Comparison of Actical and activPAL measures of sedentary behaviour in preschool children

Systematic review of accelerometer-based methods for 24-h physical behavior assessment in young children (0-5 years old)

BACKGROUND

Accurate accelerometer-based methods are required for assessment of 24-h physical behavior in young children. We aimed to summarize evidence on measurement properties of accelerometer-based methods for assessing 24-h physical behavior in young children.

METHODS

We searched PubMed (MEDLINE) up to June 2021 for studies evaluating reliability or validity of accelerometer-based methods for assessing physical activity (PA), sedentary behavior (SB), or sleep in 0-5-year-olds. Studies using a subjective comparison measure or an accelerometer-based device that did not directly output time series data were excluded. We developed a Checklist for Assessing the Methodological Quality of studies using Accelerometer-based Methods (CAMQAM) inspired by COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN).

RESULTS

Sixty-two studies were included, examining conventional cut-point-based methods or multi-parameter methods. For infants (0-12 months), several multi-parameter methods proved valid for classifying SB and PA. From three months of age, methods were valid for identifying sleep. In toddlers (1-3 years), cut-points appeared valid for distinguishing SB and light PA (LPA) from moderate-to-vigorous PA (MVPA). One multi-parameter method distinguished toddler specific SB. For sleep, no studies were found in toddlers. In preschoolers (3-5 years), valid hip and wrist cut-points for assessing SB, LPA, MVPA, and wrist cut-points for sleep were identified. Several multi-parameter methods proved valid for identifying SB, LPA, and MVPA, and sleep. Despite promising results of multi-parameter methods, few models were open-source. While most studies used a single device or axis to measure physical behavior, more promising results were found when combining data derived from different sensor placements or multiple axes.

CONCLUSIONS

Up to age three, valid cut-points to assess 24-h physical behavior were lacking, while multi-parameter methods proved valid for distinguishing some waking behaviors. For preschoolers, valid cut-points and algorithms were identified for all physical behaviors. Overall, we recommend more high-quality studies evaluating 24-h accelerometer data from multiple sensor placements and axes for physical behavior assessment. Standardized protocols focusing on including well-defined physical behaviors in different settings representative for children's developmental stage are required. Using our CAMQAM checklist may further improve methodological study quality.

PROSPERO REGISTRATION NUMBER

CRD42020184751.

Parental Views on the Acceptability and Feasibility of Measurement Tools Used to Assess Movement Behaviour of Pre-School Children: A Qualitative Study

Movement behaviours (physical activity, sedentary behaviour, and sleep) are important for the health and development of pre-school children (aged 3-4 years). There is limited qualitative research examining the acceptability and feasibility of tools used to assess movement behaviours in pre-schoolers. This study explored parental views on various measurement tools in three deprived areas in England, UK (West Yorkshire, County Durham and Northumberland). The study consisted of a demonstration of the different tools (accelerometers, a diary and a questionnaire), directly followed by focus group discussions. Three focus group discussions with a total of eleven parents and carers were transcribed verbatim and analysed using thematic analysis. Findings revealed four main themes: (1) importance of contextual information when using any measurement tool (e.g., child illness, capturing different routines); (2) practical issues associated with devices (e.g., aversion to devices being attached directly to the skin of their child; concern of larger devices during sleep time); (3) encouraging children to wear a device (e.g., making devices attractive to children-'superpowers'); and (4) presentation of diaries and questionnaires (e.g., age-appropriate movement activities, preference for real-time recording over recall). Practical recommendations for the use of the tools to measure movement behaviours of pre-school children are provided.

A systematic review of the validity, reliability, and feasibility of measurement tools used to assess the physical activity and sedentary behaviour of pre-school aged children

Physical activity (PA) and sedentary behaviour (SB) of pre-school aged children are associated with important health and developmental outcomes. Accurate measurement of these behaviours in young children is critical for research and practice in this area. The aim of this review was to examine the validity, reliability, and feasibility of measurement tools used to assess PA and SB of pre-school aged children.Searches of electronic databases, and manual searching, were conducted to identify articles that examined the measurement properties (validity, reliability or feasibility) of measurement tools used to examine PA and/or SB of pre-school aged children (3-7 years old). Following screening, data were extracted and risk of bias assessment completed on all included articles.A total of 69 articles, describing 75 individual studies were included. Studies assessed measurement tools for PA (n = 27), SB (n = 5), and both PA and SB (n = 43). Outcome measures of PA and SB differed between studies (e.g. moderate to vigorous activity, step count, posture allocation). Most studies examined the measurement properties of one measurement tool only (n = 65). Measurement tools examined included: calorimetry, direct observation, combined heart rate and accelerometry, heart rate monitors, accelerometers, pedometers, and proxy report (parent, carer or teacher reported) measures (questionnaires or diaries). Studies most frequently assessed the validity (criterion and convergent) (n = 65), face and content validity (n = 2), test-retest reliability (n = 10) and intra-instrument reliability (n = 1) of the measurement tools. Feasibility data was abstracted from 41 studies.Multiple measurement tools used to measure PA and SB in pre-school aged children showed some degree of validity, reliability and feasibility, but often for different purposes. Accelerometers, including the Actigraph (in particular GT3X versions), Actical, ActivPAL and Fitbit (Flex and Zip), and proxy reported measurement tools used in combination may be useful for a range of outcome measures, to measure intensity alongside contextual information.

Physical activity and sedentary time of youth in structured settings: a systematic review and meta-analysis

BACKGROUND

Structured settings, such as school, childcare, afterschool programs, summer camps, and physical activity/sport programs are crucial to promoting physical activity (PA) opportunities and reducing sedentary (ST) for children and adolescents. However, little is known about how much PA and ST children and adolescents accumulate in structured settings. The aim of this study is to conduct a systematic review and meta-analysis of the absolute amount of time youth spend physically active and sedentary in different structured settings (Prospero number: 42018111804).

METHODS

Observational and experimental/quasi-experimental studies (baseline data only) with full-text available, written in English and published in a peer-reviewed journal, reporting the total amount of objectively measured PA (light, moderate, vigorous, and/or total physical activity) and/or time spent ST during structured settings among youth (3 to 18 years) were eligible. Adjusted meta-analysis was conducted to estimate the pooled mean of time spent in PA and ST, by settings and sex.

RESULTS

A total of 187 studies (childcare n=60; school n=91; afterschool programs n=14; summer camp n=4; and Physical activity/ sport programs n=18) from 30 countries (47.9% United States), representing 74,870 youth (mean age 8.6 years old) were included. Overall, there was a high variation between studies in outcomes and settings. The meta-analyses revealed, on average, youth spend 221.8 minutes (36.7 min/hour) in ST and 32.1 minutes (5.1 min/hour) in MVPA during childcare hours, and 223.9 minutes (36.7min/hour) in ST and 27.8 min (4.4 min/hour) in MVPA at school. Relatively, youth are engaged in more MVPA in afterschool programs (11.7 min/hour), PA/ sport programs (20.9 min/hour), and summer camps (6.4 min/hour), when compared to childcare and school.

CONCLUSION

Total PA accumulated during childcare and MVPA accumulated during schools hours were close to recommendations, despite high proportion of ST. Afterschool programs, summer camp and PA/ sport programs are important settings that can contribute to daily PA and reduced ST. Ensuring all youth have access to these structured settings may be an important step forward for public health.

Physical activity and sedentary time among preschoolers in centre-based childcare: a systematic review

Background

Many preschoolers spend a substantial portion of their day enrolled in centre-based childcare; the amounts of physical activity and sedentary time accumulated in this environment are critical to preschoolers’ ability to meet movement guidelines. The purpose of this systematic review was to provide a comprehensive overview of the objectively assessed physical activity and sedentary time of preschoolers in centre-based childcare (registration no. CRD42016033502).

Methods

Eight online databases were searched using terms related to physical activity, sedentary time, preschoolers and centre-based childcare. Published, peer-reviewed primary studies written in English that objectively assessed (via accelerometry) the physical activity and sedentary time of preschoolers (2-5 years) in centre-based childcare were included.

Results

Fifty-five studies (published 2004-2017) from 11 countries, representing 13,956 participants were included. Studies reported light physical activity (n=38) ranging from 2.94 to 29.96 mins/hr, moderate-to-vigorous physical activity (n=46) which ranged from 1.29 to 22.66 mins/hr, and total physical activity (n=42) ranging from 4.23 to 47.17 mins/hr. Sedentary time (n=47) ranged from 12.38 to 55.77 mins/hr.

Conclusion

Physical activity and sedentary time were highly varied and inconsistent between studies; therefore, it is difficult to determine preschoolers’ true amount of physical activity and sedentary time during childcare hours. Despite this variability, preschoolers were noted to participate in high rates of sedentary time in this setting. The lack of homogeneity is an important finding in and of itself as it highlights the lack of consistency in measuring, processing, and reporting paediatric physical activity data.

Use of Objective Measures to Estimate Sedentary Time in Youth

The purpose of the study was to conduct a comprehensive evaluation of the ActiGraph GT3X+ (AG) and activPAL (AP) for assessing time spent in sedentary behaviors (SB) in youth using structured and free-living activities. Forty-four participants (mean±SD; age, 12.7±0.8 yrs) completed up to 8 structured activities and approximately 2-hrs of free-living activity while wearing an AG (right hip) and AP (right thigh). A Cosmed K4b² was used for measured energy expenditure (MET_y; activity VO₂ divided by resting VO₂). Direct observation was used during the structured activities. SB time was estimated using the inclinometer function of the AP and AG, and count thresholds with AG (<75 vector magnitude (VM) counts/10-s, < 25 vertical axis (VA) counts/10-s, and <50, 100, 150, and 200 VA counts/min). For the structured activities, the AG inclinometer and AP correctly classified supine rest about 45% of the time, seated activities 54.6% and 65.1% of the time, respectively, and walking and running >96% of the time. For the free-living measurement, the VA <25 counts/10-s has the lowest RMSE (20.6 min), while the VM <75 counts/10-s had the lowest MAPE (69.2%). The AG inclinometer was within 0.2 minutes of measured time, but had the highest MAPE (107.1%). The AP was within 1.6 minutes of measured time, but had the highest RMSE (28.5 minutes). Compared to measured SB time, the VA <25 counts/10-s and VM <75 counts/10-s provided the most precise estimates of SB during free-living activity. Further refinement is needed to improve the AP and AG posture estimates.

Measurement of obesity prevention in childcare settings: A systematic review of current instruments

OBJECTIVE

The incidence of childhood obesity is highest among children entering kindergarten. Overweight and obesity in early childhood track through adulthood. Programs increasingly target children in early life for obesity prevention. However, the published literature lacks a review on tools available for measuring behaviour and environmental level change in child care. The objective is to describe measurement tools currently in use in evaluating obesity-prevention in preschool-aged children.

METHODS

Literature searches were conducted in PubMed using the keywords "early childhood obesity," "early childhood measurement," "early childhood nutrition" and "early childhood physical activity." Inclusion criteria included a discussion of: (1) obesity prevention, risk assessment or treatment in children ages 1-5 years; and (2) measurement of nutrition or physical activity.

RESULTS

One hundred thirty-four publications were selected for analysis. Data on measurement tools, population and outcomes were abstracted into tables. Tables are divided by individual and environmental level measures and further divided into physical activity, diet and physical health outcomes. Recommendations are made for weighing advantages and disadvantages of tools.

CONCLUSION

Despite rising numbers of interventions targeting obesity-prevention and treatment in preschool-aged children, there is no consensus for which tools represent a gold standard or threshold of accuracy.

Accelerometer-based measures in physical activity surveillance: current practices and issues

OBJECTIVE

Self-reports of physical activity (PA) have been the mainstay of measurement in most non-communicable disease (NCD) surveillance systems. To these, other measures are added to summate to a comprehensive PA surveillance system. Recently, some national NCD surveillance systems have started using accelerometers as a measure of PA. The purpose of this paper was specifically to appraise the suitability and role of accelerometers for population-level PA surveillance.

METHODS

A thorough literature search was conducted to examine aspects of the generalisability, reliability, validity, comprehensiveness and between-study comparability of accelerometer estimates, and to gauge the simplicity, cost-effectiveness, adaptability and sustainability of their use in NCD surveillance.

CONCLUSIONS

Accelerometer data collected in PA surveillance systems may not provide estimates that are generalisable to the target population. Accelerometer-based estimates have adequate reliability for PA surveillance, but there are still several issues associated with their validity. Accelerometer-based prevalence estimates are largely dependent on the investigators' choice of intensity cut-off points. Maintaining standardised accelerometer data collections in long-term PA surveillance systems is difficult, which may cause discontinuity in time-trend data. The use of accelerometers does not necessarily produce useful between-study and international comparisons due to lack of standardisation of data collection and processing methods. To conclude, it appears that accelerometers still have limitations regarding generalisability, validity, comprehensiveness, simplicity, affordability, adaptability, between-study comparability and sustainability. Therefore, given the current evidence, it seems that the widespread adoption of accelerometers specifically for large-scale PA surveillance systems may be premature.

A review of preschool children's physical activity and sedentary time using objective measures

CONTEXT

Identifying current physical activity levels and sedentary time of preschool children is important for informing government policy and community initiatives. This paper reviewed studies reporting on physical activity and time spent sedentary among preschool-aged children (2-5 years) using objective measures.

EVIDENCE ACQUISITION

Databases were searched for studies published up to and including April 2013 that reported on, or enabled the calculation of, the proportion of time preschool children spent sedentary and in light- and moderate to vigorous-intensity physical activity. A total of 40 publications met the inclusion criteria for physical activity and 31 met the inclusion criteria for sedentary time. Objective measures included ActiGraph, Actiwatch, Actical, Actiheart, and RT3 accelerometers, direct observation, and Quantum XL telemetry heart rate monitoring. Data were analyzed in May 2013.

EVIDENCE SYNTHESIS

Considerable variation in prevalence estimates existed. The proportion of time children spent sedentary ranged from 34% to 94%. The time spent in light-intensity physical activity and moderate to vigorous-intensity physical activity ranged from 4% to 33% and 2% to 41%, respectively.

CONCLUSIONS

The considerable variation of prevalence estimates makes it difficult to determine the "true" prevalence of physical activity and sedentary time in preschool children. Future research should aim to reduce inconsistencies in the employed methodologies to better understand preschoolers' physical activity levels and sedentary behavior.

Evaluation of Actical equations and thresholds to predict physical activity intensity in young children

Abstract This study examined the validity of current Actical activity energy expenditure (AEE) equations and intensity cut-points in preschoolers using AEE and direct observation as criterion measures. Forty 4-6-year-olds (5.3 ± 1.0 years) completed a ~150-min room calorimeter protocol involving age-appropriate sedentary behaviours (SBs), light intensity physical activities (LPAs) and moderate-to-vigorous intensity physical activities (MVPAs). AEE and/or physical activity intensity were calculated using Actical equations and cut-points by Adolph, Evenson, Pfeiffer and Puyau. Predictive validity was examined using paired sample t-tests. Classification accuracy was evaluated using weighted kappas, sensitivity, specificity and area under the receiver operating characteristic curve. The Pfeiffer equation significantly overestimated AEE during SB and underestimated AEE during LPA (P < 0.0125 for both). There was no significant difference between measured and predicted AEEs during MVPA. The Adolph cut-point showed significantly higher accuracy for classifying SB, LPA and MVPA than all others. The available Actical equation does not provide accurate estimates of AEE across all intensities in preschoolers. However, the Pfeiffer equation performed reasonably well for MVPA. Using cut-points of ≤6 counts · 15 s(-1), 7-286 counts · 15 s(-1) and ≥ 287 counts · 15 s(-1) when classifying SB, LPA and MVPA, respectively, is recommended.

Assessment of physical activity and energy expenditure: an overview of objective measures

The ability to assess energy expenditure (EE) and estimate physical activity (PA) in free-living individuals is extremely important in the global context of non-communicable diseases including malnutrition, overnutrition (obesity), and diabetes. It is also important to appreciate that PA and EE are different constructs with PA defined as any bodily movement that results in EE and accordingly, energy is expended as a result of PA. However, total energy expenditure, best assessed using the criterion doubly labeled water (DLW) technique, includes components in addition to physical activity energy expenditure, namely resting energy expenditure and the thermic effect of food. Given the large number of assessment techniques currently used to estimate PA in humans, it is imperative to understand the relative merits of each. The goal of this review is to provide information on the utility and limitations of a range of objective measures of PA and their relationship with EE. The measures discussed include those based on EE or oxygen uptake including DLW, activity energy expenditure, physical activity level, and metabolic equivalent; those based on heart rate monitoring and motion sensors; and because of their widespread use, selected subjective measures.

Interdevice baseline signal magnitude variability of the ActivPAL3 activity monitor

The aim of this study was to evaluate the interdevice variability in baseline signal magnitude of the ActivPAL3 (PALTechnologies, Glasgow, UK) activity monitor. Twenty consecutively numbered ActivPAL3 triaxial physical activity monitors were assessed for baseline signal magnitudes oriented in the X (vertical standing), Y (side lying), and Z (lying flat) planes. Data was recorded for 60s in each orientation and the three orthogonal axes were analysed for baseline signal magnitude each orientation. Our batch of devices demonstrated approximately 6.6% difference in raw acceleration calibration values compared to manufacturer specifications. Analysis of interdevice variability revealed three devices which exhibited baseline signal variability greater than 1.96 SD of the group mean. The impact of interdevice variability in baseline signal magnitude on data collection is unknown, however, supports the argument in the literature for the systematic checking of interdevice variability as routine maintenance.

Acceptability of standing workstations in elementary schools: a pilot study

OBJECTIVE

To examine the acceptability of introducing standing workstations in elementary-school classrooms; and to quantify changes in children's time spent sitting, standing, and walking; step counts; sit-to-stand transitions; and musculoskeletal discomfort.

METHODS

A controlled trial was conducted in two elementary schools in Auckland, New Zealand (March-May 2012). Participants were 30 third and fourth graders (n = 23 intervention, n = 7 control). Intervention classes received standing workstations; control class retained usual sitting desks. Children wore ActivPAL monitors over 7 days at baseline and during the fourth week of the intervention.

RESULTS

Children spoke enthusiastically of the standing workstations. School staffs were supportive of the standing workstations because they offered "flexibility in learning". Overall, children in the intervention group sat less (intervention: 8.27 (1.45), mean (SD); control: 9.00 (0.80) h/day), stood longer (3.75 (0.88); 2.85 (0.30) h/day), and engaged in fewer transitions from sitting to standing (93 (17); 98 (26) counts) compared to the control group. Effect size ranged from small-large (-0.49; 95% confidence limits (0.64)%, 0.71; (0.48), -0.96; (0.54)% respectively). Results for time spent stepping and step counts were unclear.

CONCLUSION

Standing workstations can be successfully integrated in classroom environments and appear to decrease overall sedentariness.