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JMIR Mhealth Uhealth. 2019 Jan 9;7(1):e10418. doi: 10.2196/10418.

How Well iPhones Measure Steps in Free-Living Conditions: Cross-Sectional Validation Study.

Author information

1
Department of Preventive Medicine and Public Health, Tokyo Medical University, Shinjuku-ku, Japan.
2
Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
3
Department of Health Sociology and Health Education, School of Public Health, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Japan.
4
Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Japan.
5
Division of Preventive Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
6
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States.

Abstract

BACKGROUND:

Smartphones have great potential for monitoring physical activity. Although a previous laboratory-based study reported that smartphone apps were accurate for tracking step counts, little evidence on their accuracy in free-living conditions currently exists.

OBJECTIVE:

We aimed to investigate the accuracy of step counts measured using iPhone in the real world.

METHODS:

We recruited a convenience sample of 54 adults (mean age 31 [SD 10] years) who owned an iPhone and analyzed data collected in 2016 and 2017. Step count was simultaneously measured using a validated pedometer (Kenz Lifecorder) and the iPhone. Participants were asked to carry and use their own iPhones as they typically would while wearing a pedometer on the waist for 7 consecutive days during waking hours. To assess the agreement between the two measurements, we calculated Spearman correlation coefficients and prepared a Bland-Altman plot.

RESULTS:

The mean step count measured using the iPhone was 9253 (3787) steps per day, significantly lower by 12% (1277/10,530) than that measured using the pedometer, 10,530 (3490) steps per day (P<.001). The Spearman correlation coefficient between devices was 0.78 (P<.001). The largest underestimation of steps by the iPhone was observed among those who reported to have seldom carried their iPhones (seldom carry: mean -3036, SD 2990, steps/day; sometimes carry: mean -1424, SD 2619, steps/day; and almost always carry: mean -929, SD 1443, steps/day; P for linear trend=.08).

CONCLUSIONS:

Smartphones may be of practical use to individuals, clinicians, and researchers for monitoring physical activity. However, their data on step counts should be interpreted cautiously because of the possibility of underestimation due to noncarrying time.

KEYWORDS:

epidemiology; free-living conditions; mobile phone; pedometer; physical activity; population; step count; validation

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