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Wearable Technology-Based Physical Activity Measurement and Intervention
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Wearable Technology-Based Physical Activity Measurement and Intervention

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (19 March 2021) | Viewed by 15830

Special Issue Editor

Dr. Wonwoo Byun

E-Mail Website
Guest Editor
Department of Health and Kinesiology, University of Utah, Salt Lake City, UT 84112, USA
Interests: epidemiology; preschool children; intervention; reliability; sedentary behavior; wearable technology; validity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

Despite the well-established health benefits of regular physical activity, only a small proportion of population in developed and/or developing countries is physically active enough to achieve the associated health benefits, placing physical inactivity as one of the major public health concerns worldwide. Researchers, clinicians, and practitioners are increasingly adopting wearable technologies in various settings to measure and/or promote physical activity in diverse populations (e.g., children and youth, patients, pregnant women, older adults); however, the development of effective strategies for improving physical activity particularly using novel wearable technologies that also provide accurate and consistent monitoring of physical activity is understudied. In fact, available data are limited and sometimes inconsistent, leaving a gap in understanding the utilization of innovative and affordable wearable technologies in physical activity interventions and the effects of those interventions in in various population groups. As part of continuous efforts to extend our desire for developing cost-effective and sustainable interventions to improve physical activity and human health, this Special Issue calls for research studies on a broad range of topics in wearable technology-based physical activity management and intervention to promote human health across varying health domains and populations.

We invite investigators to submit original as well as review articles addressing topics on a broad range in wearable technology-based physical activity management and intervention to promote human health across varying health. Potential topics covered in this Special Issue can be found below.

  • Studies examining the effects of (wearable) technology-based interventions to promote physical activity and/or reduce sedentary behavior in diverse populations (e.g., children and youth, patients, pregnant women, older adults);
  • Studies identifying processes of implementing technology-based physical activity interventions;
  • Observational or experimental studies understanding the role of technology, including games and social media, in promoting physical activity;
  • Studies examining the optimal approaches to promote physical activity using wearable technologies;
  • Utilizing wearable and other technologies to describe the levels and/or trends of physical activity/sedentary behavior across diverse populations;
  • Methodological studies examining the validity and/or reliability of wearable technology-based devices for assessing physical activity and sedentary behavior.

Prof. Dr. Wonwoo Byun
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

14 pages, 5063 KiB  
Article
The Function of Color and Structure Based on EEG Features in Landscape Recognition
by Yuting Wang, Shujian Wang and Ming Xu
Int. J. Environ. Res. Public Health 2021, 18(9), 4866; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094866 - 03 May 2021
Cited by 8 | Viewed by 3050
Abstract
Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape images, structure images with filtered [...] Read more.
Both color and structure make important contributions to human visual perception, as well as the evaluation of landscape quality and landscape aesthetics. The EEG equipment liveamp32 was used to record the EEG signals of humans when viewing landscape images, structure images with filtered color, and color images with a filtered structure. The results show that the SVM classifier was the most suitable classifier for landscape classification based on EEG features. The classification accuracy of the landscape picture recognition was up to 98.3% when using beta waves, while the accuracy of the color recognition was 97.5%, and that of the structure recognition was 93.9% when using gamma waves. Secondly, color and structure played a major role in determining the alpha and gamma wave responses, respectively, for all the landscape types, including forest, desert, and water. Furthermore, structure only played a decisive role in forest, while color played a major role in desert and water when using beta waves. Lastly, statistically significant differences between landscape groups and scenario groups with regard to alpha, beta, and gamma rhythms in brain waves were confirmed. The reasonable usage and layout of structure and color will have a very important guiding value for landscape aesthetics in future landscape design and landscape planning. Full article
(This article belongs to the Special Issue Wearable Technology-Based Physical Activity Measurement and Intervention)
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9 pages, 316 KiB  
Article
A Comparison of Sedentary Behavior as Measured by the Fitbit and ActivPAL in College Students
by Chelsea Carpenter, Chih-Hsiang Yang and Delia West
Int. J. Environ. Res. Public Health 2021, 18(8), 3914; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18083914 - 08 Apr 2021
Cited by 12 | Viewed by 3257
Abstract
Previous studies have examined the ability of the Fitbit to measure physical activity compared to research-grade accelerometers. However, few have examined whether Fitbits accurately measure sedentary behavior. This study examined whether the Fitbit Charge 3 adequately quantifies sedentary behavior compared to the gold [...] Read more.
Previous studies have examined the ability of the Fitbit to measure physical activity compared to research-grade accelerometers. However, few have examined whether Fitbits accurately measure sedentary behavior. This study examined whether the Fitbit Charge 3 adequately quantifies sedentary behavior compared to the gold standard in objectively measured sedentary behavior assessment, the activPAL. Eleven adults wore a Fitbit Charge 3 and activPAL device for 14 days and self-reported their sedentary behavior each week. ActivPAL epoch data were summed into minute-by-minute data and processed with two cutpoints (activPAL_Half and activPAL_Full) to compare to Fitbit data. Paired t-tests were used to examine differences between the two devices for sedentary behavior variables. Intraclass correlations were used to examine device agreement. There was no significant difference in sedentary time between activPAL_Half and Fitbit data, but activPAL_Full estimated significantly lower sedentary time than Fitbit. Intraclass correlations showed high agreement. We suggest that Fitbit could replace activPAL when measuring total sedentary time. Full article
(This article belongs to the Special Issue Wearable Technology-Based Physical Activity Measurement and Intervention)
11 pages, 2312 KiB  
Article
Inter-Device Agreement between Fitbit Flex 1 and 2 for Assessing Sedentary Behavior and Physical Activity
by Sunku Kwon, Ryan D. Burns, Youngwon Kim, Yang Bai and Wonwoo Byun
Int. J. Environ. Res. Public Health 2021, 18(5), 2716; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18052716 - 08 Mar 2021
Viewed by 1929
Abstract
This study examined the inter-model agreement between the Fitbit Flex (FF) and FF2 in estimating sedentary behavior (SED) and physical activity (PA) during a free-living condition. 33 healthy adults wore the FF and FF2 on non-dominant wrist for 14 consecutive days. After excluding [...] Read more.
This study examined the inter-model agreement between the Fitbit Flex (FF) and FF2 in estimating sedentary behavior (SED) and physical activity (PA) during a free-living condition. 33 healthy adults wore the FF and FF2 on non-dominant wrist for 14 consecutive days. After excluding sleep and non-wear time, data from the FF and FF2 was converted to the time spent (min/day) in SED and PA using a proprietary algorithm. Pearson’s correlation was used to evaluate the association between the estimates from FF and FF2. Mean absolute percent errors (MAPE) were used to examine differences and measurement agreement in SED and PA estimates between FF and FF2. Bland-Altman (BA) plots were used to examine systematic bias between two devices. Equivalence testing was conducted to examine the equivalence between the FF and FF2. The FF2 had strong correlations with the FF in estimating SED and PA times. Compared to the FF, the FF2 yielded similar SED and PA estimates along with relatively low measurement discords and did not have significant systematic biases for SED and Moderate-to-vigorous PA estimates. Our findings suggest that researchers may choose FF2 as a measurement of SED and PA when FF is not available in the market during the longitudinal PA research. Full article
(This article belongs to the Special Issue Wearable Technology-Based Physical Activity Measurement and Intervention)
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16 pages, 1725 KiB  
Article
Using an Accelerometer-Based Step Counter in Post-Stroke Patients: Validation of a Low-Cost Tool
by Francesco Negrini, Giulio Gasperini, Eleonora Guanziroli, Jacopo Antonino Vitale, Giuseppe Banfi and Franco Molteni
Int. J. Environ. Res. Public Health 2020, 17(9), 3177; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17093177 - 02 May 2020
Cited by 10 | Viewed by 3274
Abstract
Monitoring the real-life mobility of stroke patients could be extremely useful for clinicians. Step counters are a widely accessible, portable, and cheap technology that can be used to monitor patients in different environments. The aim of this study was to validate a low-cost [...] Read more.
Monitoring the real-life mobility of stroke patients could be extremely useful for clinicians. Step counters are a widely accessible, portable, and cheap technology that can be used to monitor patients in different environments. The aim of this study was to validate a low-cost commercial tri-axial accelerometer-based step counter for stroke patients and to determine the best positioning of the step counter (wrists, ankles, and waist). Ten healthy subjects and 43 post-stroke patients were enrolled and performed four validated clinical tests (10 m, 50 m, and 6 min walking tests and timed up and go tests) while wearing five step counters in different positions while a trained operator counted the number of steps executed in each test manually. Data from step counters and those collected manually were compared using the intraclass coefficient correlation and mean average percentage error. The Bland–Altman plot was also used to describe agreement between the two quantitative measurements (step counter vs. manual counting). During walking tests in healthy subjects, the best reliability was found for lower limbs and waist placement (intraclass coefficient correlations (ICCs) from 0.46 to 0.99), and weak reliability was observed for upper limb placement in every test (ICCs from 0.06 to 0.38). On the contrary, in post-stroke patients, moderate reliability was found only for the lower limbs in the 6 min walking test (healthy ankle ICC: 0.69; pathological ankle ICC: 0.70). Furthermore, the Bland–Altman plot highlighted large average discrepancies between methods for the pathological group. However, while the step counter was not able to reliably determine steps for slow patients, when applied to the healthy ankle of patients who walked faster than 0.8 m/s, it counted steps with excellent precision, similar to that seen in the healthy subjects (ICCs from 0.36 to 0.99). These findings show that a low-cost accelerometer-based step counter could be useful for measuring mobility in select high-performance patients and could be used in clinical and real-world settings. Full article
(This article belongs to the Special Issue Wearable Technology-Based Physical Activity Measurement and Intervention)
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9 pages, 464 KiB  
Article
Agreement between the Apple Series 1, LifeTrak Core C200, and Fitbit Charge HR with Indirect Calorimetry for Assessing Treadmill Energy Expenditure
by Peng Zhang, Ryan Donald Burns, You Fu, Steven Godin and Wonwoo Byun
Int. J. Environ. Res. Public Health 2019, 16(20), 3812; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph16203812 - 10 Oct 2019
Cited by 3 | Viewed by 2881
Abstract
The purpose of this study was to examine agreement in energy expenditure between the Apple Series 1 Watch, LifeTrak Core C200, and Fitbit Charge HR with indirect calorimetry during various treadmill speeds in young adults. Participants were a sample of college-aged students (mean [...] Read more.
The purpose of this study was to examine agreement in energy expenditure between the Apple Series 1 Watch, LifeTrak Core C200, and Fitbit Charge HR with indirect calorimetry during various treadmill speeds in young adults. Participants were a sample of college-aged students (mean age = 20.1 (1.7) years; 13 females, 17 males). Participants completed six structured 10-minute exercise sessions on a treadmill with speeds ranging from 53.6 m·min−1 to 187.7 m·min−1. Indirect calorimetry was used as the criterion. Participants wore the Apple Watch, LifeTrak, and Fitbit activity monitors on their wrists. Group-level agreement was examined using equivalence testing, relative agreement was examined using Spearman’s rho, and individual-level agreement was examined using Mean Absolute Percent Error (MAPE) and Bland-Altman Plots. Activity monitor agreement with indirect calorimetry was supported using the Apple Watch at 160.9 m·min−1 (Mean difference = −2.7 kcals, 90% C.I.: −8.3 kcals, 2.8 kcals; MAPE = 11.9%; rs = 0.64) and 187.7 m·min−1 (Mean difference = 3.7 kcals, 90% C.I.: −2.2 kcals, 9.7 kcals; MAPE = 10.7%; rs = 0.72) and the Fitbit at 187.7 m·min−1 (Mean difference = −0.2 kcals, 90% C.I.: −8.8 kcals, 8.5 kcals; MAPE = 20.1%; rs = 0.44). No evidence for statistical equivalence was seen for the LifeTrak at any speed. Bland-Altman Plot Limits of Agreement were narrower for the Apple Series 1 Watch compared to other monitors, especially at slower treadmill speeds. The results support the utility of the Apple Series 1 Watch and Fitbit Charge HR for assessing energy expenditure during specific treadmill running speeds in young adults. Full article
(This article belongs to the Special Issue Wearable Technology-Based Physical Activity Measurement and Intervention)
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