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Background: Tummy time is recommended by the World Health Organization as part of its global movement guidelines for infant physical activity. To enable objective measurement of tummy time, accelerometer wear and nonwear time requires validation. The purpose of this study was to validate GENEActiv wear and nonwear time for use in infants.
Methods: The analysis was conducted on accelerometer data from 32 healthy infants (4-25 wk) wearing a GENEActiv (right hip) while completing a positioning protocol (3 min each position). Direct observation (video) was compared with the accelerometer data. The accelerometer data were analyzed by receiver operating characteristic curves to identify optimal cut points for second-by-second wear and nonwear time. Cut points (accelerometer data) were tested against direct observation to determine performance. Statistical analysis was conducted using leave-one-out validation and Bland-Altman plots.
Results: Mean temperature (0.941) and z-axis (0.889) had the greatest area under the receiver operating characteristic curve. Cut points were 25.6°C (temperature) and -0.812g (z-axis) and had high sensitivity (0.84, 95% confidence interval, 0.838-0.842) and specificity (0.948, 95% confidence interval, 0.944-0.948).
Conclusions: Analyzing GENEActiv data using temperature (>25.6°C) and z-axis (greater than -0.812g) cut points can be used to determine wear time among infants for the purpose of measuring tummy time.
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http://dx.doi.org/10.1123/jpah.2019-0486 | DOI Listing |
Sci Rep
August 2025
Department of Psychiatry and Behavioral Neuroscience, The University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA.
Wrist-worn alcohol biosensors can continuously track alcohol consumption, but their measurements are disrupted when the device is removed. Left unaddressed, non-wear data compromises observations of alcohol use and subsequent predictions of intoxication. To advance beyond commonly used temperature cutoffs and enable more precise detection of non-wear, we trained a random forest algorithm using laboratory ground truth data.
View Article and Find Full Text PDFChild Care Health Dev
July 2025
Child Health & Exercise Medicine Program, Department of Pediatrics, McMaster University, Hamilton, Canada.
Background: Accelerometers are increasingly used to measure physical activity and sedentary time in toddlers. Data cleaning or wear time validation can impact outcomes of interest, particularly in young children who spend less time awake. However, no study has systematically compared wear time validation strategies in toddlers.
View Article and Find Full Text PDFMethodsX
June 2025
Husqvarna Group, Huskvarna, Sweden.
Products often end up in landfills after serving their purpose, e.g. after their end-of-life.
View Article and Find Full Text PDFSci Rep
July 2025
College of Science, Nanjing Forestry University, Nanjing, 210037, People's Republic of China.
With increasing awareness of healthy living and social pressure, more and more people have begun to pay attention to their sleep state. Most existing methods that utilize wrist-worn devices data for detection rely on heuristic algorithms or traditional machine learning, which suffer from low classification efficiency and insufficient accuracy. This study explores an improved feature extractor based on the Constrained Cross Network to enhance the accuracy of the sleep-wake binary classification problem.
View Article and Find Full Text PDFSports Med Open
March 2025
Research Centre in Child Studies, University of Minho, Braga, Portugal.
Background: There are no reviews describing current measurement protocols and accelerometer processing decisions that are being used in 24-h MovBeh studies, across the lifespan. We aim to synthesise information on methods for assessing 24-h movement behaviors using accelerometry across all age groups.
Main Body: PubMed, PsycINFO, SPORTDiscus, and EMBASE were searched until December 2022.