. 2023 Mar 8;20(1):26.

doi: 10.1186/s12966-022-01388-9.

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review

Richard M Pulsford¹, Laura Brocklebank², Sally A M Fenton³, Esmée Bakker⁴, Gregore I Mielke⁵, Li-Tang Tsai^{6

7}, Andrew J Atkin^{8

9}, Danielle L Harvey⁹, Joanna M Blodgett¹⁰, Matthew Ahmadi¹¹, Le Wei¹¹, Alex Rowlands^{12

13

14}, Aiden Doherty¹⁵, Vegar Rangul¹⁶, Annemarie Koster¹⁷, Lauren B Sherar¹⁸, Andreas Holtermann¹⁹, Mark Hamer^#²⁰, Emmanuel Stamatakis^#¹¹

Affiliations

¹ Faculty of Health and Life Sciences, University of Exeter, St Lukes Campus. EX12LU, Exeter, UK.
² Department of Behavioural Science and Health, University College London, London, WC1E 7HB, UK.
³ School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
⁴ Radboud University Medical Centre, 6500 HB, Nijmegen, The Netherlands.
⁵ School of Public Health, The University of Queensland, ST Lucia qld, Australia.
⁶ Center On Aging and Mobility, University Hospital Zurich, Zurich City Hospital - Waid and University of Zurich, Zurich , Switzerland.
⁷ Department of Aging Medicine and Aging Research, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
⁸ Norwich Epidemiology Centre, University of East Anglia, Norwich, UK.
⁹ School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, NR47TJ, UK.
¹⁰ Institute of Sport Exercise and Health, Division of Surgery and Interventional Science, University College London, London, W1T 7HA, UK.
¹¹ Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
¹² Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW, UK.
¹³ NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.
¹⁴ Alliance for Research in Exercise, Nutrition and Activity (ARENA), Division of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide, Australia.
¹⁵ Big Data Institute, Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
¹⁶ Department of Public Health and Nursing, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway.
¹⁷ Department of Social Medicine, CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.
¹⁸ School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, LE113TU, UK.
¹⁹ National Research Centre for the Working Environment, Copenhagen, Denmark.
²⁰ Institute of Sport Exercise and Health, Division of Surgery and Interventional Science, University College London, London, W1T 7HA, UK. m.hamer@ucl.ac.uk.

^# Contributed equally.

PMID: 36890553
PMCID: PMC9993720
DOI: 10.1186/s12966-022-01388-9

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review

Richard M Pulsford et al. Int J Behav Nutr Phys Act. 2023.

. 2023 Mar 8;20(1):26.

doi: 10.1186/s12966-022-01388-9.

Authors

Affiliations

¹ Faculty of Health and Life Sciences, University of Exeter, St Lukes Campus. EX12LU, Exeter, UK.
² Department of Behavioural Science and Health, University College London, London, WC1E 7HB, UK.
³ School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
⁴ Radboud University Medical Centre, 6500 HB, Nijmegen, The Netherlands.
⁵ School of Public Health, The University of Queensland, ST Lucia qld, Australia.
⁶ Center On Aging and Mobility, University Hospital Zurich, Zurich City Hospital - Waid and University of Zurich, Zurich , Switzerland.
⁷ Department of Aging Medicine and Aging Research, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
⁸ Norwich Epidemiology Centre, University of East Anglia, Norwich, UK.
⁹ School of Health Sciences, Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, NR47TJ, UK.
¹⁰ Institute of Sport Exercise and Health, Division of Surgery and Interventional Science, University College London, London, W1T 7HA, UK.
¹¹ Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
¹² Diabetes Research Centre, University of Leicester, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW, UK.
¹³ NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK.
¹⁴ Alliance for Research in Exercise, Nutrition and Activity (ARENA), Division of Health Sciences, Sansom Institute for Health Research, University of South Australia, Adelaide, Australia.
¹⁵ Big Data Institute, Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK.
¹⁶ Department of Public Health and Nursing, HUNT Research Centre, Norwegian University of Science and Technology, Levanger, Norway.
¹⁷ Department of Social Medicine, CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, The Netherlands.
¹⁸ School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, LE113TU, UK.
¹⁹ National Research Centre for the Working Environment, Copenhagen, Denmark.
²⁰ Institute of Sport Exercise and Health, Division of Surgery and Interventional Science, University College London, London, W1T 7HA, UK. m.hamer@ucl.ac.uk.

^# Contributed equally.

PMID: 36890553
PMCID: PMC9993720
DOI: 10.1186/s12966-022-01388-9

Abstract

Background: Accelerometer measures of physical behaviours (physical activity, sedentary behaviour and sleep) in observational studies offer detailed insight into associations with health and disease. Maximising recruitment and accelerometer wear, and minimising data loss remain key challenges. How varying methods used to collect accelerometer data influence data collection outcomes is poorly understood. We examined the influence of accelerometer placement and other methodological factors on participant recruitment, adherence and data loss in observational studies of adult physical behaviours.

Methods: The review was in accordance with the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA). Observational studies of adults including accelerometer measurement of physical behaviours were identified using database (MEDLINE (Ovid), Embase, PsychINFO, Health Management Information Consortium, Web of Science, SPORTDiscus and Cumulative Index to Nursing & Allied Health Literature) and supplementary searches to May 2022. Information regarding study design, accelerometer data collection methods and outcomes were extracted for each accelerometer measurement (study wave). Random effects meta-analyses and narrative syntheses were used to examine associations of methodological factors with participant recruitment, adherence and data loss.

Results: 123 accelerometer data collection waves were identified from 95 studies (92.5% from high-income countries). In-person distribution of accelerometers was associated with a greater proportion of invited participants consenting to wear an accelerometer (+ 30% [95% CI 18%, 42%] compared to postal distribution), and adhering to minimum wear criteria (+ 15% [4%, 25%]). The proportion of participants meeting minimum wear criteria was higher when accelerometers were worn at the wrist (+ 14% [ 5%, 23%]) compared to waist. Daily wear-time tended to be higher in studies using wrist-worn accelerometers compared to other wear locations. Reporting of information regarding data collection was inconsistent.

Conclusion: Methodological decisions including accelerometer wear-location and method of distribution may influence important data collection outcomes including recruitment and accelerometer wear-time. Consistent and comprehensive reporting of accelerometer data collection methods and outcomes is needed to support development of future studies and international consortia. Review supported by the British Heart Foundation (SP/F/20/150002) and registered (Prospero CRD42020213465).

Keywords: Accelerometers; Adherence; Epidemiology; Health; Measurement; Methods; Observational Studies; Physical Activity; Physical Behaviour; Recruitment; Sedentary Behaviour.

PubMed Disclaimer

Conflict of interest statement

AD receives research funding from: i) Novo Nordisk to investigate the genetic drivers of wearable-measured traits; and ii) SwissRe to investigate the epidemiological association of wearable-measured traits with disease outcomes. No other authors have competing interests to declare.

Figures

**Fig. 1**
Adapted PRISMA flow diagram. Overview of the process for identification and selection of study waves

**Fig. 2**
Cumulative frequency of observational studies of adult physical behaviours using accelerometers worn at different body locations (Panel A) and the cumulative number of participants (Panel B) Legend. Panel A shows the cumulative frequency of observational studies identified in this review that measure physical behaviours using accelerometers at different wear locations over time (from initiation of accelerometer measurement). Panel B shows the cumulative number of participants who consented to wear an accelerometer within these studies. Device wear locations are differentiated using different line styles

**Fig. 3**
Average reported accelerometer wear (hrs/day) according to study sample size and accelerometer wear location Legend. Accelerometer wear (hours/day) according to study sample size (quartiles) and accelerometer wear location. Marker colour denotes accelerometer wear location. Study sample size quartiles were: Q1 = 122–629, Q2 = 630–1412, Q3 = 1412–3421, Q4 = > 3421. Numeric values = study review ID number, connecting lines indicate studies with multiple measures (repeated cross-sectional measures of different samples, or repeated measures in the same sample)

See this image and copyright information in PMC

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The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review

Affiliations

The impact of selected methodological factors on data collection outcomes in observational studies of device-measured physical behaviour in adults: A systematic review

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical