Evidence map›Paper›PMID 33322117›Full record

ArticleInternational journal of environmental research and public health2020

Feasibility Study Comparing Physical Activity Classifications from Accelerometers with Wearable Camera Data.

Alyse Davies, Margaret Allman-Farinelli, Katherine Owen, Louise Signal, Cameron Hosking, Leanne Wang, Adrian Bauman

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Alyse DaviesNutrition and Dietetics Group, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-9448-7539
Margaret Allman-FarinelliNutrition and Dietetics Group, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.ORCID 0000-0002-6478-1374
Katherine OwenPrevention Research Centre, School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia.
Louise SignalHealth Promotion & Policy Research Unit, Department of Public Health, University of Otago, P.O. Box 7343, Wellington South, Wellington 6242, New Zealand.
Cameron HoskingTransformational Bioinformatics Group, Commonwealth Scientific and Industrial Research Organization, North Ryde, Sydney, NSW 2113, Australia.
Leanne WangNutrition and Dietetics Group, Charles Perkins Centre, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.
Adrian BaumanPrevention Research Centre, School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Device-based assessments are frequently used to measure physical activity (PA) but contextual measures are often lacking. There is a need for new methods, and one under-explored option is the use of wearable cameras. This study tested the use of wearable cameras in PA measurement by comparing intensity classifications from accelerometers with wearable camera data. Seventy-eight 18-30-year-olds wore an Actigraph GT9X link accelerometer and Autographer wearable camera for three consecutive days. An image coding schedule was designed to assess activity categories and activity sub-categories defined by the 2011 Compendium of Physical Activities (Compendium). Accelerometer hourly detailed files processed using the Montoye (2020) cut-points were linked to camera data using date and time stamps. Agreement was examined using equivalence testing, intraclass correlation coefficient (ICC) and Spearman's correlation coefficient (rho). Fifty-three participants contributing 636 person-hours were included. Reliability was moderate to good for sedentary behavior (rho = 0.77), light intensity activities (rho = 0.59) and moderate-to-vigorous physical activity (MVPA) (rho = 0.51). The estimates of sedentary behavior, light activity and MVPA from the two methods were similar, but not equivalent. Wearable cameras are a potential complementary tool for PA measurement, but practical challenges and limitations exist. While wearable cameras may not be feasible for use in large scale studies, they may be feasible in small scale studies where context is important.

Indexed as

ExerciseWearable Electronic DevicesAccelerometryAdolescentAdultFeasibility StudiesFemaleHumansMalePublic HealthReproducibility of ResultsSedentary BehaviorYoung Adultaccelerometeractivity intensitiescompendiummeasurementmethodsphysical activitypublic healthsedentary behaviorwearable camerasyoung adults

Identifiers

PMID33322117
PMCPMC7764508

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.