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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 13642

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Special Issue Editors

Dr. Richard R. Suminski

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Guest Editor

Behavioral Health & Nutrition, University of Delaware, 021 Carpenter Sports Bldg, Newark, DE 19711, USA
Interests: physical activity; human behavior; measurement; direct observation; video technology; social and physical determinants of physical activity

Dr. Gregory M. Dominick

E-Mail Website
Guest Editor

Behavioral Health & Nutrition, University of Delaware, 012 Carpenter Sports Bldg, Newark, DE 19711, USA
Interests: physical activity; human behavior; measurement; wearable and video technology; machine learning; social and physical determinants of physical activity

Special Issue Information

Dear Colleagues,

Both physical and mental maladies associated with low levels of physical activity continue to plague people of all ages living throughout the world. In the U.S.A., for instance, only 23% of adults achieve the recommended amounts of physical activity in a given week. Thus, effective physical activity promotion strategies need to be developed, tested and implemented; however, for this to happen, feasible, reliable, and valid physical activity assessment methods must be available. Of particular interest are assessment methodologies for evaluating group or population-level physical activity within free-living settings such as parks, neighborhoods, public open spaces, etc. Further, it is critical a multi-disciplinary approach is taken to ensure the latest and most innovative technologies are incorporated into the assessment paradigm. Data capture procedures and devices (e.g., video cameras, sensors), data processing and analysis systems (e.g., computer vision, artificial intelligence), and data access/display formats (e.g., dashboards) are just some areas starting to arise in the physical activity assessment literature. For the current Special Issue, we are seeking high quality, scientific papers addressing topics related to the areas mentioned, especially those combining rigorous and reproducible scholarship with practical relevance for the study and promotion of physical activity behavior.

Dr. Richard R. Suminski
Dr. Gregory M. Dominick
Guest Editors

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Keywords

Physical activity
assessment
innovation
technology
methods
human behavior

Published Papers (6 papers)

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Research

12 pages, 1606 KiB

Open AccessArticle

Assessing Physical Activity Levels among Chinese College Students by BMI, HR, and Multi-Sensor Activity Monitors

by Dansong Liu, Xiaojuan Li, Qi Han, Bo Zhang, Xin Wei, Shuang Li, Xuemei Sui and Qirong Wang

Int. J. Environ. Res. Public Health 2023, 20(6), 5184; https://doi.org/10.3390/ijerph20065184 - 15 Mar 2023

Viewed by 1450

Abstract

We investigated the use of multi-sensor physical activity monitors, body mass index (BMI), and heart rate (HR) to measure energy expenditure (EE) of various physical activity levels among Chinese collegiate students, compared with portable indirect calorimetry. Methods: In a laboratory experiment, 100 college students, 18–25 years old, wore the SenseWear Pro3 Armband™ (SWA; BodyMedia, Inc., Pittsburg, PA, USA) and performed 7 different physical activities. EE was measured by indirect calorimetry, while body motion and accelerations were measured with an SWA accelerometer. Special attention was paid to the analysis of unidirectional and three-directional accelerometer output. Results: Seven physical activities were recorded and distinguished by SWA, and different physical activities demonstrated different data features. The mean values of acceleration ACz (longitudinal accel point, axis Z) and VM (vector magnitude) were significantly different (p = 0.000, p < 0.05) for different physical activities, whereas no significant difference was found in one single physical activity with varied speeds (p = 0.9486, p > 0.05). When all physical activities were included in a correlation regression analysis, a strong linear correlation between the EE and accelerometer reporting value was found. According to the correlation analysis, sex, BMI, HR, ACz, and VM were independent variables, and the EE algorithm model demonstrated a high correlation coefficient R² value of 0.7. Conclusions: The predictive energy consumption model of physical activity based on multi-sensor physical activity monitors, BMI, and HR demonstrated high accuracy and can be applied to daily physical activity monitoring among Chinese collegiate students. Full article

(This article belongs to the Special Issue Innovation in Physical Activity Assessment)

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11 pages, 712 KiB

Open AccessArticle

Assessing Park Quality with a Wearable Video Device and an Unmanned Aerial System

by Richard R. Suminski, Gregory M. Dominick and Eric Plautz

Int. J. Environ. Res. Public Health 2022, 19(18), 11717; https://doi.org/10.3390/ijerph191811717 - 16 Sep 2022

Viewed by 1257

Abstract

Parks are ideal places for promoting physical activity, which is vital for achieving and sustaining good health. Thus, it is important to develop and provide the best methods for assessing aspects of parks that could influence physical activity. This study examined the use of high-tech video capture for describing park quality. Videos were obtained with a wearable video device (WVD) and an unmanned aerial system (UAS) at 28 and 17 parks, respectively. In-person audits of park attributes were performed using the Physical Activity Readiness Assessment (PARA) instrument while video was simultaneously captured. The PARA provides quality ratings of park attributes that range from poor to good. Kappa statistics were calculated to compare in-person PARA outcomes with PARA outcomes obtained by reviewing the WVD and UAS videos. Substantial and almost-perfect agreements were found between WVD and in-person PARAs on the quality of features and amenities and the severity of incivilities. Agreements between UAS and in-person PARAs on feature and amenity quality and incivility severity were unacceptable (mostly fair and moderate). In conclusion, being able to reliably assess park quality using video provides advantages over in-person assessments (e.g., retrospective analysis). In addition, it sets up the possibility of utilizing computer vision to automate the video analysis process. Full article

(This article belongs to the Special Issue Innovation in Physical Activity Assessment)

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16 pages, 1160 KiB

Open AccessArticle

Conducting Physical Activity Research on Racially and Ethnically Diverse Adolescents Using Social Network Analysis: Case Studies for Practical Use

by Tyler Prochnow, Meg Patterson, M. Renée Umstattd Meyer, Joseph Lightner, Luis Gomez and Joseph Sharkey

Int. J. Environ. Res. Public Health 2022, 19(18), 11545; https://doi.org/10.3390/ijerph191811545 - 14 Sep 2022

Cited by 3 | Viewed by 1617

Abstract

Adolescent physical activity (PA) is significantly impacted by peer behaviors through peer influence, peer selection, and popularity. However, the scales for these social constructs may not fully capture the detailed social networks and mechanisms responsible for PA behavior changes. This level of detail and granularity can be quantified and analyzed through social network analysis (SNA). To demonstrate the variety, utility, and efficacy of SNA in adolescent PA research, this article aims to provide four case studies on the collection of social network and PA data on ethnically and racially diverse adolescents. Through case studies, this article provides tangible ways in which SNA can be used to evaluate social influences on PA behaviors. Case studies are presented on: (1) Youth Engagement in Sport—an egocentric analysis of middle school youth participation in an experiential sport program with 3- and 6-month follow-ups; (2) Summer care program networks—an egocentric and whole network longitudinal study of adolescents at summer care programs; (3) The Convoy method—a qualitative egocentric discussion activity with adolescents from colonias on the Texas-Mexico border; and (4) A father-focused, family-centered health program—an egocentric experimental analysis of children participating in a health program. Data collection procedures are listed and example surveys are provided. Descriptive analyses are included, as are recommendations on further analysis techniques for each type of network data. Using SNA, researchers can understand social contexts in a more specific manner, better positioning interventions to alter such influences. Full article

(This article belongs to the Special Issue Innovation in Physical Activity Assessment)

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15 pages, 1729 KiB

Open AccessArticle

Accelerometer-Measured Physical Activity Levels and Patterns Vary in an Age- and Sex-Dependent Fashion among Finnish Children and Adolescents

by Anne-Mari Jussila, Pauliina Husu, Henri Vähä-Ypyä, Kari Tokola, Sami Kokko, Harri Sievänen and Tommi Vasankari

Int. J. Environ. Res. Public Health 2022, 19(11), 6950; https://doi.org/10.3390/ijerph19116950 - 06 Jun 2022

Cited by 9 | Viewed by 2726

Abstract

Background: The purpose of this study was to measure physical activity (PA), sedentary behavior (SB), and hour-by-hour PA patterns with an accelerometer in a population-based sample of Finnish children and adolescents. Methods: A total of 3274 participants (3rd, 5th, 7th, 9th graders) from 176 schools wore a hip-worn triaxial accelerometer for seven days during waking hours. Mean amplitude deviation of the acceleration data was used to assess PA intensity that was converted to metabolic equivalents and categorized into light, moderate, and vigorous PA. Angle for posture estimation was used to measure SB and standing. Results: The majority of participants’ PA consisted of light PA, and they were sedentary for more than half of their waking hours. Children were more active than adolescents, and boys were more active than girls. Participants took, on average, 9890 steps daily, and one third met the PA recommendation. The participants were divided into tertiles based on daily steps to investigate the variation in PA patterns. Compared to the least active tertile, the most active tertile took twice as many steps on weekdays and nearly three times as many steps on the weekend. Conclusions: The majority of the participants were not active enough, and there was a great variation in PA levels and patterns, especially among the adolescents and on weekends. Full article

(This article belongs to the Special Issue Innovation in Physical Activity Assessment)

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12 pages, 1443 KiB

Open AccessArticle

Novel Physical Fitness Fuzzy Evaluation Model for Individual Health Promotion

by Kuen-Suan Chen and Tzung-Hua Hsieh

Int. J. Environ. Res. Public Health 2022, 19(9), 5060; https://doi.org/10.3390/ijerph19095060 - 21 Apr 2022

Cited by 2 | Viewed by 1751

Abstract

Physical fitness level plays a significant role in health promotion. Cardiorespiratory endurance, muscular endurance, muscle power, and flexibility are the four key indicators of physical fitness level, listed as one of the important fields of preventive medicine. Some studies targeted at students, based on statistical inference, have put forward a set of physical fitness evaluation methods to see whether they have reached the level of healthy physical fitness. Testing and monitoring of individual physical fitness takes up little time and requires a small sample dataset; this paper hence proposed an evaluation and analysis model that suits individual physical fitness by means of a fuzzy evaluation method suitable for evaluating small sample datasets. This paper developed the evaluation model based on the upper confidence limit of the physical fitness evaluation index so that it could reduce the risk of misjudgment caused by sampling error. At the same time, a simple and easy-to-use fuzzy evaluation form was developed as an evaluation interface, which can present the whole picture of all evaluation indicators as well as have good and convenient management performance. Accordingly, it can help every individual simultaneously monitor multiple physical fitness indicators to ensure that each physical fitness index can meet the requirement of healthy physical fitness. Full article

(This article belongs to the Special Issue Innovation in Physical Activity Assessment)

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17 pages, 3644 KiB

Open AccessArticle

Repurposing an EMG Biofeedback Device for Gait Rehabilitation: Development, Validity and Reliability

by Reza Koiler, Elham Bakhshipour, Joseph Glutting, Amy Lalime, Dexter Kofa and Nancy Getchell

Int. J. Environ. Res. Public Health 2021, 18(12), 6460; https://doi.org/10.3390/ijerph18126460 - 15 Jun 2021

Cited by 2 | Viewed by 3592

Abstract

Gait impairment often limits physical activity and negatively impacts quality of life. EMG-Biofeedback (EMG-BFB), one of the more effective interventions for improving gait impairment, has been limited to laboratory use due to system costs and technical requirements, and has therefore not been tested on a larger scale. In our research, we aimed to develop and validate a cost-effective, commercially available EMG-BFB device for home- and community-based use. We began by repurposing mTrigger^® (mTrigger LLC, Newark, DE, USA), a cost-effective, portable EMG-BFB device, for gait application. This included developing features in the cellphone app such as step feedback, success rate, muscle activity calibration, and cloud integration. Next, we tested the validity and reliability of the mTrigger device in healthy adults by comparing it to a laboratory-grade EMG system. While wearing both devices, 32 adults walked overground and on a treadmill at four speeds (0.3, 0.6, 0.9, and 1.2 m/s). Statistical analysis revealed good to excellent test–retest reliability (r > 0.89) and good to excellent agreement in the detection of steps (ICC > 0.85) at all speeds between two systems for treadmill walking. Our results indicated that mTrigger compared favorably to a laboratory-grade EMG system in the ability to assess muscular activity and to provide biofeedback during walking in healthy adults. Full article

(This article belongs to the Special Issue Innovation in Physical Activity Assessment)

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