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Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis

Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis

Category A includes countries with more than 1000 users, Category B with 500 to 999 users, Category C with 250 to 499 users, and Category D with 100 to 249 users. The analysis is visually supported by shaded background colors which represent different years: green for 2019, red for 2020, orange for 2021, and yellow for 2022. This color scheme provides a clear, visual distinction of changes in physical activity levels and user engagement per country over the studied period.

Bastien Wyatt, Nicolas Forstmann, Nolwenn Badier, Anne-Sophie Hamy, Quentin De Larochelambert, Juliana Antero, Arthur Danino, Vincent Vercamer, Paul De Villele, Benjamin Vittrant, Thomas Lanz, Fabien Reyal, Jean-François Toussaint, Lidia Delrieu

J Med Internet Res 2025;27:e68199

Improving Early Dementia Detection Among Diverse Older Adults With Cognitive Concerns With the 5-Cog Paradigm: Protocol for a Hybrid Effectiveness-Implementation Clinical Trial

Improving Early Dementia Detection Among Diverse Older Adults With Cognitive Concerns With the 5-Cog Paradigm: Protocol for a Hybrid Effectiveness-Implementation Clinical Trial

(D) IUH: negative 5-Cog result. At both sites, a paper token (Figure 4) is used as an additional feature to help ensure that care providers review the patients’ 5-Cog results. Patients are handed this token after they complete the 5-Cog battery and are asked to hand it to their care provider at their scheduled visit, generally within 30 minutes after the 5-Cog battery administration. Token to alert care provider for patient's 5-Cog participation.

Rachel Beth Rosansky Chalmer, Emmeline Ayers, Erica F Weiss, Nicole R Fowler, Andrew Telzak, Diana Summanwar, Jessica Zwerling, Cuiling Wang, Huiping Xu, Richard J Holden, Kevin Fiori, Dustin D French, Celeste Nsubayi, Asif Ansari, Paul Dexter, Anna Higbie, Pratibha Yadav, James M Walker, Harrshavasan Congivaram, Dristi Adhikari, Mairim Melecio-Vazquez, Malaz Boustani, Joe Verghese

JMIR Res Protoc 2025;14:e60471

Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation

Accuracy of Smartphone-Mediated Snore Detection in a Simulated Real-World Setting: Algorithm Development and Validation

Analytical validation reference table showing the schematic and equationsa,b,c,d,e used to report snore detection performance of the Sleep Watch app in this study. a Accuracy = (true positive + true negative)/(true positive + true negative + false positive + false negative). b Sensitivity = true positive/(true positive + false negative). c Specificity = true negative/(false positive + true negative). d Positive predictive value = true positive/(true positive + false positive). e Negative predictive value = true negative

Jeffrey Brown, Zachary Mitchell, Yu Albert Jiang, Ryan Archdeacon

JMIR Form Res 2025;9:e67861

Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis

Each observation was represented by the triplet {X,T,S}, where X⊆Rd is a d-dimensional feature vector, T∈(0,Emax] is an observed event or censoring time over a finite time horizon, and S∈{0,1} indicates whether T is a right-censoring time (S=0) or an event time (S=1). The observed time T is the minimum of the event time E and the right-censoring time C, that is, T=min(E, C).

De Rong Loh, Elliot D Hill, Nan Liu, Geraldine Dawson, Matthew M Engelhard

JMIR AI 2025;4:e62985

Theory-Based Social Media Intervention for Nonmedical Use of Prescription Opioids in Young Adults: Protocol for a Randomized Controlled Trial

Theory-Based Social Media Intervention for Nonmedical Use of Prescription Opioids in Young Adults: Protocol for a Randomized Controlled Trial

In terms of the aim for the preliminary efficacy evaluation (ie, secondary outcomes of psychosocial and behavioral factors associated with NMUPO), we estimate an effect size according to a previous review on digital interventions for illicit drug use [111] and found small-to-medium effect sizes (Cohen d=–0.17 to –0.34) with a 6-month follow-up assessment. G-power analysis [112] estimated a sample size of 10 to 35 per arm for an RCT (repeated measures analysis of covariance).

Cheuk Chi Tam, Sean D Young, Sayward Harrison, Xiaoming Li, Alain H Litwin

JMIR Res Protoc 2025;14:e65847

Treatment of Substance Use Disorders With a Mobile Phone App Within Rural Collaborative Care Management (Senyo Health): Protocol for a Mixed Methods Randomized Controlled Trial

Treatment of Substance Use Disorders With a Mobile Phone App Within Rural Collaborative Care Management (Senyo Health): Protocol for a Mixed Methods Randomized Controlled Trial

Panel (C) shows a behavioral activation task, and panel (D) showcases the points awarded for completing this task. Senyo Health chat feature being displayed from the perspective of the recovery coach. The left is conversations with multiple patients. Once selected, the full conversation appears in the center of the screen, with the recovery coach able to text back and forth. Surveys, modules, and activation tasks can also be assigned to the participant through the chat.

Tyler S Oesterle, Nicholas L Bormann, Margaret M Paul, Scott A Breitinger, Benjamin Lai, Jamie L Smith, Cindy J Stoppel, Stephan Arndt, Mark D Williams

JMIR Res Protoc 2025;14:e65693