Journal of Medical Internet Research

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Perceptions of the Use of Mobile Apps to Assess Sleep-Dependent Memory in Older Adults With Subjective and Objective Cognitive Impairment: Focus Group Approach

Aaron Lam, Simone Simonetti, Angela D'Rozario, David Ireland, DanaKai Bradford, Jurgen Fripp, Sharon L Naismith

JMIR Aging 2025;8:e68147

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The Motivations of Citizens to Attend an eHealth Course in the Public Library: Qualitative Interview Study

Lucille Standaar, Adriana Margje Israel, Rosalie van der Vaart, Brigitta Keij, Frank J van Lenthe, Roland Friele, Mariëlle A Beenackers, Lilian Huibertina Davida van Tuyl

JMIR Form Res 2025;9:e60612

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Gamification in mHealth Apps for Rehabilitation: Protocol for a Scoping Review

Jacqueline Dawson, Randall Nee, Christian Ramirez, Sharlene Reyes, David Sanchez, Tulsi Sukhadia, Andrew Bartlett

JMIR Res Protoc 2025;14:e63600

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Application of a Sociotechnical Framework to Uncover Factors That Influence Effective User Engagement With Digital Mental Health Tools in Clinical Care Contexts: Scoping Review

Brian Lo, Keri Durocher, Rebecca Charow, Sarah Kimball, Quynh Pham, Sanjeev Sockalingam, David Wiljer, Gillian Strudwick

J Med Internet Res 2025;27:e67820

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Sex Differences in the Variability of Physical Activity Measurements Across Multiple Timescales Recorded by a Wearable Device: Observational Retrospective Cohort Study

The PV quantifies variability by calculating the average percentage difference between all combinations of measurements [37-40]: where n is the total number of values, z is a list of values on which pair-wise comparisons are calculated, and i and j are indices of any 2 different values. The PV improves upon CV because it is not mean dependent, and it is less sensitive to rare events [37] (Figure 2 B).

Kristin J Varner, Lauryn Keeler Bruce, Severine Soltani, Wendy Hartogensis, Stephan Dilchert, Frederick M Hecht, Anoushka Chowdhary, Leena Pandya, Subhasis Dasgupta, Ilkay Altintas, Amarnath Gupta, Ashley E Mason, Benjamin L Smarr