Validation of the Phillips et al. GENEActiv accelerometer wrist cut-points in children aged 5-8 years old

Combined Virtual-Reality- and Gym-Based Physical Activity Intervention for Children With a Developmental Disability: Effects on Physical Activity Levels, Motor Skills, and Social Skills

This study examined the effects of a combined virtual-reality- and gym-based physical activity (PA) program on PA levels, motor skills, and social skills of children with a developmental disability (DD). Twenty-five children with DD were randomly assigned to experimental and control groups. The intervention was conducted for 60 min, two times a week, for 12 weeks. Pre- and postintervention assessments encompassing PA levels measured via Gravity Estimator of Normal Everyday Activity, motor skills evaluated using the Test of Gross Motor Development-Third Edition, and social skills gauged via the Social Skills Rating System-Parent were conducted. Additionally, a follow-up assessment was administered to the experimental group 12 weeks postintervention. The findings unequivocally demonstrate that the combined virtual-reality- and gym-based PA program yielded significant enhancements in PA levels, motor skills, and social skills among children with DD in the experimental group. Notably, these improvements were sustained 12 weeks after the intervention. These findings may help professionals develop and implement better PA programs for children with DD.

Validation of an accelerometer system for measuring physical activity and sedentary behavior in healthy children and adolescents

The study aims to assess the concurrent validity of the SENS motion® accelerometer system for device-based measurement of physical activity and sedentary behavior in healthy children and adolescents. Thirty-six healthy children and adolescents (mean ± standard deviation (SD) age, 10.2 ± 2.3 years) were fitted with three SENS sensors while performing standardized activities including walking, fast walking, sitting/lying, and arm movements. Data from the sensors were compared with video observations (reference criteria). The agreement between SENS motion® and observation was analyzed using Student's t-test and illustrated in Bland-Altman plots. The concurrent validity was further evaluated using intraclass correlation coefficient (ICC) and was expressed as standard error of measurement (SEM) and minimal detectable change (MDC). Strong agreement was found between SENS and observation for walking time, sedentary time, and lying time. In contrast, moderate agreement was observed for number of steps, sitting time, and time with and without arm movement. ICC2.1 values were overall moderate to excellent (0.5-0.94), with correspondingly low SEM% for walking time, sedentary time, lying time, and time with arm movement (2-9%). An acceptable SEM% level was reached for both steps and sitting time (11% and 12%). For fast walking time, the results showed a weak agreement between the measurement methods, and the ICC value was poor.

CONCLUSION

SENS motion® seems valid for detecting physical activity and sedentary behavior in healthy children and adolescents with strong agreement and moderate to excellent ICC values. Furthermore, the explorative results on arm movements seem promising.

WHAT IS KNOWN

• Inactivity and sedentary behavior follow an increasing trend among children and adolescents. • SENS motion® seems to be valid for measuring physical activity and sedentary behavior in adults and elderly patients.

WHAT IS NEW

• SENS motion® seems valid with strong agreement between video observations and SENS measurement, and ICC values are moderate to excellent when measuring physical activity and sedentary behavior in healthy children and adolescents. • SENS motion® seems promising for detection of arm movements.

The effect of a virtual reality exergame on motor skills and physical activity levels of children with a developmental disability

BACKGROUND

It is well documented that regular physical activity (PA) participation improves physical functions of children with a developmental disability (DD). Researchers have begun to pay attention to virtual reality (VR) based PA programs, but there is a lack of research evidence.

AIM

We aimed to examine the effect of a VR-based PA program on motor skills and PA levels in children with DD.

METHODS AND PROCEDURES

Twenty-three children with DD were randomly assigned to an experimental and a control group. The intervention was conducted for 24 sessions, 40 min each, and twice a week. Each participant rode a stationary bike with a cadence sensor wearing a VR headset. TGMD-3 and a GENEActiv accelerometer were used to measure motor skills and PA levels one week before and after the intervention.

OUTCOMES AND RESULTS

Children in the experimental group showed a significant increase in locomotor skills. Ball skills also increased but did not have significant differences. For PA levels, both groups did not have significant increase after the intervention.

CONCLUSIONS AND IMPLICATIONS

A VR-based PA program was effective in improving locomotor skills among children with DD. To significantly change ball skills and PA levels of children with DD, VR-based PA program mixed with reality-based PA program is probably necessary.

The Validity of the Energy Expenditure Criteria Based on Open Source Code through two Inertial Sensors

Through this study, we developed and validated a system for energy expenditure calculation, which only requires low-cost inertial sensors and open source R software. Five healthy subjects ran at ten different speeds while their kinematic variables were recorded on the thigh and wrist. Two ActiGraph wireless inertial sensors and a low-cost Bluetooth-based inertial sensor (Lis2DH12), assembled by SensorID, were used. Ten energy expenditure equations were automatically calculated in a developed open source R software (our own creation). A correlation analysis was used to compare the results of the energy expenditure equations. A high interclass correlation coefficient of estimated energy expenditure on the thigh and wrist was observed with an Actigraph and Sensor ID accelerometer; the corrected Freedson equation showed the highest values, and the Santos-Lozano vector magnitude equation and Sasaki equation demonstrated the lowest one. Energy expenditure was compared between the wrist and thigh and showed low correlation values. Despite the positive results obtained, it was necessary to design specific equations for the estimation of energy expenditure measured with inertial sensors on the thigh. The use of the same formula equation in two different placements did not report a positive interclass correlation coefficient.

Multi-Component Physical Activity Interventions in the UK Must Consider Determinants of Activity to Increase Effectiveness

Interventions to increase physical activity in children have adopted broad approaches and achieved varying success. There is a need to adopt approaches underpinned with a theoretical basis. Accordingly, the aim here was to implement and evaluate a 12-week intervention designed using the concepts of the COM-B model to determine the effect this has on physical activity levels. One hundred and forty-seven school-age children (mean age 8.9 ± 1.3 years) took part in a 12-week program delivered in a school setting. Topics included physical activity, healthy eating, sleep quality and reducing screen time/sedentary activities when not in school. A sample of participants wore a wrist-worn accelerometer for seven days pre-and post-intervention (N = 11). The physical activity frequency was unchanged (2.9 ± 1.0 AU) when compared with post-intervention values (3.1 ± 0.8 AU, mean increase 6.8 ± 3.7%, p > 0.05). Changes were observed in the daily consumption of fruit and vegetables (pre-intervention 44.6% vs. post-intervention 60.2%, p < 0.05). Sedentary time, light activity, moderate activity and vigorous activity were unchanged post-intervention (p > 0.05). There is a need to adopt a broader approach that incorporates a theoretical basis and considers the complex ways by which physical activity behaviours are influenced.

Reliability of GENEActiv accelerometers to estimate sleep, physical activity, and sedentary time in children

Background

Reliable estimates of habitual sleep, physical activity, and sedentary time are essential to investigate the associations between these behaviours and health outcomes. While the number of days needed and hours/day for estimates of physical activity and sedentary time are generally known, the criteria for sleep estimates are more uncertain. The objective of this study was to identify the number of nights needed to obtain reliable estimates of habitual sleep behaviour using the GENEActiv wrist worn accelerometer. The number of days to obtain reliable estimate of physical activity was also examined.

Methods

Data was used from a two-year longitudinal study. Children wore an accelerometer for up to 8 days 24 h/day across three timepoints. The sample included 2,745 children (51 % girls) between the ages of 7-12-years-old (mean = 9.8 years, SD = 1.1 year) with valid accelerometer data from any timepoint. Reliability estimates were calculated for sleep duration, sleep efficiency, sleep onset, wake time, time in bed, light physical activity, moderate physical activity, moderate-to-vigorous physical activity, vigorous physical activity, and sedentary time.

Results

Intraclass correlations and the Spearman Brown prophecy formula were used to determine the nights and days needed for reliable estimates. We found that between 3 and 5 nights were needed to achieve acceptable reliability (ICC = 0.7) in sleep outcomes, while physical activity and sedentary time outcomes required between 3 and 4 days.

Conclusions

To obtain reliable estimates, researchers should consider these minimum criteria when designing their studies and prepare strategies to ensure sufficient wear time compliance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12966-021-01143-6.

Impact of The Daily Mile on children's physical and mental health, and educational attainment in primary schools: iMprOVE cohort study protocol

INTRODUCTION

School-based active mile initiatives such as The Daily Mile (TDM) are widely promoted to address shortfalls in meeting physical activity recommendations. The iMprOVE Study aims to examine the impact of TDM on children's physical and mental health and educational attainment throughout primary school. METHODS AND ANALYSIS: iMprOVE is a longitudinal quasi-experimental cohort study. We will send a survey to all state-funded primary schools in Greater London to identify participation in TDM. The survey responses will be used for non-random allocation to either the intervention group (Daily Mile schools) or to the control group (non-Daily Mile schools). We aim to recruit 3533 year 1 children (aged 5-6 years) from 77 primary schools and follow them up annually until the end of their primary school years. Data collection taking place at baseline (children in school year 1) and each primary school year thereafter includes device-based measures of moderate-to-vigorous physical activity (MVPA) and questionnaires to measure mental health (Strengths and Difficulties Questionnaire) and educational attainment (ratings from 'below expected' to 'above expected levels'). The primary outcome is the mean change in MVPA minutes from baseline to year 6 during the school day among the intervention group compared with controls. We will use multilevel linear regression models adjusting for sociodemographic data and participation in TDM. The study is powered to detect a 10% (5.5 min) difference between the intervention and control group which would be considered clinically significant.

ETHICS AND DISSEMINATION

Ethics has been approved from Imperial College Research Ethics Committee, reference 20IC6127. Key findings will be disseminated to the public through research networks, social, print and media broadcasts, community engagement opportunities and schools. We will work with policy-makers for direct application and impact of our findings.

Pre-schoolers fundamental movement skills predict BMI, physical activity, and sedentary behavior: A longitudinal study

AIMS

This study aimed to predict the variation in future fundamental movement skills (FMS), physical activity (PA) and body mass index (BMI) from prior FMS, PA, and BMI in British pre-schoolers.

METHODS

British pre-schoolers (n = 177) underwent assessment of FMS, via the Test of Gross Motor Development 2 (TGMD-2), BMI, and PA, via accelerometer at two time points one year apart. Regression analysis was used to predict the change in PA and BMI in Year 2 from FMS variables in Year 1 and Year 2.

RESULTS

Variation in FMS performance between individuals predicted a significant amount of change in BMI, with 12.3 and 9.8% of the change in Year 2 BMI data explained by change in Year 1 and Year 2 FMS data, respectively. Change in FMS performance between individuals predicted a significant amount of change in PA, but was better at predicting change in sedentary activity rather than light or moderate-vigorous activities. A combination of run, throw, dribble, and kick performance predicted 46.1% of the change in sedentary activity between individuals in the 2nd year of testing.

CONCLUSIONS

FMS mastery at four years of age predicts children's BMI and time spent sedentary at five years of age.

Seafarers' Physical Activity and Sleep Patterns: Results from Asia-Pacific Sea Routes

Prolonged ocean voyages constrain the regular physical activity and sleep patterns of seafarers. However, there is a lack of information on seafarers’ physical activity and sleep behavior. The purpose of this study was to systematically examine physical activity and sleep patterns among seafarers using a single wrist-worn accelerometer. Fifty-one senior maritime students (mean age = 22.8 years; 80.0% male) in a university navigation department participated in the study. Data were collected from participants on three sea voyages in the Asia-Pacific region. Indicators of moderate to vigorous intensity physical activity (MVPA) and sleep patterns were compared between several conditions: (1) moored versus sailing, (2) on-navigation duty and off- navigation duty, and (3) day versus night navigation duty. Regardless of conditions, low levels of physical activity and short sleep durations were observed. Independent sample t-tests revealed that time spent doing MVPA was significantly higher when participants were off-duty than when they were on-duty (p < 0.001). Physical activity did not significantly differ between the other two conditions. While total sleep duration was not significantly different between mooring and sailing, the results showed that participants awakened more frequently (p = 0.007) and their sleep was more restless (p < 0.001) while sailing. The results demonstrated that developing effective programs to promote physical activity should be a public health priority for the seafaring population, and serious consideration is required to mitigate sleep disruption during sailing.

Cross-validation of Actigraph derived accelerometer cut-points for assessment of sedentary behaviour and physical activity in children aged 8-11 years

AIM

To cross-validate previously calibrated Actigraph cut-points in children.

METHODS

Twenty-eight children (50% boys) aged between 8 and 11 years of age (9.4 ± 1.4 years) performed a series of 5 minute bouts of activity reflective of different levels of PA from sedentary behaviour (SB) to moderate to vigorous physical activity (MVPA); V ˙ O₂ was assessed using breath-by-breath indirect calorimetry, and activity was assessed using Actigraph accelerometers worn on the hip and non-dominant wrist. The V ˙ O₂ values were then converted into age-specific METs (measured METs) and coded as SB, light PA and MVPA. Accelerometer data were analysed using previously calibrated cut-points at different epochs, that is 5, 15, 30 and 60 seconds.

RESULTS

Receiver operating characteristic (ROC) curve analysis indicated that there was excellent discrimination of SB using the Evenson et al (15 seconds), Romanzini (15 seconds), Treuth et al (30 seconds), Freedson et al (60 seconds), Treuth et al (60 seconds) and Troiano et al (60 seconds) cut-points. ROC analysis indicated poor discrimination for LPA irrespective of the cut-points used. Good discrimination of MVPA was evident for all existing cut-points using a 60-second epoch.

CONCLUSION

There is considerable variation in the performance of existing cut-points for assessment of SB, LPA and MPA in children.

Predicting children's energy expenditure during physical activity using deep learning and wearable sensor data

This study examined a series of machine learning models, evaluating their effectiveness in assessing children's energy expenditure, in terms of the metabolic equivalents (MET) of physical activity (PA), from triaxial accelerometery. The study also determined the impact of the sensor placement (waist, ankle or wrist) on the machine learning model's predictive performance. Twenty-eight healthy Caucasian children aged 8-11years (13 girls, 15 boys) undertook a series of activities reflective of different levels of PA (lying supine, seated and playing with Lego, slow walking, medium walking, and a medium paced run, instep passing a football, overarm throwing and catching and stationary cycling). Energy expenditure and physical activity were assessed during all activities using accelerometers (GENEActiv monitor) worn on four locations (i.e. non-dominant wrist, dominant wrist, dominant waist, dominant ankle) and breath-by-breath calorimetry data. MET values ranged from 1.2 ± 0.2 for seated playing with Lego to 4.1 ± 0.8 for running at 6.5 kmph^-1. Machine learning models were used to determine the MET values from the accelerometer data and to determine which placement location performed more effectively in predicting the PA data. The study identified that novel machine learning models can be used to accurately predict METs, with 90% accuracy. The models showed a preference towards the dominant wrist or ankle as the movement in those positions were more consistent during PA. It was evident that machine learning models using these locations can be effectively used to accurately predict METs for PA in children.

Development of raw acceleration cut-points for wrist and hip accelerometers to assess sedentary behaviour and physical activity in 5-7-year-old children

This study validated sedentary behaviour (SB), moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) accelerometer cut-points in 5-7-year-old children. Participants (n = 49, 55% girls) wore an ActiGraph GT9X accelerometer, recording data at 100 Hz downloaded in 1 s epochs, on both wrists and the right hip during a standardised protocol and recess. Cut-points were generated using ROC analysis with direct observation as a criterion. Subsequently, cut-points were optimised using Confidence intervals equivalency analysis and then cross-validated in a cross-validation group. SB cut-points were 36 mg (Sensitivity (Sn) = 79.8%, Specificity (Sp) = 56.8%) for non-dominant wrist, 39 mg (Sn = 75.4%, Sp = 70.2%) for dominant wrist and 20 mg (Sn = 78%, Sp = 50.1%) for hip. MVPA cut-points were 189 mg (Sn = 82.6%, Sp = 78%) for non-dominant wrist, 181 mg (Sn = 79.1%, Sp = 76%) for dominant wrist and 95 mg (Sn = 79.3%, Sp = 75.6%) for hip. VPA cut-points were 536 mg (Sn = 75.1%, Sp = 68.7%) for non-dominant wrist, 534 mg (Sn = 67.6%, Sp = 95.6%) for dominant wrist and 325 mg (Sn = 78.2%, Sp = 96.1%) for hip. All placements demonstrated adequate levels of accuracy for SB and PA assessment.

Actigraphic Sleep and Dietary Macronutrient Intake in Children Aged 6-9 Years Old: A Pilot Study

The objective of this study was to examine the relationship between different sleep parameters and energy and macronutrient intake in school-aged children. A total of 203 children 6 to 9 years of age participated in this cross-sectional study. Anthropometric measurements were taken first. Diet was assessed with 3-day food logs and sleep was measured with a questionnaire on sleep quality and a wrist actigraph worn for at least 7 days. A decrease of 165.45 kcal was observed per each additional hour of sleep during the week (β (95% CI) = −165.45 (−274.01, −56.88); p = 0.003). This relationship was also observed for fat (β (95% CI) = −11.14 (−18.44, −3.84); p = 0.003) and protein (β (95% CI) = −13.27 (−22.52, −4.02); p = 0.005). An increase in weekend sleep efficiencies for those under the recommended threshold of 85% also had a similar association with energy (β (95% CI) = −847.43 (−1566.77, 128.09); p = 0.021) and carbohydrate (β (95% CI) = −83.96 (−161.76, −6.15); p = 0.035)) intake. An increase in habitual sleep variability was related with a slight increase in protein intake (β (95% CI) = 0.32 (0.031, 0.62); p = 0.031). Children who slept less had a higher energy intake, especially from fat and protein and those who presented inefficient sleep had a higher carbohydrate intake. Strategies to enhance sleep quality and quantity combined with dietary recommendations could help to improve energy and macronutrient intake levels in children.

Estimating Physical Activity in Children Aged 8-11 Years Using Accelerometry: Contributions From Fundamental Movement Skills and Different Accelerometer Placements

Accelerometers are widely used to assess physical activity, but it is unclear how effective accelerometers are in capturing fundamental movement skills in children. This study examined the energy expenditure during different physical activities (PA) and calibrated triaxial accelerometry, worn at the wrist, waist and ankle, during children's PA with attention to object control movement skills and cycling. Thirty children (14 girls) aged 8 to 11 years wore a GENEActiv accelerometer on their non-dominant wrist, dominant wrist, waist and ankle. Children undertook eight, 5-min bouts of activity comprising being lay supine, playing with Lego, slow walking, medium walking, medium paced running, overarm throwing and catching, instep passing a football and cycling at 35 W. VO₂ was assessed concurrently using indirect calorimetry. Indirect calorimetry indicated that being lay supine and playing with Lego were classified as sedentary in nature (<1.5 METs), slow paced walking, medium placed walking and throwing and catching were classified as light (1.51-2.99 METs) and running, cycling and instep passing were classified as moderate intensity (>3 METs). ROC curve analysis indicated that discrimination of sedentary activity was excellent for all placements although the ankle performed better than other locations. This pattern was replicated for moderate physical activity (MPA) where the ankle performed better than other locations. Data were reanalyzed removing cycling from the data set. When this analysis was undertaken discrimination of sedentary activity remained excellent for all locations. For MPA discrimination of activity was considered good for waist and ankle placement and fair for placement on either wrist. The current study is the first to quantify energy expenditure in object control fundamental movement skills via indirect calorimetry in children aged 8-11 years whilst also calibrating GENEActiv accelerometers worn at four body locations. Results suggest throwing and catching is categorized as light intensity and instep kicking a football moderate intensity, resulting in energy expenditure equivalent to slow or medium paced walking or cycling and running, respectively. Ankle worn accelerometry appears to provide the most suitable wear location to quantify MPA including ambulatory activity, object control skills and cycling, in children aged 8-11 years.

Accuracy of Accelerometers for Measuring Physical Activity and Levels of Sedentary Behavior in Children: A Systematic Review

Objectives: This systematic review evaluated the accuracy of triaxial and omnidirectional accelerometers for measuring physical activity and sedentary behavior in children. Design: Systematic review of the literature. Methods: We comprehensively searched several databases for studies published from January 1996 through June 2018 that reported diagnostic accuracy measures in children and adolescents (age 3-18 years) and compared accelerometers with energy expenditure using indirect calorimetry. Results: We included 11 studies that enrolled 570 participants. All studies used indirect calorimetry as the reference standard. Across the studies, median sensitivity ranged from 46% to 96% and median specificity ranged from 71% to 96%. Median area under the curve ranged from 69% to 98%. Conclusions: Accuracy measures were greatest when detecting sedentary behavior and lowest when detecting light physical activity. Accuracy was higher when the accelerometer was placed on the hip compared with the wrist. The current evidence suggests that triaxial and omnidirectional accelerometers are accurate in measuring sedentary behavior and physical activity levels in children.

Wrist Acceleration Cut Points for Moderate-to-Vigorous Physical Activity in Youth

PURPOSE

This study aimed to examine the validity of wrist acceleration cut points for classifying moderate (MPA), vigorous (VPA), and moderate-to-vigorous (MVPA) physical activity.

METHODS

Fifty-seven children (5-12 yr) completed 15 semistructured activities. Three sets of wrist cut points (>192 mg, >250 mg, and >314 mg), previously developed using Euclidian norm minus one (ENMO192+), GENEActiv software (GENEA250+), and band-pass filter followed by Euclidian norm (BFEN314+), were evaluated against indirect calorimetry. Analyses included classification accuracy, equivalence testing, and Bland-Altman procedures.

RESULTS

All cut points classified MPA, VPA, and MVPA with substantial accuracy (ENMO192+: κ = 0.72 [95% confidence interval = 0.72-0.73], MVPA: area under the receiver operating characteristic curve (ROC-AUC) = 0.85 [0.85-0.86]; GENEA250+: κ = 0.75 [0.74-0.76], MVPA: ROC-AUC = 0.85 [0.85-0.86]; BFEN314+: κ = 0.73 [0.72-0.74], MVPA: ROC-AUC = 0.86 [0.86-0.87]). BFEN314+ misclassified 19.7% non-MVPA epochs as MPA, whereas ENMO192+ and GENEA250+ misclassified 32.6% and 26.5% of MPA epochs as non-MVPA, respectively. Group estimates of MPA time were equivalent (P < 0.01) to indirect calorimetry for the BFEN314+ MPA cut point (mean bias = -1.5%, limits of agreement [LoA] = -57.5% to 60.6%), whereas estimates of MVPA time were equivalent (P < 0.01) to indirect calorimetry for the ENMO192+ (mean bias = -1.1%, LoA = -53.7% to 55.9%) and GENEA250+ (mean bias = 2.2%, LoA = -56.5% to 52.2%) cut points. Individual variability (LoA) was large for MPA (min: BFEN314+, -60.6% to 57.5%; max: GENEA250+, -42.0% to 104.1%), VPA (min: BFEN314+, -238.9% to 54.6%; max: ENMO192+, -244.5% to 127.4%), and MVPA (min: ENMO192+, -53.7% to 55.0%; max: BFEN314+, -83.9% to 25.3%).

CONCLUSION

Wrist acceleration cut points misclassified a considerable proportion of non-MVPA and MVPA. Group-level estimates of MVPA were acceptable; however, error for individual-level prediction was larger.

The Use of a Fitbit Device for Assessing Physical Activity and Sedentary Behavior in Preschoolers

OBJECTIVE

To determine the accuracy of the Fitbit Flex (FF) activity monitor for assessing preschoolers' physical activity (PA) and sedentary behavior (SED) in free-living conditions.

STUDY DESIGN

This is a cross-sectional study of 27 preschoolers (41% girls; age, 4.9 years) who each wore the FF and ActiGraph GT3X+ (AG) simultaneously for 24 hours. Raw data from each device were temporally matched and summarized into estimates of SED, moderate-to-vigorous PA, and total PA after removing sleep and nonwear time periods. Equivalence testing was performed to compare the 90% CIs of the estimates from the FF with the respective equivalence zone (±15% of the mean estimates) from the AG.

RESULTS

The FF yielded equivalent estimates of SED (mean difference, 42-71 minutes/day) and total PA (mean difference, 42-70 minutes/day), but lower moderate-to-vigorous PA (mean difference, 29-67 minutes/day) than the AG, on average. The FF showed strong correlations and relatively low mean absolute percent errors (MAPEs) in relation to the AG for SED (r = 0.87; MAPE = 9.1%) and total PA (r = 0.71; MAPE = 14.3%), but a moderate correlation and high MAPE for moderate-to-vigorous PA (r = 0.59; MAPE = 70.1%).

CONCLUSIONS

The FF activity monitor accurately estimated the amount of time spent in SED and overall PA in preschool-aged children, but with an underestimation of moderate-to-vigorous PA. Our findings support the use of the FF as a feasible and a relatively accurate PA monitoring tool in clinical and research settings.

Calibration of GENEActiv accelerometer wrist cut-points for the assessment of physical activity intensity of preschool aged children

This study sought to validate cut-points for use of wrist-worn GENEActiv accelerometer data, to analyse preschool children's (4 to 5 year olds) physical activity (PA) levels via calibration with oxygen consumption values (VO₂). This was a laboratory-based calibration study. Twenty-one preschool children, aged 4.7 ± 0.5 years old, completed six activities (ranging from lying supine to running) whilst wearing the GENEActiv accelerometers at two locations (left and right wrist), these being the participants' non-dominant and dominant wrist, and a Cortex face mask for gas analysis. VO₂ data was used for the assessment of criterion validity. Location specific activity intensity cut-points were established via receiver operator characteristic curve (ROC) analysis. The GENEActiv accelerometers, irrespective of their location, accurately discriminated between all PA intensities (sedentary, light, and moderate and above), with the dominant wrist monitor providing a slightly more precise discrimination at light PA and the non-dominant at the sedentary behaviour and moderate and above intensity levels (area under the curve (AUC) for non-dominant = 0.749-0.993, compared to AUC dominant = 0.760-0.988).

CONCLUSION

This study establishes wrist-worn physical activity cut-points for the GENEActiv accelerometer in preschoolers. What is Known: • GENEActiv accelerometers have been validated as a PA measurement tool in adolescents and adults. • No study to date has validated the GENEActiv accelerometers in preschoolers. What is New: • Cut-points were determined for the wrist-worn GENEActiv accelerometer in preschoolers. • These cut-points can be used in future research to help classify and increase preschoolers' compliance rates with PA.