ArticleSensors (Basel, Switzerland)2015
Sedentary Behaviour Profiling of Office Workers: A Sensitivity Analysis of Sedentary Cut-Points.
Article in Sensors (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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.
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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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applicability of Physiological Monitoring Systems within Occupational Groups: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Using Internet of Things to Reduce Office Workers' Sedentary Behavior: Intervention Development Applying the Behavior Change Wheel and Human-Centered Design Approach.JMIR mHealth and uHealth · 2020Article
- An intervention study to assess potential effect and user experience of an mHealth intervention to reduce sedentary behaviour among older office workers.BMJ health & care informatics · 2019Article
- Validation of the VitaBit Sit-Stand Tracker: Detecting Sitting, Standing, and Activity Patterns.Sensors (Basel, Switzerland) · 2018Article
- Physical Behavior Profiles in Chronic Cancer-Related Fatigue.International journal of behavioral medicine · 2018Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measuring sedentary behaviour and physical activity with wearable sensors provides detailed information on activity patterns and can serve health interventions. At the basis of activity analysis stands the ability to distinguish sedentary from active time. As there is no consensus regarding the optimal cut-point for classifying sedentary behaviour, we studied the consequences of using different cut-points for this type of analysis. We conducted a battery of sitting and walking activities with 14 office workers, wearing the Promove 3D activity sensor to determine the optimal cut-point (in counts per minute (m·s(-2))) for classifying sedentary behaviour. Then, 27 office workers wore the sensor for five days. We evaluated the sensitivity of five sedentary pattern measures for various sedentary cut-points and found an optimal cut-point for sedentary behaviour of 1660 × 10(-3) m·s(-2). Total sedentary time was not sensitive to cut-point changes within ±10% of this optimal cut-point; other sedentary pattern measures were not sensitive to changes within the ±20% interval. The results from studies analyzing sedentary patterns, using different cut-points, can be compared within these boundaries. Furthermore, commercial, hip-worn activity trackers can implement feedback and interventions on sedentary behaviour patterns, using these cut-points.
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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.