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Monnaatsie M, Mielke GI, Biddle SJH, Kolbe-Alexander TL. Ecological momentary assessment of physical activity and sedentary behaviour in shift workers and non-shift workers: Validation study. J Sports Sci 2024:1-10. [PMID: 38899730 DOI: 10.1080/02640414.2024.2369443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 06/11/2024] [Indexed: 06/21/2024]
Abstract
This study examined the criterion validity of an ecological momentary assessment (EMA)-reported physical activity and sedentary time compared with accelerometry in shift workers and non-shift workers. Australian workers (n = 102) received prompts through a mobile EMA app and wore the Actigraph accelerometer on the right hip for 7-10 days. Participants received five EMA prompts per day at 3-hour intervals on their mobile phones. EMA prompts sent to shift workers (SW-T) were tailored according to their work schedule. Non-shift workers (NSW-S) received prompts at standardised times. To assess criterion validity, the association of EMA-reported activities and the Actigraph accelerometer activity counts and number of steps were used. Participants were 36 ± 11 years and 58% were female. On occasions where participants reported physical activity, acceleration counts per minute (CPM) and steps were significantly higher (β = 1184 CPM, CI 95%: 1034, 1334; β = 20.9 steps, CI 95%: 18.2, 23.6) than each of the other EMA activities. Acceleration counts and steps were lower when sitting was reported than when no sitting was reported by EMA. Our study showed that EMA-reported physical activity and sedentary time was significantly associated with accelerometer-derived data. Therefore, EMA can be considered to assess shift workers' movement-related behaviours with accelerometers to provide rich contextual data.
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Affiliation(s)
- Malebogo Monnaatsie
- School of Health and Medical Sciences, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Ipswich, Queensland, Australia
- Centre for Health Research, University of Southern Queensland, Springfield, Queensland, Australia
- Department of Sport Science, Faculty of Education, University of Botswana, Gaborone, Botswana
| | - Gregore I Mielke
- School of Public Health, University of Queensland, Brisbane, Australia
| | - Stuart J H Biddle
- Centre for Health Research, University of Southern Queensland, Springfield, Queensland, Australia
- Faculty of Sport & Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Tracy L Kolbe-Alexander
- School of Health and Medical Sciences, Faculty of Health, Engineering and Sciences, University of Southern Queensland, Ipswich, Queensland, Australia
- Centre for Health Research, University of Southern Queensland, Springfield, Queensland, Australia
- Division of Exercise Science and Sports Medicine, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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Zablocki RW, Hartman SJ, Di C, Zou J, Carlson JA, Hibbing PR, Rosenberg DE, Greenwood-Hickman MA, Dillon L, LaCroix AZ, Natarajan L. Using functional principal component analysis (FPCA) to quantify sitting patterns derived from wearable sensors. Int J Behav Nutr Phys Act 2024; 21:48. [PMID: 38671485 PMCID: PMC11055353 DOI: 10.1186/s12966-024-01585-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). METHODS The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. RESULTS At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. CONCLUSION In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. TRIAL REGISTRATION ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145 ; International Registered Report Identifier (IRRID): DERR1-10.2196/28684.
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Affiliation(s)
- Rong W Zablocki
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA
| | - Sheri J Hartman
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA
| | - Chongzhi Di
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, Washington, USA
| | - Jingjing Zou
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA
| | - Jordan A Carlson
- Center for Children's Healthy Lifestyles and Nutrition, Children's Mercy Kansas City, 610 E. 22nd St., Kansas City, 64108, Missouri, USA
| | - Paul R Hibbing
- Department of Kinesiology and Nutrition, University of Illinois Chicago, 1919 W Taylor St, Chicago, IL, 60612, USA
| | - Dori E Rosenberg
- Kaiser Permanente Washington Health Research Institute, 1730 Minor Ave, Suite 1600, Seattle, 98101, Washington, USA
| | | | - Lindsay Dillon
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA
| | - Loki Natarajan
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California at San Diego, 9500 Gilman Drive, La Jolla, 92093, California, USA.
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Etzkorn LH, Heravi AS, Knuth ND, Wu KC, Post WS, Urbanek JK, Crainiceanu CM. Classification of Free-Living Body Posture with ECG Patch Accelerometers: Application to the Multicenter AIDS Cohort Study. STATISTICS IN BIOSCIENCES 2024; 16:25-44. [PMID: 38715709 PMCID: PMC11073799 DOI: 10.1007/s12561-023-09377-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 05/11/2023] [Accepted: 05/30/2023] [Indexed: 05/12/2024]
Abstract
Purpose As health studies increasingly monitor free-living heart performance via ECG patches with accelerometers, researchers will seek to investigate cardio-electrical responses to physical activity and sedentary behavior, increasing demand for fast, scalable methods to process accelerometer data. We extend a posture classification algorithm for accelerometers in ECG patches when researchers do not have ground-truth labels or other reference measurements (i.e., upright measurement). Methods Men living with and without HIV in the Multicenter AIDS Cohort study wore the Zio XT® for up to two weeks (n = 1,250). Our novel extensions for posture classification include (1) estimation of an upright posture for each individual without a reference upright measurement; (2) correction of the upright estimate for device removal and re-positioning using novel spherical change-point detection; and (3) classification of upright and recumbent periods using a clustering and voting process rather than a simple inclination threshold used in other algorithms. As no posture labels exist in the free-living environment, we perform numerous sensitivity analyses and evaluate the algorithm against labelled data from the Towson Accelerometer Study, where participants wore accelerometers at the waist. Results On average, 87.1% of participants were recumbent at 4am and 15.5% were recumbent at 1pm. Participants were recumbent 54 minutes longer on weekends compared to weekdays. Performance was good in comparison to labelled data in a separate, controlled setting (accuracy = 96.0%, sensitivity = 97.5%, specificity = 95.9%). Conclusions Posture may be classified in the free-living environment from accelerometers in ECG patches even without measuring a standard upright position. Furthermore, algorithms that fail to account for individuals who rotate and re-attach the accelerometer may fail in the free-living environment.
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Affiliation(s)
| | | | | | | | | | - Jacek K. Urbanek
- School of Medicine, Johns Hopkins University
- Regeneron Pharmaceuticals Inc., 777 Old Saw Mill River Rd, Tarrytown NY 10591
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LaMonte MJ, LaCroix AZ, Nguyen S, Evenson KR, Di C, Stefanick ML, Hyde ET, Anuskiewicz B, Eaton CB. Accelerometer-Measured Physical Activity, Sedentary Time, and Heart Failure Risk in Women Aged 63 to 99 Years. JAMA Cardiol 2024; 9:336-345. [PMID: 38381446 PMCID: PMC10882503 DOI: 10.1001/jamacardio.2023.5692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 02/22/2024]
Abstract
Importance Heart failure (HF) prevention is paramount to public health in the 21st century. Objective To examine incident HF and its subtypes with preserved ejection fraction (HFpEF) and reduced EF (HFrEF) according to accelerometer-measured physical activity (PA) and sedentary time. Design, Setting, and Participants This was a prospective cohort study, the Objective Physical Activity and Cardiovascular Health (OPACH) in Older Women study, conducted from March 2012 to April 2014. Included in the analysis were women aged 63 to 99 years without known HF, who completed hip-worn triaxial accelerometry for 7 consecutive days. Follow-up for incident HF occurred through February 2022. Data were analyzed from March to December 2023. Exposure Daily PA (total, light, moderate to vigorous PA [MVPA], steps) and sedentary (total, mean bout duration) behavior. Main Outcomes and Measures Adjudicated incident HF, HFpEF, and HFrEF. Results A total of 5951 women (mean [SD] age, 78.6 [6.8] years) without known HF were included in this analysis. Women self-identified with the following race and ethnicity categories: 2004 non-Hispanic Black (33.7%), 1022 Hispanic (17.2%), and 2925 non-Hispanic White (49.2%). There were 407 HF cases (257 HFpEF; 110 HFrEF) identified through a mean (SD) of 7.5 (2.6) years (range, 0.01-9.9 years) of follow-up. Fully adjusted hazard ratios (HRs) for overall HF, HFpEF, and HFrEF associated with a 1-SD increment were 0.85 (95% CI, 0.75-0.95), 0.78 (95% CI, 0.67-0.91), and 1.02 (95% CI, 0.81-1.28) for minutes per day total PA; 0.74 (95% CI, 0.63-0.88), 0.71 (95% CI, 0.57-0.88), and 0.83 (95% CI, 0.62-1.12) for steps per day; and 1.17 (95% CI, 1.04-1.33), 1.29 (95% CI, 1.10-1.51), and 0.94 (95% CI, 0.75-1.18) for minutes per day total sedentary. Cubic spline curves for overall HF and HFpEF were significant inverse for total PA and steps per day and positive for total sedentary. Light PA and MVPA were inversely associated with overall HF (HR per 1 SD: 0.88; 95% CI, 0.78-0.98 and 0.84; 95% CI, 0.73-0.97) and HFpEF (0.80; 95% CI, 0.70-0.93 and 0.85; 95% CI, 0.72-1.01) but not HFrEF. Associations did not meaningfully differ when stratified by age, race and ethnicity, body mass index, physical function, or comorbidity score. Results for sedentary bout duration were inconsistent. Conclusions and Relevance Higher accelerometer-measured PA (MVPA, light PA, steps per day) was associated with lower risk (and greater total sedentary time with higher risk) of overall HF and HFpEF in a racially and ethnically diverse cohort of older women. Increasing PA and reducing sedentary time for primary HFpEF prevention may have relevant implications for cardiovascular resilience and healthy aging in later life.
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Affiliation(s)
| | | | | | | | - Chongzhi Di
- Fred Hutchinson Cancer Center, Seattle, Washington
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Vähä-Ypyä H, Husu P, Sievänen H, Vasankari T. Measurement of Sedentary Behavior-The Outcomes of the Angle for Posture Estimation (APE) Method. SENSORS (BASEL, SWITZERLAND) 2024; 24:2241. [PMID: 38610452 PMCID: PMC11014150 DOI: 10.3390/s24072241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024]
Abstract
Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth's gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = -0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term "sedentary behavior" for studies wherein the classification of SB is based on both intensity and posture.
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Affiliation(s)
- Henri Vähä-Ypyä
- The UKK Institute for Health Promotion Research, 33500 Tampere, Finland; (P.H.); (H.S.); (T.V.)
| | - Pauliina Husu
- The UKK Institute for Health Promotion Research, 33500 Tampere, Finland; (P.H.); (H.S.); (T.V.)
| | - Harri Sievänen
- The UKK Institute for Health Promotion Research, 33500 Tampere, Finland; (P.H.); (H.S.); (T.V.)
| | - Tommi Vasankari
- The UKK Institute for Health Promotion Research, 33500 Tampere, Finland; (P.H.); (H.S.); (T.V.)
- Faculty of Medicine and Health Technology, Tampere University, 33014 Tampere, Finland
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Nguyen S, Bellettiere J, Anuskiewicz B, Di C, Carlson J, Natarajan L, LaMonte MJ, LaCroix AZ. Prospective Associations of Accelerometer-Measured Machine-Learned Sedentary Behavior With Death Among Older Women: The OPACH Study. J Am Heart Assoc 2024; 13:e031156. [PMID: 38410939 PMCID: PMC10944026 DOI: 10.1161/jaha.123.031156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/14/2023] [Indexed: 02/28/2024]
Abstract
BACKGROUND Sedentary behavior is a recognized mortality risk factor. The novel and validated convolutional neural network hip accelerometer posture algorithm highly accurately classifies sitting and postural changes compared with accelerometer count cut points. We examined the prospective associations of convolutional neural network hip accelerometer posture-classified total sitting time and mean sitting bout duration with all-cause and cardiovascular disease (CVD) death. METHODS AND RESULTS Women (n=5856; mean±SD age, 79±7 years; 33% Black women, 17% Hispanic or Latina women, 50% White women) in the Women's Health Initiative Objective Physical Activity and Cardiovascular Health (OPACH) Study wore the ActiGraph GT3X+ for ~7 days from May 2012 to April 2014 and were followed through February 19, 2022 for all-cause and CVD death. The convolutional neural network hip accelerometer posture algorithm classified total sitting time and mean sitting bout duration from GT3X+ output. Over follow-up (median, 8.4 years; range, 0.1-9.9), there were 1733 deaths (632 from CVD). Adjusted Cox regression hazard ratios (HRs) comparing women in the highest total sitting time quartile (>696 min/d) to those in the lowest (<556.0 min/d) were 1.57 (95% CI; 1.35-1.83; P-trend<0.001) for all-cause death and 1.78 (95% CI; 1.36-2.31; P-trend<0.001) for CVD death. HRs comparing women in the longest mean sitting bout duration quartile (>15 minutes) to the shortest (<9.3 minutes) were 1.43 (95% CI; 1.23-1.66; P-trend<0.001) for all-cause death and 1.52 (95% CI; 1.18-1.96; P-trend<0.001) for CVD death. Apparent nonlinear associations for total sitting time suggested higher all-cause death (P nonlinear=0.009) and CVD death (P nonlinear=0.008) risk after ~660 to 700 min/d. CONCLUSIONS Higher total sitting time and longer mean sitting bout duration are associated with higher all-cause and CVD mortality risk among older women. These data support interventions aimed at reducing both total sitting time and interrupting prolonged sitting.
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Affiliation(s)
- Steve Nguyen
- Division of EpidemiologyHerbert Wertheim School of Public Health, University of California San DiegoLa JollaCAUSA
| | - John Bellettiere
- Division of EpidemiologyHerbert Wertheim School of Public Health, University of California San DiegoLa JollaCAUSA
| | - Blake Anuskiewicz
- Division of EpidemiologyHerbert Wertheim School of Public Health, University of California San DiegoLa JollaCAUSA
| | - Chongzhi Di
- Division of Public Health SciencesFred Hutchinson Cancer CenterSeattleWAUSA
| | - Jordan Carlson
- Center for Children’s Healthy Lifestyles and Nutrition, Children’s Mercy Kansas CityKansas CityMOUSA
| | - Loki Natarajan
- Division of EpidemiologyHerbert Wertheim School of Public Health, University of California San DiegoLa JollaCAUSA
| | - Michael J. LaMonte
- Department of Epidemiology and Environmental Health, School of Public Health and Health ProfessionsUniversity at Buffalo – SUNYBuffaloNYUSA
| | - Andrea Z. LaCroix
- Division of EpidemiologyHerbert Wertheim School of Public Health, University of California San DiegoLa JollaCAUSA
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Hibbing PR, Carlson JA, Steel C, Greenwood-Hickman MA, Nakandala S, Jankowska MM, Bellettiere J, Zou J, LaCroix AZ, Kumar A, Katzmarzyk PT, Natarajan L. Low movement, deep-learned sitting patterns, and sedentary behavior in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Obes (Lond) 2023; 47:1100-1107. [PMID: 37580374 PMCID: PMC10714872 DOI: 10.1038/s41366-023-01364-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 07/27/2023] [Accepted: 08/08/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND/OBJECTIVES Sedentary behavior (SB) has both movement and postural components, but most SB research has only assessed low movement, especially in children. The purpose of this study was to compare estimates and health associations of SB when derived from a standard accelerometer cut-point, a novel sitting detection technique (CNN Hip Accelerometer Posture for Children; CHAP-Child), and both combined. METHODS Data were from the International Study of Childhood Obesity, Lifestyle, and the Environment (ISCOLE). Participants were 6103 children (mean ± SD age 10.4 ± 0.56 years) from 12 countries who wore an ActiGraph GT3X+ accelerometer on the right hip for approximately one week. We calculated SB time, mean SB bout duration, and SB breaks using a cut-point (SBmovement), CHAP-Child (SBposture), and both methods combined (SBcombined). Mixed effects regression was used to test associations of SB variables with pediatric obesity variables (waist circumference, body fat percentage, and body mass index z-score). RESULTS After adjusting for MVPA, SBposture showed several significant obesity associations favoring lower mean SB bout duration (b = 0.251-0.449; all p < 0.001) and higher SB breaks (b = -0.005--0.052; all p < 0.001). Lower total SB was unexpectedly related to greater obesity (b = -0.077--0.649; p from <0.001-0.02). For mean SB bout duration and SB breaks, more associations were observed for SBposture (n = 5) than for SBmovement (n = 3) or SBcombined (n = 1), and tended to have larger magnitude as well. CONCLUSIONS Using traditional measures of low movement as a surrogate for SB may lead to underestimated or undetected adverse associations between SB and obesity. CHAP-Child allows assessment of sitting posture using hip-worn accelerometers. Ongoing work is needed to understand how low movement and posture are related to one another, as well as their potential health implications.
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Affiliation(s)
- Paul R Hibbing
- Department of Kinesiology and Nutrition, University of Illinois Chicago, Chicago, IL, USA.
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, Kansas City, MO, USA.
| | - Jordan A Carlson
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, Kansas City, MO, USA
- Department of Pediatrics, University of Missouri Kansas City, Kansas City, MO, USA
| | - Chelsea Steel
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, Kansas City, MO, USA
| | | | - Supun Nakandala
- Databricks Inc, San Francisco, CA, USA
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Marta M Jankowska
- Beckman Research Institute, Population Sciences, City of Hope, Duarte, CA, USA
| | - John Bellettiere
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Jingjing Zou
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Arun Kumar
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Loki Natarajan
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
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Mielke GI, de Almeida Mendes M, Ekelund U, Rowlands AV, Reichert FF, Crochemore-Silva I. Absolute intensity thresholds for tri-axial wrist and waist accelerometer-measured movement behaviors in adults. Scand J Med Sci Sports 2023; 33:1752-1764. [PMID: 37306308 DOI: 10.1111/sms.14416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 05/03/2023] [Accepted: 05/19/2023] [Indexed: 06/13/2023]
Abstract
AIM This study was aimed to: (1) compare raw triaxial acceleration data from GENEActiv (GA) and ActiGraph GT3X+ (AG) placed on the non-dominant wrist; (2) compare AG placed on the non-dominant and dominant wrist, and waist; (3) derive brand- and placement-specific absolute intensity thresholds for inactive and sedentary time, and physical activity intensity in adults. METHODS Eighty-six adults (44 men; 34.6 ± 10.8 years) performed nine activities while simultaneously wearing GA and AG on wrist and waist. Acceleration (in gravitational equivalent units; mg) was compared with oxygen uptake (measured with indirect calorimetry). RESULTS Increases in acceleration mirrored increases in intensity of activities, regardless of device brand and placement. Differences in acceleration between GA and AG worn at the non-dominant wrist were small but tended to be high at lower intensity activities. Thresholds for differentiating inactivity (<1.5 MET) from activity (≥1.5 MET) ranged from 25 mg (AG non-dominant wrist; sensitivity 93%, specificity 95%) to 40 mg (AG waist; sensitivity 78%, specificity 100%). For moderate intensity (≥3 METs), thresholds ranged from 65 mg (AG waist; sensitivity 96%, specificity 94%) to 92 mg (GA non-dominant; sensitivity 93%, specificity 98%); vigorous intensity (≥6 METs) thresholds ranged from 190 mg (AG waist; sensitivity 82%, specificity 92%) to 283 mg (GA non-dominant; sensitivity 93%, specificity 98%). CONCLUSION Raw triaxial acceleration outputs from two widely used accelerometer brands may have limited comparability in low intensity activities. Thresholds derived in this study can be utilized in adults to reasonably classify movement behaviors into categories of intensity.
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Affiliation(s)
- Gregore Iven Mielke
- School of Public Health, The University of Queensland, Queensland, Brisbane, Australia
| | | | - Ulf Ekelund
- Norwegian School of Sport Sciences, Oslo, Norway
- Department of Chronic Diseases, Norwegian Institute of Public Health, Oslo, Norway
| | - Alex V Rowlands
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK
- NIHR Leicester Biomedical Research Centre, Leicester, UK
| | | | - Inacio Crochemore-Silva
- Post-graduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
- Post-graduate Program in Physical Education, Federal University of Pelotas, Pelotas, Brazil
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Nguyen S, LaCroix AZ, Hayden KM, Di C, Palta P, Stefanick ML, Manson JE, Rapp SR, LaMonte MJ, Bellettiere J. Accelerometer-measured physical activity and sitting with incident mild cognitive impairment or probable dementia among older women. Alzheimers Dement 2023; 19:3041-3054. [PMID: 36695426 PMCID: PMC10366337 DOI: 10.1002/alz.12908] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 01/26/2023]
Abstract
INTRODUCTION Physical activity (PA) is prospectively inversely associated with dementia risk, but few studies examined accelerometer measures of PA and sitting with rigorously-adjudicated mild cognitive impairment (MCI) and dementia risk. METHODS We examined the associations of accelerometer measures (PA and sitting) with incident MCI/probable dementia in the Women's Health Initiative (n = 1277; mean age = 82 ± 6 years) RESULTS: Over a median follow-up of 4.2 years, 267 MCI/probable dementia cases were identified. Adjusted Cox regression HRs (95% CI) across moderate-to-vigorous PA (MVPA) min/d quartiles were 1.00 (reference), 1.28 (0.90 to 1.81), 0.79 (0.53 to 1.17), and 0.69 (0.45 to 1.06); P-trend = 0.01. Adjusted HRs (95% CI) across steps/d quartiles were 1.00 (reference), 0.73 (0.51 to 1.03), 0.64 (0.43 to 0.94), and 0.38 (0.23 to 0.61); P-trend < 0.001. The HR (95% CI) for each 1-SD increment in MVPA (31 min/d) and steps/d (1865) were 0.79 (0.67 to 0.94) and 0.67 (0.54 to 0.82), respectively. Sitting was not associated with MCI/probable dementia. DISCUSSION Findings suggest ≥ moderate intensity PA, particularly stepping, associates with lower MCI and dementia risk. HIGHLIGHTS Few studies have examined accelerometer-measured physical activity, including steps, and sitting with incident ADRD. Moderate-to-vigorous physical activity and steps, but not light physical activity or sitting, were inversely associated with lower ADRD risk. Among older women, at least moderate intensity physical activity may be needed to reduce ADRD risk.
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Affiliation(s)
- Steve Nguyen
- Division of Epidemiology, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
| | - Andrea Z. LaCroix
- Division of Epidemiology, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
| | - Kathleen M. Hayden
- Department of Social Sciences & Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Chongzhi Di
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Priya Palta
- Departments of Medicine and Epidemiology, Columbia University Irving Medical Center, New York, NY, USA
| | - Marcia L. Stefanick
- Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - JoAnn E. Manson
- Division of Preventive Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephen R. Rapp
- Department of Psychiatry & Behavioral Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Michael J. LaMonte
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo - SUNY, Buffalo, NY, USA
| | - John Bellettiere
- Division of Epidemiology, Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, USA
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10
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Dooley EE, Winkles JF, Colvin A, Kline CE, Badon SE, Diaz KM, Karvonen-Gutierrez CA, Kravitz HM, Sternfeld B, Thomas SJ, Hall MH, Gabriel KP. Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep-wake cycles. JOURNAL OF ACTIVITY, SEDENTARY AND SLEEP BEHAVIORS 2023; 2:8. [PMID: 37694170 PMCID: PMC10492590 DOI: 10.1186/s44167-023-00017-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/02/2023] [Indexed: 09/12/2023]
Abstract
Background Daily 24-h sleep-wake cycles have important implications for health, however researcher preferences in choice and location of wearable devices for behavior measurement can make 24-h cycles difficult to estimate. Further, missing data due to device malfunction, improper initialization, and/or the participant forgetting to wear one or both devices can complicate construction of daily behavioral compositions. The Method for Activity Sleep Harmonization (MASH) is a process that harmonizes data from two different devices using data from women who concurrently wore hip (waking) and wrist (sleep) devices for ≥ 4 days. Methods MASH was developed using data from 1285 older community-dwelling women (ages: 60-72 years) who concurrently wore a hip-worn ActiGraph GT3X + accelerometer (waking activity) and a wrist-worn Actiwatch 2 device (sleep) for ≥ 4 days (N = 10,123 days) at the same time. MASH is a two-tiered process using (1) scored sleep data (from Actiwatch) or (2) one-dimensional convolutional neural networks (1D CNN) to create predicted wake intervals, reconcile sleep and activity data disagreement, and create day-level night-day-night pairings. MASH chooses between two different 1D CNN models based on data availability (ActiGraph + Actiwatch or ActiGraph-only). MASH was evaluated using Receiver Operating Characteristic (ROC) and Precision-Recall curves and sleep-wake intervals are compared before (pre-harmonization) and after MASH application. Results MASH 1D CNNs had excellent performance (ActiGraph + Actiwatch ROC-AUC = 0.991 and ActiGraph-only ROC-AUC = 0.983). After exclusions (partial wear [n = 1285], missing sleep data proceeding activity data [n = 269], and < 60 min sleep [n = 9]), 8560 days were used to show the utility of MASH. Of the 8560 days, 46.0% had ≥ 1-min disagreement between the devices or used the 1D CNN for sleep estimates. The MASH waking intervals were corrected (median minutes [IQR]: -27.0 [-115.0, 8.0]) relative to their pre-harmonization estimates. Most correction (-18.0 [-93.0, 2.0] minutes) was due to reducing sedentary behavior. The other waking behaviors were reduced a median (IQR) of -1.0 (-4.0, 1.0) minutes. Conclusions Implementing MASH to harmonize concurrently worn hip and wrist devices can minimizes data loss and correct for disagreement between devices, ultimately improving accuracy of 24-h compositions necessary for time-use epidemiology.
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Affiliation(s)
- Erin E. Dooley
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - J. F. Winkles
- Epidemiology Data Center, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Alicia Colvin
- Department of Epidemiology, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Christopher E. Kline
- Department of Health and Human Development, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Sylvia E. Badon
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Keith M. Diaz
- Center for Behavioral Cardiovascular Health, Columbia University Medical Center, New York, NY, USA
| | | | - Howard M. Kravitz
- Department of Psychiatry and Behavioral Sciences and Department of Preventive Medicine, Rush University Medical Center, Chicago, IL, USA
| | - Barbara Sternfeld
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - S. Justin Thomas
- Department of Psychiatry and Behavioral Neurobiology, The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Martica H. Hall
- Department of Psychiatry, School of Medicine, The University of Pittsburgh, Pittsburgh, PA, USA
| | - Kelley Pettee Gabriel
- Department of Epidemiology, The University of Alabama at Birmingham, Birmingham, AL, USA
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11
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Posis AIB, Bellettiere J, Salem RM, LaMonte MJ, Manson JE, Casanova R, LaCroix AZ, Shadyab AH. Associations of Accelerometer-Measured Physical Activity and Sedentary Time With All-Cause Mortality by Genetic Predisposition for Longevity. J Aging Phys Act 2023; 31:265-275. [PMID: 36002033 PMCID: PMC9950283 DOI: 10.1123/japa.2022-0067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/08/2022] [Accepted: 07/20/2022] [Indexed: 11/18/2022]
Abstract
The goal of this study was to examine associations between accelerometer-measured physical activity (PA) and sedentary time (ST) with mortality by a genetic risk score (GRS) for longevity. Among 5,446 women, (mean [SD]: age, 78.2 [6.6] years), 1,022 deaths were observed during 33,350 person-years of follow-up. Using multivariable Cox proportional hazards models, higher light PA and moderate to vigorous PA were associated with lower mortality across all GRS for longevity categories (low/medium/high; all ptrend < .001). Higher ST was associated with higher mortality (ptrend across all GRS categories < .001). Interaction tests for PA and ST with the GRS were not statistically significant. Findings support the importance of higher PA and lower ST for reducing mortality risk in older women, regardless of genetic predisposition for longevity.
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Affiliation(s)
- Alexander Ivan B. Posis
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
- School of Public Health, San Diego State University, San Diego, CA, USA
| | - John Bellettiere
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Rany M. Salem
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Michael J. LaMonte
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, NY, USA
| | - JoAnn E. Manson
- Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, MA, USA
| | - Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Andrea Z. LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Aladdin H. Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
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12
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Bellettiere J, Nakandala S, Tuz-Zahra F, Winkler EAH, Hibbing PR, Healy GN, Dunstan DW, Owen N, Greenwood-Hickman MA, Rosenberg DE, Zou J, Carlson JA, Di C, Dillon LW, Jankowska MM, LaCroix AZ, Ridgers ND, Zablocki R, Kumar A, Natarajan L. CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35. JOURNAL FOR THE MEASUREMENT OF PHYSICAL BEHAVIOUR 2022; 5:215-223. [PMID: 38260182 PMCID: PMC10803054 DOI: 10.1123/jmpb.2021-0062] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35-99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results Mean errors (activPAL - CHAP-Adult) and 95% limits of agreement were: sedentary time -10.5 (-63.0, 42.0) min/day, breaks in sedentary time 1.9 (-9.2, 12.9) breaks/day, mean bout duration -0.6 (-4.0, 2.7) min, usual bout duration -1.4 (-8.3, 5.4) min, alpha .00 (-.04, .04), and time in ≥30-min bouts -15.1 (-84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: -2.0% (4.0%), -4.7% (12.2%), 4.1% (11.6%), -4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson's correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.
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Affiliation(s)
- John Bellettiere
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Supun Nakandala
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Fatima Tuz-Zahra
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | | | - Paul R Hibbing
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Hospital, Kansas City, MO, USA
| | - Genevieve N Healy
- School of Public Health, the University of Queensland, Brisbane, QLD, Australia
| | - David W Dunstan
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, VIC, Australia
| | - Neville Owen
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Centre for Urban Transitions, Swinburne University of Technology, Melbourne, VIC, Australia
| | | | - Dori E Rosenberg
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jingjing Zou
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Jordan A Carlson
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Hospital, Kansas City, MO, USA
- Department of Pediatrics, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Chongzhi Di
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Lindsay W Dillon
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Marta M Jankowska
- Qualcomm Institute/Calit2, University of California San Diego, La Jolla, CA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Nicola D Ridgers
- School of Exercise and Nutrition Sciences, Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC, Australia
| | - Rong Zablocki
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Arun Kumar
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Loki Natarajan
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
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13
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Carlson JA, Ridgers ND, Nakandala S, Zablocki R, Tuz-Zahra F, Bellettiere J, Hibbing PR, Steel C, Jankowska MM, Rosenberg DE, Greenwood-Hickman MA, Zou J, LaCroix AZ, Kumar A, Natarajan L. CHAP-child: an open source method for estimating sit-to-stand transitions and sedentary bout patterns from hip accelerometers among children. Int J Behav Nutr Phys Act 2022; 19:109. [PMID: 36028890 PMCID: PMC9419346 DOI: 10.1186/s12966-022-01349-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 08/15/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Hip-worn accelerometer cut-points have poor validity for assessing children's sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. METHODS Participants were 278, 8-11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). RESULTS Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. CONCLUSIONS There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.
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Affiliation(s)
- Jordan A Carlson
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, 610 E. 22ndSt., Kansas City, MO, 64108, USA.
- Department of Pediatrics, University of Missouri - Kansas City, Kansas City, MO, USA.
| | - Nicola D Ridgers
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia
- Alliance for Research in Exercise, Nutrition and Activity, Allied Health and Human Performance, University of South Australia, Adelaide, Australia
| | - Supun Nakandala
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Rong Zablocki
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Fatima Tuz-Zahra
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - John Bellettiere
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Paul R Hibbing
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, 610 E. 22ndSt., Kansas City, MO, 64108, USA
| | - Chelsea Steel
- Center for Children's Healthy Lifestyles & Nutrition, Children's Mercy Kansas City, 610 E. 22ndSt., Kansas City, MO, 64108, USA
| | - Marta M Jankowska
- Population Sciences, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Dori E Rosenberg
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | - Jingjing Zou
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Andrea Z LaCroix
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Arun Kumar
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
| | - Loki Natarajan
- Herbert Wertheim School of Public Health and Longevity Science, University of California San Diego, La Jolla, CA, USA
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14
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Curran F, Dowd KP, Peiris CL, van der Ploeg HP, Tremblay MS, O’Donoghue G. A Standardised Core Outcome Set for Measurement and Reporting Sedentary Behaviour Interventional Research: The CROSBI Consensus Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:9666. [PMID: 35955024 PMCID: PMC9367894 DOI: 10.3390/ijerph19159666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Heterogeneity of descriptors and outcomes measured and reported in sedentary behaviour (SB) research hinder the meta-analysis of data and accumulation of evidence. The objective of the Core Research Outcomes for Sedentary Behaviour Interventions (CROSBI) consensus study was to identify and validate, a core outcome set (COS) to report (what, how, when to measure) in interventional sedentary behaviour studies. Outcomes, extracted from a systematic literature review, were categorized into domains and data items (COS v0.0). International experts (n = 5) provided feedback and identified additional items, which were incorporated into COS v0.1. A two round online Delphi survey was conducted to seek consensus from a wider stakeholder group and outcomes that achieved consensus in the second round COS (v0.2), were ratified by the expert panel. The final COS (v1.0) contains 53 data items across 12 domains, relating to demographics, device details, wear-time criteria, wear-time measures, posture-related measures, sedentary breaks, sedentary bouts and physical activity. Notably, results indicate that sedentary behaviour outcomes should be measured by devices that include an inclinometry or postural function. The proposed standardised COS is available openly to enhance the accumulation of pooled evidence in future sedentary behaviour intervention research and practice.
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Affiliation(s)
- Fiona Curran
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Kieran P. Dowd
- Department of Sport and Health Sciences, Technological University of Shannon, N37 HD68 Athlone, Ireland
| | - Casey L. Peiris
- Department of Physiotherapy, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne 3086, Australia
| | - Hidde P. van der Ploeg
- Amsterdam UMC, Department of Public and Occupational Health, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands
| | - Mark S. Tremblay
- Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON K1H 8L1, Canada
- Department of Pediatrics, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Department of Health Sciences, Carleton University, Ottawa, ON K1S 5B6, Canada
| | - Grainne O’Donoghue
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, D04 V1W8 Dublin, Ireland
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15
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Daily and hourly patterns of physical activity and sedentary behavior of older adults: Atherosclerosis risk in communities (ARIC) study. Prev Med Rep 2022; 28:101859. [PMID: 35711287 PMCID: PMC9194653 DOI: 10.1016/j.pmedr.2022.101859] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/13/2022] [Accepted: 06/06/2022] [Indexed: 11/23/2022] Open
Abstract
Older adult activity patterns varied by day of the week with Sunday the least active. Most physical activity was accrued during the morning hours (9 AM-12 PM). Men and those with overweight and obesity had highest sedentary minutes. Older adult movement patterns show opportunities for physical activity promotion.
This cross-sectional study of older adults ≥ 65 years describes daily and hourly patterns of accelerometer-derived steps, sedentary, and physical activity behaviors and examines differences by day of the week and sociodemographic and health-related factors to identify time-use patterns. Data were from 459 Atherosclerosis Risk in Communities (ARIC) study participants (60% female; mean ± SD age = 78.3 ± 4.6 years; 20% Black) who wore a hip accelerometer ≥ 4 of 7 days, for ≥ 10 h/day in 2016. We used linear mixed models to examine daily patterns of steps, sedentary, low light, high light, and moderate-to-vigorous intensity physical activity (MVPA). Differences by sex, median age (</≥ 78 years), body mass index, self-rated health, depressive symptoms, and performance in a two-minute walk test were explored. Men (vs women), and those with overweight and obesity (vs normal weight), had significantly higher sedentary minutes and lower minutes of low light per day. For each additional meter walked during the two-minute walk test, sedentary behavior was lower while high light, MVPA, and daily steps were higher. No significant differences in time-use behaviors were found by self-reported race, age, education, self-rated health, or depressive symptoms. Participants were least active (22.5 min MVPA, 95% CI: 11.5, 33.5) and most sedentary (453.9 min, 95% CI: 417.7, 490.2) on Sunday. Most activity was accrued in the morning (before 12 PM) while the evening hours (3–11 PM) were spent ≥ 50% sedentary. Movement patterns suggest opportunities for promotion of activity and reduction in sedentary time on Sundays, in the evening hours, and for those with overweight or obesity.
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