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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

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

Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation

The Fisher exact test yielded P=.01, indicating a statistically significant difference. As shown in Table 2, the average word count of the original notes was 320 words, and the average length reduction of the H-summaries and U-summaries was 22% (SD 15%) and 23% (SD 15%) words, respectively. A negative number for length reduction in Table 2 indicates that the summary generated had more words than the original text. In our analysis, we identified 3 instances of false information in U-summaries.

Mahshad Koohi Habibi Dehkordi, Yehoshua Perl, Fadi P Deek, Zhe He, Vipina K Keloth, Hao Liu, Gai Elhanan, Andrew J Einstein

JMIR Med Inform 2025;13:e66476

Impact of Ecological Momentary Assessment Participation on Short-Term Smoking Cessation: quitSTART Ecological Momentary Assessment Incentivization Randomized Trial

Impact of Ecological Momentary Assessment Participation on Short-Term Smoking Cessation: quitSTART Ecological Momentary Assessment Incentivization Randomized Trial

Mean EMAs completed in the incentivized arm was 13.3 (SD 11.2, range 0‐40, average completion rate of 31.7% out of 42 total EMA prompts) and 4.7 (SD 5.8, range 0‐28, average completion rate of 11.2% out of 42 total EMA prompts) in the nonincentivized arm (P Smoking cessation outcomes overall and by group. a EMA: ecological momentary assessment.

Kara P Wiseman, Alex Budenz, Leeann Siegel, Yvonne M Prutzman

J Med Internet Res 2025;27:e67630