Chukwuemeka Uchechukwu Martha, Nnalue Arinze Damian, Obiekwe Sochima Johnmark, Maruf Fatai Adesina, Anakor Anthony Chinedu, Moses Monday Omoniyi, Amaechi Chinedum, Okonkwo Uchenna Prosper, Amaechi Ifeoma Adaigwe
Department of Medical Rehabilitation, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Department of Research, Medical Research Circle (Medrec), Bukavu, Democratic Republic of Congo.
BMC Digit Health. 2025;3(1):20. doi: 10.1186/s44247-025-00159-3. Epub 2025 Jul 8.
Step counting stands out as a highly practical and widely utilised method for assessing an individual's level of physical activity (PA). Although the progress of step counting has undergone a significant transformation in recent times, the need to validate PA applications (apps) is even more pressing to ensure their accuracy. This study aimed to compare the criterion validity of Pacer, Pedometer by ITO Technologies Inc., and Google Fit in measuring step counts in semi-structured laboratory-based conditions.
This comparative experimental study involved 50 students who were fitted with Android phones running the three step counting applications (Pedometer, Pacer and Google Fit) simultaneously while they walked a 30-m walkway at a normal and fast pace during which a video of their walking was recorded with Techno Pouvoir 4 Pro running Android version 11. The steps in the recorded videos served as the criterion compared with the steps recorded by the apps and were counted only when the foot is lifted off the ground and placed in a new location. They were counted independently by two reviewers, who recounted where their level of agreement was more than 3 steps until their report was not more than 2 steps different. The Spearman's correlation was used for a relationship, while Mean Absolute Percentage Error (MAPE) and Bland plot were for validity testing at an Alpha level of 0.05.
While there was no significant difference in step counts among the three apps, a significant difference was found between the steps recorded by the apps and those counted by the video criterion during normal-paced walking but not for fast-paced walking ( > 0.05). The MAPEs for the three applications were moderate, with Google Fit showing 6.6% for normal pace walking and Pedometer and Pacer showing 9.2%. For fast-paced walking, the MAPE was lower at 5.4% across all three applications.
Our findings suggest that a pedometer, Pacer and Google Fit could be used as outcome measures in a general population for counting steps, but Google Fit might be a better step counter when normal pace walking is assessed. However, the study's relatively short duration may have overlooked variations in the applications'performance across different conditions over a longer period; hence, future studies should consider comparing the validity of these applications for a longer duration and among diverse populations.
步数计数作为一种评估个体身体活动（PA）水平的高度实用且广泛应用的方法脱颖而出。尽管近年来步数计数取得了重大变革，但为确保PA应用程序（应用）的准确性，对其进行验证的需求更为迫切。本研究旨在比较Pacer、ITO Technologies Inc.的计步器和谷歌健身在基于半结构化实验室条件下测量步数时的标准效度。
这项对比实验研究涉及50名学生，他们同时佩戴运行这三款步数计数应用程序（计步器、Pacer和谷歌健身）的安卓手机，在一条30米的通道上以正常和快速步伐行走，期间使用运行安卓11版本的Techno Pouvoir 4 Pro录制他们行走的视频。录制视频中的步数作为与应用程序记录的步数进行比较的标准，仅当脚离开地面并置于新位置时才进行计数。由两名评审员独立计数，当他们的一致程度超过3步时重新计数，直到他们的报告差异不超过2步。使用斯皮尔曼相关性来分析关系，而平均绝对百分比误差（MAPE）和布兰德图用于在α水平为0.05时进行效度测试。
虽然三款应用程序之间的步数没有显著差异，但在正常步伐行走期间，应用程序记录的步数与视频标准计数的步数之间存在显著差异，而快速步伐行走时则没有（>0.05）。三款应用程序的MAPE适中，谷歌健身在正常步伐行走时为6.6%，计步器和Pacer为9.2%。对于快速步伐行走，所有三款应用程序的MAPE较低，为5.4%。
我们的研究结果表明，计步器、Pacer和谷歌健身可作为普通人群中计算步数的结果指标，但在评估正常步伐行走时，谷歌健身可能是更好的步数计数器。然而，该研究相对较短的持续时间可能忽略了这些应用程序在更长时间段内不同条件下性能的变化；因此，未来的研究应考虑比较这些应用程序在更长持续时间和不同人群中的效度。
