CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children

Carlson, Jordan; Ridgers, Nicola D.; Nakandala, Supun; Zablocki, Rong; Tuz-Zahra, Fatima; Bellettiere, John; Hibbing, Paul R.; Steel, Chelsea; Jankowska, Marta M.; Rosenberg, Dori E.; Greenwood-Hickman, Mikael Anne; Zou, Jingjing; LaCroix, Andrea Z.; Kumar, Arun

doi:10.1186/s12966-022-01349-2

Cited by 7 publications

(4 citation statements)

References 48 publications

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“…Previous reviews have highlighted that in the absence of a consistent definition of activity patterns, as well as inconsistency in the operationalisation and assessment of activity patterns, it has been difficult to draw conclusions about how activity is accumulated by different populations and how such patterns are associated with health and well-being [ 15 , 17 ]. Examples of activity pattern components examined within the literature include sporadic and prolonged bouts of different movement intensities [ 13 , 33 ], breaks in sitting time [ 34 ], postural transitions [ 35 ], tempo of activities [ 36 ], and time accumulated in different time periods of the day (e.g., hourly; [ 31 ]). In this Delphi study, such examples were considered in the operationalisation of activity pattern components rather than the definition of activity patterns.…”

Section: Discussionmentioning

confidence: 99%

“…It should be noted that this study did not aim to achieve consensus on how to define, for example, short or long activity bouts [ 13 ], or what time intervals would define a transition from one posture or intensity to another [ 35 ]. It is acknowledged that components such as bout lengths that have been examined vary within and between different age groups [ 17 , 28 , 37 ], and therefore general definitions for durations (e.g., short versus long) may not be realistic.…”

Section: Discussionmentioning

confidence: 99%

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Defining and reporting activity patterns: a modified Delphi study

Ridgers¹,

Denniss²,

Burnett³

et al. 2023

Int J Behav Nutr Phys Act

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Background Despite significant interest in assessing activity patterns in different populations, there has been no consensus concerning the definition and operationalisation of this term. This has limited the comparability, interpretability, and synthesis of study findings to date. The aim of this study was to establish a consensus regarding the way in which activity patterns and activity pattern components are defined and reported. Methods The activity patterns literature was searched to identify experts to be invited to participate and to develop a proposed definition of activity patterns and activity pattern components. A three-round modified Delphi survey was conducted online (November 2021 to May 2022). In Round 1, participants were asked to rate their agreement with a proposed activity patterns definition, which also included six activity pattern components (e.g., activity intensity, activity bout, transitions), six examples of activity patterns (e.g., frequency of postural transitions in discrete time periods) and eight items for reporting activity patterns in future research (n = 21 items). Open-ended questions enabled participants to provide further comments and suggestions for additional items. Consensus was defined a priori as ≥ 80% participants rating their agreement with an item. In Round 2, participants were asked to rate their agreement with 25 items (13 original items, eight amended, and four new). In Round 3, participants rated their agreement with 10 items (five original items, four amended, and one new). Results Twenty experts in activity patterns research participated in Round 1, with response rates of 80% and 60% in Rounds 2 and 3, respectively. The proposed activity pattern definition, all activity pattern components definitions, four of the six activity pattern examples, and 10 items in the activity patterns reporting framework achieved consensus. The removal of one activity component item between Rounds 1 and 2 achieved consensus. Conclusion This modified Delphi study achieved consensus for defining and reporting activity patterns for the first time. This consensus definition enables standardisation of activity patterns terminology, which is important given the significant interest in quantifying how individuals accumulate their physical activity and sedentary behaviour across the lifespan to inform the development of future public health guidelines and interventions efforts.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Defining and reporting activity patterns: a modified Delphi study

Ridgers¹,

Denniss²,

Burnett³

et al. 2023

Int J Behav Nutr Phys Act

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“…While a novel computational method has been developed that classifies sitting posture from hip-mounted accelerometers among children [19], postural-detection devices (e.g. activPAL) can differentiate between sitting and standing but have rarely been used to explore associations between sitting time and cardiometabolic risk factors among youth [20,21].…”

Section: Introductionmentioning

confidence: 99%

The association between device-measured sitting time and cardiometabolic health risk factors in children

Contardo Ayala,

Ridgers,

Timperio

et al. 2024

BMC Public Health

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Background There is limited evidence of the associations between postural-derived sitting time, waist-worn derived sedentary time and children’s health and the moderation effect of physical activity (PA). This study examined associations of children’s device-measured sitting time with cardiometabolic health risk factors, including moderation by physical activity. Methods Cross-sectional baseline data from children (mean-age 8.2 ± 0.5 years) in Melbourne, Australia (2010) participating in the TransformUs program were used. Children simultaneously wore an activPAL to assess sitting time and an ActiGraph GT3X to assess sedentary time and physical activity intensity. Cardiometabolic health risk factors included: adiposity (body mass index [BMI], waist circumference [WC]), systolic and diastolic blood pressure (SBP, DBP), high-density lipoprotein (HDL), low-density lipoprotein (LDL), cholesterol, triglycerides, fasting plasma glucose (FPG), serum insulin, and 25-hydroxyvitaminD (25[OH]D). Linear regression models (n = 71–113) assessed associations between sitting time with each health risk factor, adjusted for different PA intensities (i.e. light [LIPA], moderate-vigorous intensities [MVPA], separately on each model), age, sex, adiposity, and clustering by school. Interaction terms examined moderation. The analyses were repeated using device-measured sedentary time (i.e. ActiGraph GT3X) for comparison. Results Sitting time was positively associated with SBP (b = 0.015; 95%CI: 0.004, 0.026), DBP (b = 0.012; 95%CI:0.004, 0.020), and FPG (b = 0.001; 95%CI: 0.000, 0.000), after adjusting for higher PA intensities. The association between sitting time and insulin (b = 0.003; 95%CI: 0.000, 0.006) was attenuated after adjusting for higher PA intensities. When the models were adjusted for LIPA and MVPA, there was a negative association with LDL (b=-0.001; 95%CI: -0.002, -0.000 and b=-0.001; 95%CI: -0.003, -0.000, respectively). There was a negative association of sedentary time with WCz (b=-0.003; 95%CI: -0.005, 0.000) and BMIz (b=-0.003; 95%CI: -0.006, -0.000) when the models were adjusted by MVPA. Sedentary time was positively associated with triglycerides (b = 0.001; 95%CI: 0.000, 0.001) but attenuated after adjusting for MVPA. No evidence of moderation effects was found. Conclusions Higher volumes of sitting and sedentary time were associated with some adverse associations on some cardiometabolic health risk factors in children. These associations were more evident when sitting time was the predictor. This suggests that reducing time spent sitting may benefit some cardiometabolic health outcomes, but future experimental research is needed to confirm causal relationships and identify the biological mechanisms that might be involved. Trial registration Australian New Zealand Clinical Trials Registry: ACTRN12609000715279.

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“…Thus, there is a need to increase the transparency and accessibility of device-based intake-balance assessments. Research-grade devices may be especially useful for this purpose, given the growing emphasis on open-source methodology when using such devices [ 23 – 26 ].…”

Section: Introductionmentioning

confidence: 99%

Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique

Hibbing,

Welk,

Ries

et al. 2023

Int J Behav Nutr Phys Act

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Background Intake-balance assessments measure energy intake (EI) by summing energy expenditure (EE) with concurrent change in energy storage (ΔES). Prior work has not examined the validity of such calculations when EE is estimated via open-source techniques for research-grade accelerometry devices. The purpose of this study was to test the criterion validity of accelerometry-based intake-balance methods for a wrist-worn ActiGraph device. Methods Healthy adults (n = 24) completed two 14-day measurement periods while wearing an ActiGraph accelerometer on the non-dominant wrist. During each period, criterion values of EI were determined based on ΔES measured by dual X-ray absorptiometry and EE measured by doubly labeled water. A total of 11 prediction methods were tested, 8 derived from the accelerometer and 3 from non-accelerometry methods (e.g., diet recall; included for comparison). Group-level validity was assessed through mean bias, while individual-level validity was assessed through mean absolute error, mean absolute percentage error, and Bland–Altman analysis. Results Mean bias for the three best accelerometry-based methods ranged from -167 to 124 kcal/day, versus -104 to 134 kcal/day for the non-accelerometry-based methods. The same three accelerometry-based methods had mean absolute error of 323–362 kcal/day and mean absolute percentage error of 18.1-19.3%, versus 353–464 kcal/day and 19.5-24.4% for the non-accelerometry-based methods. All 11 methods demonstrated systematic bias in the Bland–Altman analysis. Conclusions Accelerometry-based intake-balance methods have promise for advancing EI assessment, but ongoing refinement is necessary. We provide an R package to facilitate implementation and refinement of accelerometry-based methods in future research (see paulhibbing.com/IntakeBalance).

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CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children

Cited by 7 publications

References 48 publications

Defining and reporting activity patterns: a modified Delphi study

Defining and reporting activity patterns: a modified Delphi study

The association between device-measured sitting time and cardiometabolic health risk factors in children

Criterion validity of wrist accelerometry for assessing energy intake via the intake-balance technique

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