ArticleJournal for the measurement of physical behaviour2021
A Transparent Method for Step Detection using an Acceleration Threshold.
Article in Journal for the measurement of physical behaviour, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02650258 (Cadence and Intensity Across the Adult Lifespan), which is not on this map. Cited by 14 papers.
What it found
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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.
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.
Cadence and Intensity Across the Adult Lifespan
Who cites it
14 citing papers in PubMed.
- Validation of an Algorithmic Pipeline for Wrist-Worn Devices to Estimate Walking Speed in People with Multiple Long-Term Conditions.Sensors (Basel, Switzerland) · 2026Article
- Gait Event Detection and Gait Parameter Estimation from a Single Waist-Worn IMU Sensor.Sensors (Basel, Switzerland) · 2025Article
- Real-World Wrist-Derived Digital Mobility Outcomes in People with Multiple Long-Term Conditions: A Comparison of Algorithms.Bioengineering (Basel, Switzerland) · 2025Article
- Every Step Counts-How Can We Accurately Count Steps with Wearable Sensors During Activities of Daily Living in Individuals with Neurological Conditions?Sensors (Basel, Switzerland) · 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
- Accurate measurement of simulated slow and altered walking activity: Apple Watch best in class wearable devices.PloS one · 2025Article
- Laboratory comparison of consumer-grade and research-established wearables for monitoring heart rate, body temperature, and physical acitivity in sub-Saharan Africa.Frontiers in physiology · 2025Article
- Article
- Wearable Sensors as a Preoperative Assessment Tool: A Review.Sensors (Basel, Switzerland) · 2024Review
- Accuracy and Reliability of a Suite of Digital Measures of Walking Generated Using a Wrist-Worn Sensor in Healthy Individuals: Performance Characterization Study.JMIR human factors · 2023Article
- Development and Validation of a Machine Learning Wrist-worn Step Detection Algorithm with Deployment in the UK Biobank.medRxiv : the preprint server for health sciences · 2023Article
- Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm.Sensors (Basel, Switzerland) · 2022Article
- A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-adults study.The international journal of behavioral nutrition and physical activity · 2022Article
- Analytical Validation of Wrist-Worn Accelerometer-Based Step-Count Methods during Structured and Free-Living Activities.Digital biomarkersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Step-based metrics provide simple measures of ambulatory activity, yet device software either includes undisclosed proprietary step detection algorithms or simply do not compute step-based metrics. We aimed to develop and validate a simple algorithm to accurately detect steps across various ambulatory and non-ambulatory activities. Seventy-five adults (21-39 years) completed seven simulated activities of daily living (e.g., sitting, vacuuming, folding laundry) and an incremental treadmill protocol from 0.22-2.2ms
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Registered trials
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.