e.g. mhealth
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Skip search results from other journals and go to results- 52 JMIR Formative Research
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Physical activity and sleep are 2 biopsychosocial factors critical to the experience of pediatric pain and are identified as core outcome domains for its treatment and study [6]. Prior research has identified bidirectional relations between decreased physical activity and increased pain [10] and between decreased sleep duration and quality and increased pain [11,12]. A reliable and valid assessment of physical activity and sleep is necessary for intervention efforts in pediatric pain.
JMIR Form Res 2025;9:e59074
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For ease of exposition, we first define the different resolutions at which Fitbit sleep data can be analyzed. At the broadest level, sleep can be conceptualized as an entire sleep period. A single sleep period may consist of 1 or more sleep logs, including any interruptions that occur—defined as intervening wakefulness of ≥1 hour between sleep logs.
Each sleep log consists of a consecutive sequence of sleep segments separated by less than 1 hour and may be classified as either primary or nonprimary sleep.
J Med Internet Res 2025;27:e71718
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Information about RA included disease duration, menopausal status, other associated rheumatic diseases (osteoarthritis, spondyloarthropathy or ankylosing spondylitis, fibromyalgia or chronic widespread pain, gout or other crystal arthritis, Sjögren syndrome, thyroid disorder, diabetes, multiple sclerosis, and hypertension), sleep-related problems (restless leg syndrome and obstructive sleep apnea or snoring), current medications (sleep medicine and pain medicine), and disease activity, measured by Routine Assessment
JMIR Mhealth Uhealth 2025;13:e64889
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Let’s Grow is a novel m Health intervention designed to support parents in improving the movement behaviors (ie, physical activity, sedentary behavior, and sleep) of their children aged 2 years via a purpose-built app [17].
JMIR Mhealth Uhealth 2025;13:e60478
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Sleep is a modifiable behavior and is highly intervenable [35]. Problematic sleep conditions, such as insomnia and obstructive sleep apnea, increase with age among older adults [36]. A meta-analysis [37] suggested that the proportion of individuals with sleep disorders in older adults was 30.5%, indicating that more attention should be paid to the sleep health of this group.
J Med Internet Res 2025;27:e75938
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Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis
The measures and domains assessed in the app include (1) social support, using 3 items from the Schuster Social Support Scale [24]; (2) personal functioning, using 3 items about educational and employment engagement and achievement [25]; (3) psychological distress, using the K6 (Kessler-6) scale for psychological distress [26]; (4) sleep, using 4 sleep items, including feeling refreshed after sleep, trouble falling asleep, and subjective energy [27]; (5) physical activity, 4 items from the International Physical
J Med Internet Res 2025;27:e71305
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These can include vasomotor symptoms (VMS) such as hot flashes (sudden, temporary onset of warmth in the body, flushing, and sweating) and night sweats (episodes of excessive sweating that happen during sleep), as well as depression, anxiety, and sleep disturbances [3]. In the United States, approximately 6000 women transition into menopause each day, many of whom face these challenges for years before and after menopause [4].
JMIR Mhealth Uhealth 2025;13:e58204
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Therefore, CWs can track vital parameters and behaviors, such as heart rate, physical activity (PA), or sleep [1].
The usage of CWs in the population has increased enormously within the last decade. In 2022, around 500 million of these devices were sold worldwide, which means that the sales increased by the factor 16 compared with 2014 [2]. In Germany, 7.2 million CWs were sold in 2022 [3].
JMIR Mhealth Uhealth 2025;13:e59199
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Data were collected on daily activity (number of steps) and sleep.
Fitbit data were obtained via the Fitbit API, which provides preprocessed JSON files reflecting proprietary algorithms for step counting and sleep [50]. For sleep, each participant had one JSON file that spanned the duration of the study, which included a sleep log with timestamps for sleep onset and offset and a breakdown of sleep stages (eg, light, rapid eye movement [REM], deep).
JMIR Form Res 2025;9:e66187
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Poor sleep quality is another common observance among postsecondary students [16], with conditions such as delayed sleep phase syndrome being 50% more prevalent among postsecondary students compared to the general population [17]. A distinctive combination of social, work, and academic pressures renders this population particularly susceptible to poor sleep quality [18,19].
JMIR Form Res 2025;9:e66131
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