scholarly journals Adapted motivational interviewing for brief healthcare consultations: protocol for a systematic review and meta-analysis of treatment fidelity in real-world evaluations of behaviour change counselling

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028417 ◽  
Author(s):  
Alison K Beck ◽  
Erin Forbes ◽  
Amanda L Baker ◽  
Ben Britton ◽  
Christopher Oldmeadow ◽  
...  
Keyword(s):  
Behaviour Change ◽  
Health Behaviour ◽  
Real World ◽  
Treatment Effects ◽  
Random Effect ◽  
Treatment Fidelity ◽  
Systematic Evaluation ◽  
Complex Behaviour ◽  
Behaviour Change Counselling ◽  
The Impact

IntroductionTreatment fidelity is an important and often neglected component of complex behaviour change research. It is central to understanding treatment effects, especially for evaluations conducted outside of highly controlled research settings. Ensuring that promising interventions can be delivered adequately (ie, with fidelity) by real-world clinicians within real-world settings is an essential step in developing interventions that are both effective and ‘implementable’. Whether this is the case for behaviour change counselling, a complex intervention developed specifically for maximising the effectiveness of real-world consultations about health behaviour change, remains unclear. To improve our understanding of treatment effects, best practice guidelines recommend the use of strategies to enhance, monitor and evaluate what clinicians deliver during patient consultations. There has yet to be a systematic evaluation of whether and how these recommendations have been employed within evaluations of behaviour change counselling, nor the impact on patient health behaviour and/or outcome. We seek to address this gap.Methods and analysisMethods are informed by published guidelines. Ten electronic databases (Medline, PubMed, EMBASE, PsycINFO, CINAHL Complete, ScienceDirect, Taylor and Francis; Wiley, ProQuest and Open Grey) will be searched for published and unpublished articles that evaluate behaviour change counselling within real-world clinical settings (randomised and non-randomised). Eligible papers will be rated against the National Institute of Health fidelity framework. A synthesis, evaluation and critical overview of fidelity practices will be reported and linear regression used to explore change across time. Random-effect meta-regression is planned to explore whether fidelity (outcomes reported and methods used) is associated with the impact of behaviour change counselling. Standardised effect sizes will be calculated using Hedges’ g (continuous outcomes) and ORs (binary/dichotomous outcomes).Ethics and disseminationNo ethical issues are foreseen. Findings will be disseminated via journal publication and conference presentation(s).PROSPERO registration numberCRD42019131169

scholarly journals Assessing Adherence, Competence and Differentiation in a Stepped-Wedge Randomised Clinical Trial of a Complex Behaviour Change Intervention

Nutrients ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2332
Author(s):  
Alison Kate Beck ◽  
Amanda L. Baker ◽  
Gregory Carter ◽  
Chris Wratten ◽  
Judith Bauer ◽  
...  
Keyword(s):  
Behaviour Change ◽  
Real World ◽  
Best Practice ◽  
Research Question ◽  
Randomised Clinical Trial ◽  
Routine Care ◽  
Stepped Wedge ◽  
Behaviour Change Intervention ◽  
Complex Behaviour ◽  
Behaviour Change Counselling

Background: A key challenge in behavioural medicine is developing interventions that can be delivered adequately (i.e., with fidelity) within real-world consultations. Accordingly, clinical trials should (but tend not to) report what is actually delivered (adherence), how well (competence) and the distinction between intervention and comparator conditions (differentiation). Purpose: To address this important clinical and research priority, we apply best practice guidelines to evaluate fidelity within a real-world, stepped-wedge evaluation of “EAT: Eating As Treatment”, a new dietitian delivered health behaviour change intervention designed to reduce malnutrition in head and neck cancer (HNC) patients undergoing radiotherapy. Methods: Dietitians (n = 18) from five Australian hospitals delivered a period of routine care and following a randomly determined order each site received training and began delivering the EAT Intervention. A 20% random stratified sample of audio-recorded consultations (control n = 196; intervention n = 194) was coded by trained, independent, raters using a study specific checklist and the Behaviour Change Counselling Inventory. Intervention adherence and competence were examined relative to apriori benchmarks. Differentiation was examined by comparing control and intervention sessions (adherence, competence, non-specific factors, and dose), via multiple linear regression, logistic regression, or mixed-models. Results: Achievement of adherence benchmarks varied. The majority of sessions attained competence. Post-training consultations were clearly distinct from routine care regarding motivational and behavioural, but not generic, skills. Conclusions: Although what level of fidelity is “good enough” remains an important research question, findings support the real-world feasibility of integrating EAT into dietetic consultations with HNC patients and provide a foundation for interpreting treatment effects.

scholarly journals The impact of nature-based interventions on public health: a review using pathways, mechanisms and behaviour change techniques from environmental social science and health behaviour change

2021 ◽  
Vol 9s7 ◽  
pp. 33-61
Author(s):  
Stephanie Wilkie ◽  
Nicola Davinson
Keyword(s):  
Public Health ◽  
Social Science ◽  
Health Outcomes ◽  
Behaviour Change ◽  
Health Behaviour ◽  
Behaviour Change Techniques ◽  
Change Models ◽  
Health Behaviour Change ◽  
The Impact ◽  
Environmental Social Science

The aim of this narrative review is to explore whether nature-based interventions improved individual public health outcomes and health behaviours, using a conceptual framework that included pathways and pathway domains, mechanisms, and behaviour change techniques derived from environmental social science theory and health behaviour change models. A two-stage scoping methodology was used to identified studies published between 2000 and 2021. Peer reviewed, English-language reports of nature-based interventions with adults (N = 9) were included if the study met the definition of a health�behaviour change intervention and reported at least one measured physical/mental health outcome. Interventions focused on the restoring or building capacities pathway domains as part of the nature contact/experience pathway; varied health behaviour change mechanisms and techniques were present but environmental social-science-derived mechanisms to influence health outcomes were used less. Practical recommendations for future interventions include explicit statement of the targeted level of causation, as well as utilisation of both environmental social science and health behaviour change theories and varied public health outcomes to allow simultaneously testing of theoretical predictions.

scholarly journals Training physicians in motivational communication to improve behaviour change counselling competency: A behavioural medicine approach to NCD prevention and management

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
K Lavoie
Keyword(s):  
Behaviour Change ◽  
Skill Acquisition ◽  
Assessment Tool ◽  
Health Behaviours ◽  
Evidence Based ◽  
Training Dose ◽  
Face To Face ◽  
Behaviour Change Counselling ◽  
Communication Competencies ◽  
The Impact

Abstract Background Despite the importance of changing health behaviours in the context of preventing and managing non-communicable chronic diseases (NCD's), physician use of evidence-based behaviour change counselling (BCC) is low, and BCC skills competency is generally poor. Motivational communication (MC) is a patient-centred, evidence-based BCC approach used by healthcare providers, designed to increase patient motivation to adopt a healthy lifestyle. MC-based approaches improved a range of health behaviours (smoking, diet, physical activity) in patients with NCDs, leading to increased demand for physician training. Despite the widespread dissemination of training programs, data on their efficacy in achieving competency among physicians is limited. This is likely due to a lack of consensus on the core communication competencies to be achieved, and in the absence of acceptable, valid and reliable tools to measure skill acquisition. Results Using an integrated knowledge translation (iKT) approach that engaged 199 international physicians, behaviour change experts and health administrators, we have identified 11 core evidence-based communication competencies that physicians should acquire in the context of NCD prevention/management. They have been incorporated into a basic 4 hr face-to-face MC training program called “LEARN THE BASICs”. To assess MC competency, we have also developed a reliable, engaging, efficient, 'user-friendly' case-based digital assessment tool called the MC-Competency Assessment Test (MC-CAT). Conclusions Strategies for optimizing and tailoring this program, including finding the most cost-effective training dose, the impact of supplemental training components (e.g., in person vs. digital coaching; booster sessions), and delivery modes (e.g., face-to-face vs digital/online), will be discussed in the context of optimizing implementation success.

scholarly journals The impact of Healthy Conversation Skills training on health professionals’ barriers to having behaviour change conversations: a pre-post survey using the Theoretical Domains Framework

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jenna L. Hollis ◽  
Lucy Kocanda ◽  
Kirsty Seward ◽  
Clare Collins ◽  
Belinda Tully ◽  
...  
Keyword(s):  
Behaviour Change ◽  
Health Behaviour ◽  
Health Professionals ◽  
Theoretical Domains Framework ◽  
Clinical Settings ◽  
Conversation Skills ◽  
Health Behaviour Change ◽  
The Impact

Abstract Background Changing people’s behaviour by giving advice and instruction, as traditionally provided in healthcare consultations, is usually ineffective. Healthy Conversation Skills (HCS) training enhances health professionals’ communication skills and ability to empower and motivate people in health behaviour change. Guided by the Theoretical Domains Framework (TDF), this study examined the impact of HCS training on health professional barriers to conducting behaviour change conversations in both clinical and non-clinical settings. Secondary aims were to i) identify health professionals’ barriers to having behaviour change conversations, and explore the ii) effect of HCS training on health professionals’ competence and attitudes to adopting HCS, iii) feasibility, acceptability and appropriateness of using HCS in their clinical and non-clinical roles, and iv) acceptability and quality of HCS training. Methods HCS training was conducted in October-November 2019 and February 2020. Pre-training (T1), post-training (T2) and follow-up (T3; 6-10 weeks post-training) surveys collected data on demographics and changes in competence, confidence, importance and usefulness (10-point Likert scale, where 10 = highest score) of conducting behaviour change conversations. Validated items assessing barriers to having these conversations were based on eight TDF domains. Post-training acceptability and quality of training was assessed. Data were summarised using descriptive statistics, and differences between TDF domain scores at the specific time points were analysed using Wilcoxon matched-pairs signed-rank tests. Results Sixty-four participants consented to complete surveys (97% women; 16% identified as Aboriginal), with 37 employed in clinical settings and 27 in non-clinical settings. The training improved scores for the TDF domains of skills (T1: median (interquartile range) = 4.7(3.3-5.3); T3 = 5.7(5.3-6.0), p < 0.01), belief about capabilities (T1 = 4.7(3.3-6.0); T3 = 5.7(5.0-6.0), p < 0.01), and goals (T1 = 4.3(3.7-5.0); T3 = 4.7(4.3-5.3), p < 0.01) at follow-up. Competence in using ‘open discovery questions’ increased post-training (T1 = 25% of responses; T2 = 96% of responses; T3 = 87% of responses, p < 0.001), as did participants’ confidence for having behaviour change conversations (T1 = 6.0(4.7-7.6); T2 = 8.1(7.1-8.8), p < 0.001), including an increased confidence in having behaviour change conversations with Aboriginal clients (T1 = 5.0(2.7-6.3); T2 = 7.6(6.4-8.3), p < 0.001). Conclusions Provision of additional support strategies to address intentions; memory, attention and decision processes; and behavioural regulation may enhance adoption and maintenance of HCS in routine practice. Wider implementation of HCS training could be an effective strategy to building capacity and support health professionals to use a person-centred, opportunistic approach to health behaviour change.

scholarly journals Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort

2019 ◽  
Vol 26 (10) ◽  
pp. 977-988 ◽  
Author(s):  
Gang Fang ◽  
Izabela E Annis ◽  
Jennifer Elston-Lafata ◽  
Samuel Cykert
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Treatment Effects ◽  
Model Performance ◽  
Virtual Patient ◽  
Gradient Boosting ◽  
Individual Treatment ◽  
Patient Cohort ◽  
Individual Outcome ◽  
The Impact

Abstract Objective We aimed to investigate bias in applying machine learning to predict real-world individual treatment effects. Materials and Methods Using a virtual patient cohort, we simulated real-world healthcare data and applied random forest and gradient boosting classifiers to develop prediction models. Treatment effect was estimated as the difference between the predicted outcomes of a treatment and a control. We evaluated the impact of predictors (ie, treatment predictors [X1], confounders [X2], treatment effects modifiers [X3], and other outcome risk factors [X4]) with known effects on treatment and outcome using real-world data, and outcome imbalance on predicting individual outcome. Using counterfactuals, we evaluated percentage of patients with biased predicted individual treatment effects Results The X4 had relatively more impact on model performance than X2 and X3 did. No effects were observed from X1. Moderate-to-severe outcome imbalance had a significantly negative impact on model performance, particularly among subgroups in which an outcome occurred. Bias in predicting individual treatment effects was significant and persisted even when the models had a 100% accuracy in predicting health outcome. Discussion Inadequate inclusion of the X2, X3, and X4 and moderate-to-severe outcome imbalance may affect model performance in predicting individual outcome and subsequently bias in predicting individual treatment effects. Machine learning models with all features and high performance for predicting individual outcome still yielded biased individual treatment effects. Conclusions Direct application of machine learning might not adequately address bias in predicting individual treatment effects. Further method development is needed to advance machine learning to support individualized treatment selection.

Diverse Pre-Treatment Effects in Survey Experiments

2017 ◽  
Vol 5 (2) ◽  
pp. 148-158
Author(s):  
Katerina Linos ◽  
Kimberly Twist
Keyword(s):  
Mass Media ◽  
Real World ◽  
Information Dissemination ◽  
Treatment Effects ◽  
Survey Experiments ◽  
Pre Treatment ◽  
World Information ◽  
The Impact ◽  
First Time ◽  
Clear Information

AbstractResearchers using survey experiments typically assume respondents are blank slates, encountering information for the first time. We study how prior real-world information dissemination through the mass media shapes experimental results. We show prior exposure can lead us to both under- and overestimate true framing effects in experiments. Message clarity moderates the impact of pre-treatment, with clear information more likely to produce pre-treatment effects than unclear information.

scholarly journals Is fidelity to a complex behaviour change intervention associated with patient outcomes? Exploring the relationship between dietitian adherence and competence and the nutritional status of intervention patients in a successful stepped-wedge randomised clinical trial of eating as treatment (EAT)

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Alison K. Beck ◽  
Amanda L. Baker ◽  
Gregory Carter ◽  
Laura Robinson ◽  
Kristen McCarter ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Nutritional Status ◽  
Behaviour Change ◽  
Trial Registration ◽  
Stepped Wedge ◽  
Behaviour Change Intervention ◽  
Intervention Effects ◽  
Complex Behaviour ◽  
Behaviour Change Counselling ◽  
The Relationship

AbstractBackgroundBehavioural medicine is characterised by findings for the effectiveness and efficacy of complex behaviour change interventions. Comparatively, scant attention has been paid to key intervention components or mechanisms of action. Evaluating relationships between process variables (fidelity) and intervention effects is central to addressing this imbalance. Accordingly, in the current study, we sought to explore the magnitude and direction of effect between fidelity predictors (dietitian adherence and competence) and intervention effects (patient nutritional status) during the intervention phase of a real-world, stepped-wedge evaluation of ‘EAT: Eating As Treatment’.MethodsThe EAT clinical trial was conducted within five major Australian hospitals located in Queensland, Western Australia, Victoria and South Australia between 2013 and 2016. EAT is a dietitian-delivered health behaviour change intervention designed to reduce malnutrition in head and neck cancer (HNC) patients undergoing radiotherapy. Dietitian adherence and competence ratings were derived from a 20% random sample of audio-recorded dietetic consultations (n=194) conducted after dietitians (n=18) were trained in EAT. Sessions were coded by trained, independent, coders using a study checklist, the Behaviour Change Counselling Index (BECCI) and items from the Cognitive Therapy Scale-Revised (CTS-R). Patient nutritional status was measured using the Patient-Generated Subjective Global Assessment (PGSGA).ResultsDietitian adherence to a written nutrition plan (β=7.62, 95% CI=0.65 to 14.58,p=0.032), dietitian adherence to behaviour change counselling (β=0.69, 95% CI =0.02 to 1.38,p=0.045) and competence in delivering behaviour change counselling (β=3.50, 95% CI =0.47 to 6.53,p=0.024) were significant predictors of patient nutritional status. Dietitian adherence and competence ratings were higher during consultations with intervention patients at greater risk of malnutrition.ConclusionsThis study contributes new insights into the relationship between fidelity and treatment outcome by demonstrating that dietitian adherence and competence is greater when working with more challenging patients. This is likely central to the demonstrated success of the EAT intervention in reducing malnutrition and highlights the importance of ensuring that providers are adequately equipped to flexibly integrate intervention elements according to patient need.Trial registrationThis study is a process analysis of a stepped-wedge randomised controlled trial prospectively registered on the Australian New Zealand Clinical Trials Registry (ACTRN12613000320752; Date of registration 21/03/2013).

scholarly journals A systematic evaluation of assumptions in centrality measures by empirical flow data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mareike Bockholt ◽  
Katharina A. Zweig
Keyword(s):  
Real World ◽  
Process Model ◽  
Systematic Evaluation ◽  
Centrality Measures ◽  
The Real ◽  
Link Type ◽  
Network Process ◽  
The One ◽  
Almost All ◽  
The Impact

AbstractWhen considering complex systems, identifying themost importantactors is often of relevance. When the system is modeled as a network, centrality measures are used which assign each node a value due to its position in the network. It is often disregarded that they implicitly assume a network process flowing through a network, and also make assumptions ofhowthe network process flows through the network. A node is then central with respect to this network process (Borgatti in Soc Netw 27(1):55–71, 2005,10.1016/j.socnet.2004.11.008). It has been shown that real-world processes often do not fulfill these assumptions (Bockholt and Zweig, in Complex networks and their applications VIII, Springer, Cham, 2019,10.1007/978-3-030-36683-4_7). In this work, we systematically investigate the impact of the measures’ assumptions by using four datasets of real-world processes. In order to do so, we introduce several variants of the betweenness and closeness centrality which, for each assumption, use either the assumed process model or the behavior of the real-world process. The results are twofold: on the one hand, for all measure variants and almost all datasets, we find that, in general, the standard centrality measures are quite robust against deviations in their process model. On the other hand, we observe a large variation of ranking positions of single nodes, even among the nodes ranked high by the standard measures. This has implications for the interpretability of results of those centrality measures. Since a mismatch of the behaviour of the real network process and the assumed process model does even affect the highly-ranked nodes, resulting rankings need to be interpreted with care.

scholarly journals Bridge Category Models: Development of Bayesian Modelling Procedures to Account for Bridge Ordinal Ratings for Disease Staging

2021 ◽  
Author(s):  
Joshua Levy ◽  
Carly Bobak ◽  
Nasim Azizgolshani ◽  
Michael Andersen ◽  
Arief Suriawinata ◽  
...  
Keyword(s):  
Clinical Research ◽  
Real World ◽  
Treatment Effects ◽  
Bayesian Modelling ◽  
Statistical Methodology ◽  
Significant Bias ◽  
Disease Staging ◽  
Liver Biopsies ◽  
The Impact ◽  
Research Studies

Disease grading and staging is accomplished through the assignment of an ordinal rating. Bridge ratings occur when a rater assigns two adjacent categories. Most statistical methodology necessitates the use of a single ordinal category. Consequently, bridge ratings often go unreported in clinical research studies. We propose three methodologies (Expanded, Mixture, and Collapsed) Bridge Category Models, to account for bridge ratings. We perform simulations to examine the impact of our approaches on detecting treatment effects, and comment on a real-world scenario of staging liver biopsies. Results indicate that if bridge ratings are not accounted for, disease staging models may exhibit significant bias and precision loss. All models worked well when they corresponded to the data generating mechanism.

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