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Danilevicz IM, van Hees VT, van der Heide FCT, Jacob L, Landré B, Benadjaoud MA, Sabia S. Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data. BMC Med Res Methodol 2024; 24:132. [PMID: 38849718 PMCID: PMC11157888 DOI: 10.1186/s12874-024-02255-w] [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: 11/02/2023] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset.
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Affiliation(s)
- Ian Meneghel Danilevicz
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | | | - Frank C T van der Heide
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | - Louis Jacob
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | - Benjamin Landré
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | - Mohamed Amine Benadjaoud
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), 31 Av Division Leclerc, 92260, Fontenay-Aux-Roses, France
| | - Séverine Sabia
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France.
- Department of Epidemiology and Public Health, University College London, London, UK.
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Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [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: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
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Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
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Farrahi V, Clare P. Artificial Intelligence and Machine Learning-Powerful Yet Underutilized Tools and Algorithms in Physical Activity and Sedentary Behavior Research. J Phys Act Health 2024; 21:320-322. [PMID: 38335946 DOI: 10.1123/jpah.2024-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 01/17/2024] [Indexed: 02/12/2024]
Affiliation(s)
- Vahid Farrahi
- Institute for Sport and Sport Science, TU Dortmund University, Dortmund, Germany
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
| | - Philip Clare
- Prevention Research Collaboration, School of Public Health, The University of Sydney, Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- National Drug and Alcohol Research Centre, UNSW Sydney, Sydney, NSW, Australia
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Farrahi V, Collings PJ, Oussalah M. Deep learning of movement behavior profiles and their association with markers of cardiometabolic health. BMC Med Inform Decis Mak 2024; 24:74. [PMID: 38481262 PMCID: PMC10936042 DOI: 10.1186/s12911-024-02474-7] [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: 11/24/2023] [Accepted: 03/04/2024] [Indexed: 03/17/2024] Open
Abstract
BACKGROUND Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. Such averaged values cannot naturally capture the complex interplay between the duration, timing, and patterns of accumulation of movement behaviors, that altogether may be codependently related to health outcomes in adults. In this study, we introduce a novel approach to visually represent recorded movement behaviors as images using original accelerometer outputs. Subsequently, we utilize these images for cluster analysis employing deep convolutional autoencoders. METHODS Our method involves converting minute-by-minute accelerometer outputs (activity counts) into a 2D image format, capturing the entire spectrum of movement behaviors performed by each participant. By utilizing convolutional autoencoders, we enable the learning of these image-based representations. Subsequently, we apply the K-means algorithm to cluster these learned representations. We used data from 1812 adult (20-65 years) participants in the National Health and Nutrition Examination Survey (NHANES, 2003-2006 cycles) study who worn a hip-worn accelerometer for 7 seven consecutive days and provided valid accelerometer data. RESULTS Deep convolutional autoencoders were able to learn the image representation, encompassing the entire spectrum of movement behaviors. The images were encoded into 32 latent variables, and cluster analysis based on these learned representations for the movement behavior images resulted in the identification of four distinct movement behavior profiles characterized by varying levels, timing, and patterns of accumulation of movement behaviors. After adjusting for potential covariates, the movement behavior profile characterized as "Early-morning movers" and the profile characterized as "Highest activity" both had lower levels of insulin (P < 0.01 for both), triglycerides (P < 0.05 and P < 0.01, respectively), HOMA-IR (P < 0.01 for both), and plasma glucose (P < 0.05 and P < 0.1, respectively) compared to the "Lowest activity" profile. No significant differences were observed for the "Least sedentary movers" profile compared to the "Lowest activity" profile. CONCLUSIONS Deep learning of movement behavior profiles revealed that, in addition to duration and patterns of movement behaviors, the timing of physical activity may also be crucial for gaining additional health benefits.
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Affiliation(s)
- Vahid Farrahi
- Institute for Sport and Sport Science, TU Dortmund University, Dortmund, Germany.
| | - Paul J Collings
- Physical Activity, Sport and Health Research Group, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Mourad Oussalah
- Centre of Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
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Danilevicz IM, van Hees VT, van der Heide F, Jacob L, Landré B, Benadjaoud MA, Sabia S. Measures of fragmentation of rest activity patterns: mathematical properties and interpretability based on accelerometer real life data. RESEARCH SQUARE 2023:rs.3.rs-3543711. [PMID: 37986973 PMCID: PMC10659546 DOI: 10.21203/rs.3.rs-3543711/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index metric, an adaptation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical worth in a large dataset.
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Affiliation(s)
- Ian Meneghel Danilevicz
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | | | - Frank van der Heide
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | - Louis Jacob
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | - Benjamin Landré
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
| | - Mohamed Amine Benadjaoud
- Institut de Radioprotection et de Sûreté Nucléaire (IRSN), 31 Av Division Leclerc, 92260, Fontenay-Aux-Roses, France
| | - Séverine Sabia
- Université Paris Cité, INSERM, U1153, CRESS, Epidemiology of Ageing and Neurodegenerative Diseases, 10 Av de Verdun, 75010, Paris, France
- Department of Epidemiology and Public Health, University College London, London, United Kingdom
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