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      Motivation states to move, be physically active and sedentary vary like circadian rhythms and are associated with affect and arousal

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          Abstract

          Introduction

          Motivation to be physically active and sedentary is a transient state that varies in response to previous behavior. It is not known: (a) if motivational states vary from morning to evening, (b) if they are related to feeling states (arousal/hedonic tone), and (c) whether they predict current behavior and intentions. The primary purpose of this study was to determine if motivation states vary across the day and in what pattern. Thirty adults from the United States were recruited from Amazon MTurk.

          Methods

          Participants completed 6 identical online surveys each day for 8 days beginning after waking and every 2–3 h thereafter until bedtime. Participants completed: (a) the CRAVE scale (Right now version) to measure motivation states for Move and Rest, (b) Feeling Scale, (c) Felt Arousal Scale, and (d) surveys about current movement behavior (e.g., currently sitting, standing, laying down) and intentions for exercise and sleep. Of these, 21 participants (mean age 37.7 y; 52.4% female) had complete and valid data.

          Results

          Visual inspection of data determined that: a) motivation states varied widely across the day, and b) most participants had a single wave cycle each day. Hierarchical linear modelling revealed that there were significant linear and quadratic time trends for both Move and Rest. Move peaked near 1500 h when Rest was at its nadir. Cosinor analysis determined that the functional waveform was circadian for Move for 81% of participants and 62% for Rest. Pleasure/displeasure and arousal independently predicted motivation states (all p's < .001), but arousal had an association twice as large. Eating, exercise and sleep behaviors, especially those over 2 h before assessment, predicted current motivation states. Move-motivation predicted current body position (e.g., laying down, sitting, walking) and intentions for exercise and sleep more consistently than rest, with the strongest prediction of behaviors planned for the next 30 min.

          Discussion

          While these data must be replicated with a larger sample, results suggest that motivation states to be active or sedentary have a circadian waveform for most people and influence future behavioral intentions. These novel results highlight the need to rethink the traditional approaches typically utilized to increase physical activity levels.

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            The behaviour change wheel: A new method for characterising and designing behaviour change interventions

            Background Improving the design and implementation of evidence-based practice depends on successful behaviour change interventions. This requires an appropriate method for characterising interventions and linking them to an analysis of the targeted behaviour. There exists a plethora of frameworks of behaviour change interventions, but it is not clear how well they serve this purpose. This paper evaluates these frameworks, and develops and evaluates a new framework aimed at overcoming their limitations. Methods A systematic search of electronic databases and consultation with behaviour change experts were used to identify frameworks of behaviour change interventions. These were evaluated according to three criteria: comprehensiveness, coherence, and a clear link to an overarching model of behaviour. A new framework was developed to meet these criteria. The reliability with which it could be applied was examined in two domains of behaviour change: tobacco control and obesity. Results Nineteen frameworks were identified covering nine intervention functions and seven policy categories that could enable those interventions. None of the frameworks reviewed covered the full range of intervention functions or policies, and only a minority met the criteria of coherence or linkage to a model of behaviour. At the centre of a proposed new framework is a 'behaviour system' involving three essential conditions: capability, opportunity, and motivation (what we term the 'COM-B system'). This forms the hub of a 'behaviour change wheel' (BCW) around which are positioned the nine intervention functions aimed at addressing deficits in one or more of these conditions; around this are placed seven categories of policy that could enable those interventions to occur. The BCW was used reliably to characterise interventions within the English Department of Health's 2010 tobacco control strategy and the National Institute of Health and Clinical Excellence's guidance on reducing obesity. Conclusions Interventions and policies to change behaviour can be usefully characterised by means of a BCW comprising: a 'behaviour system' at the hub, encircled by intervention functions and then by policy categories. Research is needed to establish how far the BCW can lead to more efficient design of effective interventions.
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              Out of Control: Visceral Influences on Behavior

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                Author and article information

                Contributors
                Journal
                Front Sports Act Living
                Front Sports Act Living
                Front. Sports Act. Living
                Frontiers in Sports and Active Living
                Frontiers Media S.A.
                2624-9367
                18 April 2023
                2023
                : 5
                : 1094288
                Affiliations
                [ 1 ]Department of Psychology, Southern Connecticut State University , New Haven, CT, United States
                [ 2 ]Division of Digestive Health, Yale New Haven Hospital , New Haven, CT, United States
                [ 3 ]Department of Biobehavioral Sciences, Teachers College—Columbia University , New York, NY, United States
                [ 4 ]Science Research Program, Dobbs Ferry High School , Dobbs Ferry, NY, United States
                [ 5 ]Department of Kinesiology and Health Education, The University of Texas at Austin , Austin, TX, United States
                [ 6 ]Faculty of Physical Activity and Sports Sciences, Universidad de León , León, Spain
                [ 7 ]College of Healthcare Sciences, James Cook University , Townsville, Australia
                [ 8 ]Graduate Program in Movement Sciences, Integrated Institute of Health, Federal University of Mato Grosso do Sul, Campo Grande , Brazil
                [ 9 ]Center for Pain, Research, Informatics, Medical Comorbidities and Education Center (PRIME), VA Connecticut Healthcare System , West Haven, CT, United States
                [ 10 ]Center for Medical Informatics, Yale School of Medicine , New Haven, CT, United States
                [ 11 ]Department of Psychiatry, Yale Medical School , New Haven, CT, United States
                [ 12 ]Department of Psychology, University of Notre Dame , Notre Dame, IN, United States
                [ 13 ]Patel College of Osteopathic Medicine, Nova Southeastern University , Fort Lauderdale, FL, United States
                [ 14 ]Department of Mathematics and Statistics, Texas Tech University , Lubbock, TX, United States
                Author notes

                Edited by: June J. Pilcher, Clemson University, United States

                Reviewed by: Drew Morris, Centre College, United States Gary Giumetti, Quinnipiac University, United States

                [* ] Correspondence: Matthew Stults-Kolehmainen matthew.stults@ 123456ynhh.org
                [ † ]

                These authors share first authorship

                Specialty Section: This article was submitted to Movement Science and Sport Psychology, a section of the journal Frontiers in Sports and Active Living

                Article
                10.3389/fspor.2023.1094288
                10151587
                37143586
                20af6561-3eed-4bb0-8ef4-e05ce707cae3
                © 2023 Budnick, Stults-Kolehmainen, Dadina, Bartholomew, Boullosa, Ash, Sinha, Blacutt, Haughton and Lu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 November 2022
                : 16 March 2023
                Page count
                Figures: 5, Tables: 3, Equations: 0, References: 98, Pages: 0, Words: 0
                Funding
                Funded by: American Heart Association
                Award ID: #852679
                Funded by: DB was supported
                Award ID: RYC2021-031098-I, MCIN/AEI/10.13039/501100011033
                Funded by: European Union NextGenerationEU/PRTR
                Award ID:  
                GA was supported by a fellowship from the Office of Academic Affiliations at the United States Veterans Health Administration, a Robert E. Leet and Clara Guthrie Patterson Trust Mentored Research Award, Bank of America, N.A., Trustee, and American Heart Association Grant #852679 (GA, 2021–2024). DB was supported by Grant RYC2021-031098-I funded by MCIN/AEI/10.13039/501100011033, by “European Union NextGenerationEU/PRTR” and by a productivity research grant (PQ1D) from CNPq (Brazil).
                Categories
                Sports and Active Living
                Original Research

                affectively charged,motivation states,affect,arousal,exercise,physical activity,sedentary activity,body position,sleep

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