Unraveling the Factors Associated With Digital Health Intervention Uptake: Cross-Sectional Study [0.03%]
探究与数字健康干预采纳相关的因素：横断面研究
Ilona Ruotsalainen,Mikko Valtanen,Riikka Kärsämä et al.
Ilona Ruotsalainen et al.
Background: Chronic noncommunicable diseases (NCDs) remain a leading health challenge worldwide, and reducing modifiable lifestyle risk factors is a key prevention strategy. Digital health interventions (DHIs) offer scala...
Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional Study With AI-Based Analysis [0.03%]
考虑时间变异性探讨基于人工智能分析的手机按键动态特征的年龄相关模式：横断面研究
Junhyung Moon,Yu Lim Huh,Hee Young Cho et al.
Junhyung Moon et al.
Background: Keystroke dynamics on smartphones have emerged as a promising form of passive digital biomarker. While previous studies have explored their utility in several diseases and disorders, relatively few have examin...
Calorie Counting Apps for Monitoring and Managing Calorie Intake in Adults living with Weight-Related Chronic Diseases: A Decade-long Scoping Review (2013-2024) [0.03%]
用于体重相关慢性病成人患者监测和管理热量摄入的计卡应用程序：2013-2024十年回顾性综述
Kaylee Rose Dugas,Marie-Andrée Giroux,Abdelatif Guerroudj et al.
Kaylee Rose Dugas et al.
Background: Overweight and obesity, as defined by the World Health Organization, correspond to body mass index (BMI) values of 25.0-29.9 kg/m² for overweight and ≥ 30 kg/m² for obesity. Both conditions remain major pub...
Health Motivation as a Predictor of mHealth Engagement Across BMI: Cross-Sectional Survey [0.03%]
基于BMI的健康动机对移动医疗参与的预测作用：横断面调查研究
Shao-Hsuan Chang,Lung-Kun Yeh,Daishi Chen et al.
Shao-Hsuan Chang et al.
Background: Digital health tools, such as mobile apps and wearable devices, have been widely adopted to support self-management of health behaviors. However, user engagement remains inconsistent, particularly among popula...
Smartphone-Based Approach-Avoidance Bias Modification Training for Depression: Randomized Clinical Trial [0.03%]
基于智能手机的针对抑郁症的方向回避偏见修正训练：随机临床试验
Maximilian Blomberg,Hilmar Gero Zech,Maximilian Kluge et al.
Maximilian Blomberg et al.
Background: Effective treatments for depression are available, yet many patients do not respond to treatment or experience relapse. Cognitive bias modification aims to ameliorate cognitive biases that contribute to the de...
Randomized Controlled Trial
JMIR mHealth and uHealth. 2025 Nov 26:13:e69033. DOI:10.2196/69033 2025
Evaluation of a Pilot mHealth Intervention to Engage Primary Care Clients at an Urban Clinic Serving Marginalized Populations: Mixed-Methods Cohort Study [0.03%]
面向边缘群体的城市门诊初级保健患者的移动健康干预试点评估：混合方法队列研究
Lauren Harrison,Christina Fulton,Antonio Marante Changir et al.
Lauren Harrison et al.
Background: Many individuals in urban low-income settings face barriers to engaging in primary care and experience systemic challenges such as homelessness and discrimination in the health care system. This study was cond...
Transdiagnostic Cognitive Control Training for Patients Waiting for Outpatient Psychotherapy: Randomized Clinical Trial [0.03%]
等待门诊心理治疗患者的跨诊断认知控制训练：随机临床试验
Maximilian Blomberg,Lisa Oberender,Ernst Koster et al.
Maximilian Blomberg et al.
Background: Various mental disorders are associated with impaired cognitive control, which is crucial for effective emotion regulation. Cognitive control training has demonstrated promise in enhancing emotion regulation a...
Randomized Controlled Trial
JMIR mHealth and uHealth. 2025 Nov 26:13:e65867. DOI:10.2196/65867 2025
Interpretable Machine Learning Models for Analyzing Determinants Affecting the Use of mHealth Apps Among Family Caregivers of Patients With Stroke in Chinese Communities: Cross-Sectional Survey Study [0.03%]
用于分析移动健康应用程序在华人群体中家庭护理人员使用情况的决定因素的可解释机器学习模型：横断面调查研究
Yun Du,Jun-Ying Fan,Guang-Zhi Liu et al.
Yun Du et al.
Background: Mobile health (mHealth) apps are believed to be an effective method to support family caregivers to better care for patients with stroke. This study's purpose was to explore the status and the influencing fact...
Authors' Reply: Methodological Considerations in Evaluating Mental Health Apps: Ensuring Reliability and Patient Safety [0.03%]
作者回应：评估心理健康APP的方法学问题：确保可靠性和患者安全
Seema Mehrotra,Ravikesh Tripathi,Pramita Sengupta et al.
Seema Mehrotra et al.
Methodological Considerations in Evaluating Mental Health Apps: Ensuring Reliability and Patient Safety [0.03%]
评估心理健康应用程序的方法学考虑：确保可靠性和患者安全
Harikrishnan Balakrishna
Harikrishnan Balakrishna