Evidence map›Paper›PMID 40635993›Full record

ArticleBMC digital health2025

Comparative validity assessment of three android step counter applications; a semi-structured laboratory-based study.

Uchechukwu Martha Chukwuemeka, Arinze Damian Nnalue, Sochima Johnmark Obiekwe, Fatai Adesina Maruf, Anthony Chinedu Anakor, Monday Omoniyi Moses, Chinedum Amaechi, Uchenna Prosper Okonkwo, Ifeoma Adaigwe Amaechi

Abstract read
In one paragraph

Article in BMC digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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

9 authors.

Uchechukwu Martha ChukwuemekaDepartment of Medical Rehabilitation, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Arinze Damian NnalueDepartment of Medical Rehabilitation, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Sochima Johnmark ObiekweDepartment of Medical Rehabilitation, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Fatai Adesina MarufDepartment of Medical Rehabilitation, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Anthony Chinedu AnakorDepartment of Physiology, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Monday Omoniyi MosesDepartment of Physiotherapy and Sports Science, Kuame Nkurumah University of Science and Technology, Kumasi, Ghana.
Chinedum AmaechiDepartment of Computer Sciences, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Uchenna Prosper OkonkwoDepartment of Medical Rehabilitation, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.
Ifeoma Adaigwe AmaechiDepartment of Medical Rehabilitation, Nnamdi Azikiwe University, Awka, Anambra State Nigeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Method: 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. Result: 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 ( Conclusion: 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.

Indexed as

Google FitPacerPedometerPhysical activityStep countingValidation study

Identifiers

PMID40635993
PMCPMC12234585

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LicenceCC BY-NC-ND
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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.