Dataset Information

The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults.

ABSTRACT: The activPAL (AP) monitor is well established for distinguishing sitting, standing, and stepping time. However, its validity in predicting time in physical activity intensity categories in a free-living environment has not been determined.This study aimed to determine the validity of the AP in estimating time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) in a free-living environment.Thirteen participants (mean ± SD age = 24.8 ± 5.2 yr, BMI = 23.8 ± 1.9 kg·m) were directly observed for three 10-h periods wearing an AP. A custom R program was developed and used to summarize detailed active and sedentary behavior variables from the AP. AP estimates were compared with direct observation.The AP accurately and precisely estimated time in activity intensity categories (bias [95% confidence interval]; sedentary = 0.8 min [-2.9 to 4.5], light = 1.7 min [2.2-5.7], and -2.6 min [-5.8 to 0.7]). The overall accuracy rate for time in intensity categories was 96.2%. The AP also accurately estimated guideline minutes, guideline bouts, prolonged sitting minutes, and prolonged sitting bouts.The AP can be used to accurately capture individualized estimates of active and sedentary behavior variables in free-living settings.

SUBMITTER: Lyden K

PROVIDER: S-EPMC5469371 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults.

Lyden Kate K Keadle Sarah Kozey SK Staudenmayer John J Freedson Patty S PS

Medicine and science in sports and exercise 20170501 5

The activPAL (AP) monitor is well established for distinguishing sitting, standing, and stepping time. However, its validity in predicting time in physical activity intensity categories in a free-living environment has not been determined.<h4>Purpose</h4>This study aimed to determine the validity of the AP in estimating time spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) in a free-living environment.<h4>Methods</h4>Thirteen participants (mean ± SD age = 24.8 ± 5.2 y ...[more]

PMID: 28410327

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Project description:Study objectivesTo investigate whether a foundational transformer model using 8-hour, multichannel data from polysomnograms can outperform existing artificial intelligence (AI) methods for sleep stage classification.MethodsWe utilized the Sleep Heart Health Study (SHHS) visits 1 and 2 for training and validation and the Multi-Ethnic Study of Atherosclerosis (MESA) for testing of our model. We trained a self-supervised foundational transformer (called PFTSleep) that encodes 8-hour long sleep studies at 125 Hz with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels. These encodings are used as input for training of an additional model to classify sleep stages, without adjusting the weights of the foundational transformer. We compared our results to existing AI methods that did not utilize 8-hour data or the full set of signals but did report evaluation metrics for the SHHS dataset.ResultsWe trained and validated a model with 8,444 sleep studies with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels and tested on an additional 2,055 studies. In total, we trained and tested 587,944 hours of sleep study signal data. Area under the precision recall curve (AUPRC) scores were 0.82, 0.40, 0.53, 0.75, and 0.82 and area under the receiving operating characteristics curve (AUROC) scores were 0.99, 0.95, 0.96, 0.98, and 0.99 for wake, N1, N2, N3, and REM, respectively, on the SHHS validation set. For MESA, the AUPRC scores were 0.56, 0.16, 0.40, 0.45, and 0.65 and AUROC scores were 0.94, 0.77, 0.87, 0.91, and 0.96, respectively. Our model was compared to the longest context window state-of-the-art model and showed increases in macro evaluation scores, notably sensitivity (3.7% increase) and multi-class REM (3.39% increase) and wake (0.97% increase) F1 scores.ConclusionsUtilizing full night, multi-channel PSG data encodings derived from a foundational transformer improve sleep stage classification over existing methods.

Dataset Information

The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults.

Publications

The activPALTM Accurately Classifies Activity Intensity Categories in Healthy Adults.

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