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Background <p>While wearables, such as Fitbit devices, are specifically designed to measure physical activity and physiological functioning, they have less consumer uptake than smartphones, which contain many similar sensors that can also measure physical activity (although not cardiac activity). This study’s objective was to assess the agreement between physical and cardiac activity measures collected from Fitbit wearable devices and physical activity measures derived from the naturalistic use of smartphones over 28 days in a large, diverse sample (<i>n</i> = 10,085). Agreement between smartphone and Fitbit features was quantified via intraclass correlation coefficients (ICC; two-way random effects, absolute agreement) computed within participants across aligned days.</p> Results <p>Although the level of agreement between smartphone and Fitbit metrics was statistically significant in all comparisons examined, the effect sizes ranged from ‘poor’ to ‘good’ across metrics and individuals. The strongest agreement was observed for measurements of smartphone activity categories with strongly related categories derived from Fitbit exercise data. Some effects were stronger when data was filtered to focus on participants with higher activity levels in both smartphone and Fitbit usage.</p> Conclusions <p>Although variations in data agreement and missingness highlight the need for careful interpretation of results, these findings do provide support for the use of smartphone sensors to measure certain aspects of physical activity at the population level when the scalability of the measurement is critical.</p>

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Comparison of smartphone- and fitbit-derived measures of physical activity in a large sample under naturalistic conditions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Ari Winbush,
  • Daniel McDuff,
  • Allen Jiang,
  • Andrew Barakat,
  • Benjamin Nelson,
  • Conor Heneghan,
  • John Hernandez,
  • Nicholas B. Allen

摘要

Background

While wearables, such as Fitbit devices, are specifically designed to measure physical activity and physiological functioning, they have less consumer uptake than smartphones, which contain many similar sensors that can also measure physical activity (although not cardiac activity). This study’s objective was to assess the agreement between physical and cardiac activity measures collected from Fitbit wearable devices and physical activity measures derived from the naturalistic use of smartphones over 28 days in a large, diverse sample (n = 10,085). Agreement between smartphone and Fitbit features was quantified via intraclass correlation coefficients (ICC; two-way random effects, absolute agreement) computed within participants across aligned days.

Results

Although the level of agreement between smartphone and Fitbit metrics was statistically significant in all comparisons examined, the effect sizes ranged from ‘poor’ to ‘good’ across metrics and individuals. The strongest agreement was observed for measurements of smartphone activity categories with strongly related categories derived from Fitbit exercise data. Some effects were stronger when data was filtered to focus on participants with higher activity levels in both smartphone and Fitbit usage.

Conclusions

Although variations in data agreement and missingness highlight the need for careful interpretation of results, these findings do provide support for the use of smartphone sensors to measure certain aspects of physical activity at the population level when the scalability of the measurement is critical.