A Standardised Core Outcome Set for Measurement and Reporting Sedentary Behaviour Interventional Research: The CROSBI Consensus Study

Curran, F.; Dowd, Kieran P.; Peiris, Casey L; Ploeg, Hidde P. van der; Tremblay, Mark S.; O’Donoghue, Gráinne

doi:10.3390/ijerph19159666

Cited by 3 publications

(3 citation statements)

References 58 publications

(83 reference statements)

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“…The definition of activity patterns includes physical activity and sedentary behaviour, identified as movement behaviours, to reflect advances in the literature. Moreover, the focus on the temporal structure during the waking day is consistent with previous definitions of sitting patterns [ 19 , 41 ] and the assessment of physical activity patterns, though noting the considerable variability in how patterns were operationalised [ 15 ].…”

Section: Discussionsupporting

confidence: 61%

“…Due to a technical error in the survey, one item in the activity patterns reporting framework was not presented in the Round 3 survey, though consensus for inclusion was already achieved in the Round 1 survey. Snowball sampling was utilised to identify potential participants, which has been used in previous Delphi studies (e.g., [ 41 ]). However, it is possible that this may have introduced sampling bias into the study, particularly if participants held similar views on the definition of an activity pattern or pattern component.…”

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: Discussionsupporting

confidence: 61%

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

View full text Add to dashboard Cite

show abstract

“…Moreover, only a limited number of outcomes were reported in this study [ 15 ]. Given the recently proposed standardized Core Research Outcomes for Sedentary Behaviour research [ 19 ], it is important that future SB research begins to report these outcomes in order to enhance the accumulation of pooled evidence and opportunities for meta-analyses.…”

Section: Introductionmentioning

confidence: 99%

Comparing the activPAL CREA and GHLA Algorithms for the Classification of Postures and Activity in Free-Living Children

Buchan

Ugbolue

2022

IJERPH

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The activPAL accelerometer has been used extensively in research to assess sedentary behaviour (SB) and physical activity (PA) outcomes. The aim of this study was to assess the comparability of PA and SB outcomes from two automated algorithms (CREA and GHLA) applied to the activPAL accelerometer. One hundred and twenty participants aged 8–12 years wore an activPAL accelerometer on their right thigh continuously for seven days on two occasions, providing valid data from 1058 days. The PALbatch software downloaded the data after applying the CREA and GHLA (latest) algorithms. The comparability of the algorithms were assessed using the mean absolute percent error (MAPE), intra-class correlation coefficients (ICC), and equivalence testing. Comparisons for daily wear time, primary lying, sitting and standing time, sedentary and stepping time, upright time, total number of steps, sit–stand transitions and stepping time ≤ 1 min revealed mainly small MAPE (≤2%), excellent ICCs (lower bound 95% CI ≥ 0.97), and equivalent outcomes. Time spent in sitting bouts > 60 min and stepping bouts > 5 min were not equivalent with the absolute zone needed to reach equivalence (≥7%). Comparable outcomes were provided for wear time and postural outcomes using the CREA or GHLA algorithms, but not for time spent in sitting bouts > 60 min and stepping bouts > 5 min.

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Can the bias of self-reported sitting time be corrected? A statistical model validation study based on data from 23 993 adults in the Norwegian HUNT study

Kongsvold,

Flaaten,

Logacjov

et al. 2023

Int J Behav Nutr Phys Act

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Background Despite apparent shortcomings such as measurement error and low precision, self-reported sedentary time is still widely used in surveillance and research. The aim of this study was threefold; (i) to examine the agreement between self-reported and device-measured sitting time in a general adult population; (ii), to examine to what extent demographics, lifestyle factors, long-term health conditions, physical work demands, and educational level is associated with measurement bias; and (iii), to explore whether correcting for factors associated with bias improves the prediction of device-measured sitting time based on self-reported sitting time. Methods A statistical validation model study based on data from 23 993 adults in the Trøndelag Health Study (HUNT4), Norway. Participants reported usual sitting time on weekdays using a single-item questionnaire and wore two AX3 tri-axial accelerometers on the thigh and low back for an average of 3.8 (standard deviation [SD] 0.7, range 1–5) weekdays to determine their sitting time. Statistical validation was performed by iteratively adding all possible combinations of factors associated with bias between self-reported and device-measured sitting time in a multivariate linear regression. We randomly selected 2/3 of the data (n = 15 995) for model development and used the remaining 1/3 (n = 7 998) to evaluate the model. Results Mean (SD) self-reported and device-measured sitting time were 6.8 (2.9) h/day and 8.6 (2.2) h/day, respectively, corresponding to a mean difference of 1.8 (3.1) h/day. Limits of agreement ranged from − 8.0 h/day to 4.4 h/day. The discrepancy between the measurements was characterized by a proportional bias with participants device-measured to sit less overestimating their sitting time and participants device-measured to sit more underestimating their sitting time. The crude explained variance of device-measured sitting time based on self-reported sitting time was 10%. This improved to 24% when adding age, body mass index and physical work demands to the model. Adding sex, lifestyle factors, educational level, and long-term health conditions to the model did not improve the explained variance. Conclusions Self-reported sitting time had low validity and including a range of factors associated with bias in self-reported sitting time only marginally improved the prediction of device-measured sitting time.

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A Standardised Core Outcome Set for Measurement and Reporting Sedentary Behaviour Interventional Research: The CROSBI Consensus Study

Cited by 3 publications

References 58 publications

Defining and reporting activity patterns: a modified Delphi study

Defining and reporting activity patterns: a modified Delphi study

Comparing the activPAL CREA and GHLA Algorithms for the Classification of Postures and Activity in Free-Living Children

Can the bias of self-reported sitting time be corrected? A statistical model validation study based on data from 23 993 adults in the Norwegian HUNT study

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