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Effectiveness and Cost-Effectiveness of Using a Social Robot in Residential Care for Individuals With Challenges in Daily Structure and Planning: Protocol for a Multiple-Baseline Single Case Trial and Health Economic Evaluation

Effectiveness and Cost-Effectiveness of Using a Social Robot in Residential Care for Individuals With Challenges in Daily Structure and Planning: Protocol for a Multiple-Baseline Single Case Trial and Health Economic Evaluation

With 24 participants, a significant intervention effect (P Primary outcome data will be analyzed using a multilevel model (R, version 4.0+; package lme4) to investigate whether there is a significant difference between the level of professional care support moments (frequency or duration) per week in the baseline phase and the effect phase. The dependent continuous variable is the frequency and duration of professional care support moments per week.

Kirstin N van Dam, Marieke F M Gielissen, Nienke M Siebelink, Ghislaine A P G van Mastrigt, Wouter den Hollander, Brigitte Boon

JMIR Res Protoc 2025;14:e67841

What Matters Most to Veterans When Deciding to Use Technology for Health: Cross-Sectional Analysis of a National Survey

What Matters Most to Veterans When Deciding to Use Technology for Health: Cross-Sectional Analysis of a National Survey

However, a greater proportion of veterans with (compared to without) prevalent mental health conditions reported the following considerations to be “very important”: seeing information about DHTs on social media (those with mental health conditions: 42/428, 9.8%; those without mental health conditions: 19/328, 5.8%; χ22=6.2; P=.05); having community support through Veteran Service Organizations, churches, libraries, or other organizations to use DHTs (with: 56/427, 13.1%; without: 25/327, 7.6%; χ22=7.9; P=.02

Bella Etingen, Bridget M Smith, Stephanie L Shimada, Stephanie A Robinson, Robin T Higashi, Ndindam Ndiwane, Kathleen L Frisbee, Jessica M Lipschitz, Eric Richardson, Dawn Irvin, Timothy P Hogan

JMIR Form Res 2025;9:e77113

eHealth Literacy and Participation in Remote Blood Pressure Monitoring Among Patients With Hypertension: Cross-Sectional Study

eHealth Literacy and Participation in Remote Blood Pressure Monitoring Among Patients With Hypertension: Cross-Sectional Study

With 47.3% adult population with hypertension in the United States in 2021 [29], using 5% type 1 error (P=.05), the minimum sample size required to estimate participation in RBPM was 383 participants [30]. A minimum of 500 sample size has been recommended for detecting differences between the sample estimates and the population in observational studies involving logistic regression [31]. We stopped recruitment as soon as possible when we reached a sample size of 500.

Chinwe E Eze, Michael P Dorsch, Antoinette B Coe, Corey A Lester, Lorraine R Buis, Karen B Farris

J Med Internet Res 2025;27:e71926

Efficacy of Mobile App–Based Dietary Interventions Among Cancer Survivors: Systematic Review and Meta-Analysis

Efficacy of Mobile App–Based Dietary Interventions Among Cancer Survivors: Systematic Review and Meta-Analysis

For breast cancer survivors, their mobile app–based dietary interventions neither showed statistically significant effects on weight changes (Hedges g=0.20, 95% CI −2.24 to 2.64; P=.49; I2=0%; see Figure 2) nor on BMI (Hedges g=−0.23, 95% CI −6.15 to 5.69; P=.71; I2=69%; see Figure 3) compared to the control groups (see Table 1).

Krista Ching Wai Chung, Naomi Takemura, Wendy Wing Tak Lam, Mandy Man Ho, Antoinette Marie Lee, Wynnie Yuen Yee Chan, Daniel Yee Tak Fong

JMIR Mhealth Uhealth 2025;13:e65505

Pharmacoepidemiologic Research Based on Common Data Models: Systematic Review and Bibliometric Analysis

Pharmacoepidemiologic Research Based on Common Data Models: Systematic Review and Bibliometric Analysis

A 2-tailed P value of A total of 37,880 studies were identified, of which 308 met the inclusion criteria, comprising 307 in English and 1 in Chinese (Figure 1). The publications originated from 32 countries, involving 1580 authors and published across 140 journals.

Yongqi Zheng, Meng Zhang, Conghui Wang, Ling Gao, Junqing Xie, Peng Shen, Yexiang Sun, Mengling Feng, Seng Chan You, Feng Sun

JMIR Med Inform 2025;13:e72225

Population-Based Digital Health Interventions to Deliver at-Home COVID-19 Testing: SCALE-UP II Randomized Clinical Trial

Population-Based Digital Health Interventions to Deliver at-Home COVID-19 Testing: SCALE-UP II Randomized Clinical Trial

Reach-Accept testing in the Chatbot arm was lower than in SMS text messaging (174/1051, 16.6% vs 555/1066, 52.1%; a RR 0.317, 98.33% CI 0.27‐0.38; P Reach-Accept testing was higher among participants messaged every 10 days vs every 30 days (860/15,717, 5.5% vs 752/15,722, 4.8%; a RR 1.144, 97.5% CI 1.03‐1.28; P=.01; Table 2), and lower if the participants were offered access to PN compared with those in the no PN condition (680/15,718, 4.3% vs 932/15,721, 5.9%; a RR 0.729, 97.5% CI 0.65‐0.81; P Out of 2117 participants

Guilherme Del Fiol, Tatyana V Kuzmenko, Brian Orleans, Jonathan J Chipman, Tom Greene, Ray Meads, Kimberly A Kaphingst, Bryan Gibson, Kensaku Kawamoto, Andy J King, Tracey Siaperas, Shlisa Hughes, Alan Pruhs, Courtney Pariera Dinkins, Cho Y Lam, Joni H Pierce, Ryzen Benson, Emerson P Borsato, Ryan C Cornia, Leticia Stevens, Richard L Bradshaw, Chelsey R Schlechter, David W Wetter

J Med Internet Res 2025;27:e74145

Prevalence of Frailty and Its Predictors Among Patients With Cancer at the Chemotherapy Stage: Systematic Review

Prevalence of Frailty and Its Predictors Among Patients With Cancer at the Chemotherapy Stage: Systematic Review

Notably, we observed an excessive between-study heterogeneity (I²=98.2%; χ²10=562.4, P Forest chart of prevalence of frailty in patients with cancer during chemotherapy [20-27,29,30,32]. GLMM: generalized linear mixed model. Subgroup analyses were conducted to explore potential sources of heterogeneity.

Tingting Wang, Jinxia Jiang, Zihe Song, Xianliang Liu, Minhui Zhong, Chan Yu, Runa Zhang, Xia Duan

JMIR Cancer 2025;11:e69936