PainRhythms: Machine learning prediction of chronic pain from circadian dysregulation using actigraph data — a preliminary study

Sarwar, Atifa; Agu, Emmanuel; Polcari, Justin; Ciroli, Jack; Nephew, Benjamin C.; King, Jean A.

doi:10.1016/j.smhl.2022.100344

Cited by 4 publications

(2 citation statements)

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“…The intraday variability of rest–activity rhythm was the most predictive feature, with elevated values in pain associated with disturbed sleep. Rest–activity rhythms can effectively detect subjects with chronic pain [ 148 ].…”

Section: Resultsmentioning

confidence: 99%

Nutrition and Chronobiology as Key Components of Multidisciplinary Therapeutic Interventions for Fibromyalgia and Associated Chronic Fatigue Syndrome: A Narrative and Critical Review

Carrasco-Querol,

Cabricano-Canga,

Bueno Hernández

et al. 2024

Nutrients

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Fibromyalgia (FM) is often accompanied by chronic fatigue syndrome (CFS). It is a poorly understood disorder that mainly affects women and leads to chronic pain, fatigue, and insomnia, among other symptoms, which decrease quality of life. Due to the inefficiency of current pharmacological treatments, increasing interest is being directed towards non-pharmacological multicomponent therapies. However, nutrition and chronobiology are often overlooked when developing multicomponent therapies. This narrative and critical review explore the relevance of nutritional and chronobiological strategies in the therapeutic management of FM and the often-associated CFS. Reviewed literature offers scientific evidence for the association of dietary habits, nutrient levels, body composition, gut microbiota imbalance, chronobiological alterations, and their interrelation with the development and severity of symptoms. This review highlights the key role of nutrition and chronobiology as relevant and indispensable components in a multidisciplinary approach to FM and CFS.

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Section: Resultsmentioning

confidence: 99%

Nutrition and Chronobiology as Key Components of Multidisciplinary Therapeutic Interventions for Fibromyalgia and Associated Chronic Fatigue Syndrome: A Narrative and Critical Review

Carrasco-Querol,

Cabricano-Canga,

Bueno Hernández

et al. 2024

Nutrients

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“…Recently, CosinorPy, a Python package for the identification and characterisation of rhythmic behaviour has been reported [9]. This package has already been used in different studies ranging from a the analysis of ’circadian reprogramming’ in cell lines using qPCR data [20] to predicting sleep quality metrics [21] and chronic pain predictions [22]. CosinorPy implements single- as well as multicomponent cosinor models, which can be used in a combination with the population as well as count models.…”

Section: Introductionmentioning

confidence: 99%

Identification and characterisation of variable rhythms using CosinorPy

Moškon

2022

Preprint

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Background and objectiveAnalysis of rhythmic data has become an important aspect in biological and medical as well as in other fields of science. Several nonparametric methods aimed at such analyses have been reported in recent years. However, parametric methods based on trigono-metric regression still reflect several advantages. Different software packages for parametric analysis of rhythmic data have been made available recently. These are mostly based on a single-component cosinor model, which is not able to describe asymmetric oscillations that might reflect multiple peaks per period. Moreover, a basic cosinor model is unable to describe oscillations that are changed (namely forced or damped) with time. Here, we present some important extensions of the recently developed CosinorPy Python package to address these gaps.MethodsThe extended version of CosinorPy provides functionalities to perform a detailed individual or comparative analysis of rhythmic behaviour reflecting (1) multiple asymmetric peaks per period, (2) forced, damped or sustained oscillations, and (3) accumulation or reduction of MESOR with time. In all these cases the package is able to assess the (differential) rhyth-micity parameters, evaluate their significance and confidence intervals, and provide a set of publication-ready figures.ResultsWe demonstrate the package on some typical scenarios incorporating different types of oscillatory dynamics, such as asymmetric, damped, and forced oscillations. We show that the proposed implementation of a generalised cosinor model is able to reduce the error of estimated rhythmicity parameters in cases that tend to be problematic for alternative models. The implementation of the presented package is available together with the scripts to reproduce the reported results at https://github.com/mmoskon/CosinorPy.ConclusionAccording to our knowledge, CosinorPy currently presents the only implementation that is able to cover the above scenarios using a parametric model with vast applications in biology and medicine as well as in other scientific domains.

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Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports

Yang,

Amin,

Shihada

2024

ACM Comput. Surv.

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Wearable devices or wearables, designed to be attached to the human body, can gather personalized real-time data and continuously monitor an individual’s health status and physiological disposition in a non-invasive manner. Intelligent wearables integrate advanced machine learning (ML) algorithms to process complex data patterns and provide accurate insights. As a result, intelligent wearables have emerged as a groundbreaking innovation in the fields of sports and health, introducing a new paradigm in kinematic analysis and patient data evaluation. For example, virtual coaches offer feedback on athletes’ performance, while virtual physicians assist in customizing medication for patients. This article provides an overview of various types of intelligent wearables and their applications in health and sports, categorizes ML algorithms, and introduces the wireless body area sensor network (WBASN) used for communication in wearable sensors. Additionally, we discuss potential challenges and development directions that could shape the future of intelligent wearables and propose effective solutions for their continued enhancement. This article offers valuable insights into the exciting potential of intelligent wearables to transform healthcare and sports.

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PainRhythms: Machine learning prediction of chronic pain from circadian dysregulation using actigraph data — a preliminary study

Cited by 4 publications

References 49 publications

Nutrition and Chronobiology as Key Components of Multidisciplinary Therapeutic Interventions for Fibromyalgia and Associated Chronic Fatigue Syndrome: A Narrative and Critical Review

Nutrition and Chronobiology as Key Components of Multidisciplinary Therapeutic Interventions for Fibromyalgia and Associated Chronic Fatigue Syndrome: A Narrative and Critical Review

Identification and characterisation of variable rhythms using CosinorPy

Intelligent Wearable Systems: Opportunities and Challenges in Health and Sports

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