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Perski O, Copeland A, Allen J, Pavel M, Rivera DE, Hekler E, Hankonen N, Chevance G. The iterative development and refinement of health psychology theories through formal, dynamical systems modelling: a scoping review and initial expert-derived 'best practice' recommendations. Health Psychol Rev 2024:1-44. [PMID: 39260381 DOI: 10.1080/17437199.2024.2400977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 09/01/2024] [Indexed: 09/13/2024]
Abstract
This scoping review aimed to synthesise methodological steps taken by researchers in the development of formal, dynamical systems models of health psychology theories. We searched MEDLINE, PsycINFO, the ACM Digital Library and IEEE Xplore in July 2023. We included studies of any design providing that they reported on the development or refinement of a formal, dynamical systems model unfolding at the within-person level, with no restrictions on population or setting. A narrative synthesis with frequency analyses was conducted. A total of 17 modelling projects reported across 29 studies were included. Formal modelling efforts have largely been concentrated to a small number of interdisciplinary teams in the United States (79.3%). The models aimed to better understand dynamic processes (69.0%) or inform the development of adaptive interventions (31.0%). Models typically aimed to formalise the Social Cognitive Theory (31.0%) or the Self-Regulation Theory (17.2%) and varied in complexity (range: 3-30 model components). Only 3.4% of studies reported involving stakeholders in the modelling process and 10.3% drew on Open Science practices. We conclude by proposing an initial set of expert-derived 'best practice' recommendations. Formal, dynamical systems modelling is poised to help health psychologists develop and refine theories, ultimately leading to more potent interventions.
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Affiliation(s)
- Olga Perski
- Faculty of Social Sciences, Tampere University, Tampere, Finland
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA
| | - Amber Copeland
- School of Psychology, University of Sheffield, Sheffield, UK
| | - Jim Allen
- MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
| | - Misha Pavel
- Khoury College of Computer Sciences, Northeastern University, Burlington, VT, USA
| | - Daniel E Rivera
- Control Systems Engineering Laboratory, Arizona State University, Tempe, AZ, USA
| | - Eric Hekler
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, CA, USA
| | - Nelli Hankonen
- Faculty of Social Sciences, Tampere University, Tampere, Finland
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Perski O, Kale D, Leppin C, Okpako T, Simons D, Goldstein SP, Hekler E, Brown J. Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development. PLOS DIGITAL HEALTH 2024; 3:e0000594. [PMID: 39178183 PMCID: PMC11343380 DOI: 10.1371/journal.pdig.0000594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 07/28/2024] [Indexed: 08/25/2024]
Abstract
Specific moments of lapse among smokers attempting to quit often lead to full relapse, which highlights a need for interventions that target lapses before they might occur, such as just-in-time adaptive interventions (JITAIs). To inform the decision points and tailoring variables of a lapse prevention JITAI, we trained and tested supervised machine learning algorithms that use Ecological Momentary Assessments (EMAs) and wearable sensor data of potential lapse triggers and lapse incidence. We aimed to identify a best-performing and feasible algorithm to take forwards in a JITAI. For 10 days, adult smokers attempting to quit were asked to complete 16 hourly EMAs/day assessing cravings, mood, activity, social context, physical context, and lapse incidence, and to wear a Fitbit Charge 4 during waking hours to passively collect data on steps and heart rate. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested, without and with the sensor data. Their ability to predict lapses for out-of-sample (i) observations and (ii) individuals were evaluated. Next, a series of individual-level and hybrid (i.e., group- and individual-level) algorithms were trained and tested. Participants (N = 38) responded to 6,124 EMAs (with 6.9% of responses reporting a lapse). Without sensor data, the best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.899 (95% CI = 0.871-0.928). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.524-0.994; median AUC = 0.639). 15/38 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.855 (range: 0.451-1.000). Hybrid algorithms could be constructed for 25/38 participants, with a median AUC of 0.692 (range: 0.523 to 0.998). With sensor data, the best-performing group-level algorithm had an AUC of 0.952 (95% CI = 0.933-0.970). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.494-0.979; median AUC = 0.745). 11/30 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.983 (range: 0.549-1.000). Hybrid algorithms could be constructed for 20/30 participants, with a median AUC of 0.772 (range: 0.444 to 0.968). In conclusion, high-performing group-level lapse prediction algorithms without and with sensor data had variable performance when applied to out-of-sample individuals. Individual-level and hybrid algorithms could be constructed for a limited number of individuals but had improved performance, particularly when incorporating sensor data for participants with sufficient wear time. Feasibility constraints and the need to balance multiple success criteria in the JITAI development and implementation process are discussed.
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Affiliation(s)
- Olga Perski
- Faculty of Social Sciences, Tampere University, Finland
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, California, United States of America
- Department of Behavioural Science and Health, University College London, United Kingdom
| | - Dimitra Kale
- Department of Behavioural Science and Health, University College London, United Kingdom
| | - Corinna Leppin
- Department of Behavioural Science and Health, University College London, United Kingdom
| | - Tosan Okpako
- Department of Behavioural Science and Health, University College London, United Kingdom
| | - David Simons
- Centre for Emerging, Endemic and Exotic Diseases, Royal Veterinary College, United Kingdom
| | - Stephanie P. Goldstein
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University & The Miriam Hospital/Weight Control and Diabetes Research Center, United States of America
| | - Eric Hekler
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, California, United States of America
| | - Jamie Brown
- Department of Behavioural Science and Health, University College London, United Kingdom
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Kim M, Patrick K, Nebeker C, Godino J, Stein S, Klasnja P, Perski O, Viglione C, Coleman A, Hekler E. The Digital Therapeutics Real-World Evidence Framework: An Approach for Guiding Evidence-Based Digital Therapeutics Design, Development, Testing, and Monitoring. J Med Internet Res 2024; 26:e49208. [PMID: 38441954 PMCID: PMC10951831 DOI: 10.2196/49208] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 01/13/2024] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
Digital therapeutics (DTx) are a promising way to provide safe, effective, accessible, sustainable, scalable, and equitable approaches to advance individual and population health. However, developing and deploying DTx is inherently complex in that DTx includes multiple interacting components, such as tools to support activities like medication adherence, health behavior goal-setting or self-monitoring, and algorithms that adapt the provision of these according to individual needs that may change over time. While myriad frameworks exist for different phases of DTx development, no single framework exists to guide evidence production for DTx across its full life cycle, from initial DTx development to long-term use. To fill this gap, we propose the DTx real-world evidence (RWE) framework as a pragmatic, iterative, milestone-driven approach for developing DTx. The DTx RWE framework is derived from the 4-phase development model used for behavioral interventions, but it includes key adaptations that are specific to the unique characteristics of DTx. To ensure the highest level of fidelity to the needs of users, the framework also incorporates real-world data (RWD) across the entire life cycle of DTx development and use. The DTx RWE framework is intended for any group interested in developing and deploying DTx in real-world contexts, including those in industry, health care, public health, and academia. Moreover, entities that fund research that supports the development of DTx and agencies that regulate DTx might find the DTx RWE framework useful as they endeavor to improve how DTxcan advance individual and population health.
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Affiliation(s)
- Meelim Kim
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
- The Qualcomm Institute, University of California San Diego, La Jolla, CA, United States
- The Design Lab, University of California San Diego, La Jolla, CA, United States
| | - Kevin Patrick
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
- The Qualcomm Institute, University of California San Diego, La Jolla, CA, United States
| | - Camille Nebeker
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
- The Qualcomm Institute, University of California San Diego, La Jolla, CA, United States
- The Design Lab, University of California San Diego, La Jolla, CA, United States
| | - Job Godino
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
- The Qualcomm Institute, University of California San Diego, La Jolla, CA, United States
- Laura Rodriguez Research Institute, Family Health Centers of San Diego, San Diego, CA, United States
| | | | - Predrag Klasnja
- School of Information, University of Michigan, Ann Arbor, MI, United States
| | - Olga Perski
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
- Faculty of Social Sciences, Tampere University, Tampere, Finland
| | - Clare Viglione
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
| | - Aaron Coleman
- Small Steps Labs LLC dba Fitabase Inc, San Diego, CA, United States
| | - Eric Hekler
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, United States
- The Qualcomm Institute, University of California San Diego, La Jolla, CA, United States
- The Design Lab, University of California San Diego, La Jolla, CA, United States
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Mendorf S, Heimrich KG, Mühlhammer HM, Prell T, Schönenberg A. Trajectories of quality of life in people with diabetes mellitus: results from the survey of health, ageing and retirement in Europe. Front Psychol 2024; 14:1301530. [PMID: 38274698 PMCID: PMC10808439 DOI: 10.3389/fpsyg.2023.1301530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 12/29/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Previous longitudinal studies identified various factors predicting changes in Quality of Life (QoL) in people with diabetes mellitus (PwDM). However, in these studies, the stability of QoL has not been assessed with respect to individual differences. Methods We studied the predictive influence of variables on the development of QoL in PwDM across three waves (2013-2017) from the cross-national panel dataset Survey of Health, Ageing, and Retirement in Europe (SHARE). To determine clinically meaningful changes in QoL, we identified minimal clinically important difference (MCID). Linear regressions and Linear Mixed Models (LMM) were conducted to determine factors associated with changes in QoL. Results On average, QoL remained stable across three waves in 2989 PwDM, with a marginal difference only present between the first and last wave. However, when looking at individual trajectories, 19 different longitudinal patterns of QoL were identified across the three time-points, with 38.8% of participants showing stable QoL. Linear regression linked lower QoL to female gender, less education, loneliness, reduced memory function, physical inactivity, reduced health, depression, and mobility limitations. LMM showed that the random effect of ID had the strongest impact on QoL across the three waves, suggesting highly individual QoL patterns. Conclusion This study enhances the understanding of the stability of QoL measures, which are often used as primary endpoints in clinical research. We demonstrated that using traditional averaging methods, QoL appears stable on group level. However, our analysis indicated that QoL should be measured on an individual level.
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Affiliation(s)
- Sarah Mendorf
- Department of Neurology, University Hospital Jena, Jena, Germany
| | - Konstantin G. Heimrich
- Department of Neurology, University Hospital Jena, Jena, Germany
- Department of Geriatrics, University Hospital Jena, Jena, Germany
| | - Hannah M. Mühlhammer
- Department of Neurology, University Hospital Jena, Jena, Germany
- Department of Geriatrics, University Hospital Halle, Halle, Germany
| | - Tino Prell
- Department of Neurology, University Hospital Jena, Jena, Germany
- Department of Geriatrics, University Hospital Halle, Halle, Germany
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Heino MTJ, Proverbio D, Marchand G, Resnicow K, Hankonen N. Attractor landscapes: a unifying conceptual model for understanding behaviour change across scales of observation. Health Psychol Rev 2023; 17:655-672. [PMID: 36420691 PMCID: PMC10261543 DOI: 10.1080/17437199.2022.2146598] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022]
Abstract
Models and theories in behaviour change science are not in short supply, but they almost exclusively pertain to a particular facet of behaviour, such as automaticity or reasoned action, or to a single scale of observation such as individuals or communities. We present a highly generalisable conceptual model which is widely used in complex systems research from biology to physics, in an accessible form to behavioural scientists. The proposed model of attractor landscapes can be used to understand human behaviour change on different levels, from individuals to dyads, groups and societies. We use the model as a tool to present neglected ideas in contemporary behaviour change science, such as hysteresis and nonlinearity. The model of attractor landscapes can deepen understanding of well-known features of behaviour change (research), including short-livedness of intervention effects, problematicity of focusing on behavioural initiation while neglecting behavioural maintenance, continuum and stage models of behaviour change understood within a single accommodating framework, and the concept of resilience. We also demonstrate potential methods of analysis and outline avenues for future research.
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Affiliation(s)
| | | | | | - Kenneth Resnicow
- School of Public Health, University of Michigan. Rogel Cancer Center University of Michigan
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Charlton JM, Xia H, Shull PB, Eng JJ, Li LC, Hunt MA. Multi-day monitoring of foot progression angles during unsupervised, real-world walking in people with and without knee osteoarthritis. Clin Biomech (Bristol, Avon) 2023; 105:105957. [PMID: 37084548 DOI: 10.1016/j.clinbiomech.2023.105957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 03/03/2023] [Accepted: 04/13/2023] [Indexed: 04/23/2023]
Abstract
BACKGROUND Foot progression angle is a biomechanical target in gait modification interventions for knee osteoarthritis. To date, it has only been evaluated within laboratory settings. METHODS Adults with symptomatic knee osteoarthritis (n = 30) and healthy adults (n = 15) completed two conditions: 1) treadmill walking in the laboratory (5-min), and 2) real-world walking outside of the laboratory (1-week). Foot progression angle was estimated via shoe-embedded inertial sensing. We calculated the foot progression angle magnitude (median) and variability (interquartile range, coefficient of variation), and used mixed models to compare outcomes between the conditions, participant groups, and disease severities. Reliability was quantified by the intraclass correlation coefficient, standardized error of the measurement, and the minimum detectable change. FINDINGS Foot progression angle magnitude did not differ between groups or conditions but variability significantly higher in real-world walking (P < 0.001). Structural and symptomatic severity were unrelated to FPA in either walking condition, except for real-world coefficient of variation which was higher for moderate-severe structural osteoarthritis compared to the treadmill for those with mild structural severity (P < 0.034). All real-world outcomes showed excellent reliability including intraclass correlation coefficients above 0.95. The participants recorded a mean (standard deviation) of 298 (33) and 10,447 (5232) steps in the laboratory and real-world walking conditions, respectively. INTERPRETATION This study provides the first characterization of foot progression angles during real-world walking in people with and without symptomatic knee osteoarthritis. These results indicate that foot progression angles can be feasibly and reliably measured in unsupervised real-world walking conditions.
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Affiliation(s)
- Jesse M Charlton
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Graduate Programs in Rehabilitation Sciences, University of British Columbia, Vancouver, Canada; Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, Canada; Centre for Aging SMART at Vancouver Coastal Health, Vancouver, Canada.
| | - Haisheng Xia
- Department of Automation, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China
| | - Peter B Shull
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Janice J Eng
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Centre for Aging SMART at Vancouver Coastal Health, Vancouver, Canada
| | - Linda C Li
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Arthritis Research Canada, Vancouver, Canada
| | - Michael A Hunt
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada; Motion Analysis and Biofeedback Laboratory, University of British Columbia, Vancouver, Canada; Centre for Aging SMART at Vancouver Coastal Health, Vancouver, Canada
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Baretta D, Koch S, Cobo I, Castaño-Vinyals G, de Cid R, Carreras A, Buekers J, Garcia-Aymerich J, Inauen J, Chevance G. Resilience characterized and quantified from physical activity data: A tutorial in R. PSYCHOLOGY OF SPORT AND EXERCISE 2023; 65:102361. [PMID: 37665834 DOI: 10.1016/j.psychsport.2022.102361] [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: 06/15/2022] [Revised: 10/25/2022] [Accepted: 12/05/2022] [Indexed: 09/06/2023]
Abstract
Consistent physical activity is key for health and well-being, but it is vulnerable to stressors. The process of recovering from such stressors and bouncing back to the previous state of physical activity can be referred to as resilience. Quantifying resilience is fundamental to assess and manage the impact of stressors on consistent physical activity. In this tutorial, we present a method to quantify the resilience process from physical activity data. We leverage the prior operationalization of resilience, as used in various psychological domains, as area under the curve and expand it to suit the characteristics of physical activity time series. As use case to illustrate the methodology, we quantified resilience in step count time series (length = 366 observations) for eight participants following the first COVID-19 lockdown as a stressor. Steps were assessed daily using wrist-worn devices. The methodology is implemented in R and all coding details are included. For each person's time series, we fitted multiple growth models and identified the best one using the Root Mean Squared Error (RMSE). Then, we used the predicted values from the selected model to identify the point in time when the participant recovered from the stressor and quantified the resulting area under the curve as a measure of resilience for step count. Further resilience features were extracted to capture the different aspects of the process. By developing a methodological guide with a step-by-step implementation, we aimed at fostering increased awareness about the concept of resilience for physical activity and facilitate the implementation of related research.
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Affiliation(s)
- Dario Baretta
- Institute of Psychology, University of Bern, Fabrikstrasse 8, 3012, Bern, Switzerland.
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), C/ Doctor Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002, Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), C/ Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Inés Cobo
- Barcelona Institute for Global Health (ISGlobal), C/ Doctor Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002, Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), C/ Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Gemma Castaño-Vinyals
- Barcelona Institute for Global Health (ISGlobal), C/ Doctor Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002, Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), C/ Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Rafael de Cid
- Genomes for Life-GCAT Lab, Germans Trias i Pujol Research Institute (IGTP), Camí de Les Escoles s/n, 08916, Badalona, Barcelona, Spain
| | - Anna Carreras
- Genomes for Life-GCAT Lab, Germans Trias i Pujol Research Institute (IGTP), Camí de Les Escoles s/n, 08916, Badalona, Barcelona, Spain
| | - Joren Buekers
- Barcelona Institute for Global Health (ISGlobal), C/ Doctor Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002, Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), C/ Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health (ISGlobal), C/ Doctor Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002, Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), C/ Monforte de Lemos 3-5, 28029, Madrid, Spain
| | - Jennifer Inauen
- Institute of Psychology, University of Bern, Fabrikstrasse 8, 3012, Bern, Switzerland
| | - Guillaume Chevance
- Barcelona Institute for Global Health (ISGlobal), C/ Doctor Aiguader, 88, 08003, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Plaça de la Mercè, 10-12, 08002, Barcelona, Spain; CIBER de Epidemiología y Salud Pública (CIBERESP), C/ Monforte de Lemos 3-5, 28029, Madrid, Spain
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Bittel KM, O'Briant KY, Ragaglia RM, Buseth L, Murtha C, Yu J, Stanely JM, Hudgins BL, Hevel DJ, Maher JP. Associations Between Social Cognitive Determinants and Movement-Related Behaviors in Studies using Ecological Momentary Assessment Methods: A Systematic Review (Preprint). JMIR Mhealth Uhealth 2022; 11:e44104. [PMID: 37027185 PMCID: PMC10131703 DOI: 10.2196/44104] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/26/2023] [Accepted: 02/27/2023] [Indexed: 03/02/2023] Open
Abstract
BACKGROUND The social cognitive framework is a long-standing framework within physical activity promotion literature to explain and predict movement-related behaviors. However, applications of the social cognitive framework to explain and predict movement-related behaviors have typically examined the relationships between determinants and behavior across macrotimescales (eg, weeks and months). There is more recent evidence suggesting that movement-related behaviors and their social cognitive determinants (eg, self-efficacy and intentions) change across microtimescales (eg, hours and days). Therefore, efforts have been devoted to examining the relationship between social cognitive determinants and movement-related behaviors across microtimescales. Ecological momentary assessment (EMA) is a growing methodology that can capture movement-related behaviors and social cognitive determinants as they change across microtimescales. OBJECTIVE The objective of this systematic review was to summarize evidence from EMA studies examining associations between social cognitive determinants and movement-related behaviors (ie, physical activity and sedentary behavior). METHODS Studies were included if they quantitatively tested such an association at the momentary or day level and excluded if they were an active intervention. Using keyword searches, articles were identified across the PubMed, SPORTDiscus, and PsycINFO databases. Articles were first assessed through abstract and title screening followed by full-text review. Each article was screened independently by 2 reviewers. For eligible articles, data regarding study design, associations between social cognitive determinants and movement-related behaviors, and study quality (ie, Methodological Quality Questionnaire and Checklist for Reporting Ecological Momentary Assessment Studies) were extracted. At least 4 articles were required to draw a conclusion regarding the overall associations between a social cognitive determinant and movement-related behavior. For the social cognitive determinants in which a conclusion regarding an overall association could be drawn, 60% of the articles needed to document a similar association (ie, positive, negative, or null) to conclude that the association existed in a particular direction. RESULTS A total of 24 articles including 1891 participants were eligible for the review. At the day level, intentions and self-efficacy were positively associated with physical activity. No other associations could be determined because of conflicting findings or the small number of studies investigating associations. CONCLUSIONS Future research would benefit from validating EMA assessments of social cognitive determinants and systematically investigating associations across different operationalizations of key constructs. Despite the only recent emergence of EMA to understand social cognitive determinants of movement-related behaviors, the findings indicate that daily intentions and self-efficacy play an important role in regulating physical activity in everyday life. TRIAL REGISTRATION PROSPERO CRD42022328500; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=328500.
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Affiliation(s)
- Kelsey M Bittel
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Kate Y O'Briant
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Rena M Ragaglia
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Lake Buseth
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Courtney Murtha
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Jessica Yu
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Jennifer M Stanely
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Brynn L Hudgins
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Derek J Hevel
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
| | - Jaclyn P Maher
- Department of Kinesiology, University Of North Carolina Greensboro, Greensboro, NC, United States
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Hojjatinia S, Lee AM, Hojjatinia S, Lagoa CM, Brunke-Reese D, Conroy DE. Physical Activity Dynamics During a Digital Messaging Intervention Changed After the Pandemic Declaration. Ann Behav Med 2022; 56:1188-1198. [PMID: 35972330 PMCID: PMC9384787 DOI: 10.1093/abm/kaac051] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic adversely impacted physical activity, but little is known about how contextual changes following the pandemic declaration impacted either the dynamics of people's physical activity or their responses to micro-interventions for promoting physical activity. PURPOSE This paper explored the effect of the COVID-19 pandemic on the dynamics of physical activity responses to digital message interventions. METHODS Insufficiently-active young adults (18-29 years; N = 22) were recruited from November 2019 to January 2020 and wore a Fitbit smartwatch for 6 months. They received 0-6 messages/day via smartphone app notifications, timed and selected at random from three content libraries (Move More, Sit Less, and Inspirational Quotes). System identification techniques from control systems engineering were used to identify person-specific dynamical models of physical activity in response to messages before and after the pandemic declaration on March 13, 2020. RESULTS Daily step counts decreased significantly following the pandemic declaration on weekdays (Cohen's d = -1.40) but not on weekends (d = -0.26). The mean overall speed of the response describing physical activity (dominant pole magnitude) did not change significantly on either weekdays (d = -0.18) or weekends (d = -0.21). In contrast, there was limited rank-order consistency in specific features of intervention responses from before to after the pandemic declaration. CONCLUSIONS Generalizing models of behavioral dynamics across dramatically different environmental contexts (and participants) may lead to flawed decision rules for just-in-time physical activity interventions. Periodic model-based adaptations to person-specific decision rules (i.e., continuous tuning interventions) for digital messages are recommended when contexts change.
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Affiliation(s)
- Sahar Hojjatinia
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, USA
| | - Alexandra M Lee
- Department of Kinesiology, The Pennsylvania State University, University Park, PA, USA
| | | | - Constantino M Lagoa
- School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, USA
| | - Deborah Brunke-Reese
- Department of Kinesiology, The Pennsylvania State University, University Park, PA, USA
| | - David E Conroy
- Department of Kinesiology, The Pennsylvania State University, University Park, PA, USA
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
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10
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Thivel D, Corteval A, Favreau JM, Bergeret E, Samalin L, Costes F, Toumani F, Dualé C, Pereira B, Eschalier A, Fearnbach N, Duclos M, Tournadre A. Fine Detection of Human Motion During Activities of Daily Living as a Clinical Indicator for the Detection and Early Treatment of Chronic Diseases: The E-Mob Project. J Med Internet Res 2022; 24:e32362. [PMID: 35029537 PMCID: PMC8800083 DOI: 10.2196/32362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 10/24/2021] [Accepted: 10/29/2021] [Indexed: 12/16/2022] Open
Abstract
Methods to measure physical activity and sedentary behaviors typically quantify the amount of time devoted to these activities. Among patients with chronic diseases, these methods can provide interesting behavioral information, but generally do not capture detailed body motion and fine movement behaviors. Fine detection of motion may provide additional information about functional decline that is of clinical interest in chronic diseases. This perspective paper highlights the need for more developed and sophisticated tools to better identify and track the decomposition, structuration, and sequencing of the daily movements of humans. The primary goal is to provide a reliable and useful clinical diagnostic and predictive indicator of the stage and evolution of chronic diseases, in order to prevent related comorbidities and complications among patients.
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Affiliation(s)
| | | | | | | | - Ludovic Samalin
- Clermont Ferrand University Hospital, Clermont-Ferrand, France
| | - Frédéric Costes
- Clermont Ferrand University Hospital, Clermont-Ferrand, France
| | | | - Christian Dualé
- Clermont Ferrand University Hospital, Clermont-Ferrand, France
| | - Bruno Pereira
- Clermont Ferrand University Hospital, Clermont-Ferrand, France
| | - Alain Eschalier
- Clermont Ferrand University Hospital, Clermont-Ferrand, France
| | - Nicole Fearnbach
- Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Martine Duclos
- Clermont Ferrand University Hospital, Clermont-Ferrand, France
| | - Anne Tournadre
- Clermont Ferrand University Hospital, Clermont-Ferrand, France
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11
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Heino MTJ, Knittle K, Noone C, Hasselman F, Hankonen N. Studying Behaviour Change Mechanisms under Complexity. Behav Sci (Basel) 2021; 11:77. [PMID: 34068961 PMCID: PMC8156531 DOI: 10.3390/bs11050077] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/22/2021] [Accepted: 04/28/2021] [Indexed: 01/13/2023] Open
Abstract
Understanding the mechanisms underlying the effects of behaviour change interventions is vital for accumulating valid scientific evidence, and useful to informing practice and policy-making across multiple domains. Traditional approaches to such evaluations have applied study designs and statistical models, which implicitly assume that change is linear, constant and caused by independent influences on behaviour (such as behaviour change techniques). This article illustrates limitations of these standard tools, and considers the benefits of adopting a complex adaptive systems approach to behaviour change research. It (1) outlines the complexity of behaviours and behaviour change interventions; (2) introduces readers to some key features of complex systems and how these relate to human behaviour change; and (3) provides suggestions for how researchers can better account for implications of complexity in analysing change mechanisms. We focus on three common features of complex systems (i.e., interconnectedness, non-ergodicity and non-linearity), and introduce Recurrence Analysis, a method for non-linear time series analysis which is able to quantify complex dynamics. The supplemental website provides exemplifying code and data for practical analysis applications. The complex adaptive systems approach can complement traditional investigations by opening up novel avenues for understanding and theorising about the dynamics of behaviour change.
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Affiliation(s)
- Matti T. J. Heino
- Faculty of Social Sciences, University of Helsinki, P.O. Box 54, 00014 Helsinki, Finland; (M.T.J.H.); (K.K.)
| | - Keegan Knittle
- Faculty of Social Sciences, University of Helsinki, P.O. Box 54, 00014 Helsinki, Finland; (M.T.J.H.); (K.K.)
| | - Chris Noone
- School of Psychology, National University of Ireland, H91 TK33 Galway, Ireland;
| | - Fred Hasselman
- Behavioural Science Institute, Radboud University Nijmegen, Postbus 9104, 500 HE Nijmegen, The Netherlands;
| | - Nelli Hankonen
- Faculty of Social Sciences, University of Helsinki, P.O. Box 54, 00014 Helsinki, Finland; (M.T.J.H.); (K.K.)
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