ArticleJMIR cancer2023
Open-Source, Step-Counting Algorithm for Smartphone Data Collected in Clinical and Nonclinical Settings: Algorithm Development and Validation Study.
Article in JMIR cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Clinical utility and methodological implementation of smartphone-based mobility analysis: a scoping review.Journal of neurology · 2026Article
- Short prescribed exercises can quantify upper limb functioning in neurodegenerative disease.Journal of neuroengineering and rehabilitation · 2026Article
- Analytical and clinical validation of step counting method in people living with amyotrophic lateral sclerosis.Scientific reports · 2025Article
- Smartphone-Based Measures as Indicators of Functional Status in Patients With Advanced Cancer.JAMA network open · 2025Article
- Predicting the strength of next-day negative emotion states in body dysmorphic disorder using passive smartphone data: An intensive longitudinal assessment study.Internet interventions · 2025Article
- Comparing Step-Counting Algorithms for High-Resolution Wrist Accelerometry Data in Older Adults in the ARIC Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Article
- Measuring Environmental and Behavioral Drivers of Chronic Diseases Using Smartphone-Based Digital Phenotyping: Intensive Longitudinal Observational mHealth Substudy Embedded in 2 Prospective Cohorts of Adults.JMIR public health and surveillance · 2024Observational
- Tracking amyotrophic lateral sclerosis disease progression using passively collected smartphone sensor data.Annals of clinical and translational neurology · 2024Article
- A Pilot Trial of Telerehabilitation for Chronic Stroke Survivors: A Case-series Study of Three Individuals.Progress in rehabilitation medicine · 2024Article
- Reproducibility of continuous glucose monitoring results under real-life conditions in an adult population: a functional data analysis.Scientific reports · 2023Article
- A Smartphone Application to Measure Walking Cadence before Major Abdominal Surgery in Older Adults.Digital biomarkersArticle
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7 authors.
Funding
Abstract
backgroundStep counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.
objectiveOur goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations ("cross-body" validation), manually ascertained ground truth ("visually assessed" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer ("commercial wearable" validation).
methodsWe used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.
resultsIn the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%.
conclusionsThis study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.
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