P<.001) positively influenced consumer satisfaction, while performance attributes (β=–0.231, t=3.729, P<.001) and basic attributes (β=–0.412, t=7.132, P<.001) negatively influenced consumer dissatisfaction. The results of the analysis confirmed our proposed hypotheses. Conclusions Our study provides a novel perspective to study the active usage of mobile health applications. By categorizing the attributes of mobile health applications into 3 categories, the differential effects of different attributes can be tested. Meanwhile, consumer satisfaction and dissatisfaction are confirmed to be independent from each other.

BACKGROUND Reviews are important for consumers to make informed decisions in online communities and for organizations to predict sales in the future. Most existing literature are conducted in product fields, with little attention paid to healthcare. Whether patients prefer to use these new platforms to discuss the reputation of doctors has so far remained an open question. OBJECTIVE We investigate how patients’ propensity to post the treatment experiences (PPPTE) changes with doctors’ (individual) online reputation (medical quality and service attitude) for outpatient care service. We also investigate the moderating effects of hospital (organizational) online reputation and disease severity. METHODS The fractional logistic regression under the GLM framework based on data from 7,183 doctors within a Chinese online health community are used to get the empirical results. RESULTS Our results show that patients prefer to share their treatment experiences for doctors who have higher medical quality and service attitude and work in higher online reputation hospitals. Comparing with the doctors who treat less severe diseases, PPPTE is smaller for doctors who treat severe diseases. In addition, the hospital’s online reputation positively (negatively) moderates the relationship between medical quality (service attitude) and PPPTE. Further, the moderating effects of disease severity on the doctor’s online reputation are negative. CONCLUSIONS Our research contributes to both theory and practice by researching the impact of individual reputation on consumer behavior, investigating the moderating effects of organizational reputation and consumer characteristics in healthcare.

BACKGROUND Mobile health applications are being increasingly used for people’s health management. The different uses of mobile health applications lead to different health outcomes. Although active usage of mobile health applications is shown to be linked to the effectiveness of mobile health services, the factors that influence people’s active usage of mobile health applications are not well studied. OBJECTIVE This paper aims to examine the antecedents of active usage of mobile health applications. METHODS Grounded on the 3-factor theory, we proposed 10 attributes of mobile health applications that influence the active usage of mobile health applications through consumers’ satisfaction and dissatisfaction. We classified these 10 attributes into 3 categories (ie, excitement attributes, performance attributes, and basic attributes). Using the survey method, 494 valid responses were collected and analyzed using structural equation modeling. RESULTS Our analysis results revealed that both consumer satisfaction (β=0.351, <i>t</i>=6.299, <i>P</i>&lt;.001) and dissatisfaction (β=–0.251, <i>t</i>=5.119, <i>P</i>&lt;.001) significantly influenced active usage. With regard to the effect of attributes, excitement attributes (β=0.525, <i>t</i>=12.861, <i>P</i>&lt;.001) and performance attributes (β=0.297, <i>t</i>=6.508, <i>P</i>&lt;.001) positively influenced consumer satisfaction, while performance attributes (β=–0.231, <i>t</i>=3.729, <i>P</i>&lt;.001) and basic attributes (β=–0.412, <i>t</i>=7.132, <i>P</i>&lt;.001) negatively influenced consumer dissatisfaction. The results of the analysis confirmed our proposed hypotheses. CONCLUSIONS Our study provides a novel perspective to study the active usage of mobile health applications. By categorizing the attributes of mobile health applications into 3 categories, the differential effects of different attributes can be tested. Meanwhile, consumer satisfaction and dissatisfaction are confirmed to be independent from each other.

BACKGROUND Depressed adolescents are at significantly higher risk of suicidal thoughts and behaviors, and socio-environmental, individual psychological traits and biogenetic factors are significant causes. However, our understanding of the specific determinants of suicidal thoughts and behaviors among depressed adolescents remains limited. OBJECTIVE The objective of this study is to develop a prediction model for suicide among Chinese adolescents with Major Depressive Disorder (MDD). Then, based on detailed and comprehensive feature contributions, this study also aims to elucidate and examine the main influencing factors and perform an in-depth interpretation of the output of the prediction model. METHODS We conducted a multi-center, cross-sectional survey from December 2020 to December 2021. A total of 2343 adolescents with MDD aged 12 to 18 years from 14 psychiatric general hospitals across 9 provinces of China were enrolled. Then, we constructed a dual path framework integrating distal and proximal factors and apply random forest (RF) classification model for risk prediction of suicide ideation and/or suicide attempt (SISA) among Chinese adolescents with MDD. Hyperparameter tuning, model training, and unbiased estimation were implemented through a stratified nested cross-validation procedure. Finally, to enhance the interpretability of the RF algorithm, we adopted the Shapley Additive exPlanations (SHAP) method for in-depth interpretation and analysis from a global and a local perspective. RESULTS Random Forest model achieved excellent performance with an accuracy of 95%, an F1-score of 95% and an AUC of 0.99. In the global analysis of the SHAP method, we found that depression severity and despair severity (proximal factors) contributed the most to SISA among the sample. Additionally, it was confirmed that borderline personality played a critical role as a distal factor. What’s more, prediction results indicated that support from significant others was a risk factor rather than a protective factor. According to dependence analysis, complex interactions between distal and proximal factors were identified, for example, the co-occurrence of high levels of borderline personality and depression may together lead to an increased risk of suicide. Furthermore, an indirect impact of cognitive reappraisal on suicide occurrence was also captured. Finally, in the local analysis, adolescent girls were at higher risk of SISA than boys in the depressive subtype. It can also be inferred that depression severity and despair severity were not necessary conditions of SISA even though they were main influencing factors. CONCLUSIONS The dual path framework integrating distal and proximal factors may provide a comprehensive perspective for suicide risk assessment. RF can be effectively used to predict suicide among Chinese adolescents with MDD and contribute to early detection, preventive interventions and intensive monitoring. As an analysis technique to interpret the prediction model with black box characteristics, SHAP can provide in-depth interpretation and reference for clinical practices.