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Squires H, Kelly MP, Gilbert N, Sniehotta F, Purshouse RC, Garcia L, Breeze P, Brennan A, Gardner B, Bright S, Fischer A, Heppenstall A, Wetton JD, Hernandez-Alava M, Boyd J, Buckley C, Vlaev I, Smith R, Abbas A, Gibb R, Henney M, Moore E, Chater AM. The PHEM-B toolbox of methods for incorporating the influences on Behaviour into Public Health Economic Models. BMC Public Health 2024; 24:2794. [PMID: 39395958 PMCID: PMC11475213 DOI: 10.1186/s12889-024-20225-1] [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: 10/20/2023] [Accepted: 09/30/2024] [Indexed: 10/14/2024] Open
Abstract
BACKGROUND It is challenging to predict long-term outcomes of interventions without understanding how they work. Health economic models of public health interventions often do not incorporate the many determinants of individual and population behaviours that influence long term effectiveness. The aim of this paper is to draw on psychology, sociology, behavioural economics, complexity science and health economics to: (a) develop a toolbox of methods for incorporating the influences on behaviour into public health economic models (PHEM-B); and (b) set out a research agenda for health economic modellers and behavioural/ social scientists to further advance methods to better inform public health policy decisions. METHODS A core multidisciplinary group developed a preliminary toolbox from a published review of the literature and tested this conceptually using a case study of a diabetes prevention simulation. The core group was augmented by a much wider group that covered a broader range of multidisciplinary expertise. We used a consensus method to gain agreement of the PHEM-B toolbox. This included a one-day workshop and subsequent reviews of the toolbox. RESULTS The PHEM-B toolbox sets out 12 methods which can be used in different combinations to incorporate influences on behaviours into public health economic models: collaborations between modellers and behavioural scientists, literature reviewing, application of the Behaviour Change Intervention Ontology, systems mapping, agent-based modelling, differential equation modelling, social network analysis, geographical information systems, discrete event simulation, theory-informed statistical and econometric analyses, expert elicitation, and qualitative research/process tracing. For each method, we provide a description with key references, an expert consensus on the circumstances when they could be used, and the resources required. CONCLUSIONS This is the first attempt to rigorously and coherently propose methods to incorporate the influences on behaviour into health economic models of public health interventions. It may not always be feasible or necessary to model the influences on behaviour explicitly, but it is essential to develop an understanding of the key influences. Changing behaviour and maintaining that behaviour change could have different influences; thus, there could be benefits in modelling these separately. Future research is needed to develop, collaboratively with behavioural scientists, a suite of more robust health economic models of health-related behaviours, reported transparently, including coding, which would allow model reuse and adaptation.
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Affiliation(s)
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- University of Sheffield, Sheffield, UK
- Dark Peak Analytics, Sheffield, UK
| | - Ali Abbas
- University of Cambridge, Cambridge, UK
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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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Safdar MF, Nowak RM, Pałka P. Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review. Comput Biol Med 2024; 170:107908. [PMID: 38217973 DOI: 10.1016/j.compbiomed.2023.107908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/19/2023] [Accepted: 12/24/2023] [Indexed: 01/15/2024]
Abstract
Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012-22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling. The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%-83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%-95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.
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Affiliation(s)
- Muhammad Farhan Safdar
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland.
| | - Robert Marek Nowak
- Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
| | - Piotr Pałka
- Institute of Control and Computation Engineering, Faculty of Electronics and Information Technology, Warsaw University of Technology, 00-665 Warsaw, Poland
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Mudd AL, Bal M, Verra SE, Poelman MP, de Wit J, Kamphuis CBM. The current state of complex systems research on socioeconomic inequalities in health and health behavior-a systematic scoping review. Int J Behav Nutr Phys Act 2024; 21:13. [PMID: 38317165 PMCID: PMC10845451 DOI: 10.1186/s12966-024-01562-1] [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: 09/13/2023] [Accepted: 01/14/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Interest in applying a complex systems approach to understanding socioeconomic inequalities in health is growing, but an overview of existing research on this topic is lacking. In this systematic scoping review, we summarize the current state of the literature, identify shared drivers of multiple health and health behavior outcomes, and highlight areas ripe for future research. METHODS SCOPUS, Web of Science, and PubMed databases were searched in April 2023 for peer-reviewed, English-language studies in high-income OECD countries containing a conceptual systems model or simulation model of socioeconomic inequalities in health or health behavior in the adult general population. Two independent reviewers screened abstracts and full texts. Data on study aim, type of model, all model elements, and all relationships were extracted. Model elements were categorized based on the Commission on Social Determinants of Health framework, and relationships between grouped elements were visualized in a summary conceptual systems map. RESULTS A total of 42 publications were included; 18 only contained a simulation model, 20 only contained a conceptual model, and 4 contained both types of models. General health outcomes (e.g., health status, well-being) were modeled more often than specific outcomes like obesity. Dietary behavior and physical activity were by far the most commonly modeled health behaviors. Intermediary determinants of health (e.g., material circumstances, social cohesion) were included in nearly all models, whereas structural determinants (e.g., policies, societal values) were included in about a third of models. Using the summary conceptual systems map, we identified 15 shared drivers of socioeconomic inequalities in multiple health and health behavior outcomes. CONCLUSIONS The interconnectedness of socioeconomic position, multiple health and health behavior outcomes, and determinants of socioeconomic inequalities in health is clear from this review. Factors central to the complex system as it is currently understood in the literature (e.g., financial strain) may be both efficient and effective policy levers, and factors less well represented in the literature (e.g., sleep, structural determinants) may warrant more research. Our systematic, comprehensive synthesis of the literature may serve as a basis for, among other things, a complex systems framework for socioeconomic inequalities in health.
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Affiliation(s)
- Andrea L Mudd
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands.
| | - Michèlle Bal
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
| | - Sanne E Verra
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
| | - Maartje P Poelman
- Chair Group Consumption and Healthy Lifestyles, Wageningen University & Research, Hollandseweg 1, 6706 KN, Wageningen, the Netherlands
| | - John de Wit
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
| | - Carlijn B M Kamphuis
- Department of Interdisciplinary Social Science- Public Health, Utrecht University, PO Box 80140, 3508 TC, Utrecht, The Netherlands
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