e.g. mhealth
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Skip search results from other journals and go to results- 23 JMIR Formative Research
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Lu et al [45] also proposed a smartwatch app alongside an algorithm for evaluating compression metrics. They tested using a Resusci Anne QCPR training manikin (Laerdal) and an android ASUS Zen Watch 2 (model WI501 Q; ASUSTe K Computer Inc). The developed polynomial model predicts compression depth and rate from smartwatch accelerometer data.
JMIR Mhealth Uhealth 2025;13:e57469
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This study aimed to understand how smartwatch-based gamification should be tailored for different user groups to effectively promote physical exercise based on a more accurate and innovative user modeling approach.
JMIR Serious Games 2025;13:e66793
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In each study, participants received prompts to answer EMA questions using an in-house app running on a smartwatch or tablet. Participants were trained on how to interact with the app by a research assistant and completed practice prompts and tasks before data collection commenced.
The EMA prompts were randomly distributed within predefined time blocks throughout the day. We adjusted these time blocks when necessary to fit participant schedules.
JMIR Mhealth Uhealth 2025;13:e57018
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daily (23 h/d of wear; 1 h/d of charging time) for 4 weeks, syncing the smartwatch, and completion of a daily EMA survey.
JMIR Hum Factors 2025;12:e69952
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Reference 9: MedAi: a smartwatch-based application framework for the prediction of common diseases usingsmartwatch
JMIR Med Inform 2025;13:e62978
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For both smartwatch μEMA and online TLFB methods, participants were asked:
Question 1/6: Overall, how would you rate your experience of using the smartwatch/online system during the study, on a scale from 1 (I did not like it at all) to 10 (I really liked it)?
Question 2/7: If you were asked to use the smartwatch/online system again in another study, how likely would you be to say yes, on a scale from 1 (I would not want to use it again) to 10 (I would really like to use it again)?
JMIR Form Res 2025;9:e63184
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Using Wear Time for the Analysis of Consumer-Grade Wearables’ Data: Case Study Using Fitbit Data
Different measures were collected using surveys, a smartphone app (Roadmap 2.0), and a Fitbit smartwatch.
This dataset was collected in a study evaluating the use of a mobile health app (Roadmap 2.0) intervention for cancer caregivers and their patients [20]. Participants were recruited between September 2020 and September 2021 from the Adult and Pediatric Hematology and Oncology Units of Mott Children’s Hospital in Ann Arbor, MI.
We first compared the level of compliance and wear time for each population.
JMIR Mhealth Uhealth 2025;13:e46149
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Similarly, a prior study from our group involving 47 individuals with PD demonstrated a good criterion validity in step counting using a consumer smartwatch (Garmin Vivosmart 4), when worn on the side least affected by the disease and under well-controlled pharmacological conditions in a supervised, in-clinic setting [20].
JMIR Form Res 2025;9:e63153
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This study conducted long-term (months) continuous monitoring of high-resolution HRV data during daily life activities using a COTS smartwatch with a photoplethysmography sensor and activity (step count) data. The primary aim of this study was to evaluate the influence of missing data on HRV metrics collected from a photoplethysmography-based smartwatch both at rest and during physical activity in real-world settings.
JMIR Form Res 2025;9:e53645
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Wearable Electrocardiogram Technology: Help or Hindrance to the Modern Doctor?
Most studies are restricted to screening for AF, and a systematic review has observed overall sensitivities of around 94% and specificities of 93%-96%, depending on whether a smartphone or smartwatch was used [22].
Not only has the physical hardware become more portable and acceptable to patients, but the underlying software interpreting the acquired ECG has also improved drastically over recent years.
JMIR Cardio 2025;9:e62719
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