e.g. mhealth
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Skip search results from other journals and go to results- 23 JMIR Formative Research
- 22 JMIR mHealth and uHealth
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Ciman and Wac [26] found that smartphone screen on and off patterns could estimate sleep duration, sleep onset, wake times, and even sleep deprivation patterns, with only a 7% margin of error compared to smartwatch data. In 2019, a study conducted in the Netherlands reported a strong correlation (R2=0.9) between smartphone touch data and traditional sleep measurement methods, enabling accurate predictions of sleep onset and wake times [28].
J Med Internet Res 2025;27:e60423
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The questions regarding the usage of CWs were prefaced by short definitions of the terms “wearable,” “fitness tracker” and “smartwatch.” The ownership and the usage of a CW were assessed with the questions “Do you own a wearable?” (answer categories: yes or no) and “Do you currently use your wearable to measure your PA, fitness or other health data such as blood pressure or pulse?” (answer categories: yes or no).
JMIR Mhealth Uhealth 2025;13:e59199
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Moreover, feedback from the cardiologists involved in the study was positive regarding this smartwatch: accurate ECG recordings, quite simple for patients to use, and good battery life. It should be noted that there was no conflict of interest among the investigators in the choice of Withings Scan Watch.
The functions of the smartwatch and its application are explained to the participants.
JMIR Res Protoc 2025;14:e67875
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Additionally, we report on the results of smartwatch power consumption testing and Android app usability testing. Finally, we conclude with the results of our study and explore potential future directions for this research, emphasizing the CRS model’s readiness for deployment in real-world applications.
Sense2 Quit consists of 2 primary components: the smartphone and smartwatch apps, and an online dashboard.
J Med Internet Res 2025;27:e67186
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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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