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A Mobile Ecological Momentary Intervention for Reducing Experiential Avoidance in the Context of Rumination: Protocol for a Randomized Controlled Trial

A Mobile Ecological Momentary Intervention for Reducing Experiential Avoidance in the Context of Rumination: Protocol for a Randomized Controlled Trial

Mobile health (m Health) apps represent a dimension of digital health provision and allow for the delivery of validated psychological interventions on-demand via smartphones. Because of the ubiquity of smartphones [41] and the opportunities afforded for personalization of provision, m Health apps offer the potential to deliver rapidly scalable interventions that can expand the reach of mental health services while overcoming a number of barriers faced by traditional forms of treatment [42].

Steven Barnes, Marta Szastok, Małgorzata Para, Fabian Morawiec, Maciej Grzeszczuk, Szymon Wójcik, Barbara Karpowicz, Pavlo Zinevych, Anna Jaskulska, Wiesław Kopeć, Monika Kornacka

JMIR Res Protoc 2025;14:e66067

Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study

Using Personalized Intervention Criteria in a Mobile Just-in-Time Adaptive Intervention for Increasing Physical Activity in University Students: Pilot Study

Low levels of daily physical activity have become a global health problem, and methods to increase physical activity using mobile health (m Health) have started to be implemented [6-8]. m Health is defined as health care services supported through the use of mobile devices such as mobile phones [9].

Mai Ikegaya, Jerome Clifford Foo, Taiga Murata, Kenta Oshima, Jinhyuk Kim

JMIR Hum Factors 2025;12:e66750

Effectiveness of Digital Health Interventions for Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis

Effectiveness of Digital Health Interventions for Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis

The following Me SH keywords were included: “Pulmonary Disease, Chronic Obstructive” OR “chronic obstructive pulmonary disease” OR “COPD” OR “chronic obstructive lung disease” OR “chronic airflow obstruction” OR “emphysema” AND “digital health” OR “telehealth” OR “m Health” OR “e Health” OR “biosensor” OR “remote monitoring” OR “Smartphone” OR “Mobile Applications” OR “Apps” OR “Internet-based interventions” OR “Web-based platforms” AND “self-management” OR “self-monitoring” OR “self-care” AND “randomized controlled

Miaoqing Zhuang, Intan Idiana Hassan, Wan Muhamad Amir W Ahmad, Azidah Abdul Kadir, Xiaodong Liu, Furong Li, Yinuo Gao, Yang Guan, Shuting Song

J Med Internet Res 2025;27:e76323

Intervention With WhatsApp Messaging to Compare the Effect of Self-Designed Messages and Standardized Messages in Adherence to Antiretroviral Treatment in Young People Living With HIV in a Hospital in Lima, Peru: Protocol for a Nonblinded Randomized Controlled Trial

Intervention With WhatsApp Messaging to Compare the Effect of Self-Designed Messages and Standardized Messages in Adherence to Antiretroviral Treatment in Young People Living With HIV in a Hospital in Lima, Peru: Protocol for a Nonblinded Randomized Controlled Trial

Mobile health (m Health) interventions tailored to young people living with HIV/AIDS seem highly promising, considering that so-called Gen Z tend to be digital natives who routinely use apps. At first glance, messaging interventions to improve adherence seem practical, low-cost, and prone to tailoring to users’ needs [4,5]. However, medication adherence involves complex behavioral aspects.

Jeffrey Freidenson-Bejar, Dianne Espinoza, Rodrigo Calderon-Flores, Fernando Mejia, Elsa González-Lagos

JMIR Res Protoc 2025;14:e66941

Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024

Application of Machine Learning and Emerging Health Technologies in the Uptake of HIV Testing: Bibliometric Analysis of Studies Published From 2000 to 2024

Mobile health (m Health), which involves using mobile devices to enhance health care services and research, has proven advantageous when incorporated with machine learning [15]. A machine learning m Health technique was used to detect false-positive HIV test results in another South African study [15]. The technology demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared to conventional visual interpretations of HIV rapid diagnostic tests [15].

Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Lateef Babatunde Amusa, Hossana Twinomurinzi, Refilwe Nancy Phaswana-Mafuya

Interact J Med Res 2025;14:e64829

Effects of Mobile Health Care App "Asmile" on Physical Activity of 80,689 Users in Osaka Prefecture, Japan: Longitudinal Observational Study

Effects of Mobile Health Care App "Asmile" on Physical Activity of 80,689 Users in Osaka Prefecture, Japan: Longitudinal Observational Study

Mobile health (m Health), a health care support system that uses mobile devices to provide health care services, has recently attracted attention as a tool to maintain and improve health. Several studies have shown that m Health apps contribute to weight loss [9], blood pressure reduction [10], cardiovascular disease (CVD) risk reduction [11], and cognitive function improvement [12,13]. Thus, m Health apps are beginning to be recognized as valuable tools for various purposes in health promotion.

Asuka Oyama, Kenshiro Taguchi, Hiroe Seto, Reiko Kanaya, Jun'ichi Kotoku, Miyae Yamakawa, Hiroshi Toki, Ryohei Yamamoto

J Med Internet Res 2025;27:e65943

Mobile- and Web-Based Interventions for Promoting Healthy Diets, Preventing Obesity, and Improving Health Behaviors in Children and Adolescents: Systematic Review of Randomized Controlled Trials

Mobile- and Web-Based Interventions for Promoting Healthy Diets, Preventing Obesity, and Improving Health Behaviors in Children and Adolescents: Systematic Review of Randomized Controlled Trials

fruit and vegetable goal and then created an action plan (ie, implementation intention); (2) coping group: set a goal to eat more fruits and vegetables then created a coping plan (ie, implementation intention); (3) both groups: set a goal to eat fruits and vegetables then created both action and coping plans School CG: did not receive any food education intervention IG: computer-based game designed to increase vegetable consumption Family-based setting CG: no intervention IG: participants used the SMARTFAMILY m Health

Clara Talens, Noelia da Quinta, Folasade A Adebayo, Maijaliisa Erkkola, Maria Heikkilä, Kamilla Bargiel-Matusiewicz, Natalia Ziółkowska, Patricia Rioja, Agnieszka E Łyś, Elena Santa Cruz, Jelena Meinilä

J Med Internet Res 2025;27:e60602

Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study

Users' Perceptions and Trust in AI in Direct-to-Consumer mHealth: Qualitative Interview Study

Indeed, many direct-to-consumer AI-enabled m Health (AI-m Health) apps are already in widespread use by individuals seeking to address particular health concerns, obtain personalized insights into their health, promote health-seeking behaviors, and help set and achieve well-being goals [2].

Katie Ryan, Justin Hogg, Max Kasun, Jane Paik Kim

JMIR Mhealth Uhealth 2025;13:e64715