Activity Trackers Are Electronic Devices
Sensors (Basel). 2022 Apr; 22(8): 2960.
Health Habits and Clothing Activity Tracker Devices: Analytical Cross-Sectional Study
Héctor José Tricás-Vidal
1Unidad de Investigación en Fisioterapia, Universidad de Zaragoza, Domingo Miral, s/n, 50009 Zaragoza, Espana; moc.liamg@tpdsacirt (H.J.T.-Five.); se.razinu@ladivc (Thousand.C.V.-P.); moc.liamg@5931itnoms (Southward.M.-B.); se.razinu@sacirtmj (J.M.T.-M.)
2School of Wellness Professions, University of Mary Hardin Baylor, 900 College St., Belton, TX 76513, U.s.
María Concepción Vidal-Peracho
oneUnidad de Investigación en Fisioterapia, Universidad de Zaragoza, Domingo Miral, s/n, 50009 Zaragoza, Spain; moc.liamg@tpdsacirt (H.J.T.-V.); se.razinu@ladivc (G.C.5.-P.); moc.liamg@5931itnoms (Due south.M.-B.); se.razinu@sacirtmj (J.M.T.-M.)
iiiSection of Endocrinology and Nutrition, Infirmary Royo Villanova, SALUD, Barrio San Gregorio s/n, 50015 Zaragoza, Spain
Mario Munoz-Organero, Academic Editor and Susanna Spinsante, Academic Editor
Received 2022 Mar 15; Accepted 2022 Apr xi.
- Data Availability Argument
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The datasets analyzed during the current study are available from the corresponding author on reasonable request. All data analyzed during this report are included in this published commodity.
Abstract
Wearable activity trackers are electronic devices that facilitate self-monitoring of information related to wellness. The purpose of this written report was to examine the apply of tracker devices to record daily activity (calories) and its associations with gender, generation, BMI, and concrete activity behavior of United States of America resident adults; a cross-exclusive written report in 892 subjects recruited to participate in an anonymous online survey was performed. Beingness female person increased the odds of using a tracker device by ii.3 times. Having low cardiovascular disease mortality risk related to time spent sitting increased the odds for using a tracker device by 2.7 times, and having medium hazard one.ix times, with respect to having loftier risk. For every ane-point increase in BMI, the odds for using a tracker device increased by 5.2%. Conclusions: Subjects who had e'er used any tracker device had a college BMI. The use of tracker devices was related to lower cardiovascular disease mortality hazard related to sitting time. The amount of concrete action and the time spent walking were not associated with the usage of tracker devices. Information technology is possible that the user of tracker devices should be supported past professionals to implement deep change in wellness habits.
Keywords: habiliment action tracker, BMI, concrete activity
1. Introduction
Article of clothing activity trackers (for instance, Fitbit, Apple Watch, Polar, Garmin, or Nike FuelBand) are electronic devices that facilitate self-monitoring and tracking of activities and information related to fitness or physical activity [1]. The use of vesture activity tracker devices has increased exponentially in the last decade [ii]. The volume of shipments of these devices reached 72.half dozen 1000000 units according to data from the International Information Corporation Worldwide Quarterly Wearable Device Tracker Global, during the first quarter of 2020; thus, clothing devices shipments grew 29.7% year over twelvemonth [2]. The most sold devices in May 2020 were Apple tree, Xiaomi, Samsung, Huawei, and Fitbit [two]. In a Pew Research Center survey conducted on 3–17 June 2019, one in v Usa (United states) adults (21%) said they regularly carried a smartwatch or wear activeness tracker [3]. I in three Americans reported having had a habiliment activity tracker such as a Fitbit or smartwatch (34%) according to the global analytics and advice firm Gallup, in ane–14 Nov 2019 [four].
Article of clothing activity trackers allow monitoring of daily activity, such equally steps taken, timing and intensity of concrete activity, distance covered, calories burned, active time, slumber cess [5], and heart rate [half dozen], and may include mobile connectivity or an net application. Due to the characteristics of the information they collect, they can exist an exhaustive source of information on the wellness habits of the population that can be shared with health professionals and serve as a diagnostic tool on the quantity and quality of physical activity as well as other health-related habits such equally the quantity and quality of sleep. However, near of the time, the population uses wearable activity tracker devices, but does not necessarily share the information obtained with professionals who tin can aid them with interpretation and making it effective [7]. Sharing data from wear devices with healthcare professionals would allow them to better understand their patients' habits [vii].
Behind the increased apply of wearable activity tracker devices are a variety of factors: self-determination [eight], self-sensation, motivation, tracking progress, and staying informed [9]. Clothing action tracker devices take been shown to increase exercise motivation through different constructs [10]. Some constructs are related to social interactions [10]. Others are in relation with exercise control features and with data management features as possibilities for data analysis, information collection, progress updates [10], or constructive feedback [8]. In young populations, the importance of daily sociability, psychological factors such every bit high extroversion levels, and behavioral factors such as big network size have been established as modulators of concrete activeness implication [xi]. It has been observed that personalized feedback facilitates positive emotional responses for highly active participants. Subjects with low activity may experience negative emotional responses just also positive coping mechanisms [12].
Activeness trackers have shown the potential to increase physical activity, but the effects on weight loss remain contradictory [xiii]. Moreover, the utilize of tracker devices to record daily activeness (calories) has been related to the possibility to trigger, maintain, or exacerbate eating disorders [fourteen]. Previous studies have focused on the adoption of technology products in different age ranges [15,sixteen,17,eighteen,19], but research on the differences in the adoption of habiliment activity tracker devices betwixt generations is thin. For example, regarding the use of wearables in adolescents, the literature shows contradictory results [20,21]. Moreover, in that location are still open questions regarding tracker device use differences beyond genders. It has been shown that more women take participated in studies about the efficacy of wearable activity devices when used in a comprehensive weight loss program [22]. It is also necessary to consider that vesture activity tracker devices may function inaccurately [23]. The demand to accommodate the type of device to the characteristics of the users has also been discussed and it has been seen that it is important for long-term apply, to facilitate the user feel in terms of functionalities and aesthetics and concrete blueprint [24]. The controversial data regarding the effectiveness of tracker devices to record daily activeness to promote healthy habits suggests increasing the available evidence on its use.
At that place are few studies that relate the utilize of wearable action tracker devices to the general population in terms of health habits, such as good for you weight maintenance and concrete activity behavior. The purpose of this written report was to examine the use of tracker devices to record daily activity (calories) and its associations with gender, generation, BMI, and concrete activeness beliefs of US resident adults.
The manuscript presents in the fabric and methods department the methodology used to collect and clarify the data. The results of the information analysis are presented below, in the results section. Then, in the word department, the results are discussed according to the bachelor scientific evidence and finally the conclusions of the written report are presented.
ii. Materials and Methods
A non-experimental analytical cross-sectional report with multivariate analysis was performed.
The Bookish Commission of the Doctoral Program in Health and Sports Sciences of the University of Zaragoza canonical the report, which complied with the ethical requirements of the Announcement of Helsinki [25].
ii.i. Subjects
A accomplice of 892 subjects was recruited. It consisted of Usa residents recruited via email to participate in an bearding online survey.
An invitation via email account with the survey link was sent to former or electric current students from Queens University of Charlotte, The University of Kentucky, Oakland University, and the University of Mary Hardin Baylor. Moreover, the survey link was published on Instagram and Facebook and was connected to the Survey Monkey website. Improvidence of the link was carried out with a snowball upshot. Answers were collected using that online platform for afterwards assay.
To calculate the sample size, nosotros used the The states Population: 329,256,465 (July 2018 statistics) co-ordinate to The Globe Factbook [26]. The expected proportion used was 21% because one-in-five U.s.a. adults (21%) said they regularly carried a smartwatch or clothing fitness tracker [3]. The sample size was calculated using the GRANMO calculator [27], with the population estimation option, confidence level 0.95, with the desired precision of +/− three percent units. A minimum number of 709 subjects was obtained.
Finally, 892 participants were analyzed, later the elimination of 6 surveys that inadequately answered the question related to tracker device employ (Figure 1). Participants were required to be over eighteen years old and they needed to have an Instagram account. None of the participants were compensated for participating in this research.
Flow nautical chart of the survey sample.
The study did non ask participants questions regarding race, sexual identity, faith, political views, or other questions that could break the law regarding inquiry ethics. All subjects, afterward clicking on the link to be directed to the survey, provided consent.
two.two. Data Sources
The participants answered the following questions in the anonymous online survey (Table 1):
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Gender. Male/female.
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Age. Age was categorized according to the side by side generations: generation-Z (born 1997–2012); millennials (born 1981–1996); generation-X (born 1965–1980); boomers (built-in 1946–1964) [28].
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Elevation in feet and inches and weight in pounds. BMI was calculated: BMI = 703 × weight (pounds)/[top (inches)]2.
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Take you lot always used any of the following tracker devices to record your daily action (calories)? The possible options were: a. Fitbit, b. Apple Lookout man, c. Polar, d. Garmin, e. Nike, f. Other (please proper name), g. I accept never used any tracking device. The question was categorized every bit "Always used" (if a participant selected some selection from a to f) or "never used" (if the participant selected the option thousand).
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Concrete activity carried out by the participants was collected with the self-administered International Concrete Activity Questionnaire (IPAQ) brusque course "last seven days" [29]. It has been demonstrated that reliable and valid physical activity data can be collected with the IPAQ brusque grade [thirty]. Vigorous concrete activity (min per week), moderate concrete activity (min per week), time spent walking (min per week), and fourth dimension spent sitting (hours per day) were registered.
Tabular array 1
Information sources.
| Anonymous Online Survey | |
| Gender | Male |
| Female | |
| Generation | Generation-Z (born 1997–2012) |
| Millennials (born 1981–1996) | |
| Generation-X (built-in 1965–1980) | |
| Boomers (born 1946–1964) | |
| Trunk Mass Index | 703 × weight (pounds)/[height (inches)]ii |
| Employ of tracker device to record daily activity (calories) | Ever used |
| Never used | |
| International Physical Activity Questionnaire brusque form "last seven days" | Time spent sitting |
| Low cardiovascular illness mortality risk (sitting less than 4 h per twenty-four hours) | |
| Medium cardiovascular disease mortality chance (sitting iv–viii h per 24-hour interval) | |
| High cardiovascular disease mortality run a risk (sitting viii–eleven h per mean solar day) | |
| Very High cardiovascular disease mortality hazard (more than 11 h per solar day) | |
| Vigorous physical activeness (min per week) | |
| Moderate physical activity (min per week) | |
| Fourth dimension spent walking (min per calendar week) |
Time spent seating was recoded: low cardiovascular disease mortality adventure indicated sitting less than 4 h per day; medium risk indicated sitting iv–8 h per mean solar day; loftier take a chance indicated sitting viii–xi h per day; very high risk indicated sitting more than eleven h per day [31].
2.3. Statistical Analyses
The numerical analysis was performed using SPSS 25.0 for Mac. Statistical significance was gear up at p < 0.05.
A descriptive analysis of qualitative variables, offering the absolute frequencies, and the percentages in each category and of quantitative variables, offering the mean ± standard deviation was carried out.
To examine the relationship between variables, "use of tracker device to record daily activity (calories)" was established as the independent variable. If the dependent variable was qualitative, Chi-square was used (the maximum likelihood ratio Chi-square examination was selected if the information prepare did not meet the sample size assumption of the Chi-square examination) and if the dependent variable was quantitative the U-Mann–Whitney test was used.
To model the utilise of tracker devices to record daily activity (calories) as a function of the variables with significative relationships previously detected, one generalized linear model (GLM) was used. The model type used was main effects with Binomial as the distribution and Logit as the link part. The parameter estimation method was the hybrid method, and the calibration parameter was Pearson chi-square.
Model assumptions were verified. The goodness of fit to check for under or over dispersion of the data was tested. The dispersion coefficient is the deviance value/degrees of freedom. In models with binomial distribution, it should requite a value close to 1. If it is >ane, there is over dispersion; if it is <one, it is said that there is under dispersion.
Distribution of the deviance residuals was tested using a probability plot. The residuals are the differences between the values estimated by the model and the observed values. In the case of binomial models, this graph shows a distribution in two lines, with antisymmetric distal extremes and proximity of both medial extremes.
Relationship between deviance residuals and model predictions was verified by plotting deviance residuals versus predicted values. In the case of binomial models, this graph shows a clear design in two lines. This is because the response variable can only take two possible values for each observation, and the predicted values are grouped around these 2 values.
3. Results
Descriptive characteristics of the sample are shown in Table 2.
Tabular array 2
Descriptive characteristics of the sample.
| Characteristics | ||
|---|---|---|
| Gender (n = 890) | n (%) | |
| Male | 185 (20.8) | |
| Female | 705 (79.2) | |
| Generation (north = 892) | northward (%) | |
| Generation-Z (built-in 1997–2012) | 103 (11.5) | |
| Millennials (born 1981–1996) | 673 (75.four) | |
| Generation-10 (born 1965–1980) | 102 (11.4) | |
| Boomers (born 1946–1964) | 14 (ane.6) | |
| Apply of tracker device to tape daily activity (calories) (northward = 892) | north (%) | |
| Ever used | 687 (77) | |
| Never used | 205 (23) | |
| Time spent sitting (n = 892) | n (%) | |
| Depression cardiovascular disease bloodshed risk (due north = 315) | 315 (35.3) | |
| Medium cardiovascular disease mortality hazard (n = 408) | 408 (45.7) | |
| High cardiovascular affliction bloodshed hazard (due north = 86) | 86 (9.six) | |
| Very High cardiovascular illness mortality risk (n = 83) | 83 (9.3) | |
| Hateful | SD | |
| Body Mass Index (n = 889) | 25.2 | 5.three |
| Vigorous physical activity (min per week) (n = 762) | 297.nine | 283.5 |
| Moderate physical activity (min per week) (northward = 736) | 321.8 | 417.5 |
| Time spent walking (min per week) (n = 843) | 812.2 | 1136.3 |
Comparative assay results of the characteristics of the sample according to the use of tracker devices to record daily activity (calories) are presented in Table 3.
Table 3
Comparative analysis of the characteristics of the sample according to the use of tracker devices to record daily activity (calories).
| Use of Tracker Device to Record Daily Activeness (Calories) | |||
|---|---|---|---|
| Ever Used | Never Used | p Value | |
| Gender (n = 890) | % | % | |
| Male | 17.6 | 68.five | <0.001 |
| Female | 82.4 | 31.five | |
| Generation (n = 892) | % | % | |
| Generation-Z (born 1997–2012) | nine.five | 18.5 | 0.001 |
| Millennials (born 1981–1996) | 78.6 | 64.9 | |
| Generation-X (built-in 1965–1980) | 10.5 | 14.6 | |
| Boomers (built-in 1946–1964) | 1.5 | two.0 | |
| Time spent sitting (northward = 892) | % | % | |
| Low cardiovascular illness bloodshed hazard (n = 315) | 37.viii | 26.8 | 0.004 |
| Medium cardiovascular disease mortality adventure (north = 408) | 45.3 | 47.iii | |
| High cardiovascular disease mortality chance (n = 86) | nine.0 | 11.7 | |
| Very High cardiovascular disease bloodshed risk (northward = 83) | 7.9 | 14.1 | |
| Mean (SD) | Mean (SD) | ||
| Body Mass Alphabetize (n = 889) | 25.4 (5.three) | 24.half dozen (v.0) | 0.024 |
| Vigorous physical activity (min per week) (n = 762) | 304.1 (295.2) | 274.8 (233.9) | 0.291 |
| Moderate physical action (min per week) (n = 736) | 327.four (428.2) | 301.6 (377.0) | 0.510 |
| Time spent walking (min per week) (north = 843) | 831.9 (1156.nine) | 746.8 (1065.0) | 0.470 |
GLM validation indicated no problems. The dispersion coefficient showed a value shut to 1 (1.087) (Tabular array iv); thus, no under or over dispersion of the data was detected.
Table 4
Generalized linear model. Goodness of fit.
| Value | Degrees of Freedom | Dispersion Coefficient | |
|---|---|---|---|
| Deviance | 810.132 | 745 | 1.087 |
The probability plot showing the distribution of the deviance residuals showed an adequate distribution with antisymmetric distal extremes and proximity of both medial extremes. A higher density of points was observed at the two medial extremes, located shut to 0 (Figure 2).
Probability plot showing the distribution of the deviance residuals.
The human relationship between deviance residuals and model predictions was verified by plotting deviance residuals versus predicted values. The scatterplot of residuals versus predicted values (Figure 3) showed a articulate pattern because the response variable was binomial. A lower density of points was observed in the extremes respective to the highest accented values of the deviance residuals.
Scatterplot showing the relationship between deviance residuals and model predictions.
The numerical outputs of the parameter estimates are given in Table 5. Significant effects for being female (p < 0.001), time spent sitting: low cardiovascular disease mortality take a chance (p = 0.001); time spent sitting: medium cardiovascular disease mortality risk (p = 0.023) and BMI (p = 0.007) were detected.
Table five
Generalized linear model. Parameter estimates.
| Dependent Variable: Employ of Tracker Device to Record Daily Activity (Calories) | Odds Ratio | Wald 95% Confidence Interval for the Odds Ratio. Lower Bound/Upper Bound. | Wald Chi-Square Statistic | p Value |
|---|---|---|---|---|
| Abiding | 0.197 | 0.036/one.069 | three.546 | 0.060 |
| Female person | ii.299 | 1.567/3.372 | 18.129 | <0.001 |
| Generation-Z (born 1997–2012) | 0.677 | 0.188/2.439 | 0.356 | 0.551 |
| Millennials (born 1981–1996) | 1.632 | 0.479/5.556 | 0.615 | 0.433 |
| Generation-Ten (built-in 1965–1980) | 0.974 | 0.270/3.518 | 0.002 | 0.968 |
| Time spent sitting: Low cardiovascular disease mortality adventure | two.698 | 1.524/four.778 | 11.589 | 0.001 |
| Time spent sitting: Medium cardiovascular disease mortality chance | one.870 | ane.090/3.211 | 5.161 | 0.023 |
| Time spent sitting: High cardiovascular disease bloodshed gamble | 1.551 | 0.773/3.111 | ane.527 | 0.217 |
| Body Mass Index | 1.052 | 1.014/1.091 | 7.301 | 0.007 |
Co-ordinate to the estimate, existence female increased the odds of having used a tracker device to record daily activity (calories) past 2.iii times in relation to being a male person. Having a low cardiovascular affliction mortality chance related to time spent sitting increased the odds of having used a tracker device to record daily activity (calories) by 2.7 times with respect to having a high cardiovascular disease bloodshed gamble. Having a medium cardiovascular disease bloodshed run a risk related to fourth dimension spent sitting increased the odds of having used a tracker device to record daily action (calories) by i.9 times with respect to having a high cardiovascular illness mortality take chances. For every 1-point increase in the BMI score, the odds of having used a tracker device to record daily activity (calories) increased by 5.2%.
4. Discussion
Females and millennials had used more tracker devices to record daily activeness. Fewer subjects who had ever used any tracker device to record daily activeness had high or very high cardiovascular disease bloodshed adventure due to the time spent sitting. Subjects who had ever used whatsoever tracker device to record daily activity had a higher BMI. The amount of vigorous or moderate concrete activity or the time spent walking were not associated with the use of tracker devices to record daily activity.
Beingness female increased the odds of having used a tracker device to record daily activeness past 2.three times. To have a low or a medium cardiovascular disease mortality take chances related to time spent sitting increased the odds of having used a tracker device to tape daily activity with respect to having a high cardiovascular affliction mortality risk. For every 1-point increase in the BMI score, the odds of having used a tracker device to tape daily action increased by v.2%.
Petty is known virtually how individual characteristics affect utilize of wearable activeness trackers considering existing enquiry focuses more often than not on the use associated with technical problems. In our sample, females had used more than tracker devices to record daily action, as it has been suggested previously on the spider web [32]. For this observed deviation, it must be considered that females were predominantly represented in the sample. We did not find whatsoever previous manuscript that compared the utilize of tracker devices in adults according to gender. Gender differences stand for an increasingly significant line of research because considering gender differences allows to make more precise recommendations and facilitates debate regarding its sociological implications [33]. Co-ordinate to this, previous inquiry studied, in 2019, the perceptions of patients and family unit members regarding the credence of wearable devices as health tools. In general terms, they ended that although men had a greater involvement in wearable devices, the acceptance and utilize were also increasing in young women [34]. The results of our study seem to confirm this observation.
Generation refers to those individuals born in the same period or group of years, and who experienced the same or like environmental, political, and social influences that would mold and impact that item generation. These influences shape their beliefs, values, attitudes, and beliefs [35]. The concept of generations is relevant as one of the chief factors suggested to mold the reactions of a generation is technological advancement [36]. In our study, millennials were predominantly represented in the sample, and they had used many more tracker devices to record daily activity than the other generations in the sample; though this difference does not persist in the multivariate analysis, showing that there are factors related to the employ of tracker devices that are more relevant than historic period. Only one previous study has shown that millennials are more prone to using yard-health for lifestyle instruction only related to apps, and in a Malaysian sample, with more than difficult admission to new technologies than the sample of our study [37].
Subjects with low or a medium cardiovascular illness mortality risk related to time spent sitting in this study reported greater utilise of tracker devices to record daily action. The use of Fitbits has previously been related to the suspension of sitting time of employees during sedentary piece of work [38]. Sedentary beliefs in diabetic adults has been related with dumb cardiometabolic wellness [39] and, contained of concrete activity, with increased hazard of diabetes, cardiovascular illness, and cardiovascular and all-cause mortality [40]. Replacing sedentary behavior with continuing, sleeping (only with sleeping deficit), walking, and moderate to vigorous concrete action has been associated previously with bloodshed risk reductions [41]. Information technology has been suggested that the deleterious effects of sedentary beliefs are caused by a unique physiological pathway related to inactivity, considering that sitting too much is non the aforementioned every bit the lack of do and that sitting too much has its own metabolic consequences [42]. These metabolic consequences include: deep and fast descent in the concentration of plasma loftier-density lipoprotein cholesterol [43] and 90–95% loss of lipoprotein lipase activeness, locally, in the most oxidative skeletal muscles in the legs, which is necessary for the uptake of fat from the blood so that it can be metabolized past muscle [44]. According to data of our and previous studies, nosotros can conclude that the use of tracker devices can reduce the deleterious effects of sedentary behavior, promoting the decrease in time spent sitting, regardless of changes in physical activeness.
The amount of vigorous or moderate concrete activity or the time spent walking were not associated, in this study, with the apply of tracker devices to record daily activeness. A previous study with an adolescent sample has shown a positive clan between the apply of apps and habiliment devices and the daily moderate-to-vigorous physical activity [45]. Yet, it has also been observed that tracker devices may deed equally facilitators, but they are non useful for health behavior modify without any other intervention [46]. In fact, wearable activity tracker-based counseling intervention among patients with type 2 diabetes mellitus, overweight/obesity, cardiovascular diseases, chronic respiratory diseases, cognitive disorders, or sedentary older adults increased physical action [47]. A recent review has shown that wearable devices are able to help with the command of diabetes, as well as foreclose the complications associated with this condition [48].
Conversely to our results, contempo reviews showed that the apply of wear activity trackers effectively improves the amount of concrete action, but not sedentary behavior [49,50]. Again, both reviews take included studies that compared interventions utilizing wearable activeness trackers with interventions that do not utilize activity tracker feedback, and they did not clarify the apply of wearable activeness trackers in the general population.
In this written report, higher BMI increased the odds of using a tracker device to record daily activity, which may be related to dissatisfaction with body weight. Although previous studies take accomplished weight loss with short-term (<vi months) tracker device-based interventions [22]; it has also been reported that providing action-level feedback alone, that is, the device alone, did not result in weight loss [51]. Recommending technological support alone, in programs for long-term weight loss purposes, is discouraged [52]. To be more effective, additional behavioral alter techniques must be included, especially in individuals in whom dissatisfaction with body weight can generate feet regarding caloric intake. This is in line with the outcomes of Jakicic et al., who establish that the addition of a wearable technology device, that provided feedback on energy expenditure, to a standard behavioral intervention resulted in less weight loss in salubrious adults [53]. Otherwise, a positive association has also been shown between activity tracking frequency and weight loss [54].
The possible tracker devices used in this written report were Fitbit, Apple Watch, Polar, Garmin, and Nike. These wireless devices are a useful tool for continuously monitoring physical activities and may help patients suffering from chronic pathologies [55]. Thus, Fitbit, Apple Spotter, and Garmin take been validated to examine heart rate and energy expenditure at different practise intensities [56]. The highest measurement fault has been establish in centre rate for the 3 devices with respect to the Polar eye rate monitor in low-cal and moderate physical activity, showing that the Apple Watch was the most accurate [56]. The evaluation of the devices regarding measurement of energy expenditure reported that the three devices measured more than calories than the Parvo Medics TrueOne 2400 metabolic measurement system [56]. Information technology has been concluded previously that almost wrist-worn devices measure heart rate with an adequate error simply they poorly estimated free energy expenditure [57]. In summary, clothing devices can assistance people to monitor their activity, but the potential measurement errors must be taken into consideration if they are used to follow health habits by healthcare professionals. Withal, the technology of article of clothing devices is constantly existence improved, as well as the studies that validate their measurements, then nosotros believe that they will increasingly go a more valid tool for following the health habits of the population with greater precision.
Clinical implications of these results are that tracker devices may be useful for promoting salubrious habits, but with nuances. Information technology seems that the use of wearables can change habits such as excessive time spent sitting, unlike what happens with other habits that may require a more complex elaboration, such as carrying out a physical activity plan or modifying the nutrition co-ordinate to health standards. If the registered data of the devices was managed past healthcare professionals, information technology could provide wider benefits to users [7,58]. Wellness professionals tin advise on behavioral modify techniques and recommendations to guide their employ. They may also assess the presence of adverse effects such as anxiety regarding monitoring or the presence of eating disorders, especially in persons with body dissatisfaction [14]. With the support of healthcare professionals, when necessary, tracker devices could brand health cocky-management more achievable, positively affecting in the health of the population and the functioning of health systems, just the fault measurement of the devices must be taken into consideration.
Limitations
The present study has some limitations. It is express by the cross-exclusive design conducted in a sample using Instagram, with a predominance of females and millennials, and disseminated through universities. This tin can make it difficult to extrapolate the results to other samples less acquainted with the utilize of new technologies. The question in relation to tracker device utilise has non been validated previously. This question may have included more options for answering than yes/no, such equally rarely, sometimes, and often to larn more virtually these trends, but the need to simplify the survey to facilitate its completion prevailed. Associations were examined, but no causal human relationship can be established. Information technology was discussed that people who accept used tracker devices have reduced the time spent sitting, simply information technology could besides be interpreted that these subjects, more than decumbent to taking care of their health, were less sedentary and they take resorted to using tracker devices to monitor their wellness status. The survey included only commercially bachelor tracking devices to record daily activity (calories) and no other types of digital technologies, such equally mobile applications.
v. Conclusions
Females have used tracker devices ii.three times more than than men. For every 1-point increase in the BMI score, the odds of having used a tracker device increased by 5.2%. Subjects with depression cardiovascular illness mortality run a risk related to time spent sitting have used tracker devices 2.vii times more than than subjects with loftier cardiovascular illness bloodshed risk. The amount of vigorous or moderate concrete activity or the fourth dimension spent walking were not associated with the usage of tracker devices to tape the daily activity. These conclusions are limited by the cross-sectional pattern; thus, no causal relationship tin be established. Future enquiry should focus on testing the validity of the survey and the extension of the research to other population groups less acquainted with the employ of new technologies, collecting more exhaustive data on the characteristics of the use of tracker devices.
Acknowledgments
The authors wish to thank to all report participants for their availability and altruistic collaboration.
Writer Contributions
Conceptualization, H.J.T.-V. and J.M.T.-M.; methodology, H.J.T.-5., 1000.C.V.-P. and C.H.-G.; formal analysis, M.O.L.-L. and Southward.M.-B.; investigation, H.J.T.-V.; data curation, M.O.Fifty.-L. and S.M.-B.; writing—original draft preparation, H.J.T.-V., G.O.50.-L. and C.H.-G.; writing—review and editing, H.J.T.-Five., M.C.V.-P., Yard.O.50.-L. and C.H.-Chiliad.; visualization, J.M.T.-M.; supervision, J.M.T.-Grand. and C.H.-Thousand. All authors have read and agreed to the published version of the manuscript.
Funding
This inquiry received no external funding.
Institutional Review Board Statement
The report was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Lath, Bookish Commission of the Doctoral Programme in Health and Sports Sciences of the University of Zaragoza (protocol lawmaking 496/half dozen June 2019).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Information Availability Argument
The datasets analyzed during the current written report are available from the corresponding writer on reasonable asking. All information analyzed during this report are included in this published article.
Conflicts of Interest
The authors declare no conflict of interest.
Footnotes
Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Activity Trackers Are Electronic Devices,
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9031391/
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