Evidence map›Paper›PMID 28659255›Full record

ArticleJMIR mHealth and uHealth2017

Mobile Device Accuracy for Step Counting Across Age Groups.

François Modave, Yi Guo, Jiang Bian, Matthew J Gurka, Alice Parish, Megan D Smith, Alexandra M Lee, Thomas W Buford

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 pooled it
–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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

8 authors.

François ModaveUniversity of Florida, Department of Health Outcomes and Policy, Gainesville, FL, United States.ORCID http://orcid.org/0000-0003-4366-0757
Yi GuoUniversity of Florida, Department of Health Outcomes and Policy, Gainesville, FL, United States.ORCID http://orcid.org/0000-0003-0587-4105
Jiang BianUniversity of Florida, Department of Health Outcomes and Policy, Gainesville, FL, United States.ORCID http://orcid.org/0000-0002-2238-5429
Matthew J GurkaUniversity of Florida, Department of Health Outcomes and Policy, Gainesville, FL, United States.ORCID http://orcid.org/0000-0002-3371-4582
Alice ParishUniversity of Florida, Department of Health Outcomes and Policy, Gainesville, FL, United States.ORCID http://orcid.org/0000-0002-5775-0457
Megan D SmithUniversity of Florida, Department of Health Outcomes and Policy, Gainesville, FL, United States.ORCID http://orcid.org/0000-0003-2690-0667
Alexandra M LeeUniversity of Florida, Department of Health Outcomes and Policy, Gainesville, FL, United States.ORCID http://orcid.org/0000-0001-8146-1674
Thomas W BufordUniversity of Florida, Department of Aging and Geriatric Research, Gainesville, FL, United States.ORCID http://orcid.org/0000-0002-9541-4358

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
University of Florida Older Americans Independence Center (OAIC)P30AG028740 · NIA · UNIVERSITY OF FLORIDA · PI Stephen D Anton · 2007 to 2026
$22.8M
NCATS NIH HHS UL1 TR001427NIA NIH HHS P30 AG028740
6 · The paper itself

Abstract

backgroundOnly one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm.

objectiveWe aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups.

methodsWe recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant.

resultsAmong the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years.

conclusionsOur analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program.

Indexed as

adultsdevicesmobilephysical activityweight reduction

Identifiers

PMID28659255
PMCPMC5508112

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.