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      Measuring Physical Activity with Hip Accelerometry among U.S. Older Adults: How Many Days Are Enough?

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          Abstract

          Introduction

          Accelerometers are increasingly used in research. Four to 7 days of monitoring is preferred to estimate average activity but may be burdensome for older adults. We aimed to investigate: 1) 7-day accelerometry protocol adherence, 2) demographic predictors of adherence, 3) day of the week effect, and 4) average activity calculated from 7 versus fewer days among older adults.

          Methods

          We used the 2003–2006 older adult hip accelerometry data from the National Health and Nutrition Examination Survey (NHANES) sample. We determined proportions with 1–7 valid (10–20 hours) wear days and identified wear day correlates using ordinal logistic regression. We determined the day of week effect on 5 accelerometry measures (counts per minute, CPM; % sedentary behavior; % light-lifestyle activity; % moderate-vigorous activity, MVPA; total activity counts) using multivariate linear regression and compared averages estimated over 2 or 3 versus 7 days using correlations, linear regression, and Bland-Altman plots.

          Results

          Among 2,208 participants aged 65+, 85% of participants had ≥2 and 44% had 7 valid wear days. Increasing age ( p = 0.01) and non-white race ( p < 0.001) were associated with fewer days. Daily CPM, % MVPA, and total daily activity counts were similar Monday through Saturday, but significantly lower on Sundays (p < 0.001). Daily % sedentary behavior and % light-lifestyle activity were significantly different on Saturdays (p = 0.04–0.045) and Sundays (p < 0.001) compared to weekdays. Among participants with 7 valid days, 2 or 3 day averages were highly correlated with 7 day averages for all 5 accelerometry measures (2 versus 7 days: r = 0.90–0.93, 3 versus 7 days: r = 0.94–0.96).

          Conclusions

          Protocols of 2–3 days, adjusting for Sundays (average CPM, % moderate-vigorous activity, and average total daily activity counts) or weekends (% sedentary behavior and % light-lifestyle activity), give reliable estimates of older adult activity.

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          Statistical methods for assessing agreement between two methods of clinical measurement.

          J Bland, D Altman (1986)
          In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
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            Conducting accelerometer-based activity assessments in field-based research.

            K. McIver, Russell R. Pate, S. Trost (2005)
            The purpose of this review is to address important methodological issues related to conducting accelerometer-based assessments of physical activity in free-living individuals. We review the extant scientific literature for empirical information related to the following issues: product selection, number of accelerometers needed, placement of accelerometers, epoch length, and days of monitoring required to estimate habitual physical activity. We also discuss the various options related to distributing and collecting monitors and strategies to enhance compliance with the monitoring protocol. No definitive evidence exists currently to indicate that one make and model of accelerometer is more valid and reliable than another. Selection of accelerometer therefore remains primarily an issue of practicality, technical support, and comparability with other studies. Studies employing multiple accelerometers to estimate energy expenditure report only marginal improvements in explanatory power. Accelerometers are best placed on hip or the lower back. Although the issue of epoch length has not been studied in adults, the use of count cut points based on 1-min time intervals maybe inappropriate in children and may result in underestimation of physical activity. Among adults, 3-5 d of monitoring is required to reliably estimate habitual physical activity. Among children and adolescents, the number of monitoring days required ranges from 4 to 9 d, making it difficult to draw a definitive conclusion for this population. Face-to-face distribution and collection of accelerometers is probably the best option in field-based research, but delivery and return by express carrier or registered mail is a viable option. Accelerometer-based activity assessments requires careful planning and the use of appropriate strategies to increase compliance.
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              Number of Days Required to Estimate Habitual Activity Using Wrist-Worn GENEActiv Accelerometer: A Cross-Sectional Study

              Christina Dillon, Anthony P. Fitzgerald, Patricia Kearney … (2016)
              Introduction Objective methods like accelerometers are feasible for large studies and may quantify variability in day-to-day physical activity better than self-report. The variability between days suggests that day of the week cannot be ignored in the design and analysis of physical activity studies. The purpose of this paper is to investigate the optimal number of days needed to obtain reliable estimates of weekly habitual physical activity using the wrist-worn GENEActiv accelerometer. Methods Data are from a subsample of the Mitchelstown cohort; 475 (44.6% males; mean aged 59.6±5.5 years) middle-aged Irish adults. Participants wore the wrist GENEActiv accelerometer for 7-consecutive days. Data were collected at 100Hz and summarised into a signal magnitude vector using 60s epochs. Each time interval was categorised according to intensity based on validated cut-offs. Spearman pairwise correlations determined the association between days of the week. Repeated measures ANOVA examined differences in average minutes across days. Intraclass correlations examined the proportion of variability between days, and Spearman-Brown formula estimated intra-class reliability coefficient associated with combinations of 1–7 days. Results Three hundred and ninety-seven adults (59.7±5.5yrs) had valid accelerometer data. Overall, men were most sedentary on weekends while women spent more time in sedentary behaviour on Sunday through Tuesday. Post hoc analysis found sedentary behaviour and light activity levels on Sunday to differ to all other days in the week. Analysis revealed greater than 1 day monitoring is necessary to achieve acceptable reliability. Monitoring frame duration for reliable estimates varied across intensity categories, (sedentary (3 days), light (2 days), moderate (2 days) and vigorous activity (6 days) and MVPA (2 days)). Conclusion These findings provide knowledge into the behavioural variability in weekly activity patterns of middle-aged adults. Since Sunday differed from all other days in the week this suggests that day of the week cannot be overlooked in the design and analysis of physical activity studies and thus should be included in the study monitoring frames. Collectively our data suggest that six days monitoring, inclusive of Saturday and Sunday, are needed to reliably capture weekly habitual activity in all activity intensities using the wrist-worn GENEActiv accelerometer.
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                Author and article information

                Contributors
                Dominique Heymann: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 12 January 2017
                Publication date Collection: 2017
                Volume: 12
                Issue: 1
                Electronic Location Identifier: e0170082
                Affiliations
                [1 ]University of Chicago Medicine, Department of Public Health Sciences, Chicago, IL, United States of America
                [2 ]University of Chicago Medicine, Section of Geriatrics and Palliative Medicine, Chicago, IL, United States of America
                [3 ]University of Chicago Medicine, Department of Sociology, Chicago, IL, United States of America
                Universite de Nantes, FRANCE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: MK MHS.

                • Data curation: MK MHS.

                • Formal analysis: MK.

                • Funding acquisition: DSL MHS LW.

                • Investigation: MK MHS.

                • Methodology: MK MHS DSL WD LW.

                • Project administration: MHS.

                • Resources: MK MHS WD DSL LW.

                • Supervision: MK MHS.

                • Validation: MK MHS.

                • Visualization: MK MHS DSL WD LW.

                • Writing – original draft: MK MHS.

                • Writing – review & editing: MK MHS DSL WD LW.

                Author information
                http://orcid.org/0000-0002-3997-5791
                Article
                Publisher ID: PONE-D-16-29093
                DOI: 10.1371/journal.pone.0170082
                PMC ID: 5231361
                PubMed ID: 28081249
                SO-VID: e5c36719-ae9e-4a23-8439-4531c389c44c
                Copyright © © 2017 Kocherginsky et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 20 July 2016
                Date accepted : 28 December 2016
                Page count
                Figures: 5, Tables: 2, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: K23 AG049106
                Award Recipient :
                ORCID: http://orcid.org/0000-0002-3997-5791
                Funded by: NIH
                Award ID: R01 AG051175
                Award Recipient :
                This work was supported by National Institute on Aging K23 AG049106 to MHS and National Institute on Health R01 AG051175 to DL.
                Categories
                Subject: Research Article
                Subject: People and Places
                Subject: Population Groupings
                Subject: Age Groups
                Subject: Elderly
                Subject: Medicine and Health Sciences
                Subject: Public and Occupational Health
                Subject: Physical Activity
                Subject: Engineering and Technology
                Subject: Electronics
                Subject: Accelerometers
                Subject: Biology and Life Sciences
                Subject: Nutrition
                Subject: Medicine and Health Sciences
                Subject: Nutrition
                Subject: People and Places
                Subject: Demography
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Regression Analysis
                Subject: Linear Regression Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics (Mathematics)
                Subject: Statistical Methods
                Subject: Regression Analysis
                Subject: Linear Regression Analysis
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Musculoskeletal System
                Subject: Pelvis
                Subject: Hip
                Subject: Medicine and Health Sciences
                Subject: Anatomy
                Subject: Musculoskeletal System
                Subject: Pelvis
                Subject: Hip
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Research Monitoring
                Custom metadata
                Data Availability All survey data are available from NHANES: 1) http://wwwn.cdc.gov/nchs/nhanes/search/nhanes03_04.aspx; 2) http://wwwn.cdc.gov/Nchs/Nhanes/Search/Nhanes05_06.aspx. All NHANES accelerometry data are available in the nhanesaccel package in R: http://r-forge.r-project.org/projects/nhanesaccel.

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                Data availability:
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