Effectiveness of Interventions for Changing More Than One Behavior at a Time to Manage Chronic Conditions: A Systematic Review and Meta-analysis.

BACKGROUND: Health behaviors play a significant role in chronic disease management. Rather than being independent of one another, health behaviors often co-occur, suggesting that targeting more than one health behavior in an intervention has the potential to be more effective in promoting better health outcomes. PURPOSE: We aimed to conduct a systematic review and meta-analysis of randomized trials of interventions that target more than one behavior to examine the effectiveness of multiple health behavior change interventions in patients with chronic conditions. METHODS: Five electronic databases (Web of Science, PubMed, CINAHL, EMBASE, and Cochrane) were systematically searched in November 2023, and studies included in previous reviews were also consulted. We included randomized trials of interventions aiming to change more than one health behavior in individuals with chronic conditions. Two independent reviewers screened and extracted data, and used Cochrane's Risk of Bias 2 tool. Meta-analyses were conducted to estimate the effects of interventions on change in health behaviors. Results were presented as Cohen's d for continuous data, and risk ratio for dichotomous data. RESULTS: Sixty-one studies were included spanning a range of chronic diseases: cardiovascular (k = 25), type 2 diabetes (k = 15), hypertension (k = 10), cancer (k = 7), one or more chronic conditions (k = 3), and multiple conditions (k = 1). Most interventions aimed to change more than one behavior simultaneously (rather than in sequence) and most targeted three particular behaviors at once: "physical activity, diet and smoking" (k = 20). Meta-analysis of 43 eligible studies showed for continuous data (k = 29) a small to substantial positive effect on behavior change for all health behaviors (d = 0.081-2.003) except for smoking (d = -0.019). For dichotomous data (k = 23) all analyses showed positive effects of targeting more than one behavior on all behaviors (RR = 1.026-2.247). CONCLUSIONS: Targeting more than one behavior at a time is effective in chronic disease management and more research should be directed into developing the science of multiple behavior change.

Many recommendations suggest engaging in more than one health behavior to manage a chronic disease; however, most research trying to understand or support health behavior tends to focus on only one behavior. We wanted to clarify if interventions aiming to support people in changing more than one health behavior are effective and promote better health outcomes. We aimed to conduct a systematic review to summarize the effects of studies reporting randomized trials of interventions that target more than one behavior in people with a chronic condition. We found and analyzed 61 studies published up to November 2023 covering people with a variety of chronic diseases: cardiovascular conditions, type 2 diabetes, hypertension, cancer, and, in some studies, people with multiple conditions. Most interventions tried to change three particular behaviors at once (physical activity, diet, and smoking) and, overall, interventions that tried to change more than one behavior had positive effects on diet, physical activity, medication adherence, and alcohol consumption, but not smoking cessation. Findings highlight the benefits of targeting more than one behavior in health behavior change interventions. Future research could seek to identify if findings are similar across settings and populations and how they can inform routine healthcare and self-management interventions.

An ontology of mechanisms of action in behaviour change interventions.

Background: Behaviour change interventions influence behaviour through causal processes called "mechanisms of action" (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. Methods: To develop the MoA Ontology, we (1) defined the ontology's scope; (2) identified, labelled and defined the ontology's entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology's comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology's alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. Results: An MoA was defined as "a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour". We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 ("acceptable") for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 23 further entities were added to the ontology resulting in 284 entities organised in seven hierarchical levels. Conclusions: The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources.

Message framing to inform cancer prevention pricing interventions in the UK and USA: a factorial experiment, 2019.

OBJECTIVES: To advance understanding of how message framing can be used to maximise public support across different pricing policies for alcohol, tobacco and sugary drinks/foods that prevent consumption of cancer-causing products. DESIGN: We designed a 3×4×3 randomised factorial experiment to test responses to messages with three pricing policies, four message frames and three products. SETTING: Online survey panel (Qualtrics) in 2019. PARTICIPANTS: Adults (N=1850) from the UK and USA. INTERVENTIONS: Participants randomly viewed one of 36 separate messages that varied by pricing policy (increasing taxes, getting rid of price discounts, getting rid of low-cost products), four frames and product (alcohol, tobacco, sugary drinks/foods). PRIMARY AND SECONDARY OUTCOME MEASURES: We assessed the relationship between the message characteristics and four dependent variables. Three were related to policy support: (1) increasing taxes on the product mentioned in the message, (2) getting rid of price discounts and special offers on the product mentioned in the message and (3) getting rid of low-cost versions of the product mentioned in the message. One was related to reactance, a psychological response to having one's freedom limited. RESULTS: We found no effect for pricing policy in the message. Frames regarding children and reducing cancer risk moderated some outcomes, showing promise for real-world use. We found differences in support by product and reactance with greatest support and least reactance for tobacco policies, less support and more reactance for alcohol policies, and the least support and most reactance for sugary drinks/foods policies. CONCLUSIONS: Cancer prevention efforts using policy interventions can be informed by the message framing literature. Our results offer insights for cancer prevention advocacy efforts across the UK and USA and highlight that tax versus non-tax approaches to increasing the cost of cancer-causing products result in similar responses from consumers.