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Comparing a Guideline-Based Mobile Health Intervention Versus Usual Care for High-Risk Adolescents With Asthma: Protocol of a Randomized Controlled Trial

Comparing a Guideline-Based Mobile Health Intervention Versus Usual Care for High-Risk Adolescents With Asthma: Protocol of a Randomized Controlled Trial

We demonstrated the feasibility of implementing a smartphone-based mobile health (m Health) intervention for adolescents, and findings suggested that this innovative tool could result in significant improvements in asthma self-efficacy, as well as clinically significant improvements in ACT scores [23]. among at-risk adolescents. Next, we conducted a pilot randomized controlled trial (RCT) test among 34 adolescents over 6 months to compare the use of the PEAKm AAP prototype in paper-based AAPs [24].

Tamara T Perry, Jessica H Turner, Ariel Berlinski, Larry A Simmons, Rita H Brown, Kaymon Neal, Sarah A Marshall, Xing He, Simon Chung, Andrew Brown, Horace J Spencer 3rd, Jiang Bian

JMIR Res Protoc 2025;14:e69903

Grouping Digital Health Apps Based on Their Quality and User Ratings Using K-Medoids Clustering: Cross-Sectional Study

Grouping Digital Health Apps Based on Their Quality and User Ratings Using K-Medoids Clustering: Cross-Sectional Study

Reference 8: Patients’ perceptions of mHealth apps: meta-ethnographic review of qualitative studies Reference 9: What is the clinical value of mHealth for patients?mhealthSecurity and Privacy of mHealth and uHealth Usability of Apps and User Perceptions of mHealth

Maciej Marek Zych, Raymond Bond, Maurice Mulvenna, Lu Bai, Jorge Martinez-Carracedo, Simon Leigh

JMIR Mhealth Uhealth 2025;13:e57279

Associations Between Daily Symptoms and Pain Flares in Rheumatoid Arthritis: Case-Crossover mHealth Study

Associations Between Daily Symptoms and Pain Flares in Rheumatoid Arthritis: Case-Crossover mHealth Study

Mobile health (m Health) devices, such as smartphones and wearables, allow patients to log symptoms actively through numerical scales or visual tools [1-5]. They can also passively capture high-resolution streams of health-related and contextual information, such as sleep, physical activity, or weather with minimal burden [3,5].

Ting-Chen Chloe Hsu, Belay B Yimer, Pauline Whelan, Christopher J Armitage, Katie Druce, John McBeth

JMIR Mhealth Uhealth 2025;13:e64889

Co-Designed Mobile-Based Cognitive Training for Older Chinese Americans: Protocol for a Pilot Randomized Controlled Trial Assessing Feasibility and Acceptability

Co-Designed Mobile-Based Cognitive Training for Older Chinese Americans: Protocol for a Pilot Randomized Controlled Trial Assessing Feasibility and Acceptability

With support from our industry partner and community stakeholders, the research infrastructure created by this project is expected to produce more innovative mobile health (m Health) interventions that can be scaled up rapidly in the future.

Tingzhong Xue, Aybey Amy Wei, Bei Wu, Camilla Sanders, Eleanor Schildwachter McConnell, Hanzhang Xu

JMIR Res Protoc 2025;14:e69303

Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study

Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study

As the use of mobile health (m Health) has grown, so has interest in how patients engage with it. Indeed, most studies of m Health interventions include some measure of user engagement, ranging from app logins to message response rates [16]; however, very few capture the heterogeneity of patient interaction styles. The notion of digital phenotyping, more generally (characterizing interaction with a range of digital health tools), is a nascent but growing field [17].

Klea Profka, Agnes Wang, Emily Schriver, Ashley Batugo, Anna U Morgan, Danielle Mowery, Eric Bressman

J Med Internet Res 2025;27:e72875