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Schoenmakers M, Saygin M, Sikora M, Vaessen T, Noordzij M, de Geus E. Stress in action wearables database: A database of noninvasive wearable monitors with systematic technical, reliability, validity, and usability information. Behav Res Methods 2025; 57:171. [PMID: 40360861 PMCID: PMC12075381 DOI: 10.3758/s13428-025-02685-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2025] [Indexed: 05/15/2025]
Abstract
Ambulatory wearable monitoring of human physiology is increasingly utilized in the fields of psychology, movement sciences, and medicine. With the rapid growth of available consumer- and research-oriented wearables, researchers are faced with a multitude of devices to choose from. It is unfeasible timewise for researchers to determine all relevant technical specifications, available signals, signal sampling details, and (raw) data availability, and conduct a search of studies regarding the reliability, validity, and usability of wearables. Thus, selection of wearables for a given study proves highly challenging and will often be unsystematic and uninformed. The 10-year research program Stress in Action initiated a publicly accessible database of wearable ambulatory monitoring devices. We outline the genesis and final structure of the first version of the Stress in Action Wearables Database (SiA-WD) and a summary of the characteristics of the wearables it currently contains. Furthermore, one short-term (2 days) and one long-term (3 months) scenario from the field of stress research are provided with walkthroughs of how the SiA-WD can help select the optimal wearable for a specific research project. Insights gathered include the scarceness of studies testing wearable user-friendliness, inconsistencies in reported validity statistics, and imprecise manufacturer documentation on recorded physiological data such as sampling rate (or window) of signals and parameter extraction. The SiA-WD is the first open-access database to simultaneously include physiological sampling information and technical specifications along with a systematic reliability, validity, and usability search. It will be iteratively expanded to facilitate informed and time-efficient wearable selection. For access to the database, see the following: https://osf.io/umgvp/ .
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Affiliation(s)
- Myrte Schoenmakers
- Department of Biological Psychology, VU Amsterdam, Van Der Boechorststraat 7, 1081 BT, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands
| | - Melisa Saygin
- Department of Biological Psychology, VU Amsterdam, Van Der Boechorststraat 7, 1081 BT, Amsterdam, Netherlands
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands
| | - Magdalena Sikora
- Department of Psychology, Health and Technology, University of Twente, Enschede, The Netherlands
| | - Thomas Vaessen
- Department of Psychology, Health and Technology, University of Twente, Enschede, The Netherlands
- Center for Contextual Psychiatry, Department of Neurosciences, KU Leuven, Louvain, Belgium
| | - Matthijs Noordzij
- Department of Psychology, Health and Technology, University of Twente, Enschede, The Netherlands
| | - Eco de Geus
- Department of Biological Psychology, VU Amsterdam, Van Der Boechorststraat 7, 1081 BT, Amsterdam, Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands.
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Baroudi L, Zernicke RF, Tewari M, Carlozzi NE, Choi SW, Cain SM. Using Wear Time for the Analysis of Consumer-Grade Wearables' Data: Case Study Using Fitbit Data. JMIR Mhealth Uhealth 2025; 13:e46149. [PMID: 40116717 PMCID: PMC11951812 DOI: 10.2196/46149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 09/02/2024] [Accepted: 12/20/2024] [Indexed: 03/23/2025] Open
Abstract
Background Consumer-grade wearables allow researchers to capture a representative picture of human behavior in the real world over extended periods. However, maintaining users' engagement remains a challenge and can lead to a decrease in compliance (eg, wear time in the context of wearable sensors) over time (eg, "wearables' abandonment"). Objective In this work, we analyzed datasets from diverse populations (eg, caregivers for various health issues, college students, and pediatric oncology patients) to quantify the impact that wear time requirements can have on study results. We found evidence that emphasizes the need to account for participants' wear time in the analysis of consumer-grade wearables data. In Aim 1, we demonstrate the sensitivity of parameter estimates to different data processing methods with respect to wear time. In Aim 2, we demonstrate that not all research questions necessitate the same wear time requirements; some parameter estimates are not sensitive to wear time. Methods We analyzed 3 Fitbit datasets comprising 6 different clinical and healthy population samples. For Aim 1, we analyzed the sensitivity of average daily step count and average daily heart rate at the population sample and individual levels to different methods of defining "valid" days using wear time. For Aim 2, we evaluated whether some research questions can be answered with data from lower compliance population samples. We explored (1) the estimation of the average daily step count and (2) the estimation of the average heart rate while walking. Results For Aim 1, we found that the changes in the population sample average daily step count could reach 2000 steps for different methods of analysis and were dependent on the wear time compliance of the sample. As expected, population samples with a low daily wear time (less than 15 hours of wear time per day) showed the most sensitivity to changes in methods of analysis. On the individual level, we observed that around 15% of individuals had a difference in step count higher than 1000 steps for 4 of the 6 population samples analyzed when using different data processing methods. Those individual differences were higher than 3000 steps for close to 5% of individuals across all population samples. Average daily heart rate appeared to be robust to changes in wear time. For Aim 2, we found that, for 5 population samples out of 6, around 11% of individuals had enough data for the estimation of average heart rate while walking but not for the estimation of their average daily step count. Conclusions We leveraged datasets from diverse populations to demonstrate the direct relationship between parameter estimates from consumer-grade wearable devices and participants' wear time. Our findings highlighted the importance of a thorough analysis of wear time when processing data from consumer-grade wearables to ensure the relevance and reliability of the associated findings.
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Affiliation(s)
- Loubna Baroudi
- Department of Mechanical Engineering, University of Michigan–Ann Arbor, 2505 Hayward St, Ann Arbor, MI, 48109, United States, 1 7342626353
| | - Ronald Fredrick Zernicke
- Department of Orthopedic Surgery, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
- Exercise & Sport Science Initiative, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
| | - Muneesh Tewari
- Center for Computational Medicine and Bioinformatics, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
- Department of Internal Medicine, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
- Department of Biomedical Engineering, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
- Veterans Administration Ann Arbor Healthcare System, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
| | - Noelle E Carlozzi
- Department of Physical Medicine and Rehabilitation, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
| | - Sung Won Choi
- Department of Pediatrics, University of Michigan–Ann Arbor, Ann Arbor, MI, United States
| | - Stephen M Cain
- Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV, United States
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Woll S, Birkenmaier D, Biri G, Nissen R, Lutz L, Schroth M, Ebner-Priemer UW, Giurgiu M. Applying AI in the Context of the Association Between Device-Based Assessment of Physical Activity and Mental Health: Systematic Review. JMIR Mhealth Uhealth 2025; 13:e59660. [PMID: 40053765 PMCID: PMC11926455 DOI: 10.2196/59660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 11/29/2024] [Accepted: 02/06/2025] [Indexed: 03/09/2025] Open
Abstract
BACKGROUND Wearable technology is used by consumers worldwide for continuous activity monitoring in daily life but more recently also for classifying or predicting mental health parameters like stress or depression levels. Previous studies identified, based on traditional approaches, that physical activity is a relevant factor in the prevention or management of mental health. However, upcoming artificial intelligence methods have not yet been fully established in the research field of physical activity and mental health. OBJECTIVE This systematic review aims to provide a comprehensive overview of studies that integrated passive monitoring of physical activity data measured via wearable technology in machine learning algorithms for the detection, prediction, or classification of mental health states and traits. METHODS We conducted a review of studies processing wearable data to gain insights into mental health parameters. Eligibility criteria were (1) the study uses wearables or smartphones to acquire physical behavior and optionally other sensor measurement data, (2) the study must use machine learning to process the acquired data, and (3) the study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in 5 electronic databases. RESULTS Of 11,057 unique search results, 49 published papers between 2016 and 2023 were included. Most studies examined the connection between wearable sensor data and stress (n=15, 31%) or depression (n=14, 29%). In total, 71% (n=35) of the studies had less than 100 participants, and 47% (n=23) had less than 14 days of data recording. More than half of the studies (n=27, 55%) used step count as movement measurement, and 44% (n=21) used raw accelerometer values. The quality of the studies was assessed, scoring between 0 and 18 points in 9 categories (maximum 2 points per category). On average, studies were rated 6.47 (SD 3.1) points. CONCLUSIONS The use of wearable technology for the detection, prediction, or classification of mental health states and traits is promising and offers a variety of applications across different settings and target groups. However, based on the current state of literature, the application of artificial intelligence cannot realize its full potential mostly due to a lack of methodological shortcomings and data availability. Future research endeavors may focus on the following suggestions to improve the quality of new applications in this context: first, by using raw data instead of already preprocessed data. Second, by using only relevant data based on empirical evidence. In particular, crafting optimal feature sets rather than using many individual detached features and consultation with in-field professionals. Third, by validating and replicating the existing approaches (ie, applying the model to unseen data). Fourth, depending on the research aim (ie, generalization vs personalization) maximizing the sample size or the duration over which data are collected.
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Affiliation(s)
- Simon Woll
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Dennis Birkenmaier
- Department of Embedded Systems and Sensors Engineering, Research Center for Information Technology, Karlsruhe, Germany
| | - Gergely Biri
- Department of Embedded Systems and Sensors Engineering, Research Center for Information Technology, Karlsruhe, Germany
| | - Rebecca Nissen
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Luisa Lutz
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Marc Schroth
- Department of Embedded Systems and Sensors Engineering, Research Center for Information Technology, Karlsruhe, Germany
| | - Ulrich W Ebner-Priemer
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- German Center for Mental Health, Mannheim, Germany
| | - Marco Giurgiu
- Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany
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Giurgiu M, von Haaren-Mack B, Fiedler J, Woll S, Burchartz A, Kolb S, Ketelhut S, Kubica C, Nigg C, Timm I, Thron M, Schmidt S, Wunsch K, Müller G, Nigg CR, Woll A, Reichert M, Ebner-Priemer U, Bussmann JB. The wearable landscape: Issues pertaining to the validation of the measurement of 24-h physical activity, sedentary, and sleep behavior assessment. JOURNAL OF SPORT AND HEALTH SCIENCE 2024; 14:101006. [PMID: 39491744 PMCID: PMC11809201 DOI: 10.1016/j.jshs.2024.101006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 04/24/2024] [Accepted: 07/04/2024] [Indexed: 11/05/2024]
Affiliation(s)
- Marco Giurgiu
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany.
| | - Birte von Haaren-Mack
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Janis Fiedler
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Simon Woll
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Alexander Burchartz
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Simon Kolb
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Sascha Ketelhut
- Department of Health Science, Institute of Sport Science, University of Bern, Bern 3012, Switzerland
| | - Claudia Kubica
- Department of Health Science, Institute of Sport Science, University of Bern, Bern 3012, Switzerland
| | - Carina Nigg
- Institute of Social and Preventive Medicine, University of Bern, Bern 3012, Switzerland
| | - Irina Timm
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Maximiliane Thron
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Steffen Schmidt
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Kathrin Wunsch
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Gerhard Müller
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany; Allgemeine Ortskrankenkasse AOK Baden-Wuerttemberg, Stuttgart 70191, Germany
| | - Claudio R Nigg
- Department of Health Science, Institute of Sport Science, University of Bern, Bern 3012, Switzerland
| | - Alexander Woll
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Markus Reichert
- Department of eHealth and Sports Analytics, Faculty of Sport Science, Ruhr University Bochum (RUB), Bochum 44801, Germany
| | - Ulrich Ebner-Priemer
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany
| | - Johannes Bj Bussmann
- Department of Rehabilitation Medicine, Erasmus University Medical Center, Rotterdam 3015, The Netherlands
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Kracht CL, Burkart S, Groves CI, Balbim GM, Pfledderer CD, Porter CD, St Laurent CW, Johnson EK, Brown DMY. 24-hour movement behavior adherence and associations with health outcomes: an umbrella review. JOURNAL OF ACTIVITY, SEDENTARY AND SLEEP BEHAVIORS 2024; 3:25. [PMID: 39399355 PMCID: PMC11467106 DOI: 10.1186/s44167-024-00064-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Accepted: 09/19/2024] [Indexed: 10/15/2024]
Abstract
Background Physical activity, sedentary behavior, and sleep, collectively known as the 24-hour movement behaviors, demonstrate individual and joint benefits on physical and mental health. Examination of these behaviors has expanded beyond guideline adherence to reviews of isotemporal substitution models (ISM) and compositional data analysis (CoDA). This umbrella review sought to review existing systematic reviews to (1) characterize the breadth and scope, (2) examine prevalence estimates for 24-hour movement guideline adherence, and (3) examine the relationship between these behaviors with health outcomes based on various approaches. Methods Eight databases and multiple supplementary strategies were used to identify systematic reviews, meta-analyses and pooled analyses that included two or more of the three 24-hour movement behaviors and a multi-behavior assessment approach. Overall review characteristics, movement behavior definitions, approaches, and health outcomes assessed were extracted, and methodological quality was assessed using the AMSTAR2 tool. Review characteristics (Aim 1), guideline prevalence estimates (Aim 2), and associations with health outcomes (Aim 3) were examined. Findings Thirty-two reviews (20 systematic reviews, 10 meta-analyses, and 2 pooled analyses) were included. Reviews captured the entire lifespan, global regions, and several physical and mental health outcomes. Individual and total guideline adherence waned from preschool to adolescence, but reviews reported similar prevalence estimates and ranges (i.e., within 10%). Common approaches included ISM and CoDA, evaluating 24-hour movement behavior's interactive associations with health outcomes, guideline adherence, and profile-based analysis. Despite heterogeneous approaches, reviews found consistent evidence for beneficial associations between meeting all three guidelines and high amount of physical activity on physical and mental health outcomes, but varied assessment of sedentary behavior or sleep. Most reviews were rated as low or critically low quality. Conclusions The breadth and scope of current reviews on 24-hour movement behaviors was wide and varied in this umbrella review, including all ages and across the globe. Prevalence estimates among populations beyond children need to be synthesized. Amongst the variety of definitions and approaches, reviews found benefit from achieving healthy amounts of all three behaviors. Longitudinal multi-behavior original research studies with rigorous assessment of sleep and sedentary behavior may help improve future systematic reviews of these various approaches. Supplementary Information The online version contains supplementary material available at 10.1186/s44167-024-00064-6.
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Affiliation(s)
- Chelsea L Kracht
- University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160 USA
| | - Sarah Burkart
- Arnold School of Public Health, University of South Carolina, 921 Assembly St, Columbia, SC 29208 USA
| | - Claire I Groves
- The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249 USA
| | | | - Christopher D Pfledderer
- School of Public Health in Austin, The University of Texas Health Science Center Houston, Austin, TX 78701 USA
| | - Carah D Porter
- Department of Kinesiology, Kansas State University, 1105 Sunset Ave, Manhattan, Kansas 66502 USA
| | | | - Emily K Johnson
- The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX 78249 USA
| | - Denver M Y Brown
- Department of Kinesiology, Kansas State University, 1105 Sunset Ave, Manhattan, Kansas 66502 USA
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Huang Z, Veerubhotla AL, DeLany JP, Ding D. Preliminary field validity of ActiGraph-based energy expenditure estimation in wheelchair users with spinal cord injury. Spinal Cord 2024; 62:514-522. [PMID: 38969742 DOI: 10.1038/s41393-024-01012-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 06/12/2024] [Accepted: 06/25/2024] [Indexed: 07/07/2024]
Abstract
STUDY DESIGN Cross-sectional validation study. OBJECTIVES To develop a raw acceleration signal-based random forest (RF) model for predicting total energy expenditure (TEE) in manual wheelchair users (MWUs) and evaluate the preliminary field validity of this new model, along with four existing models published in prior literature, using the Doubly Labeled Water (DLW) method. SETTING General community and research institution in Pittsburgh, USA. METHODS A total of 78 participants' data from two previous studies were used to develop the new RF model. A seven-day cross-sectional study was conducted to collect participants' free-living physical activity and TEE data, resting metabolic rate, demographics, and anthropometrics. Ten MWUs with spinal cord injury (SCI) completed the study, with seven participants having valid data for evaluating the preliminary field validity of the five models. RESULTS The RF model achieved a mean absolute error (MAE) of 0.59 ± 0.60 kcal/min and a mean absolute percentage error (MAPE) of 23.6% ± 24.3% on the validation set. For preliminary field validation, the five assessed models yielded MAE from 136 kcal/day to 1141 kcal/day and MAPE from 6.1% to 50.2%. The model developed by Nightingale et al. in 2015 achieved the best performance (MAE: 136 ± 96 kcal/day, MAPE: 6.1% ± 4.7%), while the RF model achieved comparable performance (MAE: 167 ± 99 kcal/day, MAPE: 7.4% ± 5.1%). CONCLUSIONS Two existing models and our newly developed RF model showed good preliminary field validity for assessing TEE in MWUs with SCI and the potential to detect lifestyle change in this population. Future large-scale field validation studies and model iteration are recommended.
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Affiliation(s)
- Zijian Huang
- Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Akhila L Veerubhotla
- Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Rehabilitation Medicine, Grossman School of Medicine, New York University, New York, NY, USA
| | - James P DeLany
- AdventHealth Orlando, Translational Research Institute, Orlando, FL, USA
| | - Dan Ding
- Department of Rehabilitation Science and Technology, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
- Human Engineering Research Laboratories, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Bioengineering, School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
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Purcell SA, LaMunion SR, Chen KY, Rynders CA, Thomas EA, Melanson EL. The use of accelerometers to improve estimation of the thermic effect of food in whole room calorimetry studies. J Appl Physiol (1985) 2024; 137:1-9. [PMID: 38695352 PMCID: PMC11389891 DOI: 10.1152/japplphysiol.00763.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 04/11/2024] [Accepted: 04/23/2024] [Indexed: 06/28/2024] Open
Abstract
We tested whether spontaneous physical activity (SPA) from accelerometers could be used in a whole room calorimeter to estimate thermic effect of food (TEF). Eleven healthy participants (n = 7 females; age: 27 ± 4 yr; body mass index: 22.8 ± 2.6 kg/m2) completed two 23-h visits in randomized order: one "fed" with meals provided and one "fasted" with no food. SPA was measured by ActivPAL and Actigraph accelerometers. Criterion TEF was calculated as the difference in total daily energy expenditure (TDEE) between fed and fasted visits and compared with three methods of estimating TEF: 1) SPA-adjusted TEF (adjTEF)-difference in TDEE without SPA between visits, 2) Wakeful TEF-difference in energy expenditure obtained from linear regression and basal metabolic rate during waking hours, 3) 24-h TEF-increase in TDEE above SPA and sleeping metabolic rate. Criterion TEF was 9.4 ± 4.5% of TDEE. AdjTEF (difference in estimated vs. criterion TEF: activPAL: -0.3 ± 3.3%; Actigraph: -1.8 ± 8.0%) and wakeful TEF (activPAL: -0.9 ± 6.1%; Actigraph: -2.8 ± 7.6%) derived from both accelerometers did not differ from criterion TEF (all P > 0.05). ActivPAL-derived 24-h TEF overestimated TEF (6.8 ± 5.4%, P = 0.002), whereas Actigraph-derived 24-h TEF was not significantly different (4.3 ± 9.4%, P = 0.156). TEF estimations using activPAL tended to show better individual-level agreement (i.e., smaller coefficients of variation). Both accelerometers can be used to estimate TEF in a whole room calorimeter; wakeful TEF using activPAL is the most viable option given strong group-level accuracy and reasonable individual agreement.NEW & NOTEWORTHY Two research-grade accelerometers can effectively estimate spontaneous physical activity and improve the estimation of thermic effect of food (TEF) in whole room calorimeters. The activPAL demonstrates strong group-level accuracy and reasonable individual-level agreement in estimating wakeful TEF, whereas a hip-worn Actigraph is an acceptable approach for estimating 24-h TEF. These results highlight the promising potential of accelerometers in advancing energy balance research by improving the assessment of TEF within whole room calorimeters.
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Affiliation(s)
- Sarah A Purcell
- Division of Endocrinology Metabolism and Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Anschutz Health and Wellness Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Division of Endocrinology, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Biology, Irving K. Barber Faculty of Science, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Samuel R LaMunion
- Diabetes, Endocrinology, and Obesity Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States
| | - Kong Y Chen
- Diabetes, Endocrinology, and Obesity Branch, Intramural Research Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, United States
| | - Corey A Rynders
- Anschutz Health and Wellness Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Elizabeth A Thomas
- Division of Endocrinology Metabolism and Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Anschutz Health and Wellness Center, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Rocky Mountain Regional VA Medical Center, Aurora, Colorado, United States
| | - Edward L Melanson
- Division of Endocrinology Metabolism and Diabetes, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- Division of Geriatric Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
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Herrmann SD, Willis EA, Ainsworth BE, Barreira TV, Hastert M, Kracht CL, Schuna JM, Cai Z, Quan M, Tudor-Locke C, Whitt-Glover MC, Jacobs DR. 2024 Adult Compendium of Physical Activities: A third update of the energy costs of human activities. JOURNAL OF SPORT AND HEALTH SCIENCE 2024; 13:6-12. [PMID: 38242596 PMCID: PMC10818145 DOI: 10.1016/j.jshs.2023.10.010] [Citation(s) in RCA: 79] [Impact Index Per Article: 79.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/08/2023] [Accepted: 10/26/2023] [Indexed: 01/21/2024]
Abstract
BACKGROUND The Compendium of Physical Activities was published in 1993 to improve the comparability of energy expenditure values assigned to self-reported physical activity (PA) across studies. The original version was updated in 2000, and again in 2011, and has been widely used to support PA research, practice, and public health guidelines. METHODS This 2024 update was tailored for adults 19-59 years of age by removing data from those ≥60 years. Using a systematic review and supplementary searches, we identified new activities and their associated measured metabolic equivalent (MET) values (using indirect calorimetry) published since 2011. We replaced estimated METs with measured values when possible. RESULTS We screened 32,173 abstracts and 1507 full-text papers and extracted 2356 PA energy expenditure values from 701 papers. We added 303 new PAs and adjusted 176 existing MET values and descriptions to reflect the addition of new data and removal of METs for older adults. We added a Major Heading (Video Games). The 2024 Adult Compendium includes 1114 PAs (912 with measured and 202 with estimated values) across 22 Major Headings. CONCLUSION This comprehensive update and refinement led to the creation of The 2024 Adult Compendium, which has utility across research, public health, education, and healthcare domains, as well as in the development of consumer health technologies. The new website with the complete lists of PAs and supporting resources is available at https://pacompendium.com.
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Affiliation(s)
- Stephen D Herrmann
- Kansas Center for Metabolism and Obesity Research, University of Kansas Medical Center, Kansas City, KS 66160, USA; Division of Physical Activity and Weight Management, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA.
| | - Erik A Willis
- Center for Health Promotion and Disease Prevention, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Barbara E Ainsworth
- College of Health Solutions, Arizona State University, Phoenix, AZ 85003, USA; School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
| | - Tiago V Barreira
- Exercise Science Department, Syracuse University, Syracuse, NY 13244, USA
| | - Mary Hastert
- Kansas Center for Metabolism and Obesity Research, University of Kansas Medical Center, Kansas City, KS 66160, USA; Division of Physical Activity and Weight Management, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Chelsea L Kracht
- Clinical Sciences Division, Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA
| | - John M Schuna
- School of Exercise and Sport Science, Oregon State University, Corvallis, OR 97331, USA
| | - Zhenghui Cai
- School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
| | - Minghui Quan
- School of Exercise and Health, Shanghai University of Sport, Shanghai 200438, China
| | - Catrine Tudor-Locke
- College of Health and Human Services, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | | | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN 55454, USA
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Delobelle J, Lebuf E, Dyck DV, Compernolle S, Janek M, Backere FD, Vetrovsky T. Fitbit's accuracy to measure short bouts of stepping and sedentary behaviour: validation, sensitivity and specificity study. Digit Health 2024; 10:20552076241262710. [PMID: 38894943 PMCID: PMC11185038 DOI: 10.1177/20552076241262710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
Objective This study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies. Methods Thirty-seven adults (18-65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland-Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL. Results Fitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively. Conclusions This study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits' reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.
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Affiliation(s)
- Julie Delobelle
- Physical Activity & Health, Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
- Research Foundation Flanders (FWO), Brussels, Belgium
| | - Elien Lebuf
- Physical Activity & Health, Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
- Research Foundation Flanders (FWO), Brussels, Belgium
| | - Delfien Van Dyck
- Physical Activity & Health, Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
| | - Sofie Compernolle
- Physical Activity & Health, Department of Movement and Sports Sciences, Ghent University, Ghent, Belgium
- Research Foundation Flanders (FWO), Brussels, Belgium
| | - Michael Janek
- Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic
| | - Femke De Backere
- Faculty of Engineering and Architecture, Department of Information Technology, Ghent University, Ghent, Belgium
| | - Tomas Vetrovsky
- Faculty of Physical Education and Sport, Charles University, Prague, Czech Republic
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Wu WJ, Yu HB, Tai WH, Zhang R, Hao WY. Validity of Actigraph for Measuring Energy Expenditure in Healthy Adults: A Systematic Review and Meta-Analysis. SENSORS (BASEL, SWITZERLAND) 2023; 23:8545. [PMID: 37896640 PMCID: PMC10610851 DOI: 10.3390/s23208545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/20/2023] [Accepted: 09/28/2023] [Indexed: 10/29/2023]
Abstract
PURPOSE The objective of this systematic review and meta-analysis was to assess the validity of the Actigraph triaxial accelerometer device in measuring physical activity energy expenditure (PAEE) in healthy adults, with indirect calorimetry (IC) serving as the validity criterion. METHODS A comprehensive search was conducted using the PubMed, Web of Science, and sportdiscuss databases, in addition to manual searches for supplementary sources. Search strategies were employed that involved conducting single keyword searches using the terms "gt3x" and "Actigraph gt3x". The literature search encompassed the timeframe spanning from 1 January 2010 to 1 March 2023. The methodological quality of the studies included in the analysis was evaluated using both the Downs and Black checklist and the Consensus-Based Criteria for Selection of Measurement Instruments (COSMIN) checklist. The meta-analysis was conducted using the Review Manager 5.4 software. The standardized mean difference (SMD) was calculated and expressed as a 95% confidence interval (CI). The significance level was set at α = 0.05. A systematic assessment of the Actigraph's performance was conducted through the descriptive analysis of computed effect sizes. RESULTS A total of 4738 articles were retrieved from the initial search. After eliminating duplicate articles and excluding those deemed irrelevant, a comprehensive analysis was conducted on a total of 20 studies, encompassing a combined sample size of 1247 participants. The scores on the Downs and Black checklist ranged from 10 to 14, with a mean score of 11.35. The scores on the COSMIN checklist varied from 50% to 100%, with an average score of 65.83%. The meta-analysis findings revealed a small effect size (SMD = 0.01, 95% CI = 0.50-0.52, p = 0.97), indicating no statistically significant difference (p > 0.05). CONCLUSIONS The meta-analysis revealed a small effect size when comparing the Actigraph and IC, suggesting that the Actigraph can be utilized for assessing total PAEE. Descriptive analyses have indicated that the Actigraph device has limited validity in accurately measuring energy expenditure during specific physical activities, such as high-intensity and low-intensity activities. Therefore, caution should be exercised when utilizing this device for such purposes. Furthermore, there was a significant correlation between the activity counts measured by the Actigraph and the PAEE, indicating that activity counts can be utilized as a predictive variable for PAEE.
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Affiliation(s)
- Wen-Jian Wu
- School of Sports Science, Fujian Normal University, Fuzhou 350117, China;
- School of Physical Education, Quanzhou Normal University, Quanzhou 362000, China; (R.Z.); (W.-Y.H.)
| | - Hai-Bin Yu
- School of Physical Education, Quanzhou Normal University, Quanzhou 362000, China; (R.Z.); (W.-Y.H.)
- Graduate School, Chengdu Sport University, Chengdu 610000, China
| | - Wei-Hsun Tai
- School of Physical Education, Quanzhou Normal University, Quanzhou 362000, China; (R.Z.); (W.-Y.H.)
- Graduate School, Chengdu Sport University, Chengdu 610000, China
| | - Rui Zhang
- School of Physical Education, Quanzhou Normal University, Quanzhou 362000, China; (R.Z.); (W.-Y.H.)
- Key Laboratory of Bionic Engineering (Ministry of Education, China), Jilin University, Changchun 130022, China
| | - Wei-Ya Hao
- School of Physical Education, Quanzhou Normal University, Quanzhou 362000, China; (R.Z.); (W.-Y.H.)
- China Institute of Sport Science, General Administration of Sport of China, Beijing 100061, China
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11
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Milther C, Winther L, Stahlhut M, Curtis DJ, Aadahl M, Kristensen MT, Sørensen JL, Dall CH. Validation of an accelerometer system for measuring physical activity and sedentary behavior in healthy children and adolescents. Eur J Pediatr 2023; 182:3639-3647. [PMID: 37258775 PMCID: PMC10460328 DOI: 10.1007/s00431-023-05014-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 04/28/2023] [Accepted: 05/01/2023] [Indexed: 06/02/2023]
Abstract
The study aims to assess the concurrent validity of the SENS motion® accelerometer system for device-based measurement of physical activity and sedentary behavior in healthy children and adolescents. Thirty-six healthy children and adolescents (mean ± standard deviation (SD) age, 10.2 ± 2.3 years) were fitted with three SENS sensors while performing standardized activities including walking, fast walking, sitting/lying, and arm movements. Data from the sensors were compared with video observations (reference criteria). The agreement between SENS motion® and observation was analyzed using Student's t-test and illustrated in Bland-Altman plots. The concurrent validity was further evaluated using intraclass correlation coefficient (ICC) and was expressed as standard error of measurement (SEM) and minimal detectable change (MDC). Strong agreement was found between SENS and observation for walking time, sedentary time, and lying time. In contrast, moderate agreement was observed for number of steps, sitting time, and time with and without arm movement. ICC2.1 values were overall moderate to excellent (0.5-0.94), with correspondingly low SEM% for walking time, sedentary time, lying time, and time with arm movement (2-9%). An acceptable SEM% level was reached for both steps and sitting time (11% and 12%). For fast walking time, the results showed a weak agreement between the measurement methods, and the ICC value was poor. CONCLUSION SENS motion® seems valid for detecting physical activity and sedentary behavior in healthy children and adolescents with strong agreement and moderate to excellent ICC values. Furthermore, the explorative results on arm movements seem promising. WHAT IS KNOWN • Inactivity and sedentary behavior follow an increasing trend among children and adolescents. • SENS motion® seems to be valid for measuring physical activity and sedentary behavior in adults and elderly patients. WHAT IS NEW • SENS motion® seems valid with strong agreement between video observations and SENS measurement, and ICC values are moderate to excellent when measuring physical activity and sedentary behavior in healthy children and adolescents. • SENS motion® seems promising for detection of arm movements.
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Affiliation(s)
- Camilla Milther
- Juliane Marie Centre and Mary Elizabeths Hospital, Rigshospitalet, Copenhagen, Denmark.
| | - Lærke Winther
- Juliane Marie Centre and Mary Elizabeths Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Michelle Stahlhut
- Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark
| | - Derek John Curtis
- Child Centre Copenhagen, The Child and Youth Administration, City of Copenhagen, Denmark
| | - Mette Aadahl
- Center for Clinical Research and Prevention, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Morten Tange Kristensen
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Physical and Occupational Therapy, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark
| | - Jette Led Sørensen
- Juliane Marie Centre and Mary Elizabeths Hospital, Rigshospitalet, Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christian Have Dall
- Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Physical and Occupational Therapy, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Denmark
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12
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Giurgiu M, Ketelhut S, Kubica C, Nissen R, Doster AK, Thron M, Timm I, Giurgiu V, Nigg CR, Woll A, Ebner-Priemer UW, Bussmann JBJ. Assessment of 24-hour physical behaviour in adults via wearables: a systematic review of validation studies under laboratory conditions. Int J Behav Nutr Phys Act 2023; 20:68. [PMID: 37291598 DOI: 10.1186/s12966-023-01473-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/31/2023] [Indexed: 06/10/2023] Open
Abstract
BACKGROUND Wearable technology is used by consumers and researchers worldwide for continuous activity monitoring in daily life. Results of high-quality laboratory-based validation studies enable us to make a guided decision on which study to rely on and which device to use. However, reviews in adults that focus on the quality of existing laboratory studies are missing. METHODS We conducted a systematic review of wearable validation studies with adults. Eligibility criteria were: (i) study under laboratory conditions with humans (age ≥ 18 years); (ii) validated device outcome must belong to one dimension of the 24-hour physical behavior construct (i.e., intensity, posture/activity type, and biological state); (iii) study protocol must include a criterion measure; (iv) study had to be published in a peer-reviewed English language journal. Studies were identified via a systematic search in five electronic databases as well as back- and forward citation searches. The risk of bias was assessed based on the QUADAS-2 tool with eight signaling questions. RESULTS Out of 13,285 unique search results, 545 published articles between 1994 and 2022 were included. Most studies (73.8% (N = 420)) validated an intensity measure outcome such as energy expenditure; only 14% (N = 80) and 12.2% (N = 70) of studies validated biological state or posture/activity type outcomes, respectively. Most protocols validated wearables in healthy adults between 18 and 65 years. Most wearables were only validated once. Further, we identified six wearables (i.e., ActiGraph GT3X+, ActiGraph GT9X, Apple Watch 2, Axivity AX3, Fitbit Charge 2, Fitbit, and GENEActiv) that had been used to validate outcomes from all three dimensions, but none of them were consistently ranked with moderate to high validity. Risk of bias assessment resulted in 4.4% (N = 24) of all studies being classified as "low risk", while 16.5% (N = 90) were classified as "some concerns" and 79.1% (N = 431) as "high risk". CONCLUSION Laboratory validation studies of wearables assessing physical behaviour in adults are characterized by low methodological quality, large variability in design, and a focus on intensity. Future research should more strongly aim at all components of the 24-hour physical behaviour construct, and strive for standardized protocols embedded in a validation framework.
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Affiliation(s)
- Marco Giurgiu
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany.
- Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany.
| | - Sascha Ketelhut
- Health Science Department, Institute of Sport Science, University of Bern, Bern, Switzerland
| | - Claudia Kubica
- Health Science Department, Institute of Sport Science, University of Bern, Bern, Switzerland
| | - Rebecca Nissen
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Ann-Kathrin Doster
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Maximiliane Thron
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Irina Timm
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Valeria Giurgiu
- Baden-Wuerttemberg Cooperative State University (DHBW), Karlsruhe, Germany
| | - Claudio R Nigg
- Sport Pedagogy Department, Institute of Sport Science, University of Bern, Bern, Switzerland
| | - Alexander Woll
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
| | - Ulrich W Ebner-Priemer
- Department of Sports and Sports Science, Karlsruhe Institute of Technology (KIT), Hertzstr. 16, 76187, Karlsruhe, Germany
- Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Heidelberg, Germany
| | - Johannes B J Bussmann
- Erasmus MC, Department of Rehabilitation medicine, University Medical Center Rotterdam, Rotterdam, Netherlands
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13
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O’Brien MW, Daley WS, Schwartz BD, Shivgulam ME, Wu Y, Kimmerly DS, Frayne RJ. Characterization of Detailed Sedentary Postures Using a Tri-Monitor ActivPAL Configuration in Free-Living Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:587. [PMID: 36679384 PMCID: PMC9866492 DOI: 10.3390/s23020587] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/10/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Objective monitors such as the activPAL characterize time when the thigh is horizontal as sedentary time. However, there are physiological differences between lying, bent-legged sitting, and straight-legged sitting. We introduce a three-monitor configuration to assess detailed sedentary postures and demonstrate its use in characterizing such positions in free-living conditions. We explored time spent in each sedentary posture between prolonged (>1 h) versus non-prolonged (<1 h) sedentary bouts. In total, 35 healthy adults (16♀, 24 ± 3 years; 24 h/day for 6.8 ± 1.0 days) wore an activPAL accelerometer on their thigh, torso, and shin. Hip and knee joint flexion angle estimates were determined during sedentary bouts using the dot-product method between the torso−thigh and thigh−shin, respectively. Compared to lying (69 ± 60 min/day) or straight-legged sitting (113 ± 100 min/day), most time was spent in bent-legged sitting (439 ± 101 min/day, p < 0.001). Most of the bent-legged sitting time was accumulated in non-prolonged bouts (328 ± 83 vs. 112 ± 63 min/day, p < 0.001). In contrast, similar time was spent in straight-legged sitting and lying between prolonged/non-prolonged bouts (both, p > 0.26). We document that a considerable amount of waking time is accumulated in lying or straight-legged sitting. This methodological approach equips researchers with a means of characterizing detailed sedentary postures in uncontrolled conditions and may help answer novel research questions on sedentariness.
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Affiliation(s)
- Myles W. O’Brien
- School of Physiotherapy (Faculty of Health) & Division of Geriatric Medicine (Faculty of Medicine), Dalhousie University, Halifax, NS B3H 4R2, Canada
- Geriatric Medicine Research, Dalhousie University & Nova Scotia Health, Halifax, NS B3H 4R2, Canada
| | - W. Seth Daley
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Beverly D. Schwartz
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Madeline E. Shivgulam
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Yanlin Wu
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Derek S. Kimmerly
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Ryan J. Frayne
- Division of Kinesiology, School of Health and Human Performance, Faculty of Health, Dalhousie University, Halifax, NS B3H 4R2, Canada
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14
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Budig M, Stoohs R, Keiner M. Validity of Two Consumer Multisport Activity Tracker and One Accelerometer against Polysomnography for Measuring Sleep Parameters and Vital Data in a Laboratory Setting in Sleep Patients. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239540. [PMID: 36502241 PMCID: PMC9741062 DOI: 10.3390/s22239540] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 11/25/2022] [Accepted: 12/01/2022] [Indexed: 05/16/2023]
Abstract
Two commercial multisport activity trackers (Garmin Forerunner 945 and Polar Ignite) and the accelerometer ActiGraph GT9X were evaluated in measuring vital data, sleep stages and sleep/wake patterns against polysomnography (PSG). Forty-nine adult patients with suspected sleep disorders (30 males/19 females) completed a one-night PSG sleep examination followed by a multiple sleep latency test (MSLT). Sleep parameters, time in bed (TIB), total sleep time (TST), wake after sleep onset (WASO), sleep onset latency (SOL), awake time (WASO + SOL), sleep stages (light, deep, REM sleep) and the number of sleep cycles were compared. Both commercial trackers showed high accuracy in measuring vital data (HR, HRV, SpO2, respiratory rate), r > 0.92. For TIB and TST, all three trackers showed medium to high correlation, r > 0.42. Garmin had significant overestimation of TST, with MAE of 84.63 min and MAPE of 25.32%. Polar also had an overestimation of TST, with MAE of 45.08 min and MAPE of 13.80%. ActiGraph GT9X results were inconspicuous. The trackers significantly underestimated awake times (WASO + SOL) with weak correlation, r = 0.11−0.57. The highest MAE was 50.35 min and the highest MAPE was 83.02% for WASO for Garmin and ActiGraph GT9X; Polar had the highest MAE of 21.17 min and the highest MAPE of 141.61% for SOL. Garmin showed significant deviations for sleep stages (p < 0.045), while Polar only showed significant deviations for sleep cycle (p = 0.000), r < 0.50. Garmin and Polar overestimated light sleep and underestimated deep sleep, Garmin significantly, with MAE up to 64.94 min and MAPE up to 116.50%. Both commercial trackers Garmin and Polar did not detect any daytime sleep at all during the MSLT test. The use of the multisport activity trackers for sleep analysis can only be recommended for general daily use and for research purposes. If precise data on sleep stages and parameters are required, their use is limited. The accuracy of the vital data measurement was adequate. Further studies are needed to evaluate their use for medical purposes, inside and outside of the sleep laboratory. The accelerometer ActiGraph GT9X showed overall suitable accuracy in detecting sleep/wake patterns.
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Affiliation(s)
- Mario Budig
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
| | | | - Michael Keiner
- Department of Sports Science, German University of Health & Sport, 85737 Ismaning, Germany
- Correspondence:
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de Arriba Muñoz A, García Castellanos MT, Cajal MD, Beisti Ortego A, Ruiz IM, Labarta Aizpún JI. Automated growth monitoring app (GROWIN): a mobile Health (mHealth) tool to improve the diagnosis and early management of growth and nutritional disorders in childhood. J Am Med Inform Assoc 2022; 29:1508-1517. [PMID: 35799406 DOI: 10.1093/jamia/ocac108] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/03/2022] [Accepted: 06/24/2022] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE To assess the functionality and feasibility of the GROWIN app for promoting early detection of growth disorders in childhood, supporting early interventions, and improving children's lifestyle by analyzing data collected over 3 years (2018-2020). METHODS We retrospectively assessed the growth parameters (height, weight, body mass index [BMI], abdominal circumference) entered by users (caregivers/parents) in the GROWIN app. We also analyzed the potential health problems detected and the messages/recommendations the app showed. Finally, we assessed the possible impact/benefit of the app on the growth of the children. RESULTS A total of 21 633 users (Spanish [65%], Latin American [30%], and others [5%]) entered 10.5 ± 8.3 measurements (0-15 y old). 1200 recommendations were for low height and 550 for low weight. 1250 improved their measurements. A specialist review was recommended in 500 patients due to low height. 2567 nutrition tests were run. All children with obesity (n = 855, BMI: 27.8 kg/m2 [2.25 SD]) completed the initial test with a follow-up of ≥1 year. Initial results (score: 8.1) showed poor eating habits (fast food, commercially baked goods, candy, etc.), with >90% not having breakfast. After 3-6 months, BMI decreased ≥1 point, and test scores increased ≥2 points. This benefit was maintained beyond 1 year and was correlated with an improvement in BMI (r = -.65, P = .01). DISCUSSION/CONCLUSIONS The GROWIN app represents an innovative automated solution for families to monitor growth. It allows the early detection of abnormal growth indicators during childhood and adolescence, promoting early interventions. Additionally, in children with obesity, an improvement in healthy nutritional habits and a decrease in BMI were observed.
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Affiliation(s)
- Antonio de Arriba Muñoz
- Pediatric Endocrinology, Hospital Universitario Miguel Servet, Zaragoza, Spain.,Instituto de Investigación Sanitaria Aragón, Zaragoza, Spain
| | - María Teresa García Castellanos
- Pediatric Endocrinology, Hospital Universitario Miguel Servet, Zaragoza, Spain.,Instituto de Investigación Sanitaria Aragón, Zaragoza, Spain
| | - Mercedes Domínguez Cajal
- Pediatric Endocrinology, Hospital Universitario Miguel Servet, Zaragoza, Spain.,Instituto de Investigación Sanitaria Aragón, Zaragoza, Spain
| | - Anunciación Beisti Ortego
- Pediatric Endocrinology, Hospital Universitario Miguel Servet, Zaragoza, Spain.,Instituto de Investigación Sanitaria Aragón, Zaragoza, Spain
| | - Ignacio Martínez Ruiz
- Instituto Universitario de Investigación de Ingeniería de Aragón (I3A), Zaragoza University, Zaragoza, Spain.,eHWin New Technologies, Zaragoza, Spain
| | - José Ignacio Labarta Aizpún
- Pediatric Endocrinology, Hospital Universitario Miguel Servet, Zaragoza, Spain.,Instituto de Investigación Sanitaria Aragón, Zaragoza, Spain
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