A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity

Ahmadi, Asghar; Noetel, Michael; Schellekens, Melissa; Parker, Philip D.; Antczak, Devan; Beauchamp, Mark R.; Dicke, Theresa; Diezmann, Carmel M.; Maeder, Anthony; Ntoumanis, Nikos; Yeung, Alexander Seeshing; Lonsdale, Chris

doi:10.5093/pi2021a4

Cited by 9 publications

(4 citation statements)

References 115 publications

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“…There is no doubt that longitudinal studies are required to capture the ecosystem process of adaptation. Machine learning or AI can therefore be incorporated into both PBi and IBi evaluations of interventions [ 9 , 41 , 42 , 43 ]. The same applies to Network Analyses that examine dynamic relationships among stakeholders over time [ 44 , 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Planning Individual and Population-Based Interventions in Global Health: Applying the DEA-A Framework to Promote Behavioral, Emotional, and/or Cognitive Change among Stakeholders

Broc,

Fassier,

Raffard

et al. 2024

IJERPH

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Addressing health challenges that impact human well-being requires a comprehensive, interdisciplinary approach that would be at the crossroad of population-based prevention and individual-level clinical care, which is in line with a Global Health perspective. In the absence of a unifying theoretical framework to guide such interventions, a Dynamic Ecosystem Adaptation through the Allostasis (DEA-A) framework has been proposed, emphasizing the functional adaptation of individuals and organizations in symbiosis with their living ecosystem. While a conceptual model has been presented, this methodological contribution aims at illustrating the practical application of the DEA-A framework for planning Global Health interventions. The methodology combines Intervention Mapping and Cognitive and Behavioral Theory, extended to the ecosystem. Practical guidelines and supporting tools are provided to help public health providers and clinicians in establishing a functional ecosystem diagnosis of the issue; defining not only behavioral, but also emotional and cognitive change objectives (allostasis targets) expected for each stakeholder; and designing intervention plans targeting determinants of these allostasis. The discussion addresses implementation and evaluation perspectives of interventions based on the DEA-A framework, emphasizing the importance of considering change in its processual and ecosystem complexity. Lastly, encouragements for a deeper understanding of individual and ecosystem homeostasis/allostasis processes are made in order to promote more functional interventions.

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Section: Discussionmentioning

confidence: 99%

Planning Individual and Population-Based Interventions in Global Health: Applying the DEA-A Framework to Promote Behavioral, Emotional, and/or Cognitive Change among Stakeholders

Broc,

Fassier,

Raffard

et al. 2024

IJERPH

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“…Several methodological steps have been taken recent years to reduce these biases and improve inferences from element-level reviews of multielement interventions (e.g., Engell et al, 2020;Furukawa et al, 2021;Solheim-Kvamme et al, 2022), and the precision will continue to improve with more use of reporting standards, developments in fidelity and process measurements, data availability, and advanced statistical analyses (Engell, 2021). For instance, computational linguistics and NLP will likely help improve the precision of fidelity measures (e.g., Flemotomos et al, 2021;Gallo et al, 2015;Imel et al, 2019), albeit there are challenges to overcome before such systems are in widespread use (Ahmadi et al, 2021). Nevertheless, highly common elements of effective interventions and implementations may best be described as evidence-informed, as they are derived from empirically tested interventions across contexts (i.e., informed by them), but not necessarily tested in isolation.…”

Section: Limitationsmentioning

confidence: 99%

Common Elements Approaches to Implementation Research and Practice: Methods and Integration with Intervention Science

Engell

Stadnick²,

Aarons³

et al. 2023

Glob Implement Res Appl

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We propose that common elements approaches can advance implementation research and practice and facilitate pragmatic use of intervention and implementation evidence. Common elements are practices or processes frequently shared by interventions or implementations. Traditional common elements methodologies use synthesis, distillation, and statistics to describe and evaluate the merit of common ingredients in effective interventions. Recent developments include identifying and testing common configurations of elements, processes, and context variables across the literature of effective interventions and implementations. While common elements thinking has grown popular in intervention science, it has rarely been utilized in implementation science, and specifically, combined with the intervention literature. The goals of this conceptual methodology paper are to (1) provide an overview of the common elements concept and how it may advance implementation research and usability for practice, (2) give a step-by-step guide to systematic common elements reviews that synthesizes and distills the intervention and implementation literature together, and (3) offer recommendations for advancing element-level evidence in implementation science. A narrative review of the common elements literature was conducted with attention to applications to implementation research. A six-step guide to using an advanced common elements methodology was provided. Examples of potential results are presented, along with a review of the implications for implementation research and practice. Finally, we reviewed methodological limitations in current common elements approaches, and identified steps towards realizing their potential. Common elements methodologies can (a) synthesize and distill the implementation science literature into practical applications, (b) generate evidence-informed hypotheses about key elements and determinants in implementation and intervention processes and mechanisms, and (c) promote evidence-informed precision tailoring of intervention and implementation to context. To realize this potential, common elements approaches need improved reporting of details from both successful and unsuccessful intervention and implementation research, more data availability, and more testing and investigation of causal processes and mechanisms of change from diverse theories.

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“…Further, ML may be a cost-effective tool to assess treatment fidelity with rapid, individualised, and objective feedback. A recent systematic review found that ML performed better than chance and, in some instances, at near human-level performance when predicting fidelity for psychological treatments from verbal recordings of treatment sessions [ 71 ]. More research is needed to explore the vast utility of ML in treatment settings for eating disorders.…”

Section: Implications For Early Intervention and Treatmentmentioning

confidence: 99%

Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions

2022

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Advances in machine learning and digital data provide vast potential for mental health predictions. However, research using machine learning in the field of eating disorders is just beginning to emerge. This paper provides a narrative review of existing research and explores potential benefits, limitations, and ethical considerations of using machine learning to aid in the detection, prevention, and treatment of eating disorders. Current research primarily uses machine learning to predict eating disorder status from females’ responses to validated surveys, social media posts, or neuroimaging data often with relatively high levels of accuracy. This early work provides evidence for the potential of machine learning to improve current eating disorder screening methods. However, the ability of these algorithms to generalise to other samples or be used on a mass scale is only beginning to be explored. One key benefit of machine learning over traditional statistical methods is the ability of machine learning to simultaneously examine large numbers (100s to 1000s) of multimodal predictors and their complex non-linear interactions, but few studies have explored this potential in the field of eating disorders. Machine learning is also being used to develop chatbots to provide psychoeducation and coping skills training around body image and eating disorders, with implications for early intervention. The use of machine learning to personalise treatment options, provide ecological momentary interventions, and aid the work of clinicians is also discussed. Machine learning provides vast potential for the accurate, rapid, and cost-effective detection, prevention, and treatment of eating disorders. More research is needed with large samples of diverse participants to ensure that machine learning models are accurate, unbiased, and generalisable to all people with eating disorders. There are important limitations and ethical considerations with utilising machine learning methods in practice. Thus, rather than a magical solution, machine learning should be seen as an important tool to aid the work of researchers, and eventually clinicians, in the early identification, prevention, and treatment of eating disorders.

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A Systematic Review of Machine Learning for Assessment and Feedback of Treatment Fidelity

Cited by 9 publications

References 115 publications

Planning Individual and Population-Based Interventions in Global Health: Applying the DEA-A Framework to Promote Behavioral, Emotional, and/or Cognitive Change among Stakeholders

Planning Individual and Population-Based Interventions in Global Health: Applying the DEA-A Framework to Promote Behavioral, Emotional, and/or Cognitive Change among Stakeholders

Common Elements Approaches to Implementation Research and Practice: Methods and Integration with Intervention Science

Potential benefits and limitations of machine learning in the field of eating disorders: current research and future directions

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