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... Devices with an embedded accelerometer (e.g., smart wristbands, smartphones) are becoming increasingly popular for use in physical activity research 15,[17][18][19] . Most of them provide various activity-related information via cloud services or apps, which may increase motivation to enhance the physical activity level 20 . ...
... However, the recent development of electronics has given rise to more convenient means of long-term physical activity monitoring 31 via a variety of compact, user-friendly and inexpensive devices (e.g., smart wristbands, smart watches, smartphones) [32][33][34][35] . Such devices are sufficiently accurate in tracking the number of steps 36,37 and are becoming increasingly popular in research and clinical applications 15,[17][18][19] . ...
Article
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Physical activity session frequency and distribution over time may play a significant role on survival after major cardiovascular events. However, the existing amount-based metrics do not account for these properties, thus the physical activity pattern is not fully evaluated. The aim of this work is to introduce a metric which accounts for the difference between the actual and uniform distribution of physical activity, thus its value depends on physical activity aggregation over time. The practical application is demonstrated on a step data from 40 participants, half of them diagnosed with chronic cardiovascular disease (CVD). The metric is capable of discriminating among different daily patterns, including going to and from work, walking in a park and being active the entire day. Moreover, the results demonstrate the tendency of CVD patients being associated with higher aggregation values, suggesting that CVD patients spend more time in a sedentary behaviour compared to healthy participants. By combining the aggregation with the intensity metric, such common weekly patterns as inactivity, regular activity and “weekend warrior” can be captured. The metric is expected to have clinical relevance since it may provide additional information on the relationship between physical activity pattern and health outcomes.
... Most of them provide information about the number of steps, sedentary time, climbed floors, travelled distances, etc. It has been shown that these devices are sufficiently accurate in tracking physical activity, therefore, they are becoming increasingly popular for use in research and medical applications (Leininger et al., 2016;Althoff et al., 2017;Leth et al., 2017). In this paper, we propose a novel parameter for an objective evaluation of physical activity aggregation, allowing to express the distribution of physical activity over time in terms of a single number. ...
Conference Paper
This work introduces a novel approach to parametrization of physical activity profile. The proposed parameter, named as physical activity aggregation, is useful for evaluating a distribution of daily or weekly physical activity. The parameter takes a large value for a highly accumulated physical activity, whereas is much lower for an evenly spread activity over the monitoring period. The parameter was investigated on step data obtained using a smart wristband on a group of 71 participants with cardiovascular disease. The results of the pilot study show that the proposed parameter is capable of discriminating among different physical activity profiles, including sedentary behaviour, going to and from work, walking in a park and being active the entire day. Moreover, the results demonstrate the tendency that middle-aged and older women are associated with lower aggregation values, suggesting that they probably spend less time in sedentary behaviour compared to men of the same age. The proposed parameter has potential to be useful for characterizing physical activity profile, as well as, for investigating its relation to health outcomes, e.g., during ambulatory rehabilitation after major cardiovascular events.
... For this reason, research pertaining to wearable activity-tracking devices, in general, is fairly well recorded in the literature, particularly focusing on the features of activity trackers (Hong, 2015), a comparison of different wearable fitness devices (Kanitthika et al., 2016) and the health-empowering capabilities of activity trackers (Nelson et al., 2016). According to the literature, numerous studies have been conducted internationally pertaining to the adoption of new technology, wearable fitness device and activity-tracking device accuracy and reliability, as well as the acceptance of and intention to use wearable devices (Byun et al., 2016;Chin et al., 2008;Davis, 1989;Fritz et al., 2016;Kim & Shin, 2015;Leininger et al., 2016;Takacs et al., 2014;Wang et al., 2008;Yang et al., 2016). ...
Thesis
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Wearable activity-tracking devices have revolutionised health and fitness monitoring over the past decade. The ten different types of wearable trackers as of 2018, have allowed consumers to have real-time data regarding their health. In addition, targeted improvements can be made based on their preferred types of activity, sports performance, heart-rate data, eating regimens as well as sleep quality and patterns. The continuous technological innovation paired with an increased consumer interest has allowed the wearable activity-tracking device market to evolve both globally and in South Africa. In 2017, a significant revenue was generated from this market of approximately R101.8bn and it is expected to reach approximately R114.5bn by 2020. However, despite the significant revenue generating and health-promoting opportunities of wearable activity trackers, adoption in South Africa is trifling. In order to improve the market penetration and adoption rates of these devices in South Africa, it is important to gain an understanding of consumer behaviour as well as the factors that influence the adoption behaviour of these devices. Given the novelty of these technological devices and the lack of research on the topic, previous technology adoption theories and models can be used as a foundation in this understanding. As such, the TAM in conjunction with the TRA, with the addition of the perceived importance of devices’ brand name, was employed to establish a model of the factors that influence consumers’ adoption behaviour of activity-tracking devices in the South African context. The primary objective of this study was to propose and empirically test a model that combined the TRA and the TAM to measure the extent to which perceived ease of use, perceived usefulness, subjective norm, with the addition of the perceived importance of brand name, influence Generation Y students’ attitude towards and intention to use wearable activity-tracking devices within the South African context. A model was established, which suggests that perceived ease of use has a direct positive influence on perceived usefulness and these two factors each have a direct positive influence on attitude towards activity-tracking devices. Therefore, with the exception of the direct positive influence between perceived usefulness and intention to use, the TAM has been validated and explains Generation Y students’ probable adoption behaviour of wearable activity-tracking devices. Similarly, the TRA has been established and explains Generation Y students’ adoption behaviour of probable activity tracker adoption in that the model suggests that subjective norm and attitude have a direct positive influence on intention to use. Furthermore, the model established in this study suggests that the perceived importance of device brand name has a direct positive influence on Generation Y students’ intention to use activity-tracking devices. The sampling frame for this study comprised the 26 public registered HEI campuses in South Africa given the nature of the Generation Y cohort, more so the significant future spending potential of those individuals obtaining tertiary qualifications, namely students. From the 26 institutions, three institutions – one traditional, one university of technology and one comprehensive university – in the Gauteng province were selected based on a non-probability judgement sampling method. Lecturers working at each of the three institutions were contacted telephonically to request permission for the questionnaires to be distributed to their students during a scheduled class period. Once permission had been obtained, the questionnaires were hand-delivered to the participating academic staff and distributed by the researcher with the assistance of a trained fieldworker, during a scheduled class period. A convenience sample of 600 full-time Generation Y students, 200 per institution, was taken in 2017. Of the 600 questionnaires distributed, 480 were usable for statistical analysis. The collected data were analysed by specific statistical analysis in order to achieve the empirical objectives set in this study, namely exploratory principal components analysis, internal consistency reliability analysis, descriptive statistical analysis, correlation analysis, multicollinearity diagnostics and structural equation modelling. The findings of this study indicate that South African Generation Y students have an overwhelmingly positive attitude towards and intention to use wearable activity-tracking devices. Furthermore, Generation Y students perceive these devices as relatively easy to use to measure their activity levels and find these devices useful to their lives in general. A device’s brand name has substantial importance when it comes to the acquisition of these devices, as Generation Y students perceive that a device with a reputable brand name has less risk of leading to disappointment. However, these devices are not yet perceived as a subjective norm, which may be due to the unacquainted perceived cost and perceived value of these devices due to their novelty in the South African consumer market. This study contributes to filling the gaps in the literature pertaining to Generation Y students’ attitude towards and intention to use activity-tracking devices in the South African context; that is the extent to which the factors, namely perceived ease of use, perceived usefulness, perceived importance of brand name and subjective norm influence Generation Y students’ attitude towards and intention to use activity-tracking devices. By understanding these factors, product manufacturers, South African product developers, local businesses including retailers, marketing practitioners, possibly medical professionals, policy makers towards sustained healthy living for all South African citizens and universities, can develop appropriate marketing strategies to create awareness as well as endorse the use of activity trackers amongst the target population. Byincreasing the adoption rates of activity trackers in South Africa, it is possible to achieve a healthier standard of living through the reduction of non-communicable diseases, as well as promoting a more active nation. Further, this increased adoption can generate a significantly larger income for the country, subsequently advancing the local economy. This study is pioneering research in South Africa and provides the foundation for future research of a similar nature – leading to an increased body of knowledge regarding the adoption behaviour of activity trackers in South Africa. The findings of this study contribute to the literature on and the development of a profile of South African Generation Y students’ consumer behaviour, which is in keeping with the objectives of a larger research project at the North-West University (Vaal Triangle Campus), namely ProGenY (profiling the consumer behaviour of Generation Y in South Africa).
... Researches in WHD are in the nascent stage mostly focusing on accuracy, reliability (Byun et al., 2016;Leininger et al., 2016;Lunney et al., 2016;Mahar et al., 2014;Takacs et al., 2014;Wilson et al., 2017) and health problem-related intervention program that is post occurrence of disease/operations (Twiggs et al., 2018;Van der Walt et al., 2018). However, less studies are available to understand the various conditions for acceptance and adoption of WHD. ...
Article
Full-text available
Purpose The present study is an attempt to extend the Model of Goal-directed Behavior (MGB) to consider health consciousness and perceived privacy protection as two critical factors to predict desire and intention by individuals to adopt and use wearable health devices (WHDs). Design/methodology/approach Based on the literature review hypotheses were framed and tested using data collected through a questionnaire survey. A total of 418 self-reported complete responses were considered to analyze the hypotheses proposed in the study. Structural equation modeling, effects analysis and model comparison (MGB and extended MGB) were performed to understand the predictability of the suggested model. Findings The results of the study corroborate that along with MGB, health consciousness and perceived privacy protection also induces the intention of users toward using WHDs. Health-consciousness is found to have a positive and significant direct and indirect impact on intention to use WHDs. Further, the model comparison exhibits that the proposed extended MGB is a better predictor of intention to use WHDs. Practical implications Apart from the conventional framework of MGB, health consciousness and perceived privacy protection promote desire and intention to use WHDs. This research provides a framework for marketers to promote health consciousness among consumers by motivating them to adopt WHDs. Further, privacy protection features should be showcased in order to induce trust in consumers which in turn will trigger their intention to use WHDs to lead a healthy lifestyle. Originality/value The current study incorporates health consciousness and perceived privacy protection in MGB to fit into the context of healthcare intention study, which enhances the predictability of intention behavior of consumers and adds to the existing body of MGB and healthcare literature.
... 14 Since devices are continually being upgraded and algorithms tuned, we created a website for sharing validation data for the community and to provide a forum for users to interact with the most up to date performance evaluations from this ongoing study (http://precision.stanford.edu/). 13,[15][16][17][18] While the FDA currently considers consumer wearable sensors such as wrist worn devices as low risk (Class 1) and therefore not . CC-BY-ND 4.0 International license available under a was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. ...
Preprint
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Background The ability to measure activity and physiology through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. Objective To assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework. Methods We evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 ± 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination. Results Devices reported the lowest error for cycling and the highest for walking. Device error was higher for males,greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest. Conclusions Most wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices ( http://precision.stanford.edu/ ). Abbreviations (EE) Energy expenditure (HR) Heart rate (GEE) General estimating equation
... However, the existing studies mostly focus on establishing the reliability and accuracy of wearable devices (e.g. Byun, Barry, & Lee, 2016;Diaz et al., 2015;Huang, Xu, Yu, & Shull, 2016;Leininger, Cook, Jones, Bellumori, & Adams, 2016;Mahar, Maeda, Sung, & Mahar, 2014). In the current literature, very few studies have explored the factors influencing the use and adoption of wearable devices. ...
Article
Recently, the popularity of smart wearable technologies, such as Fitbit, has significantly increased. There are numerous potential benefits in using these devices, especially among seniors. Yet, little is known about seniors' adoption behavior. Through a mixed-methods approach, this study investigates the factors that impact seniors' intention to use wearable devices. Results from an online survey and interviews showed that seniors' perception of the complexity of working with these devices is a barrier to their adoption decisions. Looking more deeply into the role of complexity revealed that seniors' concern about the complexity of reading and interpreting the output of wearable devices is the main deterring element. Furthermore, we explored the role of two important elements: seniors' cognitive age, and the influence of their subjective well-being on their adoption behavior. Results demonstrated that cognitive age does not significantly impact use intention by itself; nonetheless, subjective well-being moderates its effect. This result revealed an interesting finding, which is that the influence of cognitive age on seniors' use intention depends on seniors' level of subjective well-being. When seniors' subjective well-being is low, surprisingly, cognitive age increases seniors' intention to use the device. These findings provide interesting implications for practice and future research.
... Similar to prior studies analyzing how if consumer-grade devices are comparable to research grade equipment [7], we are analyzing data output differences, but we are analyzing differences in consumer-grade devices. Consumers are less concerned with matching the accuracy and precision of research-grade devices and more focused on the relative accuracy between readily available consumer-grade devices (i.e. ...
Article
Differences in data output from two leading devices in the consumer-grade wearables market have been examined, namely Jawbone UP4 and Fitbit Charge HR devices, by comparing measurements that were conducted while participants wore both devices in tandem. Aggregate daily totals of steps and calories were shown to be highly correlated between devices (0.82–0.93 correlation coefficient for steps and 0.71–0.85 for calories); however, at the hourly level, differences in data output are evident, especially during hours of vigorous activity. These differences lead to both under- and over-estimation of measures such as hourly step-counts. Heart rate measurement with Jawbone and Fitbit is shown to be significantly different even at the daily level (p-value < 0.00001), which could be due to hardware differences in sensor type and possibly due to unknown differences in proprietary algorithms. Models were trained to enable adjustment of data collected from one device to the equivalent value in terms of the other device’s measurement. This approach to data integration is recommended for researchers who are comparing data from multiple wearable devices, for individual users who have switched from one device to another and could use this method to adjust their wearables data history to be comparable with the new device, or for users who are comparing data with a user who has another type of device, or for groups organizing fitness challenges and health initiatives that can track users by comparing diverse wearables data.
... Although ActiGraphs are the gold standard for objective PA assessment, commercially available accelerometers like the Fitbit have distinct advantages for use in PA-focused investigations. Such devices have a significantly lower cost and greater ease of wear, factors that make them appealing for scalability in large trials conducted over extended periods of time, leading to their increased use in research (24)(25)(26)(27)(28). Although studies have begun to investigate agreement between medical-and commercial-grade accelerometers (29)(30)(31)(32), standardized thresholds across devices have not been delineated, particularly those specific to at-risk populations with unique gait patterns, such as older individuals with knee OA (33). ...
Article
Full-text available
Objective: We sought to evaluate the performance of Fitbit in estimating ActiGraph-derived moderate-to-vigorous physical activity (MVPA) and sedentary time in the knee osteoarthritis (OA) population. Methods: We used data from two weeks of Fitbit and ActiGraph wear among knee OA subjects. In primary analyses, we used literature-based ActiGraph thresholds of 200 and 1924 counts/min (triaxial vector magnitude) for sedentary and MVPA time as the gold standard to which we compared three sets of Fitbit thresholds informed by literature and data (Youden index). We also considered personalized, stride length-based Fitbit thresholds. In sensitivity analyses, we used uniaxial, vertical axis-based as well as personalized, BMI-based ActiGraph thresholds. We calculated agreement, sensitivity, and specificity of Fitbit in classifying sedentary and MVPA time. Results: In the primary analysis (vector magnitude thresholds), maximum agreement for sedentary and MVPA time was 67.0% from the Youden index-based and 91.1% from the stride length-based Fitbit thresholds. For sedentary time, the 20 strides/min threshold had the highest sensitivity (97.6%), and Youden-derived 1 stride/min had the highest specificity (51.6%). For MVPA, Youden-derived 14 strides/min yielded 72.8% sensitivity, and using stride length yielded 98.6% specificity. MVPA time ranged from 49-323 min/d, depending on threshold used, with literature-based and personalized thresholds leading to more conservative estimates of MVPA than Youden-derived thresholds. Conclusion: Using Fitbit for MVPA and sedentary time assessment may lead to inaccurate estimates of both. Fitbit MVPA estimates were generally more conservative than ActiGraph estimates. Incorporating individuals' characteristics did not meaningfully improve Fitbit performance. Caution should be exercised when measuring activity using Fitbit.
... The main findings are that the accuracy of HR measures from both the Apple Watch and the Fitbit Charge HR 2 decreased as exercise intensity increased. Additionally, the Apple Watch revealed lower error rates for all exercise intensities and revealed significantly lower error rates for very light, moderate, vigorous, and very vigorous intensities) compared to the Fitbit Charge HR 2. In addition to this study, some studies have examined the Fitbit devices (e.g., Fitbit Surge, Fitbit Charge HR) and the Apple Watch for multiple outcome variables (Dooley, Golaszewski, & Bartholomew, 2017;Farina & Lowry, 2017;Leininger, Cook, & Adams, 2016), but to our knowledge, this is the first study to examine the validity of HR measurements of the Fitbit Charge HR 2. ...
Article
This study compared heart rate (HR) measurements for the Fitbit Charge HR 2 (Fitbit) and the Apple Watch devices with HR measurements for electrocardiogram (ECG). Thirty young adults (15/15 females/males, age 23.5 ± 3.0 years) completed the Bruce Protocol. HR measurements were recorded from the ECG and both devices every minute. Average HR for each participant was calculated for very light, light, moderate, vigorous and very vigorous intensities based on ECG-measured HR. A concordance correlation coefficient (CCC) was calculated to examine the strength of the relationship between ECG measured HR and HR measured by each device. Relative error rates (RER) were also calculated to indicate the difference between each device and ECG. An equivalence test was conducted to examine the equivalence of HRs measured by devices and ECG. The Apple Watch showed lower RER (2.4–5.1%) compared with the Fitbit (3.9–13.5%) for all exercise intensities. For both devices, the strongest relationship with ECG-measured HR was found for very light PA with very high CCC (>.90) and equivalence. The strength of the relationship declined as exercise intensity increased for both devices. These findings indicate that the accuracy of real-time HR monitoring by the Apple Watch and Fitbit Charge HR2 is reduced as exercise intensity increases.
... There is evidence for this—for example, 10,000 steps have been observed to represent between 400 kilocalories and 800 kilocalories depending on a person's height and weight[24]. Since devices are continually being upgraded and algorithms tuned, we created a website for sharing validation data for the community and to provide a forum for users to interact with the most up-to-date performance evaluations from this ongoing study (http://precision.stanford.edu/)[23,[25][26][27][28]. While the FDA currently considers consumer wearable sensors such as wrist-worn devices as low risk (Class 1) and therefore not subject to active regulation,[29]they are however expected to increasingly inform clinical decision making. ...
Article
Full-text available
The ability to measure physical activity through wrist-worn devices provides an opportunity for cardiovascular medicine. However, the accuracy of commercial devices is largely unknown. The aim of this work is to assess the accuracy of seven commercially available wrist-worn devices in estimating heart rate (HR) and energy expenditure (EE) and to propose a wearable sensor evaluation framework. We evaluated the Apple Watch, Basis Peak, Fitbit Surge, Microsoft Band, Mio Alpha 2, PulseOn, and Samsung Gear S2. Participants wore devices while being simultaneously assessed with continuous telemetry and indirect calorimetry while sitting, walking, running, and cycling. Sixty volunteers (29 male, 31 female, age 38 ± 11 years) of diverse age, height, weight, skin tone, and fitness level were selected. Error in HR and EE was computed for each subject/device/activity combination. Devices reported the lowest error for cycling and the highest for walking. Device error was higher for males, greater body mass index, darker skin tone, and walking. Six of the devices achieved a median error for HR below 5% during cycling. No device achieved an error in EE below 20 percent. The Apple Watch achieved the lowest overall error in both HR and EE, while the Samsung Gear S2 reported the highest. In conclusion, most wrist-worn devices adequately measure HR in laboratory-based activities, but poorly estimate EE, suggesting caution in the use of EE measurements as part of health improvement programs. We propose reference standards for the validation of consumer health devices (http://precision.stanford.edu/).
... An average time series for these trends is visualized in Figure 6. We observe that, on average, NT participants engaged in 30 Case studies CS1: Associations between changes in physical activity and mental health Table 3 presents the findings for changes in survey responses regarding mental health across the two academic years. We observe that for PT participants, self-reports of health, selfesteem, and body-image increased (+7%, +4%, +6%). ...
Article
Full-text available
Background: Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data. Objective: The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies. Methods: Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks’ output in supporting MVPA behavior change studies, we applied it to 2 case studies. Results: Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes. Conclusions: By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA. © Curtis L Petersen, William B Weeks, Olof Norin, James N Weinstein.
... Fitbit Zip and Fitbit Flex overestimated the number of steps during free-living physical activity. 14,15 In the meanwhile, Fitbit One was proved as an accurate device for measuring steps in treadmill walking 16 and 2-min walking test. 13 To our knowledge, the validation of popular new devices, Fitbit Charge HR (Fitbit, Inc., San Francisco, CA) and Mi Band 2 (Xiaomi, Corp., China) have not yet been covered in a laboratory-based study. ...
Article
Introduction: The purpose of this study was to assess the validity of the step count measurement of commercial electronic activity monitor devices. Two popular models, Fitbit Charge HR and Mi Band 2, were selected for treadmill walking in a single session. Materials and methods: Thirty healthy volunteers walked at five predetermined speeds (0.90, 1.12, 1.33, 1.54, and 1.78 m/s) on a treadmill with both Fitbit Charge HR and Mi Band 2 worn on their dominant hand's wrist. Observers counted the steps, with the aid of taped video, which was taken as the criterion measure for steps. The validity of the electronic activity devices was assessed by (1) Paired sample t test with the criterion measures and (2) Pearson's correlation coefficients and the corresponding p-values were calculated to compare the output of devices with manual step count. In addition, Bland-Altman plots were constructed to visually inspect the data and to assess agreement with the criterion measures. Results: There were no significant differences in step measurement between Fitbit Charge HR and Mi Band 2 with the criterion measures. Besides, there was a very strong agreement between step count measurements obtained using the Fitbit Charge HR (r = 0.99) and the Mi Band 2 (r = 0.99), at five predetermined speeds while comparing with the observed step counts. Conclusion: Both Fitbit Charge HR and Mi Band 2 provided accurate step count measurement in the treadmill walking test.
... Malgré ce foisonnement au sein de la littérature, certains auteurs (Adapa, Fui-Honn Nah, Hall, Siau & Smith, 2017 ;Lunney et al., 2016) affirment qu'à l'état actuel, la recherche scientifique sur les OCAS est encore à un stade initial, au vu de sa focalisation sur l'évaluation des OCAS, au détriment d'autres dimensions davantage liées aux perceptions et pratiques (Schüll, 2016). Ainsi, un grand nombre de recherches portent sur l'étude du degré de précision et de fiabilité d'OCAS, comme le smartwatch (voir Mo, Zhuo & Yi, 2017) en lien à des activités spécifiques telles que le sommeil, l'activité physique ou la sédentarité (Byun, Barry & Lee, 2016 ;Dvorak, 2008 ;Huang, Xu, Yu & Shull, 2016 ;Leininger, Cook, Jones, Bellumori & Adams, 2016 ;Takacs et al., 2014 ;Rosenberger, Buman, Haskell, McConnell & Carstensen, 2016 ;Carstensen, 2016). L'acceptabilité de ces outils est aussi étudiée, principalement en lien avec des spécificités de l'objet lui-même comme : leur fonctionnalité, le prix, le design, le degré de confort, ou encore leur potentiel à être utilisés et si oui à quelle fréquence (Adapa et al., 2017). ...
Article
In a contemporary context of major health challenges, the market of digital technologies has increasingly developed in past years. This article aims to explore main profiles of use in relation to connected objects and health apps, as well as attitudes related to uses, non-uses and contexts of use. Therefore, our objective is to contribute to the scientific debate by proposing an empirical study in psychology that focusses on the perspectives of consumers and non-consumers of these technologies in the French-speaking part of Switzerland. To do this, a survey was conducted among participants of a large public health exhibition (n = 760). According to our results, the majority of respondents declare not having a connected object/health app and a third of non-users does not intend to acquire such technologies. Also, there is a trend among younger generations to have a connected object/health app. Concerning the contexts of use, such technologies are employed to self-track physical activity and eating practices. The degree of satisfaction of such use is rather high. Given these results, our analyses point out a divide within our sample, between individuals who seem resistant and declare not willing to have this kind of technology and those who use it in the long run. These results cast new light upon concrete uses and contexts of use among consumers and non-consumers of connected objects/health apps beyond techno-scientific promises that prevail today in our societies.
... The main findings are that the accuracy of HR measures from both the Apple Watch and the Fitbit Charge HR 2 decreased as exercise intensity increased. Additionally, the Apple Watch revealed lower error rates for all exercise intensities and revealed significantly lower error rates for very light, moderate, vigorous, and very vigorous intensities) compared to the Fitbit Charge HR 2. In addition to this study, some studies have examined the Fitbit devices (e.g., Fitbit Surge, Fitbit Charge HR) and the Apple Watch for multiple outcome variables (Dooley, Golaszewski, & Bartholomew, 2017;Farina & Lowry, 2017;Leininger, Cook, & Adams, 2016), but to our knowledge, this is the first study to examine the validity of HR measurements of the Fitbit Charge HR 2. ...
... In addition to pulse rate, smart wristbands synchronously acquire physical activity, which is usually expressed as the number of steps per time interval (Strath et al 2013, Leininger et al 2016. Synergy of pulse rate and physical activity has been found to be useful for extracting additional information, such as sleep tracking and estimation of energy expenditure (Wallen et al 2016), however, it is still insufficiently exploited. ...
Article
Objective: The growing interest to integrate consumer smart wristbands in eHealth applications spawns the need for novel approaches of data parametrization which account for the technology-specific constraints. The present study aims to investigate the feasibility of a consumer smart wristband to be used for computing pulse rate parameters during free-living activities. Approach: The feasibility of computing pulse rate variability (PRV) as well as pulse rate and physical activity-related parameters using the smart wristband was investigated, having an electrocardiogram as a reference. The parameters were studied on the pulse rate and step data from 54 participants, diagnosed with various cardiovascular diseases. The data were acquired during free-living activities with no user lifestyle intervention. Main results: The comparison results show that the smart wristband is well-suited for computing the mean interbeat interval and the standard deviation of the averaged interbeat intervals. However, it is less reliable when estimating frequency domain and nonlinear parameters. Heart recovery time, estimated by fitting an exponential model to the events, satisfying the conditions of the 3 min step test, showed satisfactory agreement (relative error <20%) with the reference ECG in one-third of all cases. On the other hand, the heart's adaptation to physical workload, expressed as the slope of the linear regression curve, was underestimated in most cases. Significance: The present study demonstrates that pulse rate parametrization using a consumer smart wristband is in principle feasible. The results show that the smart wristband is well suited for computing basic PRV parameters which have been reported to be associated with poorer health outcomes. In addition, the study introduces a methodology for the estimation of post-exercise heart recovery time and the heart's adaptation to physical workload during free-living activities.
Preprint
BACKGROUND Moderate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data. OBJECTIVE The objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies. METHODS Using heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks’ output in supporting MVPA behavior change studies, we applied it to 2 case studies. RESULTS Among the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes. CONCLUSIONS By providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.
Article
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The lives of adolescents and young adults (AYAs) have become increasingly intertwined with technology. In this scoping review, studies about digital health tools are summarized in relation to five key affordances—social, cognitive, identity, emotional, and functional. Consideration of how a platform or tool exemplifies these affordances may help clinicians and researchers achieve the goal of using digital health technology to enhance clinical preventive services for AYAs. Across these five affordances, considerable research and development activity exists accompanied by signs of high promise, although the literature primarily reflects demonstration studies of acceptability or small sample experiments to discern impact. Digital health technology may afford an array of functions, yet its potential to enhance AYA clinical preventive services is met with three key challenges. The challenges discussed in this review are the disconnectedness between digital health tools and clinical care, threats to AYA privacy and security, and difficulty identifying high-value digital health products for AYA. The data presented are synthesized in calls to action for the use of digital health technology to enhance clinical preventive services and to ensure that the digital health ecosystem is relevant, effective, safe, and purposed for meeting the health needs of AYA.
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