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Assessing physical behavior through accelerometry – State of the science, best practices and future directions
Abstract
Accelerometers offer opportunities for researchers to capture valid data about the intensity and amount of physical behavior (PB) in real-time over a period of several days and weeks. From this multidimensional data, a great number of metrics can be derived to capture and describe the unique aspects of PB. The goal of this paper is to help the end-user of PB monitoring devices (novice to intermediate experience) wade through sometimes excessive technical details of accelerometry to outline best practices in selecting and applying devices to quantify three major behavioral categories of common interest to the research community: physical activity (PA), sedentary behavior (SB) and sleep. The effects of these decisions on the metrics (energy expenditure, activity intensity, body position, activity patterns) can occur in a variety of ways. The device, carrying position (hip, wrist, thigh) and recording parameters (epoch length (EL), frequency, memory capacity, recording frequency and filters) have a large influence on the measured activity. The different backgrounds such as study design (purpose, repeated measurements) and duration (time frame, wear time) as well as data storage and evaluation must be taken into account when determining the parameters. Finally, the evaluation must adjust several levers (raw data, context information, non-wear time, intensity classification, compliance) depending on the target variables. Looking into the future, current developments in statistical analysis are discussed, because the research community has not yet reached a consensus on the most promising approach. There are exciting developments ahead of us in the future. Sleep in particular is increasingly being seen as an influencing factor for health. Together with the technical developments in sensors which will become incrementally smaller, more accurate and in the near future will be integrated directly into our clothes or skin, accelerometry is facing exciting times and lots of data to evaluate.
... When measuring physical activity (PA) with devices, especially accelerometers, the objective is to detect it by measuring the acceleration occurring at a specific point on the human body (Burchartz et al., 2020a). To report physical behavior (PB) in public health settings, epidemiological studies use times spent at certain levels of activity intensity throughout the day and the week as result. ...
... To convert the captured accelerations into these intensity levels, the collected data needs to be preprocessed. Many parameters like device selection, carrying position and recording parameters such as recording frequency or filters influence how the measurement is transformed into the final results and largely affect the following estimation of activity (Burchartz et al., 2020a;Rowlands et al., 2018). Understanding the PA behavior of children and adolescents is severely limited by the presence of many sets of intensity-based limits for different brands and models of accelerometers. ...
... Today, there are even more approaches to analyze accelerometer data, and for these reasons, it is important to doc-ument all decisions made in recording and processing the data to allow later comparison with other studies (Arvidsson et al., 2019;Burchartz et al., 2020a;Migueles et al., 2017). In an earlier review, Cain, Sallis, Conway, van Dyck, and Calhoon (2013) reported that only about half of all studies report all decisions used during the process of data processing. ...
This study assesses three factors that influence the quantification of children’s and adolescents’ physical activity (PA) using accelerometers: selection of (1) non-wear algorithm, (2) epoch length and (3) cut-points. A total of 1525 participants from MoMo wave 3 (2018–2022), aged 6–17 years, wore GT3X accelerometers (ActiGraph, LLC, Pensacola, FL, USA) during waking hours. Acceleration counts were reintegrated into lengths of 1, 5, 15, 30, and 60 s epochs. Two non-wear time algorithms and two sets of cut-points were applied to each epoch length. Differences were found in both the comparison of the non-wear time algorithms and the comparison of the cut-points when the different epoch lengths were considered. This may result in large differences in estimated sedentary behavior and PA values. We propose to pool the data by merging and combining multiple accelerometer datasets from different studies and evaluate them in a harmonized way in the future. In addition to the need for future validation studies using short epoch lengths for young children, we also propose to conduct meta-analyses. This allows the use of data from multiple studies to validate cut-points and to propose a consensual set of cut-points that can be used in different settings and projects. The high discrepancy between results when comparing different epoch lengths has to be considered when interpreting accelerometer data and is regarded a confounding variable when comparing levels of PA between studies.
... The assessment of human movement and nonmovement behavior by continuously measured, high-resolution data has been broadly available due to recent technological advances and the development of new generations of a variety of sensors and wearables (Burchartz et al., 2020;Keadle et al., 2021). This trend has led to a rapidly growing commercial market for wearable technologies (Thompson, 2022) along with a growing number of research studies that utilize device-based measurements to capture PB data. ...
... This in turn raises the question of whether it is "prime time" for scientifically validated wearables as global PB surveillance methodologies (Troiano et al., 2020;Trost, 2020). As promising as device-based measurements are, there are still several methodological issues and considerations regarding the use of wearables in health studies that need to be addressed (e.g., data processing, monitoring protocols, or quality criteria such as validity [Burchartz et al., 2020]). ...
... The choice of an appropriate wearing location of the device according to the research question is very important to account for all dimensions of the PB spectrum (Burchartz et al., 2020) and to be able to detect transitions between distinct activities and the prediction of a variety of activities over a prolonged time (i.e., 1-2 days) (Keadle et al., 2019). The recommended wearing location varies from dimension to dimension. ...
Purpose : To raise attention to the quality of published validation protocols while comparing (in)consistencies and providing an overview on wearables, and whether they show promise or not. Methods : Searches from five electronic databases were included concerning the following eligibility criteria: (a) laboratory conditions with humans (<18 years), (b) device outcome must belong to one dimension of the 24-hr physical behavior construct (i.e., intensity, posture/activity type outcomes, biological state), (c) must include a criterion measure, and (d) published in a peer-reviewed English language journal between 1980 and 2021. Results : Out of 13,285 unique search results, 123 articles were included. In 86 studies, children <13 years were recruited, whereas in 26 studies adolescents (13–18 years) were recruited. Most studies (73.2%) validated an intensity outcome such as energy expenditure; only 20.3% and 13.8% of studies validated biological state or posture/activity type outcomes, respectively. We identified 14 wearables that had been used to validate outcomes from two or three different dimensions. Most ( n = 72) of the identified 88 wearables were only validated once. Risk of bias assessment resulted in 7.3% of studies being classified as “low risk,” 28.5% as “some concerns,” and 71.5% as “high risk.” Conclusion : Overall, laboratory validation studies of wearables are characterized by low methodological quality, large variability in design, and a focus on intensity. No identified wearable provides valid results across all three dimensions of the 24-hr physical behavior construct. Future research should more strongly aim at biological state and posture/activity type outcomes, and strive for standardized protocols embedded in a validation framework.
... Data are subsequently divided into intensities of movement such as SED, light PA (LPA), moderate to vigorous PA (MVPA), and total PA (TPA) by applying cutpoints derived from calibration studies using criterion measures (i.e., VO 2 or direct observation) within specific age ranges (Butte et al., 2014;Costa et al., 2014;Evenson et al., 2008;Freedson et al., 2005;Pate et al., 2006;Pulsford et al., 2011;Puyau et al., 2002;Sirard et al., 2005;Trost et al., 2012). Technological advances coupled with rising interest in childhood obesity has resulted in a plethora of research in this area causing large methodological inconsistencies and an overwhelming number of choices researchers must face when analyzing accelerometer data (Burchartz et al., 2020;Cliff et al., 2009;Migueles et al., 2017). ...
... After inclusion criterion was met, 10 different sets of cutpoints were applied to the 1-s epoch files to determine the average amount of time spent in SED, LPA, MVPA, and TPA. One second epochs were chosen to ensure that the sporadic nature of PA in young children was captured as recommended by previous studies (Burchartz et al., 2020;Migueles et al., 2017).These sets of cutpoints were chosen as they have all been calibrated and validated in child populations using ActiGraph devices worn on the hip; a summary of each of these cutpoints and the corresponding calibration study is provided in Table 2. For calibration studies that published cutpoints validated using both the vertical axis (VA) and vector magnitude (VM; Butte et al., 2014;Costa et al., 2014), both VA and VM cutpoints were applied to the appropriate axis by selecting the "VM" option in the software when applying VM cutpoints; where appropriate we ensured that the "VM" option was not selected for cutpoints validated using VA data. ...
... Previous research has suggested that sets of cutpoints should be applied to accelerometer data acquired from children the same age as that of the published population used in the original calibration study to improve accuracy (Burchartz et al., 2020;Migueles et al., 2017). To explore this further we applied agespecific sets of cutpoints to our sample population for toddler, preschool, and school-aged children, and reported significant differences in times spent in all intensities for each of the different sets of cutpoints applied, despite dividing our sample into age categories matching the age ranges used in the calibration studies. ...
... During the first measurement point, out of the 70 children, nine discontinued participation due to: accident or illness; overload of parents due to the general situation around the pandemic; child did not want to wear the accelerometer anymore or it was forgotten to be put on several times. In addition, four children had not enough valid wear time in accelerometry data (at least ten hours on at least three weekdays and eight hours on at least one weekend day; [37,38]). This resulted in a sample of 57 children for the first time point (see Table 1 for background characteristics). ...
... Accelerometers (GT3x, Actigraph, Pensacola, Florida) measured body accelerations on three axes. Accelerometry has the advantage to provide a dense assessment of PA over a longer time-period covering also short PA bursts, which are more frequent in children [37,48]. Devices were worn for ten consecutive days on the right wrist and only removed for sleeping, showering, or swimming. ...
... Initialization (epoch length 15 s), downloading, validity checking, and analysis were performed using Actilife software (Actilife 6.12, Pensacola, FL, USA). Data were considered valid if devices were worn for at least ten hours on at least three weekdays and for eight hours on at least one weekend day [37,38]. The activity was classified as sedentary, light, moderate, or vigorous intensity. ...
COVID-19 restrictions and the instructions to stay at home (SaH) may have had an impact on child behavior including physical activity (PA) and health-related quality of life (HRQoL) in Switzerland. Therefore, we investigated PA and HRQoL during and after the SaH in 57 Swiss school children aged 7 to 12 years (M = 10.44; SD = 1.34). PA was measured using accelerometry (Actigraph GT3X) and HRQoL using the Kid-KINDLR questionnaire. During and post data was compared using paired sample t-tests. Independent t-tests were used to compare the HRQoL of physically active children with non-physically active children. PA in light (d = −0.56), moderate (d = −0.44), moderate-to-vigorous (d = −0.28) as well as overall HRQoL (d = −0.66), psychological well-being (d = −0.48), self-esteem (d = −0.39), friends (d = −0.70) and everyday functioning (d = −0.44), were significantly lower during SaH than afterwards. Children who adhered to PA recommendations (+60 min of moderate-to-vigorous PA) during SaH had a better overall HRQoL (d = 0.61) and psychological well-being (d = 0.56) than those who did not. Since PA levels and HRQoL were lower during SaH compared to afterwards, it seems that the restrictions negatively impacted children’s PA and HRQoL. During future SaHs, promoting children’s PA and HRQoL seems important.
... Furthermore, the rising interest in short-time intermitted VPA for health benefits with a reduced time requirement is especially difficult to monitor using self-reports [20]. However, to gain reliable measurements of PA, the sensors should be worn long enough to accurately represent the measurement duration of interest (e.g., eight hours a day for at least four days can accurately represent one measurement week) [20,21]. Even though a wear time of 24 h per day is described to be the most accurate assessment method for overall behavior throughout the day (i.e., sleep, sedentary behavior, and PA) [21], accelerometers are rarely attached to the body for this duration and therefore measured PA can be impacted by wear-time bias (a wear-time of 8 to 10 h is commonly assumed to be sufficient [21] but PA can occur during the non-weartime and therefore PA is likely to be underestimated as compared to real PA during 24 h). ...
... However, to gain reliable measurements of PA, the sensors should be worn long enough to accurately represent the measurement duration of interest (e.g., eight hours a day for at least four days can accurately represent one measurement week) [20,21]. Even though a wear time of 24 h per day is described to be the most accurate assessment method for overall behavior throughout the day (i.e., sleep, sedentary behavior, and PA) [21], accelerometers are rarely attached to the body for this duration and therefore measured PA can be impacted by wear-time bias (a wear-time of 8 to 10 h is commonly assumed to be sufficient [21] but PA can occur during the non-weartime and therefore PA is likely to be underestimated as compared to real PA during 24 h). While wearables such as Fitbit can easily be attached to the body for 24 h, they are mainly designed for commercial purposes, show limited validity and reliability, and can only provide accurate step counts in adults under certain conditions (no mobility limitation and worn at the torso) but not for energy estimation [22]. ...
... However, to gain reliable measurements of PA, the sensors should be worn long enough to accurately represent the measurement duration of interest (e.g., eight hours a day for at least four days can accurately represent one measurement week) [20,21]. Even though a wear time of 24 h per day is described to be the most accurate assessment method for overall behavior throughout the day (i.e., sleep, sedentary behavior, and PA) [21], accelerometers are rarely attached to the body for this duration and therefore measured PA can be impacted by wear-time bias (a wear-time of 8 to 10 h is commonly assumed to be sufficient [21] but PA can occur during the non-weartime and therefore PA is likely to be underestimated as compared to real PA during 24 h). While wearables such as Fitbit can easily be attached to the body for 24 h, they are mainly designed for commercial purposes, show limited validity and reliability, and can only provide accurate step counts in adults under certain conditions (no mobility limitation and worn at the torso) but not for energy estimation [22]. ...
Quantification of physical activity (PA) depends on the type of measurement and analysis method making it difficult to compare adherence to PA guidelines. Therefore, test-retest reliability, validity, and stability for self-reported (i.e., questionnaire and diary) and device-based measured (i.e., accelerometry with 10/60 s epochs) PA was compared in 32 adults and 32 children from the SMARTFAMILY study to examine if differences in these measurement tools are systematic. PA was collected during two separate measurement weeks and the relationship for each quality criteria was analyzed using Spearman correlation. Results showed the highest PA values for questionnaires followed by 10-s and 60-s epochs measured by accelerometers. Levels of PA were lowest when measured by diary. Only accelerometry demonstrated reliable, valid, and stable results for the two measurement weeks, the questionnaire yielded mixed results and the diary showed only a few significant correlations. Overall, higher correlations for the quality criteria were found for moderate than for vigorous PA and the results differed between children and adults. Since the differences were not found to be systematic, the choice of measurement tools should be carefully considered by anyone working with PA outcomes, especially if vigorous PA is the parameter of interest.
... However, it can also be a curse for both consumers and researchers to select the appropriate wearable or study design to obtain meaningful and transparent results. Several methodological issues regarding wearables, especially in research, should be addressed (e.g., data processing, monitoring protocols, or quality criteria such as validity [6]). Only a small proportion of wearables have been proven effective through rigorous, independent validation. ...
... To assess all dimensions of the physical behaviour spectrum, the choice of an appropriate wear position of the device according to the research question is crucial [6]. Further, the wear position impacts the ability to detect transitions between specific activities and predict a spectrum of activities over a prolonged time (i.e., 1-2 days) [10]. ...
Background
Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing.
Methods
We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions.
Results
Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as “low risk”, while 16.5% (N = 90) were classified as “some concerns” and 79.1% (N = 431) as “high risk”.
Conclusion
Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.
... It is also important to highlight the accelerometer's placement, which is usually determined based on the algorithms that are to be used to assess the behaviour of interest (Burchartz et al., 2020;Migueles et al., 2017). The procedures for using accelerometers vary widely among the studies in our systematic review. ...
... sitting, climbing, etc.) (Ellis et al., 2016;Stevens et al., 2020). New trends in the PA measurement literature include algorithms based on thigh and wrist data (Burchartz et al., 2020). Thigh-worn accelerometers are emerging as a new safe and feasible placement . ...
Active commuting to/from school (ACS) is an efficient manner to increase daily physical activity (PA) levels. However, there seems to be no consensus on the best methodology to accurately assess ACS-PA. Therefore, this systematic review aimed (1) to compile and review the methodologies used in device-measured ACS-PA in young people, including the definition of the times (i.e. start/end times) and the locations (i.e. home/school) of the trips (i.e. when and where), and how to quantify the ACS-PA mode, intensity, and volume with devices (e.g. accelerometers, pedometers), (2) to analyse the strengths and limitations of these methodologies, and (3) to propose practical recommendations for ACS-PA measurement. A systematic search was carried out up to 2021 in five different databases. The systematic search yielded 6,274 references, of which 27 papers met the inclusion criteria (See PMC7459731). Methodologies used to assess ACS-PA were heterogenous, especially on how to determine the times when ACS takes place. The start/end times of the trips were mainly identified using predefined time intervals, even though GPS-based detection were also used in some studies. Regarding how to quantify the ACS-PA, the main mode of ACS assessed was walking and the most used device was the accelerometer to quantify the PA intensity. This systematic review provides the strengths and limitations of each method, proposes solutions to appropriately measure ACS-PA, and includes a decision tree for helping researchers’ decision-making.
PROSPERO registration number CRD42020162004A.
... This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (see Table S1) [30] and was registered to the international prospective register of systematic reviews PROSPERO [31] on 28 April 2020 (registration number: CRD42020150201). ...
... Reporting mean activity counts per minute instead of converting counts to activity levels could be one step to help improve the comparability of PA data and minimize errors in the assumptions that are made based on different cut-points. It would be desirable to report raw data processing and filtering to outcome metrics for an across device-comparison [28]. Moreover, some studies aimed at a full-day approach to examine PA locations, others focused on PA occurring in specific locations for a limited time of the day. ...
The aim of this systematic review was to examine where physical activity (PA) takes place and how much time children, adolescents and adults spend being physically active within the identified locations. A systematic literature search was carried out in five electronic databases (PubMed, CINAHL, SPORTDiscus, PsycInfo, Scopus). For inclusion, primary studies had to identify locations of PA using device-based or self-report tools, whereas minutes of PA had to be examined using device-based tools only. Thirty-two studies were included, methodological quality and sex/gender sensitivity of the studies were assessed. The narrative data synthesis revealed that the highest average amount of daily moderate-to-vigorous PA was found in home and recreational locations, followed by school and neighborhood locations. In adults, highest average amount of daily moderate-to-vigorous PA was found in neighborhood and home locations followed by workplace and recreational locations. The majority of studies had a low risk of bias in four out of six domains; eight studies reported significant sex/gender differences in location-based PA. The results indicate that different locations are used for PA to a varying degree across the lifespan. Future research on the promotion of PA should focus on location-specific design features that encourage children, adolescents and adults to be physically active.
... regular/last week/month PA recall via questionnaire or measured PA during a de ned time via accelerometry) [59]. Furthermore, the earliest study included in the reviews was published in 1997 and the complexity and capacity of sensors evolved rapidly since that time [60], allowing for more precise measurements and the combination of PA data with physiological parameters like heartrate or blood sugar [61]. This potential in uence of different sensors on intervention effectiveness however, has not been considered in the reviews. ...
... comparison of different epoch lengths [70]). The most promising aspect of device-measured outcomes and accelerometry in particular is the assessment of valid PA and SB data in real-time, resulting in a variety of outcome parameters which have the potential to be easily compared throughout different studies [60]. While device-measured assessments for HE are rarely used (but becoming more and more available [71]), HE interventions should only include validated tools and be aware of the advantages of each assessment to ensure the quality of results [72]. ...
Background: Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology.
Methods: PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized.
Results: The systematic search reveled eleven systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions.
Conclusion: Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions.
Trial registration: The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902.
... regular/last week/month PA recall via questionnaire or measured PA during a defined time via accelerometry) [59]. Furthermore, the earliest study included in the reviews was published in 1997 and the complexity and capacity of sensors evolved rapidly since that time [60], allowing for more precise measurements and the combination of PA data with physiological parameters like heartrate or blood sugar [61]. This potential influence of different sensors on intervention effectiveness however, has not been considered in the reviews. ...
... comparison of different epoch lengths [69]). The most promising aspect of device-measured outcomes and accelerometry in particular is the assessment of valid PA and SB data in real-time, resulting in a variety of outcome parameters which have the potential to be easily compared throughout different studies [60]. While device-measured assessments for HE are rarely used (but becoming more and more available [70]), HE interventions should only include validated tools and be aware of the advantages of each assessment to ensure the quality of results [71]. ...
Background
Electronic (eHealth) and mobile (mHealth) health interventions can provide a large coverage, and are promising tools to change health behavior (i.e. physical activity, sedentary behavior and healthy eating). However, the determinants of intervention effectiveness in primary prevention has not been explored yet. Therefore, the objectives of this umbrella review were to evaluate intervention effectiveness, to explore the impact of pre-defined determinants of effectiveness (i.e. theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions), and to provide recommendations for future research and practice in the field of primary prevention delivered via e/mHealth technology.
Methods
PubMed, Scopus, Web of Science and the Cochrane Library were searched for systematic reviews and meta-analyses (reviews) published between January 1990 and May 2020. Reviews reporting on e/mHealth behavior change interventions in physical activity, sedentary behavior and/or healthy eating for healthy subjects (i.e. subjects without physical or physiological morbidities which would influence the realization of behaviors targeted by the respective interventions) were included if they also investigated respective theoretical foundations, behavior change techniques, social contexts or just-in-time adaptive interventions. Included studies were ranked concerning their methodological quality and qualitatively synthesized.
Results
The systematic search revealed 11 systematic reviews and meta-analyses of moderate quality. The majority of original research studies within the reviews found e/mHealth interventions to be effective, but the results showed a high heterogeneity concerning assessment methods and outcomes, making them difficult to compare. Whereas theoretical foundation and behavior change techniques were suggested to be potential positive determinants of effective interventions, the impact of social context remains unclear. None of the reviews included just-in-time adaptive interventions.
Conclusion
Findings of this umbrella review support the use of e/mHealth to enhance physical activity and healthy eating and reduce sedentary behavior. The general lack of precise reporting and comparison of confounding variables in reviews and original research studies as well as the limited number of reviews for each health behavior constrains the generalization and interpretation of results. Further research is needed on study-level to investigate effects of versatile determinants of e/mHealth efficiency, using a theoretical foundation and additionally explore the impact of social contexts and more sophisticated approaches like just-in-time adaptive interventions.
Trial registration
The protocol for this umbrella review was a priori registered with PROSPERO: CRD42020147902 .
... Finally, to track population prevalence, inform policy, and evaluate physical activity promotion efforts at national and sub-national level in a timely fashion, it is recommended to strengthen the periodic systematic national monitoring system for physical activity. This should ensure it employs state of the science methods and measures while preserving trend measurement, and that it is adequately powered to also track progress in population subgroups with particularly low levels of physical activity (Burchartz et al., 2020;Nigg et al., 2020). ...
Physical activity guidelines are recognized as an important element of a national approach to promote physical activity. This publication summarizes the approach and process taken to update the Swiss Guidelines, presents a summary of the updated guidelines and discusses experiences and identifies challenges. The multistage project involved: 1) to summarize the scientific evidence underpinning the 2020-edition of the WHO guidelines; 2) to systematically analyze the existing Swiss guidelines for different target groups and to develop proposals for updates 3) a participatory process to gain consensus with the main interested groups 4) to finalize the guidelines. Updated guidelines were adopted for infants, pre-school children, children and young people, adults, older adults and pregnant and postpartum women, in most cases following the WHO guidelines. Children, young people and adults living with disability and adults and older adults with chronic conditions are specifically addressed in each of the general guidelines for healthy populations, rather than developing separate guidelines for each of these groups as done by the WHO. The systematic approach in identifying aspects to update, the participatory approach and a scientific consortium and project coordination group with different thematic backgrounds were key strengths in the process. Challenges included the large amount of feedback and finding scientifically sound compromises. The updated versions of the Swiss national guidelines provide an excellent basis to further promote physical activity in Switzerland. A remaining key task is to develop a range of communication tools and materials for different target groups beyond the circle of experts and interested groups, considering available evidence on optimal messaging and best outlet tools and channels. To track population prevalence, inform policy and evaluate physical activity promotion at national and sub-national level in a timely fashion, strengthening existing physical activity monitoring will be important.
... In the last years, the number of studies using device-measured PA for the analysis of ACS-related PA (hereinafter referred to as ACS-PA) have increased exponentially to answer this research question. 13 In addition to providing detailed information on PA performed in free-living conditions, 14 these devices overcome the limitations present in self-reported measures such as recognition, memory, or social desirability biases of the participants. 15 For example, as Adamo et al. 16 suggested, self-reported measures can overestimate device-measured PA levels by up to 70%, especially in the pediatric population. ...
Objective
To analyze the contribution of active commuting to and from school (ACS) to device‐measured light physical activity (LPA) and moderate‐to‐vigorous physical activity (MVPA) levels in young people aged 6 to 18 years old, as well as, in both trip directions (i.e., home‐school, school‐home).
Methods
This systematic review was conducted according to the PRISMA statement, and five different databases were used for the systematic search (PubMed, Web of Science, SPORTdiscuss, Cochrane Library, and National Transportation Library) using PECO strategy.
Results
A total of 14 studies met all the eligibility criteria, which compile 7127 participants. The overall ACS weighted LPA was 19.55 min (95% CI: 3.84‐35.26; I² = 99.9%, p < 0.001) and 68.74 min (95% CI: 6.09‐131.39; z = 2.15, p = 0.030) during the home‐school and school‐home trips, respectively. For MVPA, the overall ACS weighted MVPA was 8.98 min (95% CI: 5.33–12.62; I² = 99.95%, p < 0.001) during the home‐school trip and 20.07 min (95% CI: 13.62‐26.53; I² = 99.62%, p < 0.001) during the school‐home trip.
Conclusion
ACS may contribute about 48% of the PA recommendations in young people on school days if both trip directions are actively performed. Therefore, future studies aimed at increasing daily PA levels in young population should focus on promoting students' ACS.
PROSPERO registration number: CRD42020162004A.
... In contrast studies reporting multiple analyses contribute more weight towards vote-count totals, irrespective of whether these analyses are independent of one another. Nevertheless, the need for this approach to summarise the available literature is an important reflection in itself, and highlights the methodological heterogeneity within current literature and reinforces the need for high quality research, standardised procedures and best-practice guidance for assessing and reporting PA in studies concerned with vascular function and CVD risk [66,70]. ...
The aim of this review was to understand the association between habitual physical activity (hPA) and carotid-femoral pulse wave velocity (cfPWV) in an ostensibly healthy adult population. Searches were performed in MEDLINE Web of Science, SPORTDiscus and CINAHL databases published up to 01/01/2022 (PROSPERO, Registration No: CRD42017067159). Observational English-language studies assessing the relationship between cfPWV and hPA (measured via self-report or device-based measures) were considered for inclusion in a narrative synthesis. Studies were excluded if studying specific disease. Studies were further included in pooled analyses where a standardised association statistic for continuous hPA and cfPWV was available. 29 studies were included in narrative synthesis, of which 18 studies provided sufficient data for pooled analyses, totalling 15,573 participants. A weak, significant, negative correlation between hPA and cfPWV was observed; partial r = -0.08 95%CI [-0.15, -0.01]; P = 0.045. Heterogeneity was high (I2 = 94.5% P<0.001). Results did not differ across sub-group analyses, however the high heterogeneity within pooled analyses was largely explained by studies utilizing self-reports of PA exposures, being of poor methodological quality or providing only univariate analyses. Overall this systematic review identified a weak negative beneficial association between hPA and cfPWV suggesting that higher levels of hPA benefit vascular health even amongst an asymptomatic population. However, the variation in PA metrics reported (restricting ability to complete meta-analysis), and the heterogeneity within pooled analyses suggests that findings should be interpreted with a degree of caution. The development of methods to precisely quantify day-to-day movement behaviours should support future high-quality research in this field.
... 17 No firm consensus regarding the definition of the valid wear-time criteria exists so far, but it is recommended that the sample is restricted to participants with a minimum of 4 days with valid data. 17,18 For the measurement of PA during waking hours, the appropriate criterion for the minimum wear time per day may be shorter than for the measurement of 24-hour movement behavior to get reliable results. An analysis of the international children's accelerometer database suggests that, for the measurement of PA during waking hours, 8 hours as a criterion for a valid day is more suitable than a longer period of time due to high losses in sample size with the stricter criteria. ...
Background:
Incomplete data due to nonparticipation and noncompliance with the study protocol can bias the results of studies. We investigated how a nationwide accelerometer sample of adolescents and young adults is affected by such incomplete data.
Methods:
We analyzed cross-sectional data from 6465 participants (11-31 y old) who participated in a national health survey in Germany (KiGGS Wave 2; 2014-2017). The data included information about the participation in the measurement of physical activity using accelerometers, compliance with the wear-time protocol, and sociodemographic and health-related variables. Multivariable regression analyses were conducted to detect factors associated with incomplete data.
Results:
Of the total sample, 78.0% participated in the accelerometer part of the study, and 83.5% of the participants with data available complied with the wear-time protocol. In 11- to 17-year-olds, the likelihood of having incomplete accelerometer data was higher in boys, older adolescents, adolescents with a lower sociodemographic status, adolescents with overweight, adolescents not participating in organized sport, adolescents not speaking only German at home, current smokers, and adolescents having a higher soft drink consumption. In 18- to 31-year-olds, the likelihood of having incomplete accelerometer data was higher in men, adults with a lower educational level, adults not speaking only German at home, and adults who smoke.
Conclusions:
Our results suggest that accelerometer samples are biased such that participants with more beneficial health behaviors provide complete accelerometer data more often. This knowledge should be used to design effective recruitment strategies and should be considered when interpreting results of accelerometer studies.
... Moreover, for criterion-related validity, the results were based solely on a subjective assessment of physical activity. While the use of questionnaires to assess physical activity is advantageous in large sample sizes [45], it seems worthwhile for future studies to complement such methods with device-based methods(e.g., accelerometer [46]). Moreover, the cross-sectional study design does not allow us to assess whether enjoyment can actually contribute to the maintenance of physical activity. ...
A new measure of the short form of the Physical Activity Enjoyment Scale (PACES-S), including four items, has been developed that focuses on the subjective experience of enjoyment. As validation has so far only been conducted in a youth population, the purpose of the present article was to test the psychometric properties of the measure in an adult population in three studies. In the first study (n = 1017) the results supported the unidimensional structure of the instrument (χ2 = 10.0; df = 2; p < 0.01; CFI = 0.992; RMSEA = 0.063), revealed a satisfactory level of internal consistency (ω = 0.79), and showed that the measure is invariant across gender. The results on factorial validity and internal consistency were generally supported by the second study (n = 482), which additionally showed satisfactory test–retest reliability (r = 0.73). Finally, the third study (n = 1336) also supported the factorial validity and internal consistency of the measure and additionally showed a positive correlation with physical activity (r = 0.40), thus supporting the criterion-related validity of the measure. This more economical version of PACES seems to be particularly useful for large-scale studies.
... Some variables were self-reported and are subject to biases. Questionnaires were used to assess physical activity, but it is preferable that questionnaires and accelerometers be used to assess physical activity in surveillance studies because each method has advantages and disadvantages [45][46][47]. Participants were asked about physical activity frequency and intensity, but not duration. There is a risk of misclassification bias with physical activity questionnaires, but single-item physical activity assessment tools like the one used in the present study have been shown to be valid and reliable [18]. ...
Background
Mild cognitive impairment often precedes dementia. The purpose of this analysis was to estimate the population attributable fraction for physical activity in Colombia, which is the reduction in cases that would occur if all participants were physically active.
Methods
The sample included 20,174 men and women aged 70.04 ± 7.68 years (mean ± SD) from the National Survey of Health, Wellbeing and Ageing. Trained interviewers administered a shorter version of the mini-mental state examination and mild cognitive impairment was defined as a score of 12 or less out of 19. Logistic regression models were fitted and population attributable fractions for physical activity were calculated. All analyses were adjusted for age, sex, height, education, income, civil status, smoking, and alcohol drinking.
Results
The prevalence of physical activity was approximately 50% when defined as walking between 9 and 20 blocks at least three times per week. Theoretically, 19% of cases of mild cognitive impairment would be eliminated if all adults were to walk (95% confidence interval: 16%, 22%). The prevalence was approximately 20% when defined as taking part in vigorous sport or exercise at least three times per week. Theoretically, 23% of cases of mild cognitive impairment would be eliminated if all adults were to take part in vigorous sport or exercise (16%, 30%). Similar results were observed after removing those who reported mental health problems.
Conclusion
Physical activity, whether walking or vigorous sport and exercise, has the potential to substantially reduce the burden of mild cognitive impairment in Colombia.
... Future studies should further refine the understanding of opportune moment identification by involving participants in JITAI design and building on existing findings from ecological momentary assessment research (e.g. Giurgiu et al., 2020). These important tailoring variables like the core affective state of the participants and contextual factors like availability and weather should then be used to enhance the adaptation to participants' needs and therefore the engagement and effectiveness of JITAIs. ...
Just-in-time adaptive interventions (JITAIs) are a promising technology-based approach for health behavior change. This examination aimed to evaluate whether a JITAI after a period of inactivity can enhance physical activity (PA) in the subsequent hour depending on whether the JITAI has been answered (“engaged” condition) compared to when the trigger was not answered (“not engaged” condition). Data of the 3-week intervention period of the SMARTFAMILY2.0 trial was used for analysis. A total of 80 participants (n = 47 adults, 23 female; n = 33 children, 15 female) with 907 JITAI triggers were included in this examination. A JITAI was sent when the participant has been inactive for at least 60 min as indicated by accelerometry. Two multilevel models were calculated for metabolic equivalents (MET) and step count with measurements (Level 1) nested in participants (Level 2) under consideration of the covariates weekday/weekend, time of the day, and adult/child. Results indicated significantly higher MET (β = 0.08, p = .014) and step (β = 0.08, p = .022) counts in the subsequent hour for the engaged condition compared to the not engaged condition within-persons (Level 1). Engagement with the JITAI implemented in the SMARTFAMILY2.0 trial yielded promising results concerning physical activity enhancement in the subsequent hour. Here, the inclusion of further constraining factors like the availability of the participant or the inclusion of affective and contextual variables into the design of a JITAI might enhance the engagement in future studies.
... However, the relationship between PA and CVD risk in young CCS has not yet been thoroughly investigated, and existing studies are limited by small sample sizes and/or subjective measurement methods (15)(16)(17)(18)(19)(20)(21). Subjective measurement methods, such as questionnaires, are prone to measurement errors due to biases such as recognition-, memory-and social desirability (22), and have shown to be unreliable in pediatric populations (23). Existing studies suggest that there is an association between PA and body composition in adolescent CCS also. ...
... However, the relationship between PA and CVD risk in young CCS has not yet been thoroughly investigated, and existing studies are limited by small sample sizes and/or subjective measurement methods (15)(16)(17)(18)(19)(20)(21). Subjective measurement methods, such as questionnaires, are prone to measurement errors due to biases such as recognition-, memory-and social desirability (22), and have shown to be unreliable in pediatric populations (23). Existing studies suggest that there is an association between PA and body composition in adolescent CCS also. ...
Objectives
We aimed to compare cardiovascular disease (CVD) risk factors in childhood cancer survivors (CCS) with age- and sex-stratified reference material and examine the association between physical activity (PA) intensities and CVD risk factors in CCS.
Materials and methods
Within the cross-sectional, multicenter Physical Activity in Childhood Cancer Survivors (PACCS) study, we collected data on CVD risk factors [VO 2– peak (mL⋅kg –1 ⋅min –1 ), body mass index (BMI, kg/m ² ), systolic blood pressure (SBP, mmHg), and total-cholesterol/HDL-cholesterol (Total/HDL)] among CCS aged 9–18 years. CVD risk factors were compared to references with immediate t -tests. We transformed CVD risk factors into z -scores based on international references and generated an individual CVD risk score: [inverse ZVO 2– peak + Z BMI + Z SBP + Z Total/HDL )/4]. Multivariable mixed linear regression models were used to analyze the associations between device-measured PA intensities and CVD risk factors.
Results
We included 157 CCS aged on average 13.4 years at inclusion and 8.2 years from diagnosis. Male CCS had lower VO 2– peak compared to references (45.4 vs. 49.4 mL⋅kg –1 ⋅min –1 , P = 0.001), higher diastolic BP (67 vs. 63 mmHg, P < 0.001), lower HDL (1.35 vs. 1.44 mmol/L, P = 0.012), as well as a tendency to higher CVD risk score ( z -score=0.14 vs. 0.00, P = .075). Female CCS’ CVD risk factors were comparable to references. Vigorous-intensity PA (VPA) was associated with CVD risk factors. A 10-min increase in VPA was associated with higher VO 2– peak (β = 4.9, 95% CI, 2.1–7.7), lower Total/HDL (β = −0.3, 95% CI, −0.6 to −0.1) and a lower CVD risk score (β = −0.4, 95% CI, −0.6 to −0.2).
Conclusion
Male adolescent CCS had less favorable values of CVD risk factors compared to references. VPA in adolescent CCS is associated with clinically meaningful favorable values of CVD risk factors.
... 10 In summary, there is a growing commercial industry of wearable technology, a growing number of research studies that integrated device-based methods to capture PB data and discussions about whether it is 'prime time' for scientifically validated wearables to be global PB surveillance methodologies. 11 12 However, applying wearables in health studies offers methodological and practical challenges such as data processing, monitoring protocols or quality criteria such as validity 9 while aiming to allow for valid interstudy comparisons. ...
Objectives
Studies that assess all three dimensions of the integrative 24-hour physical behaviour (PB) construct, namely, intensity, posture/activity type and biological state, are on the rise. However, reviews on validation studies that cover intensity, posture/activity type and biological state assessed via wearables are missing.
Design
Systematic review. The risk of bias was evaluated by using the QUADAS-2 tool with nine signalling questions separated into four domains (ie, patient selection/study design, index measure, criterion measure, flow and time).
Data sources
Peer-reviewed validation studies from electronic databases as well as backward and forward citation searches (1970–July 2021).
Eligibility criteria for selecting studies
Wearable validation studies with children and adolescents (age <18 years). Required indicators: (1) study protocol must include real-life conditions; (2) validated device outcome must belong to one dimension of the 24-hour PB construct; (3) the study protocol must include a criterion measure; (4) study results must be published in peer-reviewed English language journals.
Results
Out of 13 285 unique search results, 76 articles with 51 different wearables were included and reviewed. Most studies (68.4%) validated an intensity measure outcome such as energy expenditure, but only 15.9% of studies validated biological state outcomes, while 15.8% of studies validated posture/activity type outcomes. We identified six wearables that had been used to validate outcomes from two different dimensions and only two wearables (ie, ActiGraph GT1M and ActiGraph GT3X+) that validated outcomes from all three dimensions. The percentage of studies meeting a given quality criterion ranged from 44.7% to 92.1%. Only 18 studies were classified as ‘low risk’ or ‘some concerns’.
Summary
Validation studies on biological state and posture/activity outcomes are rare in children and adolescents. Most studies did not meet published quality principles. Standardised protocols embedded in a validation framework are needed.
PROSPERO registration number
CRD42021230894.
... For example, these data allow scientists to garner in-depth insights into within-person associations between physical behavior and behavioral, biological, physiological, as well as psychological factors in the everyday life of humans. Therefore, AA comprises methods such as physical activity monitoring (e.g., via Accelerometry; Burchartz et al., 2020; Translated to research practice, a prototypical AA study may therefore investigate associations between accelerome-try-based physical activity and e-diarybased self-reports. Since AA enables researchers to capture physical activity, sport and exercise "as it is" (realtime, real-life, etc.) and within a very broad range of research applications, this method is predestined to advance many subfields of exercise, sport, and movement science beyond traditional study formats. ...
... However, the application of wearables in studies that assess health-related questions presents several methodological and practical challenges. For example, strategies for data processing, monitoring protocols, assessment limitations (eg, muscle-strengthening exercises), and quality criteria such as validity need to be taken into account [230]. ...
Background:
Wearable technology is a leading fitness trend in the growing commercial industry and an established method for collecting 24-hour physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make guided decisions on which study to rely on and which device to use. However, reviews focusing on the quality of free-living validation studies in adults are lacking.
Objective:
This study aimed to raise researchers' and consumers' attention to the quality of published validation protocols while aiming to identify and compare specific consistencies or inconsistencies between protocols. We aimed to provide a comprehensive and historical overview of which wearable devices have been validated for which purpose and whether they show promise for use in further studies.
Methods:
Peer-reviewed validation studies from electronic databases, as well as backward and forward citation searches (1970 to July 2021), with the following, required indicators were included: protocol must include real-life conditions, outcome must belong to one dimension of the 24-hour physical behavior construct (intensity, posture or activity type, and biological state), the protocol must include a criterion measure, and study results must be published in English-language journals. The risk of bias was evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2 tool with 9 questions separated into 4 domains (patient selection or study design, index measure, criterion measure, and flow and time).
Results:
Of the 13,285 unique search results, 222 (1.67%) articles were included. Most studies (153/237, 64.6%) validated an intensity measure outcome such as energy expenditure. However, only 19.8% (47/237) validated biological state and 15.6% (37/237) validated posture or activity-type outcomes. Across all studies, 163 different wearables were identified. Of these, 58.9% (96/163) were validated only once. ActiGraph GT3X/GT3X+ (36/163, 22.1%), Fitbit Flex (20/163, 12.3%), and ActivPAL (12/163, 7.4%) were used most often in the included studies. The percentage of participants meeting the quality criteria ranged from 38.8% (92/237) to 92.4% (219/237). On the basis of our classification tree to evaluate the overall study quality, 4.6% (11/237) of studies were classified as low risk. Furthermore, 16% (38/237) of studies were classified as having some concerns, and 72.9% (173/237) of studies were classified as high risk.
Conclusions:
Overall, free-living validation studies of wearables are characterized by low methodological quality, large variability in design, and focus on intensity. Future research should strongly aim at biological state and posture or activity outcomes and strive for standardized protocols embedded in a validation framework. Standardized protocols for free-living validation embedded in a framework are urgently needed to inform and guide stakeholders (eg, manufacturers, scientists, and consumers) in selecting wearables for self-tracking purposes, applying wearables in health studies, and fostering innovation to achieve improved validity.
... PA) affects different outcomes, such as weight (Mekary et al., 2013) and therefore shed some light on the relationship between different behaviours. Both PA and sedentary behaviours have been measured with questionnaires (Bouchard et al., 1983;Vizcaino et al., 2019) and accelerometers (Burchartz et al., 2020;Hart et al., 2011) with the results of the two approaches not always converging due to a range of technical and measurement issues (Hart et al., 2011). ...
Objective:
Many adolescents report a lack of physical activity (PA) and excess screen time (ST). Psychological theories aiming to understand these behaviours typically focus on predictors of only one behaviour. Yet, behaviour enactment is often a choice between options. This study sought to examine predictors of PA and ST in a single model. Variables were drawn from dual process models, which portray behaviour as the outcome of deliberative and automatic processes.
Design:
411 Finnish vocational school students (age 17-19) completed a survey, comprising variables from the Reasoned Action Approach (RAA) and automaticity pertaining to PA and ST, and self-reported PA and ST four weeks later.
Main outcome measures:
Self-reported time spent on PA and ST and their predictors.
Results:
PA and ST correlated negatively (r = -.17, p = .03). Structural equation modelling revealed that intentions and habit for PA predicted PA while ST was predicted by intentions and habit for ST and negatively by PA intentions. RAA-cognitions predicted intentions.
Conclusion:
PA and ST and their psychological predictors seem to be weakly interlinked. Future studies should assess more behaviours and related psychological influences to get a better picture of connections between different behaviours.
HighlightsPhysical activity and screen time are largely mutually exclusive classes of behaviours and might therefore be related in terms of their psychological predictors.411 adolescent vocational school students self-reported variables from the Reasoned Action Approach and behavioural automaticity related to physical activity and leisure time screen time behaviours as well as those behaviours.Structural equation modelling revealed expected within-behaviour predictions but, against expectations, no strong connections between the two behaviour classes in terms of their predictors. Only intentions to engage in physical activity negatively predicted screen time.Future research should aim to measure a wider range of mutually exclusive classes of behaviours that cover a large share of the day to uncover relations between behaviours and their respective predictors.
... Digital health tools can improve disease prevention and healthcare delivery (Auerbach, 2019). The increasing popularity of mobile and wearable devices adds to their potential to deliver effective health promotion interventions and to support health behaviour change (Arigo et al., 2019;Burchartz et al., 2020). The potential of digital health tools to revolutionise health promotion and healthcare delivery is widely recognised by healthcare providers across the globe, with national and international policy efforts focused on stimulating digital health innovation and regulation (e.g., the EU's eHealth policies, the UK National Institute for Health and Care Excellence's Evidence Standard Framework for Digital Health Technologies). ...
In this White Paper, we outline recommendations from the perspective of health psychology and behavioural science, addressing three research gaps: (1) What methods in the health psychology research toolkit can be best used for developing and evaluating digital health tools? (2) What are the most feasible strategies to reuse digital health tools across populations and settings? (3) What are the main advantages and challenges of sharing (openly publishing) data, code, intervention content and design features of digital health tools? We provide actionable suggestions for researchers joining the continuously growing Open Digital Health movement, poised to revolutionise health psychology research and practice in the coming years. This White Paper is positioned in the current context of the COVID-19 pandemic, exploring how digital health tools have rapidly gained popularity in 2020-2022, when world-wide health promotion and treatment efforts rapidly shifted from face-to-face to remote delivery. This statement is written by the Directors of the not-for-profit Open Digital Health Initiative (n = 6), Experts attending the European Health Psychology Society Synergy Expert Meeting (n = 17), and the initiative consultant following a two-day meeting (19-20th August 2021).
... In this respect, one must assume that the findings of DB measurements are more robust and therefore come closer to the true extent of PA. However, differences in minimal wear-time, epoch length, and/or devices also make DB data difficult to compare [153]. ...
With the outbreak of the Corona Virus Disease 19 (Covid-19) in late 2019, governments increasingly imposed containment strategies, including social distancing as well as restricted population movement, potentially having negative impacts on mental and physical health. A growing number of studies have examined the impact of the pandemic on different facets of physical activity (PA); an overview combining these (mixed) results, however, is missing. Thus, the objective of this systematic review and meta-analysis was to investigate whether and to which extent PA changed from before to during the Covid-19 pandemic, taking age, gender, and measurement method into account. The literature search was conducted using PubMed, Web of Science, and Scopus. Results of the main characteristics were descriptively synthesized and analyzed in a meta-analysis quantifying effects of the pandemic on PA divided by age groups, with additional subgroup analyses of the characteristics age, gender, and measurement method being narratively synthesized. Overall, 57 studies with a total sample size of 119,094 participants (N between 10 and 60,560 subjects) from 14 countries worldwide with participants aged between four and 93 years were included. Thirty-two studies revealed a significant decline in PA, whereas only five studies found a significant increase in PA during the Covid-19 pandemic. Fourteen studies revealed mixed results. PA decreased in all age groups, independent of gender. Most self-reported and all device-based measurement methods showed a reduction in PA. However, effects were not found to be significant in all age groups. Nevertheless, the declining trend should be noted and governments should strive to enable PA within periods of pandemic restrictions, or promote alternatives such as digital training to avoid negative health consequences within the population
... However, applying wearables in studies assessing health-related questions implies several methodological and practical challenges. Here, strategies for data processing, monitoring protocols, assessment limitations (e.g., muscle-strengthening exercise), or quality criteria such as validity need to be taken into account [8]. ...
BACKGROUND
Wearable technology is a leading fitness trend with a growing commercial industry and an established method to collect physical behavior data in research studies. High-quality free-living validation studies are required to enable both researchers and consumers to make a guided decision on which study to rely on and which device to use. However, reviews that focus on the quality of free-living studies in adults are missing.
OBJECTIVE
The aim of the study is to provide a systematic evaluation of the characteristics and quality of free-living validation studies in adults.
METHODS
Peer-reviewed validation studies from electronic databases as well as back- and forward citation searches (1970 to July 2021) with following required indicators: (a) study protocol must include real-life conditions; (b) validated device outcome must belong to one dimension of the 24-hour PB construct (i.e., intensity, posture/activity type, biological state); (c) the study protocol must include a criterion measure; (d) study results must be published in peer-reviewed English language journals. The risk of bias was evaluated by using the QUADAS-2 tool with nine questions separated into four domains (i.e., patient selection/study design; index measure; criterion measure; flow and time).
RESULTS
Out of 13,285 unique search results, 223 articles were included and reviewed. Most studies (69.1%) validated an intensity measure outcome such as energy expenditure, but only 21.5 % biological state and 16.1% posture/activity type outcomes. The percentage of meeting the quality criteria ranged from 38.7% to 92.4%. Only, 52 studies were classified as “low risk” or with “some concerns”.
CONCLUSIONS
Overall, free-living validation studies of wearables assessing PB in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at biological state and posture/activity outcomes, and strive for standardized protocols embedded in a validation framework.
CLINICALTRIAL
CRD42021231033
... One approach might be to collect intensive longitudinal data via ambulatory assessment, which is the state-of-the-art methodology for assessing psychological variables such as mood via smartphone-based electronic diaries (e-diaries) multiple times per day. Furthermore, e-diaries can be combined with device-based assessments of PA or SB (Burchartz et al., 2020). Sophisticated approaches allow simultaneous assessment of mood and PA or SB, and connect both devices technically via Bluetooth low energy (Giurgiu, Niermann, Ebner-Priemer, & Kanning, 2020;Reichert et al., 2020). ...
Objective
Given the increasing number of mental disorders, a growing body of studies now focuses on the relationship between physical activity (PA), sedentary behavior (SB), and mental health. However, momentary mechanisms and interrelatedness between PA, SB, and mood in daily life are highly understudied.
Methods
To investigate whether the composition of light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and SB influences mood, we conducted an Ambulatory Assessment study in the everyday life of 103 university students over five days. We measured PA and SB via accelerometers and assessed mood up to six times each day on smartphone diaries. We combined compositional data analyses (CoDA) with multilevel modeling to analyze within-person effects of the behavioral composition on mood.
Results
Higher ratio of the SB to LPA and MVPA composition within the 60 minutes prior to a diary rating negatively influenced valence (p = 0.006) and energetic arousal (p < 0.001). Furthermore, a higher ratio of LPA to SB and MVPA positively influenced energetic arousal (p < 0.001) and a higher ratio of MVPA to SB and LPA positively influenced valence (p = 0.004) and energetic arousal (p = 0.022). Simulation analyses revealed that replacing 20 minutes of SB with PA can influence mood rating up to 3.39 units [range from 0–100].
Conclusions
Findings suggest that minutely replacements of SB with PA may lead to mood enhancements. Given the high prevalence of mental disorders, more studies are warranted to deepen the understanding of momentary compositional mechanisms between SB, PA, and mood. Applying CoDA to intensive longitudinal data can serve as a starting point to identify the optimal composition of SB, LPA, and MVPA for mood enhancements in everyday life.
... However, they are less accurate for unstructured PA (i.e., not in bouts of 10 min) and sedentary and light-intensity activity and are prone to measurement error and social desirability bias (Silsbury et al., 2015). Wearable devices, defined as electronic devices worn on the body, have fundamentally changed PA assessment over the past 20 years (Burchartza et al., 2020). The most common sensor is an accelerometer that measures body movement in one or more planes, though other sensors like heart rate, gyroscope, and skin temperature are increasingly common. ...
Over the past 40 years, physical activity (PA) and public health has been established as a field of study. A robust evidence base has emerged demonstrating that participation in recommended amounts of PA results in a wide array of physical and mental health benefits. This led to the establishment of federal and global PA guidelines and surveillance programs. Strong evidence supports the efficacy of individual-level (e.g., goal setting) and environmental (e.g., policies) interventions to promote PA. There has also been progress in establishing a skilled and diverse workforce to execute the work of PA and public health. Looking forward, major challenges include stemming the obesity and chronic disease epidemics, addressing health inequities, and diversifying the workforce. Given the known benefits of PA and the availability of evidence-based interventions, efforts now must focus on implementing this knowledge to improve population health and reduce inequities through PA.
... Device-based measurement of physical activity (PA) can differ greatly depending on the chosen epoch length [1]. Aim is to investigate the discrepancy of using 10 or 60 second epochs for different parameters and displays the implications on achieving the world health organization (WHO) guidelines [2] in children and adults. ...
Background. Objective measurement of physical activity (PA) can differ greatly depending on the chosen epoch length. Therefore, this study aims to investigate the discrepancy of using 10 or 60 second epochs concerning the achievement of PA guidelines in children and adults.
Method. PA of 46 families (N=148) was measured via triaxial accelerometry (Move 3/4, movisens GmbH, Karlsruhe, Germany) during the initial week of a family-based mobile health intervention (SMARTFAMILY). Data were included if 4 out of 7 consecutive days with a maximum of 960 minutes non-wear time per day were recorded. To compare results of 10 and 60 second epoch analyses concerning sedentary behavior (SB) (>1,5 MET), light (1,5-3 MET), moderate (3-6 MET), vigorous (>6 MET) and moderate to vigorous (MVPA) (>3 MET) PA have been statistically analyzed using paired t-tests. Furthermore, the impact on achieving World Health Organization PA guidelines is displayed descriptively.
Results. 69 adults (Mage=46,4; MBMI=24,4) fulfilled the inclusion criteria. The paired t-test between 10/60 second epochs showed significantly differences (p<0,001) for all PA parameters (10/60sec in min/week: SB=4942/4311; light=431/1280; moderate=664/468; vigorous=62/43; MVPA=726/511). If compared to the PA guidelines, this translates into 100%/94% of adults achieving 150 min moderate or 75 min vigorous PA per week for 10/60 second epoch lengths, respectively. Looking at the recommendations for additional health benefits of 300 min moderate or 150 min vigorous PA, this leads to the achievement of 93%/76% adults for 10/60 seconds.
68 children (Mage= 15,4; MBMI=19,1) fulfilled the inclusion criteria. The paired t-test between 10/60 second epochs showed significant differences (p<0,001) for all PA parameters (10/60sec in min/week: SB=4610/4124; light=322/906; moderate=630/557; vigorous=93/63; MVPA=722/620). If compared to the PA guidelines, this translates into 85%/74% of children achieving 420 min MVPA per week for 10/60 second epochs.
Conclusion. Overall, this study shows that epoch length impacts the number of participants classified as reaching PA guidelines. Which epoch length to use for comparison is especially difficult since commonly used PA guidelines are not yet based on accelerometry. Furthermore, the epoch length of choice seems to depend on the PA intensity and age group of interest.
... The use of accelerometers in physical activity research has increased significantly since the mid-1990s [28]. They are especially helpful in examining day-to-day variations in physical activity [29]. The advantage of accelerometry is that dense data can be collected over a period of multiple days. ...
Background
The primary symptoms of restless legs syndrome (RLS) are sleep onset insomnia and difficulty to maintain sleep. Previous studies have shown that regular physical activity can reduce the risk of developing RLS. However, the relationship between physical activity and sleep quality parameters in individuals suffering from RLS has not yet been investigated by applying accelerometry. Thus, the present study investigates the impact of physical activity (measuring both intensity levels and duration of physical activity) during the day (7–12 h, 12–18 h, 18–23 h) on sleep quality in patients suffering from idiopathic RLS by applying a real-time approach.
Methods
In a sample of 47 participants suffering from idiopathic RLS, physical activity and sleep quality were measured over one week using accelerometers. For data analysis, physical activity levels and step counts during three periods of the day (morning, afternoon, evening) were correlated with sleep quality parameters of the subsequent night.
Results
This observational study revealed that in most instances physical activity was not correlated with sleep parameters (two exceptions exist: steps taken in the morning were negatively correlated with periodic leg movements during sleep, and physical activity in the evening was negatively correlated with total sleep time). The physical activity levels of the participants in this study, however, were unexpectedly high compared to population-level data and variance in physical activity was low. The average activity was 13,817 (SD = 4086) steps and 347 (SD = 117) minutes of moderate physical activity per day in females, and 10,636 (SD = 3748) steps and 269 (SD = 69) minutes of moderate physical activity in males, respectively. Participants did not engage in any vigorous physical activity.
Conclusions
Further interventional studies are needed to investigate the daily effects of different intensities of physical activity on RLS symptoms.
... (1) Adequate and validated measurement tools should be used. Regarding PA measurement, objective measurement should be the means of choice [66] to conduct a comprehensive quantification of PA. If for feasibility reasons or large sample size requirements PA has to be measured by self-report, validated tools like the IPAQ [45] should be used instead of single items to increase study quality. ...
Higher education students often suffer from physiological and psychological health problems caused by stress, which may negatively impact their academic performance (AP). Physical activity (PA) can be a promising strategy to buffer these stress-induced complaints. Therefore, the aim of this investigation was to summarize evidence for the tridimensional construct of PA, stress, and AP, as well as to quantify the relationships among these variables. Five databases (PubMed, Scopus, SMEI, ERIC, and Web of Science) were systematically searched in November 2019 for publications that examined PA, stress, and AP of university students, without any restrictions regarding the publication period. The systematic review includes four original research studies with a moderate-to-high risk of bias. Results of included studies were narratively summarized and quantified in a meta-analysis using random effect models. Whereas study results point to a positive relation between PA and AP, relationships between PA and stress seem to be negative, while the relation between stress and AP is undecided. The meta-analysis found no significant associations and considerable heterogeneity of the results. Findings indicate a research gap concerning the connection of PA, stress, and AP in university students. Future studies should use validated measuring tools and consider the timepoint of data collection in order to extract truly stressful periods.
Physical inactivity is a significant global health risk, contributing to one-third of all deaths worldwide. The World Health Organization (WHO) has identified inadequate physical activity as the fourth leading risk factor for mortality. Large-scale data analyses by the WHO indicate that nearly one-third of the global population does not engage in sufficient physical activity, with even greater disparities among genders and young people. Insufficient physical activity among children and adolescents poses potential health risks for their current and future well-being.
To monitor physical activity, international guidelines were established by organizations like the American College of Sports Medicine and the WHO. Germany adopted the WHO recommendations, and population-based studies like DEGS, KiGGS, and MoMo were conducted to assess adherence to these guidelines. These studies incorporated the use of physical activity questionnaires and, more recently, accelerometers to obtain continuous and objective data on activity intensity.
Accelerometers have become popular for monitoring physical activity, and their sales have increased significantly over the years. ActiGraph accelerometers were utilized in various global epidemiological studies, including KiGGS and MoMo, to ensure comparability. However, using accelerometers requires careful consideration of technical decisions to derive accurate metrics. Researchers must account for factors such as device type, wearing position, recording parameters, study design, and data preprocessing.
This dissertation aimed to address key questions concerning accelerometer use in large epidemiological studies: (1) how to make technical decisions, (2) which methodological aspects to apply, (3) the implications of different evaluation methods, (4) how accelerometer data compare to questionnaire data, and (5) differences in physical activity patterns between weekdays and weekends. The work encompassed five scientific articles.
The "Consensus Article" reflects the expert consensus on accelerometer studies and emphasizes the importance of thorough familiarization with validation studies and documenting technical decisions. The "Study Protocol" presents the methodological aspects of the MoMo study's accelerometer measurements. The "Methods Article" investigates the impact of evaluation factors on quantifying physical activity with accelerometers. The "Comparison Article" compares self-reported and accelerometer-measured physical activity in children and adolescents, revealing low adherence to WHO recommendations. Finally, the "Typical Day" article analyzes accelerometer data to understand differences in physical activity between school and weekend days.
The findings underscore the critical need to address physical inactivity in children and adolescents. More precise measurements and international collaboration are necessary to develop effective interventions. Technical advancements, shorter epoch lengths, and improved data pooling methods are crucial for comprehensive and accurate reporting. In this context, CAPA can serve as a platform for bringing together experts from various disciplines to refine data acquisition methods and provide a realistic picture of physical behavior. Failure to address this global issue could lead to severe consequences in public health.
Public health messaging calls for individuals to be more physically active and less sedentary, yet these lifestyle behaviors have been historically studied independently. Both physical activity (PA) and sedentary behavior (SB) are linked through time-use in a 24-hour day and are related to health outcomes, such as neurocognition. While the benefits of PA on brain health in late adulthood have been well-documented, the influence of SB remains to be understood. The purpose of this paper was to critically review the evolving work on SB and brain health in late adulthood and emphasize key areas of consideration to inform potential research. Overall, the existing literature studying the impact of SB on the components and mechanisms of brain health are mixed and inconclusive, provided largely by cross-sectional and observational work employing a variety of measurement techniques of SB and brain health outcomes. Further, many studies did not conceptually or statistically account for the role of PA in the proposed relationships. Therefore, our understanding of the way in which SB may influence neurocognition in late adulthood is limited. Future efforts should include more prospective longitudinal and randomized clinical trials with intentional methodological approaches to better understand the relationships between SB and the brain in late adulthood, and how these potential links are differentiated from PA.
Purpose
To examine the associations between longitudinal physical activity (PA) patterns and the development of cardiometabolic risk factors from adolescence to young adulthood.
Methods
This cohort study encompassed 250 participants recruited from sports clubs and schools, and examined at mean age 15 and 19. Device‐measured moderate‐to‐vigorous PA was grouped into five patterns (via a data‐driven method, using inactivity maintainers as a reference). The outcomes were: glucose, insulin, homeostasis model assessment for insulin resistance (HOMA‐IR), total cholesterol, HDL and LDL cholesterol, triglycerides, blood pressure, and body mass index (BMI). Linear growth curve models were applied with adjustment for sex, age, fruit/vegetable consumption, cigarette/snuff use, and change in the device wear‐time.
Results
Insulin and BMI increased among decreasers from moderate to low PA (β for insulin 0.23, 95% CI 0.03–0.46; β for BMI 0.90; CI 0.02–1.78). The concentration of HDL cholesterol decreased (β −0.18, CI −0.31 to −0.05) and that of glucose increased (β 0.18, CI 0.02–0.35) among decreasers from high to moderate PA. By contrast, among increasers, blood pressure declined (systolic β −6.43, CI −12.16 to −0.70; diastolic β −6.72, CI −11.03 to −2.41).
Conclusions
Already during the transition to young adulthood, changes in PA are associated with changes in cardiometabolic risk factors. Favorable blood pressure changes were found among PA increasers. Unfavorable changes in BMI, insulin, glucose, and HDL cholesterol were found in groups with decreasing PA. The changes were dependent on the baseline PA and the magnitude of the PA decline.
Regular physical activity (PA) supports the long-term success of bariatric surgery. However, integrating health-enhancing physical activity in daily life requires specific competences. In this study, we evaluated a multimodal exercise programme to build these competences.
Forty adults who underwent bariatric surgery were randomised to a multimodal exercise programme or control group. Primary outcomes were the facets of PA-related health competences, namely the control competence for physical training, PA-specific affect regulation, motivational competence and PA-specific self-control. Secondary outcomes were PA behaviour and subjective vitality. Outcomes were assessed before, directly after the intervention and at 3 months follow-up.
Significant medium to large treatment effects were found for control competence for physical training and PA-specific self-control. The medium treatment effect for motivational competence remained marginally non-significant, probably influenced by our rather small sample size. No effect was found for PA-specific affect regulation. Significant large treatment effects were further observed for self-reported exercise and subjective vitality, all in favour of the intervention group. In contrast, no treatment effect was found for device-based PA. Overall, this study provides a foundation for future research to optimise long-term post bariatric surgery outcomes.
Individuals at risk of Developmental Coordination Disorder (DCD) have low levels of physical activity in childhood due to impaired motor competence, however physical activity levels in adulthood have not been established. This study sought to determine the impact of DCD risk on physical activity levels in adults using accelerometry measurement. Participants (n=656) from the Arvo Ylppö Longitudinal Study cohort had their motor competence assessed at the age of five years, and their physical activity quantified via device assessment at the age of 25 years. Between group differences were assessed to differentiate physical activity measures for individuals based on DCD risk status, with general linear modelling performed to control for the effects of sex, body mass index (BMI), and maternal education. Participants at risk of DCD were found to have a lower total number of steps (d=0.3, p=.022) than those not at risk. Statistical modelling indicated that DCD risk status increased time spent in sedentary light activity (β=0.1, 95% CI 0.02 to 0.3, p=.026) and decreased time spent in vigorous physical activity via interaction with BMI (β=0.04, 95% CI 0.001 to 0.1, p=.025). Sensitivity analysis found that visuomotor impairment did not significantly impact physical activity but did increase the role of DCD risk status in some models. This 20‐year‐longitudinal study indicated that DCD risk status continues to negatively impact on levels of physical activity into early adulthood.
This review estimated the effectiveness of behavior change interventions to increase physical activity (PA) among rural adult cancer survivors. Databases were systematically searched through July 2020. Citations were screened to identify studies to increase PA in adults diagnosed with cancer residing in rural areas. Meta-analyses assessed proportion of participants achieving PA goal, differences in aerobic PA and strength training, and retention from baseline to post-intervention. Seven studies met inclusion criteria encompassing 722 participants (591 intervention, 131 controls). Overall quality of evidence was low to medium. The pooled proportion of participants achieving PA goals (150-225 minutes/week) was 39% (95% CI: 18-62%). Mean time spent engaging in aerobic PA increased from baseline to post-intervention (range: 6-52 weeks, M=97.7 minutes/week, 95% CI: 75.0-120.4), and the mean difference in time spent on strength training was 12.2 minutes/week (95% CI: -8.3-32.8). The pooled retention rate was 82% (95% CI: 69-92%) at 6-78 weeks. Due to the modest intervention effects, low quality of evidence, and small number of studies, further rigorously designed behavior change interventions, including randomized controlled trials with long-term follow up, are needed to confirm efficacy for increasing PA in rural cancer survivors and to test innovative implementation strategies to enhance reach and effectiveness.
The inter-relationship between physical activity, sedentary behaviour and sleep (collectively defined as physical behaviours) is of interest to researchers from different fields. Each of these physical behaviours has been investigated in epidemiological studies, yet their codependency and interactions need to be further explored and accounted for in data analysis. Modern accelerometers capture continuous movement through the day, which presents the challenge of how to best use the richness of these data. In recent years, analytical approaches first applied in other scientific fields have been applied to physical behaviour epidemiology (eg, isotemporal substitution models, compositional data analysis, multivariate pattern analysis, functional data analysis and machine learning). A comprehensive description, discussion, and consensus on the strengths and limitations of these analytical approaches will help researchers decide which approach to use in different situations. In this context, a scientific workshop and meeting were held in Granada to discuss: (1) analytical approaches currently used in the scientific literature on physical behaviour, highlighting strengths and limitations, providing practical recommendations on their use and including a decision tree for assisting researchers’ decision-making; and (2) current gaps and future research directions around the analysis and use of accelerometer data. Advances in analytical approaches to accelerometer-determined physical behaviours in epidemiological studies are expected to influence the interpretation of current and future evidence, and ultimately impact on future physical behaviour guidelines.
Background
The physical and cognitive benefits of moderate-vigorous intensity physical activity (MVPA) for adults have been well documented. Recently, there has been increasing interest in the independent health benefits of light-intensity physical activity (LPA). This research has primarily focused on the relationship between LPA and morbidity and mortality risk, with few studies investigating cognitive associations. The purpose of this scoping review was to catalog existing evidence on the association between device-based or technologically measured LPA and cognition among adults, identify trends in the literature, and recommend future areas for research.
Methods
Six electronic databases were searched between January and June 2020. Forty published studies met the inclusion criteria, which included both healthy and clinical young and older adult populations. Among the 40 articles were 14 acute exercise studies, four randomized control trials (RCTs), 18 cross-sectional studies, and four longitudinal studies.
Results
7/14 (50%) acute, 3/4 (75%) RCT, 10/18 (56%) cross-sectional, and 2/4 (50%) longitudinal studies reported a significant, positive relationship between LPA and one or more cognitive outcomes. These heterogeneous findings can largely be attributed to the diverse study designs and populations, as well as the numerous assessments used to test the cognitive domains.
Conclusion
These collective findings suggest LPA may be a potential lifestyle intervention to improve cognition across adulthood. However, the inconsistent approaches used among these studies suggest a more concerted, unified scientific approach is needed to further understand the LPA-cognition relationship.
Background:
To date no nationwide objective physical activity (PA) data exists for children and adolescents living in Germany. The KiGGS and MoMo-Study is a national cohort study and has in its most recent data collection wave (wave 2 since 2014) incorporated accelerometers. This wave 2 marks the first nationwide collection of objective data on PA of children and adolescents living in Germany.
Objective:
The purpose of this study protocol is to describe the methods used to capture intensity, frequency and duration of PA with accelerometers in this study.
Methods:
Participants (n=11,003; aged 6-31yrs) are instructed to wear an ActiGraph GT3X+/wGT3X-BT laterally on the right hip. Accelerometers are worn on consecutive days during waking hours to include at least four valid weekdays and one weekend day (weartime >8h) in the evaluation. A non-wear-time protocol was also implemented.
Results:
Data collection was completed by October 2017. Data harmonization took place in 2018. The first accelerometer results from this wave are anticipated to be published in 2019.
Conclusions:
This study protocol provides an overview of the technical details and basic choices when using accelerometers in large-scale epidemiological studies. At the same time the restrictions imposed by the specified filters and the evaluation routines must be taken into account.
Clinicaltrial:
Objectives: Our count-scaled algorithm automatically scores sleep across 24 hours to process sleep timing, quantity, and quality. The aim of this study was to validate the algorithm against overnight PSG in children to determine the best site placement for sleep. Methods: 28 children (5–8 years) with no history of sleep disturbance wore two types of accelerometers (ActiGraph GT3X+ and Actical) at two sites (left hip, non-dominant wrist) for 24-h. Data were processed using the count-scaled algorithm. PSG data were collected using an in-home Type 2 device. PSG-actigraphy epoch sensitivity (sleep agreement) and specificity (wake agreement) were determined and sleep outcomes compared for timing (onset and offset), quantity [sleep period time (SPT) and total sleep time (TST)], and quality metrics [sleep efficiency and waking after sleep onset (WASO)]. Results: Overall, sensitivities were high (89.1% to 99.5%) and specificities low (21.1% to 45.7%). Sleep offset was accurately measured by actigraphy, regardless of brand or placement site. By contrast, sleep onset agreed with PSG using hip-positioned but not wrist-positioned devices (difference ActiGraph : PSG 21 min, P < .001; Actical : PSG 14 min, P < .001). The ActiGraph at the wrist accurately detected WASO and sleep efficiency, but under (−34 min, P < .001) and overestimated (5.8%, P < .001) these at the hip. The Actical under- and over-estimated these variables respectively at both sites. Results for TST varied ranging from significant differences to PSG of −26 to 21 min (ActiGraph wrist and hip respectively) and 9 min (ns) to 59 min for Actical (wrist and hip respectively). Conclusion: Overall the count-scaled algorithm produced high sensitivity at the expense of low specificity in comparison with PSG. A best site placement for estimates of all sleep variables could not be determined, but overall the results suggested ActiGraph GT3X+ at the hip may be superior for sleep timing and quantity metrics, whereas the wrist may be superior for sleep quality metrics. Both devices placed at the hip performed well for sleep timing but not for sleep quality. Differences are likely linked to freedom of movement of the wrist vs the trunk (hip) during overnight sleep.
Background
Differentiating nonwear time from sleep and wake times is essential for the estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is required. Therefore, we developed a classification algorithm to determine nonwear, sleep and wake periods from accelerometer data. Our work aimed to (I) develop a new pattern recognition algorithm for identifying nonwear periods from actigraphy data based on the influence of respiration rate on the power spectrum of the acceleration signal and implement it in an automatic classification algorithm for nonwear/sleep/wake states; (II) address motion artifacts that occur during nonwear periods and are known to cause misclassification of these periods; (III) adjust the algorithm depending on the sensor position (wrist, chest); and (IV) validate the algorithm on both healthy individuals and patients with sleep disorders.
Methods
The study involved 98 participants who wore wrist and chest acceleration sensors for one day of measurements. They spent one night in the sleep laboratory and continued to wear the sensors outside of the laboratory for the remainder of the day. The results of the classification algorithm were compared to those of the reference source: polysomnography for wake/sleep and manual annotations for nonwear/wear classification.
Results
The median kappa values for the two locations were 0.83 (wrist) and 0.84 (chest). The level of agreement did not vary significantly by sleep health (good sleepers vs. subjects with sleep disorders) ( p = 0.348, p = 0.118) or by sex ( p = 0.442, p = 0.456). The intraclass correlation coefficients of nonwear total time between the reference and the algorithm were 0.92 and 0.97 with the outliers and 0.95 and 0.98 after the outliers were removed for the wrist and chest, respectively. There was no evidence of an association between the mean difference (and 95% limits of agreement) and the mean of the two methods for either sensor position (wrist p = 0.110, chest p = 0.164), and the mean differences (algorithm minus reference) were 5.11 [95% LoA −15.4–25.7] and 1.32 [95% LoA −9.59–12.24] min/day, respectively, after the outliers were removed.
Discussion
We studied the influence of the respiration wave on the power spectrum of the acceleration signal for the differentiation of nonwear periods from sleep and wake periods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer.
Apply It!
From this article, the reader should understand the following concepts:
• Articulate the differences between a fad and a trend
• Use the worldwide trends in commercial, corporate, clinical (including medical fitness), and community health fitness industry to further promote physical activity
• Study expert opinions about identified fitness trends for 2020
Background:
The analysis of associations between accelerometer-derived physical activity (PA) intensities and cardiometabolic health is a major challenge due to multicollinearity between the explanatory variables. This challenge has facilitated the application of different analytic approaches within the field. The aim of the present study was to compare association patterns of PA intensities with cardiometabolic health in children obtained from multiple linear regression, compositional data analysis, and multivariate pattern analysis.
Methods:
A sample of 841 children (age 10.2 ± 0.3 years; BMI 18.0 ± 3.0; 50% boys) provided valid accelerometry and cardiometabolic health data. Accelerometry (ActiGraph GT3X+) data were characterized into traditional (four PA intensity variables) and more detailed categories (23 PA intensity variables covering the intensity spectrum; 0-99 to ≥10,000 counts per minute). Several indices of cardiometabolic health were used to create a composite cardiometabolic health score. Multiple linear regression and multivariate pattern analyses were used to analyze both raw and compositional data.
Results:
Besides a consistent negative (favorable) association between vigorous PA and the cardiometabolic health measure using the traditional description of PA data, associations between PA intensities and cardiometabolic health differed substantially depending on the analytic approaches used. Multiple linear regression lead to instable and spurious associations, while compositional data analysis showed distorted association patterns. Multivariate pattern analysis appeared to handle the raw PA data correctly, leading to more plausible interpretations of the associations between PA intensities and cardiometabolic health.
Conclusions:
Future studies should consider multivariate pattern analysis without any transformation of PA data when examining relationships between PA intensity patterns and health outcomes.
Trial registration:
The study was registered in Clinicaltrials.gov 7th of April 2014 with identification number NCT02132494 .
Purpose:
The physical activity profile can be described from accelerometer data using two population- independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This paper aims to: 1) demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across datasets; and 2) illustrate the future potential of the metrics for generation of age and sex-specific percentile norms.
Methods:
Secondary data analyses were carried out on five diverse datasets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (T2D) (N=475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were: a) zBMI (children); b) %fat (adolescent girls and adults); c) bone health (pre- and post-menopausal women); and d) physical function (adults with T2D).
Results:
Multiple regression analyses showed the IG, but not ACC, was independently associated with zBMI/%fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile 'norms' showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC.
Conclusion:
The ACC and IG accelerometer metrics facilitate investigation of whether volume and intensity of physical activity have independent, additive or interactive effects on health markers. Future, adoption of data-driven metrics would facilitate the generation of age- and sex- specific norms that would be beneficial to researchers.
Objectives
To describe objectively measured sleep characteristics in children aged 11–12 years and in parents and to examine intergenerational concordance of sleep characteristics.
Design
Population-based cross-sectional study (the Child Health CheckPoint), nested within the Longitudinal Study of Australian Children.
Setting
Data were collected between February 2015 and March 2016 across assessment centres in Australian major cities and selected regional towns.
Participants
Of the participating CheckPoint families (n=1874), sleep data were available for 1261 children (mean age 12 years, 50% girls), 1358 parents (mean age 43.8 years; 88% mothers) and 1077 biological parent–child pairs. Survey weights were applied and statistical methods accounted for the complex sample design, stratification and clustering within postcodes.
Outcome measures
Parents and children were asked to wear a GENEActive wrist-worn accelerometer for 8 days to collect objective sleep data. Primary outcomes were average sleep duration, onset, offset, day-to-day variability and efficiency. All sleep characteristics were weighted 5:2 to account for weekdays versus weekends. Biological parent–child concordance was quantified using Pearson’s correlation coefficients in unadjusted models and regression coefficients in adjusted models.
Results
The mean sleep duration of parents and children was 501 min (SD 56) and 565 min (SD 44), respectively; the mean sleep onset was 22:42 and 22:02, the mean sleep offset was 07:07 and 07:27, efficiency was 85.4% and 84.1%, and day-to-day variability was 9.9% and 7.4%, respectively. Parent–child correlation for sleep duration was 0.22 (95% CI 0.10 to 0.28), sleep onset was 0.42 (0.19 to 0.46), sleep offset was 0.58 (0.49 to 0.64), day-to-day variability was 0.25 (0.09 to 0.34) and sleep efficiency was 0.23 (0.10 to 0.27).
Conclusions
These normative values for objective sleep characteristics suggest that, while most parents and children show adequate sleep duration, poor-quality (low efficiency) sleep is common. Parent–child concordance was strongest for sleep onset/offset, most likely reflecting shared environments, and modest for duration, variability and efficiency.
Associations between multicollinear accelerometry-derived physical activity (PA) data and cardiometabolic health in children needs to be analyzed using an approach that can handle collinearity among the explanatory variables. The aim of this paper is to provide readers a tutorial overview of interpretation of multivariate pattern analysis models using PA accelerometry data that reveals the associations to cardiometabolic health. A total of 841 children (age 10.2 ± 0.3 years) provided valid data on accelerometry (ActiGraph GT3X+) and six indices of cardiometabolic health that were used to create a composite score. We used a high-resolution PA description including 23 intensity variables covering the intensity spectrum (from 0–99 to ≥10000 counts per minute), and multivariate pattern analysis to analyze data. We report different statistical measures of the multivariate associations between PA and cardiometabolic health and use decentile groups of PA as a basis for discussing the meaning and impact of multicollinearity. We show that for high-resolution accelerometry data; considering all explanatory variables is crucial to obtain a correct interpretation of associations to cardiometabolic health; which is otherwise strongly confounded by multicollinearity in the dataset. Thus; multivariate pattern analysis challenges the traditional interpretation of findings from linear regression models assuming independent explanatory variables
Objectives:
Our aim is to demonstrate how a data-driven accelerometer metric, the acceleration above which a person's most active minutes are accumulated, can (a) quantify the prevalence of meeting current physical activity guidelines for global surveillance and (b) moving forward, could inform accelerometer-driven physical activity guidelines. Unlike cut-point methods, the metric is population-independent (e.g. age) and potentially comparable across datasets.
Design:
Cross-sectional, secondary data analysis.
Methods:
Analyses were carried out on five datasets using wrist-worn accelerometers: children (N=145), adolescent girls (N=1669), office workers (N=114), pre- (N=1218) and post- (N=1316) menopausal women, and adults with type 2 diabetes (N=475). Open-source software (GGIR) was used to generate the magnitude of acceleration above which a person's most active 60, 30 and 2min are accumulated: M60ACC; M30ACC and M2ACC, respectively.
Results:
The proportion of participants with M60ACC (children) and M30ACC (adults) values higher than accelerations representative of brisk walking (i.e., moderate-to-vigorous physical activity) ranged from 17 to 68% in children and 15 to 81% in adults, tending to decline with age. The proportion of pre-and post-menopausal women with M2ACC values meeting thresholds for bone health ranged from 6 to 13%.
Conclusions:
These metrics can be used for global surveillance of physical activity, including assessing prevalence of meeting current physical activity guidelines. As accelerometer and corresponding health data accumulate it will be possible to interpret the metrics relative to age- and sex- specific norms and derive evidence-based physical activity guidelines directly from accelerometer data for use in future global surveillance. This is where the potential advantages of these metrics lie.
Despite the health benefits associated with physical activity (PA), screen time reduction, and sleep quantity and quality, the relationships between PA, screen time, and sleep quantity and quality remain unclear in adolescents. The present study is a cross-sectional analysis of data from adolescents aged 16–19 years who participated in the 2005–2006 National Health and Nutrition Examination Survey (n = 542). Multivariable logistic regression models, adjusted for confounders, examined the relationship between objectively measured PA, self-reported screen time, and sleep quantity and quality. Respondents who met the current PA recommendation had 50% lower odds of having sufficient sleep (≥8 h) than those not meeting the recommendation (OR = 0.50, 95% CI: 0.26, 0.94). Respondents who met the screen time recommendation (≤2 h/day) had 55% lower odds of reporting poor sleep quality than those whose screen time exceeded the recommendation (OR = 0.45, 95% CI: 0.22, 0.91), with similar patterns observed for females and males. However, males who met both PA and screen time recommendations had 73% lower odds of reporting poor sleep quality than males who met neither recommendation (OR = 0.27, 95% CI: 0.07, 0.99). In conclusion, PA and screen time are associated with sleep quantity or sleep quality in adolescents, and there are differences in these associations by sex.
Isotemporal substitution model was first developed in 2009 [...]
Introduction:
A third of all children spend at least 60 min·d in moderate to vigorous physical activity (MVPA) as recommended by the World Health Organization. We investigated the distribution of objectively measured physical activity (PA) intensities achieved by schoolchildren during physical education (PE) and organized youth sports (OYS) and the contribution of PE and OYS to the achievement of the PA recommendations.
Methods:
In two surveys, PA of 396 children was measured using accelerometry during typical school weeks. Proportions (%) of sedentary behavior, light PA, and MVPA per day and particularly during sports were assessed in children 6-17 yr old. Adjusted odds ratios (AOR) were calculated to analyze the association between frequency (d·wk) and duration (min·wk) of PE and OYS and meeting the PA recommendations.
Results:
Average proportions of MVPA during PE and OYS ranged between 15% and 31%. Between 68% and 74% of children (6-10 yr) and between 27% and 37% of adolescents (11-17 yr) met the PA recommendations. In both age-groups, PE and OYS on at least 2 d·wk were both significantly associated with meeting the PA recommendations (AOR for PE = 3.60, 95% confidence interval [CI] = 2.05-6.31; AOR for OYS = 3.83, 95% CI = 2.09-6.99), compared with no PE or no OYS in that week. Likewise, significant AOR values were found for >90 min of PE per week (AOR = 3.48, 95% CI = 2.03-5.98) and >120 min of OYS per week (AOR = 3.72, 95% CI = 2.01-6.89), compared with no PE or no OYS in that week, respectively.
Conclusion:
Schoolchildren spent less than one-third of PE and OYS in MVPA. PE and OYS were found to support children and adolescents to engage sufficiently in MVPA as recommended by the World Health Organization.
Background: Objective measures using accelerometer-based activity monitors have been extensively used in physical activity (PA) and sedentary behavior (SB) research. To measure PA and SB precisely, the field is shifting towards machine learning-based (ML) approaches for calibration and validation of accelerometer-based activity monitors. Nevertheless, various parameters regarding the use and development of ML-based models, including data type (raw acceleration data versus activity counts), sampling frequency, window size, input features, ML technique, accelerometer placement, and free-living settings, affect the predictive ability of ML-based models. The effects of these parameters on ML-based models have remained elusive, and will be systematically reviewed here. The open challenges were identified and recommendations are made for future studies and directions.
Method: We conducted a systematic search of PubMed and Scopus databases to identify studies published before July 2017 that used ML-based techniques for calibration and validation of accelerometer-based activity monitors. Additional articles were manually identified from references in the identified articles. Results: A total of 62 studies were eligible to be included in the review, comprising 48 studies that calibrated and validated ML-based models for predicting the type and intensity of activities, and 22 studies for predicting activity energy expenditure.
Conclusions: It appears that various ML-based techniques together with raw acceleration data sampled at 20-30 Hz provide the opportunity of predicting the type and intensity of activities, as well as activity energy expenditure with comparable overall predictive accuracies regardless of accelerometer placement. However, the high predictive accuracy of laboratory-calibrated models is not reproducible in free-living settings, due to transitive and unseen activities together with differences in acceleration signals.
Abstract Background New physical activity guidelines for children address all movement behaviors across the 24-h day (physical activity, sedentary behavior, sleep), but how each component relates to body composition when adjusted for the compositional nature of 24-h data is uncertain. Aims To i) describe 24-h movement behaviors from 1 to 5 years of age, ii) determine cross-sectional relationships with body mass index (BMI) z-score, iii) determine whether movement behaviors from 1 to 5 years of age predict body composition and bone health at 5 years. Methods 24-h accelerometry data were collected in 380 children over 5–7 days at 1, 2, 3.5 and 5 years of age to determine the proportion of the day spent: sedentary (including wake after sleep onset), in light (LPA) and moderate-to-vigorous physical activity (MVPA), and asleep (including naps). BMI was determined at each age and a dual-energy x-ray absorptiometry (DXA) scan measured fat mass, bone mineral content (BMC) and bone mineral density (BMD) at 5 years of age. 24-h movement data were transformed into isometric log-ratio co-ordinates for multivariable regression analysis and effect sizes back-transformed. Results At age 1, children spent 49.6% of the 24-h day asleep, 38.2% sedentary, 12.1% in LPA, and 0.1% in MVPA, with corresponding figures of 44.4, 33.8, 19.8 and 1.9% at 5 years of age. Compositional time use was only related significantly to BMI z-score at 3.5 years in cross-sectional analyses. A 10% increase in mean sleep time (65 min) was associated with a lower BMI z-score (estimated difference, − 0.25; 95% CI, − 0.42 to − 0.08), whereas greater time spent sedentary (10%, 47 min) or in LPA (10%, 29 min) were associated with higher BMI z-scores (0.12 and 0.08 respectively, both p
Table of Contents Summary
The annual ACSM’s Health & Fitness Journal® worldwide survey to determine industry trends by health fitness professionals is now in its 13th consecutive year. Strength training (the #5 trend in 2018) was dropped from this year’s survey because it was determined to be too generic. In previous surveys, educated, certified, and experienced fitness professionals that was a top performing trend (#1 in 2008–2013 and in the top 10 in every survey) also was dropped from the survey because it was too broad and lacked the necessary specificity. Employing certified fitness professionals was added as a potential new trend for 2019 and was rated #6. Wearable technology regained the #1 trend after dropping to #3 in 2018. Group training (more than five participants) was rated #2, which is the spot it held in 2018. Last year’s #1 trend, high-intensity interval training, dropped to #3 for 2019. Regaining spots in the top 20 fitness trends include mobile exercise devices (#13), worksite health promotion and workplace well-being programs (#15), outcome measurements (#16), and postrehabilitation classes (#20). Dropping out of the top 20 fitness trends include circuit weight training, sport-specific training, and core training. Four international experts independently commented on the results.
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Standard statistical modelling has shown that the reallocation of sitting time to either standing or stepping may be beneficial for metabolic health. However, this overlooks the inherent dependency of time spent in all behaviours. The aim is to examine the associations between physical behaviours and markers of metabolic health (fasting glucose, fasting insulin, 2-h glucose, 2-h insulin, Homeostasis Model Assessment of Insulin Sensitivity (HOMA-IS), Matsuda Insulin Sensitivity Index (Matsuda-ISI) while quantifying the associations of reallocating time from one physical behaviour to another using compositional analysis. Objectively measured physical behaviour data were analysed (n = 435) using compositional analysis and compositional isotemporal substitutions to estimate the association of reallocating time from one behaviour to another in a population at high risk of type 2 diabetes mellitus (T2DM). Stepping time was associated with all markers of metabolic health relative to all other behaviours. Reallocating 30 min from sleep, sitting, or standing to stepping was associated with 5–6 fold lower 2-h glucose, 15–17 fold lower 2-h insulin, and higher insulin sensitivity (10–11 fold via HOMA-IS, 12–15 fold via Matsuda-ISI). Associations of reallocating time from any behaviour to stepping were maintained for 2-h glucose, 2-h insulin, and Matsuda-ISI after further adjusting for body mass index (BMI). Relocating time from stepping into sleep, sitting, or standing was associated with lower insulin sensitivity. Stepping time may be the most important behavioural composition when promoting improved metabolic health in adults at risk of T2DM.
Objectives. Sleep disruption in laboratory studies increases adiposity and decreases glucose tolerance. However, few epidemiological studies have used objective measures of sleep. This study aims to assess associations between sleep duration, timing, and regularity with measures of adiposity.
Methods. Cross‐sectional study of 188 children with obesity (age: 10.50 ± 1.39 years; body mass index: 29.25 ± 5.03 kg m‐2). Nightly sleep duration, bedtime and wake time were measured by multiple‐day actigraphy and parent reports. Percent overweight (percent over median BMI for age and sex) was chosen as the primary measure of obesity status. Objective measures of height, weight, waist circumference, blood pressure, fasting blood lipids, glucose, insulin, glycated hemoglobin, and C‐reactive protein were obtained. Television screen time and total caloric intake were assessed via parent questionnaire.
Results. Each hour later weekday bedtime was associated with an additional 6.17 percent overweight (95% CI: 1.42‐10.92). Each hour greater day‐to‐day variability in weekday bedtime and weekday wake time were associated with an additional 10.20 (95% CI: 0.50‐19.91) and 10.02 (95% CI: 1.55‐18.50) percent overweight, respectively. Associations were similar after controlling for other obesity‐related behaviors (television screen time, total caloric intake, and physical activity.)
Conclusions. Among children with obesity, later bedtime and greater variability in bedtime and wake time are associated with greater adiposity, independent of other obesity‐related behaviors. Early bedtime and wake time and consistent day‐to‐day sleep timing may be strategies to reduce adiposity in high risk children.
Background
24 h-accelerometry is now used to objectively assess physical activity (PA) in many observational studies like the German National Cohort; however, PA variability, observational time needed to estimate habitual PA, and reliability are unclear.
Methods
We assessed 24 h-PA of 50 participants using triaxial accelerometers (ActiGraph GT3X+) over 2 weeks. Variability of overall PA and different PA intensities (time in inactivity and in low intensity, moderate, vigorous, and very vigorous PA) between days of assessment or days of the week was quantified using linear mixed-effects and random effects models. We calculated the required number of days to estimate PA, and calculated PA reliability using intraclass correlation coefficients.
Results
Between- and within-person variance accounted for 34.4–45.5% and 54.5–65.6%, respectively, of total variance in overall PA and PA intensities over the 2 weeks. Overall PA and times in low intensity, moderate, and vigorous PA decreased slightly over the first 3 days of assessment. Overall PA (p = 0.03), time in inactivity (p = 0.003), in low intensity PA (p = 0.001), in moderate PA (p = 0.02), and in vigorous PA (p = 0.04) slightly differed between days of the week, being highest on Wednesday and Friday and lowest on Sunday and Monday, with apparent differences between Saturday and Sunday. In nested random models, the day of the week accounted for < 19% of total variance in the PA parameters. On average, the required number of days to estimate habitual PA was around 1 week, being 7 for overall PA and ranging from 6 to 9 for the PA intensities. Week-to-week reliability was good (intraclass correlation coefficients, range, 0.68–0.82).
Conclusions
Individual PA, as assessed using 24 h-accelerometry, is highly variable between days, but the day of assessment or the day of the week explain only small parts of this variance. Our data indicate that 1 week of assessment is necessary for reliable estimation of habitual PA.
Background: New fitness trackers and smartwatches are released to the consumer market every year. These devices are equipped with different sensors, algorithms, and accompanying mobile apps. With recent advances in mobile sensor technology, privately collected physical activity data can be used as an addition to existing methods for health data collection in research. Furthermore, data collected from these devices have possible applications in patient diagnostics and treatment. With an increasing number of diverse brands, there is a need for an overview of device sensor support, as well as device applicability in research projects.
Objective: The objective of this study was to examine the availability of wrist-worn fitness wearables and analyze availability of relevant fitness sensors from 2011 to 2017. Furthermore, the study was designed to assess brand usage in research projects, compare common brands in terms of developer access to collected health data, and features to consider when deciding which brand to use in future research.
Methods: We searched for devices and brand names in six wearable device databases. For each brand, we identified additional devices on official brand websites. The search was limited to wrist-worn fitness wearables with accelerometers, for which we mapped brand, release year, and supported sensors relevant for fitness tracking. In addition, we conducted a Medical Literature Analysis and Retrieval System Online (MEDLINE) and ClinicalTrials search to determine brand usage in research projects. Finally, we investigated developer accessibility to the health data collected by identified brands.
Results: We identified 423 unique devices from 132 different brands. Forty-seven percent of brands released only one device. Introduction of new brands peaked in 2014, and the highest number of new devices was introduced in 2015. Sensor support increased every year, and in addition to the accelerometer, a photoplethysmograph, for estimating heart rate, was the most common sensor. Out of the brands currently available, the five most often used in research projects are Fitbit, Garmin, Misfit, Apple, and Polar. Fitbit is used in twice as many validation studies as any other brands and is registered in ClinicalTrials studies 10 times as often as other brands.
Conclusions: The wearable landscape is in constant change. New devices and brands are released every year, promising improved measurements and user experience. At the same time, other brands disappear from the consumer market for various reasons. Advances in device quality offer new opportunities for research. However, only a few well-established brands are frequently used in research projects, and even less are thoroughly validated.
Background
Daily activity data are by nature compositional data. Accordingly, they occupy a specific geometry with unique properties that is different to standard Euclidean geometry. This study aimed to estimate the difference in adiposity associated with isotemporal reallocation between daily activity behaviours, and to compare the findings from compositional isotemporal subsitution to those obtained from traditional isotemporal substitution.
Methods
We estimated the differences in adiposity (body fat%) associated with reallocating fixed durations of time (isotemporal substitution) between accelerometer-measured daily activity behaviours (sleep, sedentary time and light and moderate-to-vigorous physical activity (MVPA)) among 1728 children aged 9–11 years from Australia, Canada, Finland and the UK (International Study of Childhood Obesity, Lifestyle and the Environment, 2011–2013). We generated estimates from compositional isotemporal substitution models and traditional non-compositional isotemporal substitution models.
Results
Both compositional and traditional models estimated a positive (unfavourable) difference in body fat% when time was reallocated from MVPA to any other behaviour. Unlike traditional models, compositional models found the differences in estimated adiposity (1) were not necessarily symmetrical when an activity was being displaced, or displacing another (2) were not linearly related to the durations of time reallocated, and (3) varied depending on the starting composition.
Conclusion
The compositional isotemporal model caters for the constrained and therefore relative nature of activity behaviour data and enables all daily behaviours to be included in a single statistical model. The traditional model treats data as real variables, thus the constrained nature of time is not accounted for, nor reflected in the findings. Findings from compositional isotemporal substitution support the importance of MVPA to children’s health, and suggest that while interventions to increase MVPA may be of benefit, attention should be directed towards strategies to avoid decline in MVPA levels, particularly among already inactive children. Future applications of the compositional model can extend from pair-wise reallocations to other configurations of time-reallocation, for example, increasing MVPA at the expense of multiple other behaviours.
Purpose:
Commonly used physical activity metrics tell us little about the intensity distribution across the activity profile. The purpose of this paper is to introduce a metric, the intensity gradient, which can be used in combination with average acceleration (overall activity level) to fully describe the activity profile.
Methods:
1669 adolescent girls (sample 1) and 295 adults with type 2 diabetes (sample 2) wore a GENEActiv accelerometer on their non-dominant wrist for up to 7-days. Body mass index and percent body fat were assessed in both samples and physical function (grip strength, Short Physical Performance Battery, sit-to-stand repetitions) in sample 2. Physical activity metrics were: average acceleration (AccelAV); the intensity gradient (IntensityGRAD from the log-log regression line: 25 mg intensity bins (x)/time accumulated in each bin (y)); total moderate-to-vigorous physical activity (MVPA); and bouted MVPA (sample 2 only).
Results:
Correlations between AccelAV and the IntensityGRAD (r=0.39-0.51) were similar to correlations between AccelAV and bouted MVPA (r=0.48), and substantially lower than between AccelAV and total MVPA (r>0.93). The IntensityGRAD was negatively associated with body fatness in sample 1 (p<0.05) and positively associated with physical function in sample 2 (p<0.05); associations were independent of AccelAV and potential co-variates. In contrast, MVPA was not independently associated with body fatness or physical function.
Conclusion:
AccelAV and the IntensityGRAD provide a complementary description of a person's activity profile, each explaining unique variance, and independently associated with body fatness and/or physical function. Both metrics are appropriate for reporting as standardised measures and suitable for comparison across studies using raw acceleration accelerometers. Concurrent use will facilitate investigation of the relative importance of intensity and volume of activity for a given outcome.
This study examines accelerometer-based and self-report assessment of sleep disturbance from a larger prospective cohort of youth 5 to 18 years of age with postconcussive injury. Twenty-one participants with self-reported sleep disturbance were evaluated using accelerometers. Participants completed the Pittsburgh Sleep Quality Index (PSQI) every 48 hours and also measured sleep via accelerometry. Correlations were conducted matching PSQI scores to accelerometry assessment. PSQI scores were significantly correlated only with “average number of awakenings” (r = −0.21; P = .049). Accelerometer-measured mean (standard deviation) sleep efficiency was 79.9% (5.20%), with normal sleep defined as >85%. The mean (standard deviation) PSQI global score was 10.5 (3.78) out of 21, where scores of >5 indicate subjective insomnia. Results suggest the PSQI and accelerometers may be measuring different attributes of sleep. Both may be needed as actual sleep is important but so is perception of good sleep. These findings call for further validity testing of objective sleep assessment measures and commonly used self-report tools.
Hip-worn accelerometers are widely used to estimate physical activity (PA), but the accuracy of acceleration-threshold based analysis is compromised when it comes to identifying stationary and sedentary behaviors, let alone classifying body postures into lying, sitting or standing. The purpose of this study was to devise a novel method for accurate classification of body posture using triaxial data from hip-worn accelerometer and to evaluate its performance in free-living conditions against a thigh-worn accelerometer. The posture classification rested on two facts: constant Earth's gravity vector and upright walking posture. Thirty healthy adults wore a hip-mounted accelerometer and underwent an array of lying, sitting, standing and walking tasks. Task type, their order and length were randomly assigned for each participant. During walking, the accelerometer orientation in terms of gravity vector was taken as reference, and the angle for posture estimation (APE) was determined from the incident accelerometer orientation in relation to the reference vector. Receiver operating characteristic (ROC) curve yielded an optimal cut-point APE of 64.9° (sensitivity 100% and specificity 100%) for lying and sitting and 11.6° (94.2%; 94.5%) for sitting and standing. In free-living conditions, high agreement (89.2% for original results and 90.4% for median-filtered results) in identifying sedentary periods (sitting and lying) was observed between the results from hip- and thigh-worn accelerometers. Walking provides a valid reference activity to determine the body posture. The proposed APE analysis of the raw data from hip-worn triaxial accelerometer gives accurate and specific information about daily times spent lying, sitting and standing.
Purpose:
Accelerometers are increasingly being used to assess physical activity in large-scale surveys. Establishing whether key physical activity outcomes can be considered equivalent between three widely-used accelerometer brands would be a significant step towards capitalising on the increasing availability of accelerometry data for epidemiological research.
Methods:
Twenty participants wore a GENEActiv, Axivity AX3 and ActiGraph GT9X on their non-dominant wrist and were observed for two-hours in a simulated living space. Participants undertook a series of seated and upright light/active behaviours at their own pace. All accelerometer data were processed identically using open-source software (GGIR) to generate physical activity outcomes (including average dynamic acceleration (ACC) and time within intensity cut-points). Data were analysed using pairwise 95% equivalence tests (±10% equivalence zone), intra-class correlation coefficients (ICC) and limits of agreement.
Results:
The GENEActiv and Axivity could be considered equivalent for ACC (ICC=0.95, 95% confidence interval (CI) 0.87 to 0.98), but ACC measured by the ActiGraph was approximately 10% lower (ICC: GENEActiv/ActiGraph 0.86, 95% CI 0.56 to 0.95; Axivity/ActiGraph 0.82, 95% CI 0.50 to 0.94). For time spent within intensity cut-points, all three accelerometers could be considered equivalent to each other for over 85% of outcomes (ICC≥0.69, lower 95% CI≥0.36), with the GENEActiv and Axivity equivalent for 100% of outcomes (ICC≥0.95, lower 95% CI≥0.86).
Conclusions:
GENEActiv and Axivity data processed in GGIR are largely equivalent. If comparing GENEActiv or Axivity to the ActiGraph, time spent within intensity cut-points has good agreement. These findings can be used to inform selection of appropriate outcomes if comparing outputs from these accelerometer brands.
Background: In the past years, there was an increasing development of physical activity tracker (Wearables). For recreational people, testing of these devices under walking or light jogging conditions might be sufficient. For (elite) athletes, however, scientific trustworthiness needs to be given for a broad spectrum of velocities or even fast changes in velocities reflecting the demands of the sport. Therefore, the aim was to evaluate the validity of eleven Wearables for monitoring step count, covered distance and energy expenditure (EE) under laboratory conditions with different constant and varying velocities.
Methods: Twenty healthy sport students (10 men, 10 women) performed a running protocol consisting of four 5 min stages of different constant velocities (4.3; 7.2; 10.1; 13.0 km·h⁻¹), a 5 min period of intermittent velocity, and a 2.4 km outdoor run (10.1 km·h⁻¹) while wearing eleven different Wearables (Bodymedia Sensewear, Beurer AS 80, Polar Loop, Garmin Vivofit, Garmin Vivosmart, Garmin Vivoactive, Garmin Forerunner 920XT, Fitbit Charge, Fitbit Charge HR, Xaomi MiBand, Withings Pulse Ox). Step count, covered distance, and EE were evaluated by comparing each Wearable with a criterion method (Optogait system and manual counting for step count, treadmill for covered distance and indirect calorimetry for EE).
Results: All Wearables, except Bodymedia Sensewear, Polar Loop, and Beurer AS80, revealed good validity (small MAPE, good ICC) for all constant and varying velocities for monitoring step count. For covered distance, all Wearables showed a very low ICC (<0.1) and high MAPE (up to 50%), revealing no good validity. The measurement of EE was acceptable for the Garmin, Fitbit and Withings Wearables (small to moderate MAPE), while Bodymedia Sensewear, Polar Loop, and Beurer AS80 showed a high MAPE up to 56% for all test conditions.
Conclusion: In our study, most Wearables provide an acceptable level of validity for step counts at different constant and intermittent running velocities reflecting sports conditions. However, the covered distance, as well as the EE could not be assessed validly with the investigated Wearables. Consequently, covered distance and EE should not be monitored with the presented Wearables, in sport specific conditions.
Background:
Excessive sedentary time is ubiquitous in Western societies. Previous studies have relied on self-reporting to evaluate the total volume of sedentary time as a prognostic risk factor for mortality and have not examined whether the manner in which sedentary time is accrued (in short or long bouts) carries prognostic relevance.
Objective:
To examine the association between objectively measured sedentary behavior (its total volume and accrual in prolonged, uninterrupted bouts) and all-cause mortality.
Design:
Prospective cohort study.
Setting:
Contiguous United States.
Participants:
7985 black and white adults aged 45 years or older.
Measurements:
Sedentary time was measured using a hip-mounted accelerometer. Prolonged, uninterrupted sedentariness was expressed as mean sedentary bout length. Hazard ratios (HRs) were calculated comparing quartiles 2 through 4 to quartile 1 for each exposure (quartile cut points: 689.7, 746.5, and 799.4 min/d for total sedentary time; 7.7, 9.6, and 12.4 min/bout for sedentary bout duration) in models that included moderate to vigorous physical activity.
Results:
Over a median follow-up of 4.0 years, 340 participants died. In multivariable-adjusted models, greater total sedentary time (HR, 1.22 [95% CI, 0.74 to 2.02]; HR, 1.61 [CI, 0.99 to 2.63]; and HR, 2.63 [CI, 1.60 to 4.30]; P for trend < 0.001) and longer sedentary bout duration (HR, 1.03 [CI, 0.67 to 1.60]; HR, 1.22 [CI, 0.80 to 1.85]; and HR, 1.96 [CI, 1.31 to 2.93]; P for trend < 0.001) were both associated with a higher risk for all-cause mortality. Evaluation of their joint association showed that participants classified as high for both sedentary characteristics (high sedentary time [≥12.5 h/d] and high bout duration [≥10 min/bout]) had the greatest risk for death.
Limitation:
Participants may not be representative of the general U.S. population.
Conclusion:
Both the total volume of sedentary time and its accrual in prolonged, uninterrupted bouts are associated with all-cause mortality, suggestive that physical activity guidelines should target reducing and interrupting sedentary time to reduce risk for death.
Primary funding source:
National Institutes of Health.
Background
The prominence of sedentary behavior research in health science has grown rapidly. With this growth there is increasing urgency for clear, common and accepted terminology and definitions. Such standardization is difficult to achieve, especially across multi-disciplinary researchers, practitioners, and industries. The Sedentary Behavior Research Network (SBRN) undertook a Terminology Consensus Project to address this need.
Method
First, a literature review was completed to identify key terms in sedentary behavior research. These key terms were then reviewed and modified by a Steering Committee formed by SBRN. Next, SBRN members were invited to contribute to this project and interested participants reviewed and provided feedback on the proposed list of terms and draft definitions through an online survey. Finally, a conceptual model and consensus definitions (including caveats and examples for all age groups and functional abilities) were finalized based on the feedback received from the 87 SBRN member participants who responded to the original invitation and survey.
Results
Consensus definitions for the terms physical inactivity, stationary behavior, sedentary behavior, standing, screen time, non-screen-based sedentary time, sitting, reclining, lying, sedentary behavior pattern, as well as how the terms bouts, breaks, and interruptions should be used in this context are provided.
Conclusion
It is hoped that the definitions resulting from this comprehensive, transparent, and broad-based participatory process will result in standardized terminology that is widely supported and adopted, thereby advancing future research, interventions, policies, and practices related to sedentary behaviors.
Electronic supplementary material
The online version of this article (doi:10.1186/s12966-017-0525-8) contains supplementary material, which is available to authorized users.
The health effects of daily activity behaviours (physical activity, sedentary time and sleep) are widely studied. While previous research has largely examined activity behaviours in isolation, recent studies have adjusted for multiple behaviours. However, the inclusion of all activity behaviours in traditional multivariate analyses has not been possible due to the perfect multicollinearity of 24-h time budget data. The ensuing lack of adjustment for known effects on the outcome undermines the validity of study findings. We describe a statistical approach that enables the inclusion of all daily activity behaviours, based on the principles of compositional data analysis. Using data from the International Study of Childhood Obesity, Lifestyle and the Environment, we demonstrate the application of compositional multiple linear regression to estimate adiposity from children’s daily activity behaviours expressed as isometric log-ratio coordinates. We present a novel method for predicting change in a continuous outcome based on relative changes within a composition, and for calculating associated confidence intervals to allow for statistical inference. The compositional data analysis presented overcomes the lack of adjustment that has plagued traditional statistical