Feasibility of Measuring Sedentary Time Using Data From a Thigh-Worn Accelerometer.
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ABSTRACT:
SUBMITTER: Hamer M
PROVIDER: S-EPMC7443760 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
Publications
Feasibility of Measuring Sedentary Time Using Data From a Thigh-Worn Accelerometer.
Hamer Mark M Stamatakis Emmanuel E Chastin Sebastien S Pearson Natalie N Brown Matt M Gilbert Emily E Sullivan Alice A
American journal of epidemiology 20200901 9
In large-scale cohort studies, sedentary behavior has been routinely measured using self-reports or devices that apply a count-based threshold. We employed a gold standard postural allocation technique using thigh inclination and acceleration to capture free-living sedentary behavior. Participants aged 46.8 (standard deviation (SD), 0.7) years (n = 5,346) from the 1970 British Cohort Study (United Kingdom) were fitted with a waterproofed thigh-mounted accelerometer device (activPAL3 micro; PAL T ...[more]
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Project description:BackgroundSubclinical (ie, threshold) social anxiety can greatly affect young people's lives, but existing solutions appear inadequate considering its rising prevalence. Wearable sensors may provide a novel way to detect social anxiety and result in new opportunities for monitoring and treatment, which would be greatly beneficial for persons with social anxiety, society, and health care services. Nevertheless, indicators such as skin temperature measured by wrist-worn sensors have not been used in prior work on physiological social anxiety detection.ObjectiveThis study aimed to investigate whether subclinical social anxiety in young adults can be detected using physiological data obtained from wearable sensors, including heart rate, skin temperature, and electrodermal activity (EDA).MethodsYoung adults (N=12) with self-reported subclinical social anxiety (measured using the widely used self-reported version of the Liebowitz Social Anxiety Scale) participated in an impromptu speech task. Physiological data were collected using an E4 Empatica wearable device. Using the preprocessed data and following a supervised machine learning approach, various classification algorithms such as Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbours (KNN) were used to develop models for 3 different contexts. Models were trained to differentiate (1) between baseline and socially anxious states, (2) among baseline, anticipation anxiety, and reactive anxiety states, and (3) social anxiety among individuals with social anxiety of differing severity. The predictive capability of the singular modalities was also explored in each of the 3 supervised learning experiments. The generalizability of the developed models was evaluated using 10-fold cross-validation as a performance index.ResultsWith modalities combined, the developed models yielded accuracies between 97.54% and 99.48% when differentiating between baseline and socially anxious states. Models trained to differentiate among baseline, anticipation anxiety, and reactive anxiety states yielded accuracies between 95.18% and 98.10%. Furthermore, the models developed to differentiate between social anxiety experienced by individuals with anxiety of differing severity scores successfully classified with accuracies between 98.86% and 99.52%. Surprisingly, EDA was identified as the most effective singular modality when differentiating between baseline and social anxiety states, whereas ST was the most effective modality when differentiating anxiety among individuals with social anxiety of differing severity.ConclusionsThe results indicate that it is possible to accurately detect social anxiety as well as distinguish between levels of severity in young adults by leveraging physiological data collected from wearable sensors.
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