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Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study

Investigating Measurement Equivalence of Smartphone Sensor–Based Assessments: Remote, Digital, Bring-Your-Own-Device Study

The Floodlight Open app assesses MS-related functional ability through smartphone sensor–based “active” tests (ie, assessments that require active input from the user) and was specifically developed for use in a BYOD setting [33]. It was deployed in Floodlight Open, a global, open-access, digital-only study that was designed to understand the drivers and barriers in the deployment and persistence of use of a smartphone app in a naturalistic setting and broad study population [33].

Lito Kriara, Frank Dondelinger, Luca Capezzuto, Corrado Bernasconi, Florian Lipsmeier, Adriano Galati, Michael Lindemann

J Med Internet Res 2025;27:e63090

Assessment of Pelvic Motion During Single-Leg Weight-Bearing Tasks Using Smartphone Sensors: Validity Study

Assessment of Pelvic Motion During Single-Leg Weight-Bearing Tasks Using Smartphone Sensors: Validity Study

We chose to place the smartphone over the sacrum in accordance with a prior study that demonstrated good or excellent between-day reliability [32]. The MATLAB (Math Works) mobile app was used to collect smartphone orientation and acceleration at 100 Hz; smartphone orientation data were estimated from rotation vectors collected by the smartphone’s virtual orientation sensor, which integrates accelerometer, gyroscope, and magnetometer data.

Yu Xi, Zhongsheng Li, Surendran Vatatheeswaran, Valter Devecchi, Alessio Gallina

JMIR Rehabil Assist Technol 2025;12:e65342

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

The smartphone-based, acoustical snore detection algorithm used in the Sleep Watch app was developed by the Bodymatter team in-house, using a deep neural net model trained on over 60,000 individually validated, real-world snore and nonsnore sounds. The Sleep Watch snore detection capabilities were then tested against an array of snoring and sleep audio files in a controlled acoustic setting.

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

JMIR Form Res 2025;9:e67861

Mobile App-Based Interactive Care Plan for Migraine: Survey Study of Usability and Improvement Opportunities

Mobile App-Based Interactive Care Plan for Migraine: Survey Study of Usability and Improvement Opportunities

Internet or smartphone app-based remote assessment and monitoring of migraine [9,10] may increase the efficiency of care delivery and facilitate telemedicine, electronic [11], and face-to-face visits while delivering migraine educational content.

Nathan P Young, Jennifer I Stern, Stephanie J Steel, Jon O Ebbert

JMIR Form Res 2025;9:e66763

Mobile Health Tool to Capture Social Determinants of Health and Their Impact on HIV Treatment Outcomes Among People Who Use Drugs: Pilot Feasibility Study

Mobile Health Tool to Capture Social Determinants of Health and Their Impact on HIV Treatment Outcomes Among People Who Use Drugs: Pilot Feasibility Study

In the present pilot feasibility study, we explored the potential of a smartphone app to detect disruptive life events and early warning signs of HIV care disengagement over a 1-year period among 59 people who use drugs living with HIV. Using a weekly survey within a smartphone app designed to support substance use disorder recovery, we assessed how often people who use drugs used the smartphone app and reported experiencing disruptive life events.

Rachel E Gicquelais, Caitlin Conway, Olivia Vjorn, Andrew Genz, Gregory Kirk, Ryan Westergaard

JMIR Form Res 2025;9:e59953

Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review

Disease Prediction Using Machine Learning on Smartphone-Based Eye, Skin, and Voice Data: Scoping Review

We formulated the following search string using a combination of different words related to topics, such as smartphone, smartwatch, machine learning, health, and medicine, to search different electronic databases: ((ML OR machine learning) AND (health* OR medic* OR disease) AND (smartphone OR smart phone OR smartwatch OR smart watch OR smart devices)).

Research Dawadi, Mai Inoue, Jie Ting Tay, Agustin Martin-Morales, Thien Vu, Michihiro Araki

JMIR AI 2025;4:e59094

A Smartphone-Based Timed Up and Go Test Self-Assessment for Older Adults: Validity and Reliability Study

A Smartphone-Based Timed Up and Go Test Self-Assessment for Older Adults: Validity and Reliability Study

It incorporates an innovative approach: placing a smartphone in the user’s front trouser pocket during 5 repetitions of the test. An unsupervised smartphone-based i TUG could fill a gap in the landscape of mobility assessment methods that is still dominated by patient-reported outcome measures and supervised assessments conducted in laboratory or clinical settings [25].

Melissa Johanna Böttinger, Sabato Mellone, Jochen Klenk, Carl-Philipp Jansen, Marios Stefanakis, Elena Litz, Anastasia Bredenbrock, Jan-Philipp Fischer, Jürgen M Bauer, Clemens Becker, Katharina Gordt-Oesterwind

JMIR Aging 2025;8:e67322

Patient Experiences With a Mobile Self-Care Solution for Low-Complex Orthopedic Injuries: Mixed Methods Study

Patient Experiences With a Mobile Self-Care Solution for Low-Complex Orthopedic Injuries: Mixed Methods Study

However, some expected that if injuries were more severe, they would need more assistance than a brace and a smartphone app (quote 7). An increase in perceived self-empowerment was reported in 67 out of 138 (49%) of all patients, 51 (37%) patients reported neutral results, and 20 (14%) patients reported no increase.

Jelle Spierings, Gijs Willinge, Marike Kokke, Sjoerd Repping, Wendela de Lange, Thijs Geerdink, Ruben van Veen, Detlef van der Velde, Carel Goslings, Bas Twigt, Collaboration Group

JMIR Hum Factors 2025;12:e53074