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Keywords = consumer-wearable activity tracker

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12 pages, 223 KiB  
Article
Comparison of Students’ Physical Activity at Different Times and Establishment of a Regression Model for Smart Fitness Trackers
by Xiangrong Cheng, Jingmin Liu, Ye Wang, Yue Wang, Zhengyan Tang and Hao Wang
Sensors 2025, 25(6), 1726; https://doi.org/10.3390/s25061726 - 11 Mar 2025
Viewed by 988
Abstract
Under the strategy of Healthy China, students’ physical health status not only affects their future life and studies but also influences social progress and development. By monitoring and measuring the daily PA levels of Chinese students over a week, this study aimed to [...] Read more.
Under the strategy of Healthy China, students’ physical health status not only affects their future life and studies but also influences social progress and development. By monitoring and measuring the daily PA levels of Chinese students over a week, this study aimed to fully understand the current PA status of students at different times, providing data support for improving students’ PA levels and physical health. (1) Wearable fitness trackers have advantages such as low cost, portable wearability, and intuitive test data. By exploring the differences between wearable devices and PA testing instruments, this study provides reference data to improve the accuracy of wearable devices and promote the use of fitness trackers instead of triaxial accelerometers, thereby advancing scientific research on PA and the development of mass fitness. A total of 261 students (147 males; 114 females) were randomly selected and wore both the Actigraph GT3X+ triaxial accelerometer and Huawei smart fitness trackers simultaneously to monitor their daily PA levels, energy metabolism, sedentary behavior, and step counts from the trackers over a week. The students’ PA status and living habits were also understood through literature reviews and questionnaire surveys. The validity of the smart fitness trackers was quantitatively analyzed using ActiLife software 6 Data Analysis Software and traditional analysis methods such as MedCal. Paired sample Wilcoxon signed-rank tests and mean absolute error ratio tests were used to assess the validity of the smart fitness trackers relative to the Actigraph GT3X+ triaxial accelerometer. A linear regression model was established to predict the step counts of the Actigraph GT3X+ triaxial accelerometer based on the step counts from the smart fitness trackers, aiming to improve the accuracy of human motion measurement by smart fitness trackers. There were significant differences in moderate-to-high-intensity PA time, energy expenditure, metabolic equivalents, and step counts between males and females (p < 0.01), with females having higher values than males in both moderate-to-high-intensity PA time and step counts. Sedentary behavior showed significant differences only on weekdays between males and females (p < 0.05), with females engaging in less sedentary behavior than males. (2) There was a significant difference in sedentary time between weekdays and weekends for students (p < 0.05), with sedentary time being higher on weekends than on weekdays. (3) Compared with weekends, female students had significantly different moderate-to-high-intensity PA time and sedentary time on weekdays (p < 0.01), while no significant differences were observed for male students. (4) Under free-living conditions, the average daily step count monitored by the smart fitness trackers was lower than that measured by the Actigraph GT3X+ triaxial accelerometer, with a significant difference (p < 0.01), but both showed a positive correlation (r = 0.727). (5) The linear regression equation established between the step counts monitored by the smart fitness trackers and those by the Actigraph GT3X+ triaxial accelerometer was y = 3677.3157 + 0.6069x. The equation’s R2 = 0.625, with an F-test value of p < 0.001, indicating a high degree of fit between the step counts recorded by the Huawei fitness tracker and those recorded by the triaxial accelerometer. The t-test results for the regression coefficient and constant term were t = 26.4410 and p < 0.01, suggesting that both were meaningful. The tested students were able to meet the recommended total amount of moderate-intensity PA for 150 min per week or high-intensity PA for 75 min per week according to the “Chinese Adult PA Guidelines”, as well as the recommended daily step count of more than 6000 steps per day according to the “Chinese Dietary Guidelines”. (2) Female students had significantly more moderate-to-high-intensity PA time than male students, but lower energy expenditure and metabolic equivalents, which may have been related to their lifestyle and types of exercise. On weekends, female students significantly increased their moderate-to-high-intensity PA time compared with males but also showed increased sedentary time exceeding that of males; further investigation is needed to understand the reasons behind these findings. (3) The step counts monitored by the Huawei smart fitness trackers correlated with those measured by the Actigraph GT3X+ triaxial accelerometer, but the step counts from the fitness trackers were lower, indicating that the fitness trackers underestimated PA levels. (4) There was a linear relationship between the Huawei smart fitness trackers and the Actigraph GT3X+ triaxial accelerometer. By using the step counts monitored by the Huawei fitness trackers and the regression equation, it was possible to estimate the activity counts from the Actigraph GT3X+ triaxial accelerometer. Replacing the Actigraph GT3X+ triaxial accelerometer with Huawei smart fitness trackers for step count monitoring significantly reduces testing costs while providing consumers with intuitive data. Full article
(This article belongs to the Section Biomedical Sensors)
14 pages, 1294 KiB  
Article
Encouraging Continuous Usage of Wearable Activity Trackers: The Interplay of Perceived Severity, Susceptibility and Social Media Influencers
by Anita Lennox, Re-an Müller and Isaac Sewornu Coffie
Int. J. Environ. Res. Public Health 2024, 21(12), 1549; https://doi.org/10.3390/ijerph21121549 - 22 Nov 2024
Cited by 3 | Viewed by 1519
Abstract
While past studies have provided enough evidence to show consumer attitude as a key predictor of the adoption and continuous usage intention of wearable activity trackers (WATs), limited studies have examined the antecedents of consumers’ attitudes towards the adoption and continuous usage intention [...] Read more.
While past studies have provided enough evidence to show consumer attitude as a key predictor of the adoption and continuous usage intention of wearable activity trackers (WATs), limited studies have examined the antecedents of consumers’ attitudes towards the adoption and continuous usage intention of WATs. Drawing on the health belief model and cue utilization theory, the study seeks to examine the influence of perceived severity and vulnerability as antecedents of consumers’ attitudes towards the adoption and continuous usage intention of WATs as well as the role of social media influencers (SMIs) in influencing continuous usage of WATs. Online survey data from 966 participants (Mage = 40.79, STD = 13.49) was analyzed using SPSS 29and AMOS version 29. The result shows that though perceived severity and susceptibility are key significant predictors of consumers’ attitudes towards WATs, the relationship is stronger when SMIs’ personas are used as extrinsic cues. Additionally, while perceived barriers negatively affect consumers’ attitudes towards WATs, the negative effect is neutralized through SMIs’ message framing as an extrinsic cue. Theoretically, the study provides a new insight into the interplay of perceived severity, susceptibility, SMIs’ personas, and message framing on consumers’ attitudes towards the adoption and continuous usage intention of WATs. Full article
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9 pages, 323 KiB  
Commentary
Commentary: Is Wearable Fitness Technology a Medically Approved Device? Yes and No
by Jennifer L. Scheid, Jennifer L. Reed and Sarah L. West
Int. J. Environ. Res. Public Health 2023, 20(13), 6230; https://doi.org/10.3390/ijerph20136230 - 27 Jun 2023
Cited by 7 | Viewed by 3830
Abstract
Wearable technologies, i.e., activity trackers and fitness watches, are extremely popular and have been increasingly integrated into medical research and clinical practice. To assist in optimizing health, wellness, or medical care, these devices require collaboration between researchers, healthcare providers, and wearable technology companies [...] Read more.
Wearable technologies, i.e., activity trackers and fitness watches, are extremely popular and have been increasingly integrated into medical research and clinical practice. To assist in optimizing health, wellness, or medical care, these devices require collaboration between researchers, healthcare providers, and wearable technology companies in order to clarify their clinical capabilities and educate consumers on the utilities and limitations of the wide-ranging wearable devices. Interestingly, activity trackers and fitness watches often track both health/wellness and medical information within the same device. In this commentary, we will focus our discussions regarding wearable technology on (1) defining and explaining the technical differences between tracking health, wellness, and medical information; (2) providing examples of health and wellness compared to medical tracking; (3) describing the potential medical benefits of wearable technology and its applications in clinical populations; and (4) elucidating the potential risks of wearable technology. We conclude that while wearable devices are powerful and informative tools, further research is needed to improve its clinical applications. Full article
(This article belongs to the Special Issue 2nd Edition: Wearable Technology and Health)
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11 pages, 278 KiB  
Article
Validity of Wearable Monitors and Smartphone Applications for Measuring Steps in Semi-Structured and Free-Living Settings
by Manolis Adamakis
Technologies 2023, 11(1), 29; https://doi.org/10.3390/technologies11010029 - 13 Feb 2023
Cited by 2 | Viewed by 3304
Abstract
Wearable technologies have become powerful tools for health and fitness and are indispensable everyday tools for many individuals; however, significant limitations exist related to the validity of the metrics these monitors purport to measure. Thus, the purpose of the present study was to [...] Read more.
Wearable technologies have become powerful tools for health and fitness and are indispensable everyday tools for many individuals; however, significant limitations exist related to the validity of the metrics these monitors purport to measure. Thus, the purpose of the present study was to validate the step count of three wearable monitors (i.e., Yamax 3D Power-Walker, Garmin Vivofit 3 and Medisana Vifit), as well as two Android apps (i.e., Accupedo Pedometer and Pedometer 2.0), in a sample of healthy adults. These monitors and apps were evaluated in a lab-based semi-structured study and a 3-day field study under habitual free-living conditions. A convenience sample of 24 healthy adults (14 males and 10 females; 32.6 ± 2.5 years) participated in both studies. Direct step observation and Actigraph served as the criterion methods and validity was evaluated by comparing each monitor and app with the criterion measure using mean absolute percentage errors (MAPE), Bland–Altman plots, and Intraclass Correlation Coefficients. The results revealed high validity for the three wearable monitors during the semi-structured study, with MAPE values approximately 5% for Yamax and Vifit and well below 5% for Vivofit, while the two apps showed high MAPE values over 20%. In the free-living study all monitors and apps had high MAPE, over 10%. The lowest error was observed for Yamax, Vifit and Pedometer app, while Accupedo app had the highest error, overestimating steps by 32%. The present findings cannot support the value of wearable monitors and apps as acceptable measures of PA and step count in free-living contexts. Wearable monitors and apps that might be valid in one context, might not be valid in different contexts and vice versa, and researchers should be aware of this limitation. Full article
(This article belongs to the Special Issue Wearable Technologies III)
34 pages, 5298 KiB  
Article
Using Consumer-Wearable Activity Trackers for Risk Prediction of Life-Threatening Heart Arrhythmia in Patients with an Implantable Cardioverter-Defibrillator: An Exploratory Observational Study
by Diana My Frodi, Vlad Manea, Søren Zöga Diederichsen, Jesper Hastrup Svendsen, Katarzyna Wac and Tariq Osman Andersen
J. Pers. Med. 2022, 12(6), 942; https://doi.org/10.3390/jpm12060942 - 8 Jun 2022
Cited by 1 | Viewed by 3629
Abstract
Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for [...] Read more.
Ventricular arrhythmia (VA) is a leading cause of sudden death and health deterioration. Recent advances in predictive analytics and wearable technology for behavior assessment show promise but require further investigation. Yet, previous studies have only assessed other health outcomes and monitored patients for short durations (7–14 days). This study explores how behaviors reported by a consumer wearable can assist VA risk prediction. An exploratory observational study was conducted with participants who had an implantable cardioverter-defibrillator (ICD) and wore a Fitbit Alta HR consumer wearable. Fitbit reported behavioral markers for physical activity (light, fair, vigorous), sleep, and heart rate. A case-crossover analysis using conditional logistic regression assessed the effects of time-adjusted behaviors over 1–8 weeks on VA incidence. Twenty-seven patients (25 males, median age 59 years) were included. Among the participants, ICDs recorded 262 VA events during 8093 days monitored by Fitbit (median follow-up period 960 days). Longer light to fair activity durations and a higher heart rate increased the odds of a VA event (p < 0.001). In contrast, lengthier fair to vigorous activity and sleep durations decreased the odds of a VA event (p < 0.001). Future studies using consumer wearables in a larger population should prioritize these outcomes to further assess VA risk. Full article
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26 pages, 1303 KiB  
Review
Deconstructing Commercial Wearable Technology: Contributions toward Accurate and Free-Living Monitoring of Sleep
by Lauren E. Rentz, Hana K. Ulman and Scott M. Galster
Sensors 2021, 21(15), 5071; https://doi.org/10.3390/s21155071 - 27 Jul 2021
Cited by 33 | Viewed by 6476
Abstract
Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to “measure” sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, [...] Read more.
Despite prolific demands and sales, commercial sleep assessment is primarily limited by the inability to “measure” sleep itself; rather, secondary physiological signals are captured, combined, and subsequently classified as sleep or a specific sleep state. Using markedly different approaches compared with gold-standard polysomnography, wearable companies purporting to measure sleep have rapidly developed during recent decades. These devices are advertised to monitor sleep via sensors such as accelerometers, electrocardiography, photoplethysmography, and temperature, alone or in combination, to estimate sleep stage based upon physiological patterns. However, without regulatory oversight, this market has historically manufactured products of poor accuracy, and rarely with third-party validation. Specifically, these devices vary in their capacities to capture a signal of interest, process the signal, perform physiological calculations, and ultimately classify a state (sleep vs. wake) or sleep stage during a given time domain. Device performance depends largely on success in all the aforementioned requirements. Thus, this review provides context surrounding the complex hardware and software developed by wearable device companies in their attempts to estimate sleep-related phenomena, and outlines considerations and contributing factors for overall device success. Full article
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15 pages, 1038 KiB  
Article
Gamified Wearable Fitness Tracker for Physical Activity: A Comprehensive Literature Review
by Inje Cho, Kyriaki Kaplanidou and Shintaro Sato
Sustainability 2021, 13(13), 7017; https://doi.org/10.3390/su13137017 - 22 Jun 2021
Cited by 24 | Viewed by 11661
Abstract
Recently, gamified wearable fitness trackers have received greater attention and usage among sport consumers. Although a moderate amount of aerobic physical activity can significantly reduce the risk of many serious illnesses, physical inactivity issues are still prominent. Although wearable fitness trackers have the [...] Read more.
Recently, gamified wearable fitness trackers have received greater attention and usage among sport consumers. Although a moderate amount of aerobic physical activity can significantly reduce the risk of many serious illnesses, physical inactivity issues are still prominent. Although wearable fitness trackers have the potential to contribute to physical activity engagement and sustainable health outcomes, there are dwindling engagement and discontinuance issues. Thus, examining its gamification elements and role in physical activity becomes critical. This study examined the gamification elements in wearable fitness trackers and their role in physical activity and sports engagement. A comprehensive literature review yielded 26 articles that empirically measured a variety of gamification features and the effect of the device on physical activity and sports engagement. The study suggests three key gamification themes: goal-based, social-based, and rewards-based gamification that can be a point of interest for future scholars and practitioners. Based on the review, we propose a conceptual framework that embraces motivational affordances and engagement in physical activity and sports. Full article
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31 pages, 1706 KiB  
Article
An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers
by Yu-Sheng Kao, Kazumitsu Nawata and Chi-Yo Huang
Int. J. Environ. Res. Public Health 2019, 16(18), 3227; https://doi.org/10.3390/ijerph16183227 - 4 Sep 2019
Cited by 61 | Viewed by 7898
Abstract
In recent years, IoT (Internet of Things)-based smart devices have penetrated a wide range of markets, including connected health, smart home, and wearable devices. Among the IoT-based smart devices, wearable fitness trackers are the most widely diffused and adopted IoT based devices. Such [...] Read more.
In recent years, IoT (Internet of Things)-based smart devices have penetrated a wide range of markets, including connected health, smart home, and wearable devices. Among the IoT-based smart devices, wearable fitness trackers are the most widely diffused and adopted IoT based devices. Such devices can monitor or track the physical activity of the person wearing them. Although society has benefitted from the conveniences provided by IoT-based wearable fitness trackers, few studies have explored the factors influencing the adoption of such technology. Furthermore, one of the most prevalent issues nowadays is the large attrition rate of consumers no longer wearing their device. Consequently, this article aims to define an analytic framework that can be used to explore the factors that influence the adoption of IoT-based wearable fitness trackers. In this article, the constructs for evaluating these factors will be explored by reviewing extant studies and theories. Then, these constructs are further evaluated based on experts’ consensus using the modified Delphi method. Based on the opinions of experts, the analytic framework for deriving an influence relationship map (IRM) is derived using the decision-making trial and evaluation laboratory (DEMATEL). Finally, based on the IRM, the behaviors adopted by mass customers toward IoT-based wearable fitness trackers are confirmed using the partial least squares (PLS) structural equation model (SEM) approach. The proposed analytic framework that integrates the DEMATEL and PLS-SEM was verified as being a feasible research area by empirical validation that was based on opinions provided by both Taiwanese experts and mass customers. The proposed analytic method can be used in future studies of technology marketing and consumer behaviors. Full article
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13 pages, 1120 KiB  
Article
Validity of Wrist-Worn Activity Trackers for Estimating VO2max and Energy Expenditure
by Stefanie Passler, Julian Bohrer, Lukas Blöchinger and Veit Senner
Int. J. Environ. Res. Public Health 2019, 16(17), 3037; https://doi.org/10.3390/ijerph16173037 - 22 Aug 2019
Cited by 30 | Viewed by 12671
Abstract
Activity trackers are a simple and mostly low-priced method to capture physiological parameters. Despite the high number of wrist-worn devices, there is a lack of scientific validation. The purpose of this study was to assess whether the activity trackers represent a valid alternative [...] Read more.
Activity trackers are a simple and mostly low-priced method to capture physiological parameters. Despite the high number of wrist-worn devices, there is a lack of scientific validation. The purpose of this study was to assess whether the activity trackers represent a valid alternative to gold-standard methods in terms of estimating energy expenditure (EE) and maximum oxygen uptake (VO2max). Twenty-four healthy subjects participated in this study. In total, five commercially available wrist-worn devices were tested with regard to their validity of EE and/or VO2max. Estimated values were compared with indirect calorimetry. Validity of the activity trackers was determined by paired sample t-tests, mean absolute percentage errors (MAPE), Intraclass Correlation Coefficient, and Bland-Altman plots. Within the tested devices, differences in scattering in VO2max and EE could be observed. This results in a MAPE > 10% for all evaluations, except for the VO2max-estimation of the Garmin Forerunner 920XT (7.3%). The latter significantly underestimates the VO2max (t(23) = –2.37, p = 0.027), whereas the Garmin Vivosmart HR significantly overestimates the EE (t(23) = 2.44, p = 0.023). The tested devices did not show valid results concerning the estimation of VO2max and EE. Hence, the current wrist-worn activity trackers are most likely not accurate enough to be used for neither purposes in sports, nor in health care applications. Full article
(This article belongs to the Special Issue Physical Activity and Healthy Lifestyle)
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21 pages, 9306 KiB  
Article
Development of a Multisensory Wearable System for Monitoring Cigarette Smoking Behavior in Free-Living Conditions
by Masudul Haider Imtiaz, Raul I. Ramos-Garcia, Volkan Yusuf Senyurek, Stephen Tiffany and Edward Sazonov
Electronics 2017, 6(4), 104; https://doi.org/10.3390/electronics6040104 - 28 Nov 2017
Cited by 35 | Viewed by 9251
Abstract
This paper presents the development and validation of a novel multi-sensory wearable system (Personal Automatic Cigarette Tracker v2 or PACT2.0) for monitoring of cigarette smoking in free-living conditions. The contributions of the PACT2.0 system are: (1) the implementation of a complete sensor suite [...] Read more.
This paper presents the development and validation of a novel multi-sensory wearable system (Personal Automatic Cigarette Tracker v2 or PACT2.0) for monitoring of cigarette smoking in free-living conditions. The contributions of the PACT2.0 system are: (1) the implementation of a complete sensor suite for monitoring of all major behavioral manifestations of cigarette smoking (lighting events, hand-to-mouth gestures, and smoke inhalations); (2) a miniaturization of the sensor hardware to enable its applicability in naturalistic settings; and (3) an introduction of new sensor modalities that may provide additional insight into smoking behavior e.g., Global Positioning System (GPS), pedometer and Electrocardiogram(ECG) or provide an easy-to-use alternative (e.g., bio-impedance respiration sensor) to traditional sensors. PACT2.0 consists of three custom-built devices: an instrumented lighter, a hand module, and a chest module. The instrumented lighter is capable of recording the time and duration of all lighting events. The hand module integrates Inertial Measurement Unit (IMU) and a Radio Frequency (RF) transmitter to track the hand-to-mouth gestures. The module also operates as a pedometer. The chest module monitors the breathing (smoke inhalation) patterns (inductive and bio-impedance respiratory sensors), cardiac activity (ECG sensor), chest movement (three-axis accelerometer), hand-to-mouth proximity (RF receiver), and captures the geo-position of the subject (GPS receiver). The accuracy of PACT2.0 sensors was evaluated in bench tests and laboratory experiments. Use of PACT2.0 for data collection in the community was validated in a 24 h study on 40 smokers. Of 943 h of recorded data, 98.6% of the data was found usable for computer analysis. The recorded information included 549 lighting events, 522/504 consumed cigarettes (from lighter data/self-registered data, respectively), 20,158/22,207 hand-to-mouth gestures (from hand IMU/proximity sensor, respectively) and 114,217/112,175 breaths (from the respiratory inductive plethysmograph (RIP)/bio-impedance sensor, respectively). The proposed system scored 8.3 ± 0.31 out of 10 on a post-study acceptability survey. The results suggest that PACT2.0 presents a reliable platform for studying of smoking behavior at the community level. Full article
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39 pages, 374 KiB  
Review
A Review of Activity Trackers for Senior Citizens: Research Perspectives, Commercial Landscape and the Role of the Insurance Industry
by Salvatore Tedesco, John Barton and Brendan O’Flynn
Sensors 2017, 17(6), 1277; https://doi.org/10.3390/s17061277 - 3 Jun 2017
Cited by 108 | Viewed by 18584
Abstract
The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This [...] Read more.
The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future. Full article
(This article belongs to the Section Physical Sensors)
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