Evidence map›Paper›PMID 38265864›Full record

ArticleJMIR formative research2024

A Bluetooth-Enabled Device for Real-Time Detection of Sitting, Standing, and Walking: Cross-Sectional Validation Study.

Reza Daryabeygi-Khotbehsara, Jonathan C Rawstorn, David W Dunstan, Sheikh Mohammed Shariful Islam, Mohamed Abdelrazek, Abbas Z Kouzani, Poojith Thummala, Jenna McVicar, Ralph Maddison

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Article in JMIR formative research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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.

Reza Daryabeygi-KhotbehsaraInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne Burwood, Australia.ORCID https://orcid.org/0000-0003-4064-978X
Jonathan C RawstornInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne Burwood, Australia.ORCID https://orcid.org/0000-0002-9755-7993
David W DunstanBaker-Deakin Department of Lifestyle and Diabetes, Melbourne Burwood, Australia.ORCID https://orcid.org/0000-0003-2629-9568
Sheikh Mohammed Shariful IslamInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne Burwood, Australia.ORCID https://orcid.org/0000-0001-7926-9368
Mohamed AbdelrazekSchool of Information Technology, Deakin University, Melbourne Burwood, Australia.ORCID https://orcid.org/0000-0003-3812-9785
Abbas Z KouzaniSchool of Engineering, Deakin University, Geelong, Australia.ORCID https://orcid.org/0000-0002-6292-1214
Poojith ThummalaSchool of Information Technology, Deakin University, Melbourne Burwood, Australia.ORCID https://orcid.org/0009-0000-8440-4237
Jenna McVicarInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne Burwood, Australia.ORCID https://orcid.org/0000-0001-5007-7223
Ralph MaddisonInstitute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne Burwood, Australia.ORCID https://orcid.org/0000-0001-8564-5518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants.

objectiveThe primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL.

methodsA total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement.

resultsOverall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%).

conclusionsResults suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

Indexed as

activity trackeralgorithmsdeep neural networkmachine learningreal-time datasedentary behaviorSedentary behaviOR DetectorSORDstandingvalidationwalkingwearables

Identifiers

PMID38265864
PMCPMC10851128

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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.