Do engagement and behavioural mechanisms underpin the effectiveness of the Drink Less app?

This is a process evaluation of a large UK-based randomised controlled trial (RCT) (n = 5602) evaluating the effectiveness of recommending an alcohol reduction app, Drink Less, compared with usual digital care in reducing alcohol consumption in increasing and higher risk drinkers. The aim was to understand whether participants' engagement ('self-reported adherence') and behavioural characteristics were mechanisms of action underpinning the effectiveness of Drink Less. Self-reported adherence with both digital tools was over 70% (Drink Less: 78.0%, 95% CI = 77.6-78.4; usual digital care: 71.5%, 95% CI = 71.0-71.9). Self-reported adherence to the intervention (average causal mediation effect [ACME] = -0.250, 95% CI = -0.42, -0.11) and self-monitoring behaviour (ACME = -0.235, 95% CI = -0.44, -0.03) both partially mediated the effect of the intervention (versus comparator) on alcohol reduction. Following the recommendation (self-reported adherence) and the tracking (self-monitoring behaviour) feature of the Drink Less app appear to be important mechanisms of action for alcohol reduction among increasing and higher risk drinkers.

Trace amine-associated receptor 1 (TAAR1) agonism for psychosis: a living systematic review and meta-analysis of human and non-human data.

Background: Trace amine-associated receptor 1 (TAAR1) agonism shows promise for treating psychosis, prompting us to synthesise data from human and non-human studies. Methods: We co-produced a living systematic review of controlled studies examining TAAR1 agonists in individuals (with or without psychosis/schizophrenia) and relevant animal models. Two independent reviewers identified studies in multiple electronic databases (until 17.11.2023), extracted data, and assessed risk of bias. Primary outcomes were standardised mean differences (SMD) for overall symptoms in human studies and hyperlocomotion in animal models. We also examined adverse events and neurotransmitter signalling. We synthesised data with random-effects meta-analyses. Results: Nine randomised trials provided data for two TAAR1 agonists (ulotaront and ralmitaront), and 15 animal studies for 10 TAAR1 agonists. Ulotaront and ralmitaront demonstrated few differences compared to placebo in improving overall symptoms in adults with acute schizophrenia (N=4 studies, n=1291 participants; SMD=0.15, 95%CI: -0.05, 0.34), and ralmitaront was less efficacious than risperidone (N=1, n=156, SMD=-0.53, 95%CI: -0.86, -0.20). Large placebo response was observed in ulotaront phase-III trials. Limited evidence suggested a relatively benign side-effect profile for TAAR1 agonists, although nausea and sedation were common after a single dose of ulotaront. In animal studies, TAAR1 agonists improved hyperlocomotion compared to control (N=13 studies, k=41 experiments, SMD=1.01, 95%CI: 0.74, 1.27), but seemed less efficacious compared to dopamine D 2 receptor antagonists (N=4, k=7, SMD=-0.62, 95%CI: -1.32, 0.08). Limited human and animal data indicated that TAAR1 agonists may regulate presynaptic dopaminergic signalling. Conclusions: TAAR1 agonists may be less efficacious than dopamine D 2 receptor antagonists already licensed for schizophrenia. The results are preliminary due to the limited number of drugs examined, lack of longer-term data, publication bias, and assay sensitivity concerns in trials associated with large placebo response. Considering their unique mechanism of action, relatively benign side-effect profile and ongoing drug development, further research is warranted. Registration: PROSPERO-ID: CRD42023451628.

There is a need for more effective treatments for psychosis, including schizophrenia. Psychosis is a collection of mental health symptoms, such as hearing voices, that can cause distress and impair functioning. These symptoms are thought to be caused by changes in a chemical messenger system in the brain called dopamine. Currently used antipsychotic medications target brain receptors that respond to dopamine. They are not effective in some people and can cause uncomfortable adverse events, such as weight gain and movement disorders, especially with long-term use. A new type of drug is the trace amine-associated receptor 1 (TAAR1) agonists. These drugs act on different brain receptors that can affect the activity of the dopamine system, but do not directly bind to dopamine receptors. We aimed to understand if TAAR1 agonists can reduce symptoms of psychosis, what adverse events they might have, and how they work. We did this by reviewing and collating all available evidence until November 2023. This is a "living" systematic review, so it will be regularly updated in the future. We looked at both human and animal studies investigating TAAR1 agonists. Human studies suggested that two TAAR1 agonists (namely, ulotaront or ralmitaront) might have little to no effect on reducing symptoms of psychosis compared to placebo in people with schizophrenia. They seemed to cause fewer adverse events than current antipsychotics. Data from animal studies suggested that TAAR1 agonists had some positive effects but potentially smaller than other antipsychotics. There were little to no data from both human and animal studies about how TAAR1 agonists actually work. From the current evidence we are uncertain about these results. With the ongoing development of new TAAR1 agonists, more evidence is needed to understand their potential role in the treatment of psychosis.

Evaluating the Acceptability of the Drink Less App and the National Health Service Alcohol Advice Web Page: Qualitative Interview Process Evaluation.

BACKGROUND: The extent to which interventions are perceived as acceptable to users impacts engagement and efficacy. OBJECTIVE: In this study, we evaluated the acceptability of (1) the smartphone app Drink Less (intervention) and (2) the National Health Service (NHS) alcohol advice web page (usual digital care and comparator) among adult drinkers in the United Kingdom participating in a randomized controlled trial evaluating the effectiveness of the Drink Less app. METHODS: A subsample of 26 increasing- and higher-risk drinkers (Alcohol Use Disorders Identification Test score≥8) assigned to the intervention group (Drink Less; n=14, 54%; female: n=10, 71%; age: 22-72 years; White: n=9, 64%) or usual digital care group (NHS alcohol advice web page; n=12, 46%; female: n=5, 42%; age: 23-68 years: White: n=9, 75%) took part in semistructured interviews. The interview questions were mapped on to the 7 facets of acceptability according to the Theoretical Framework of Acceptability: affective attitude, burden, perceived effectiveness, ethicality, intervention coherence, opportunity costs, and self-efficacy. Alongside these constructs, we also included a question on perceived personal relevance, which previous research has linked to acceptability and engagement. Framework and thematic analysis of data was undertaken. RESULTS: The Drink Less app was perceived as being ethical, easy, user-friendly, and effective for the period the app was used. Participants reported particularly liking the tracking and feedback sections of the app, which they reported increased personal relevance and which resulted in positive affect when achieving their goals. They reported no opportunity cost. Factors such as negative affect when not meeting goals and boredom led to disengagement in the longer term for some participants. The NHS alcohol advice web page was rated as being easy and user-friendly with no opportunity costs. However, the information presented was not perceived as being personally relevant or effective in changing drinking behavior. Most participants reported neutral or negative affect, most participants thought the alcohol advice web page was accessible, and some participants reported ethical concerns around the availability of suggested resources. Some participants reported that it had acted as a starting point or a signpost to other resources. Participants in both groups discussed motivation to change and contextual factors such as COVID-19 lockdowns, which influenced their perceived self-efficacy regardless of their assigned intervention. CONCLUSIONS: Drink Less appears to be an acceptable digital intervention among the recruited sample. The NHS alcohol advice web page was generally considered unacceptable as a stand-alone intervention among the recruited sample, although it may signpost and help people access other resources and interventions.

A data extraction template for the behaviour change intervention ontology.

Background: The Behaviour Change Intervention Ontology (BCIO) aims to improve the clarity, completeness and consistency of reporting within intervention descriptions and evidence synthesis. However, a recommended method for transparently annotating intervention evaluation reports using the BCIO does not currently exist. This study aimed to develop a data extraction template for annotating using the BCIO. Methods: The BCIO data extraction template was developed in four stages: i) scoping review of papers citing component ontologies within the BCIO, ii) development of a draft template, iii) piloting and revising the template, and iv) dissemination and maintenance of the template. Results: A prototype data extraction template using Microsoft Excel was developed based on BCIO annotations from 14 papers. The 'BCIO data extraction template v1' was produced following piloting and revision, incorporating a facility for user feedback. Discussion: This data extraction template provides a single, accessible resource to extract all necessary characteristics of behaviour change intervention scenarios. It can be used to annotate the presence of BCIO entities for evidence synthesis, including systematic reviews. In the future, we will update this template based on feedback from the community, additions of newly published ontologies within the BCIO, and revisions to existing ontologies.

Behaviour change interventions are often reported in an inconsistent and incomplete manner in study reports. This makes it difficult to build knowledge and predict outcomes. There is a need for a shared language to describe behaviour change interventions. This need was met using 'ontologies', which are classification systems that represent knowledge in a standardised way. The Behaviour Change Intervention Ontology (BCIO) has been developed to describe the different aspects of interventions in a way that is precise enough for computers as well as humans to 'read' study findings. The BCIO can be used to extract information from study reports for evidence synthesis, such as systematic literature reviews. To meet the need for a resource for annotating (coding) study reports according to the BCIO, we developed a data extraction template. The template was developed in four stages: i) reviewing existing papers using the BCIO, ii) development of a draft template, iii) piloting and revising the template, and iv) dissemination and maintenance of the template. The resulting resource is an accessible, easy-to-use template to assist with specifying the content of published papers reporting interventions and their evaluation. The template will be updated based on user feedback and future revisions to the BCIO.

Linking measures to mechanisms of action in behavior change: A qualitative analysis of expert views.

RATIONALE: Testing mechanisms of action (MoAs) hypothesized to underlie behavior change can enhance intervention effectiveness. Rigorous measurement of putative mechanisms is critical to this effort, but measures are rarely validated with respect to target MoAs. OBJECTIVE: This study aimed to elucidate challenges of linking measures to putative MoAs and to identify priorities for future research. METHOD: This study was a systematic exploration of written comments by experts in behavioral intervention research and theories of behavior change (N = 20) capturing their opinions about a task querying whether self-report measures from the Science Of Behavior Change (SOBC) Measures Repository were related to a set of MoAs identified by the Human Behaviour Change Project (HBCP). RESULTS: Six themes were identified: 1) Study Value, 2) Measure Properties, 3) Mechanism Properties, 4) Miscellaneous Measure Concerns, 5) Conceptual Challenges, and 6) Approaches to Developing Measure-Mechanism Links. Experts noted challenges such as lack of measure validation, poor measure properties (e.g., double-barreled items), overly broad MoA definitions that limited their utility, lack of clarity around the term "related," and more. Nonetheless, experts expressed the importance of the exercise. Suggestions included developing and refining measures that are validate for assessing MoAs, clarifying and elaborating MoA definitions, and conducting further, more granular research. CONCLUSION: This systematic examination of expert comments highlights issues that need further investigation to advance behavioral science, specifically pertaining to identifying valid measures of MoAs in behavioral and process research. This study highlights the challenges and opportunities for future research on linking measures and MoA in behavioral science and subsequently enhancing the efficacy of behavioral interventions.

Factors influencing uptake of protective behaviours by healthcare workers in England during the COVID-19 pandemic: A theory-based mixed-methods study.

BACKGROUND: Hospital infection control policies protect patients and healthcare workers (HCWs) and limit the spread of pathogens, but adherence to COVID-19 guidance varies. We examined hospital HCWs' enactment of social distancing and use of personal protective equipment (PPE) during the COVID-19 pandemic, factors influencing these behaviours, and acceptability and feasibility of strategies to increase social distancing. METHODS: An online, cross-sectional survey (n = 86) and semi-structured interviews (n = 22) with HCWs in two English hospitals during the first wave of the COVID-19 pandemic (May-December 2020). The Capability, Opportunity, Motivation (COM-B) model of behaviour change underpinned survey and topic guide questions. Spearman Rho correlations examined associations between COM-B domains and behaviours. Interviews were analysed using inductive and deductive thematic analysis. Potential strategies to improve social distancing were selected using the Behaviour Change Wheel and discussed in a stakeholder workshop (n = 8 participants). RESULTS: Social distancing enactment was low, with 85% of participants reporting very frequently or always being in close contact with others in communal areas. PPE use was high (88% very frequently or always using PPE in typical working day). Social distancing was associated with Physical Opportunity (e.g., size of physical space), Psychological Capability (e.g., clarity of guidance), and Social Opportunity (e.g., support from managers). Use of PPE was associated with Psychological Capability (e.g., training), Physical Opportunity (e.g., availability), Social Opportunity (e.g., impact on interactions with patients), and Reflective Motivation (e.g., beliefs that PPE is effective). Local champions and team competition were viewed as feasible strategies to improve social distancing. CONCLUSIONS: It is valuable to understand and compare the drivers of individual protective behaviours; when faced with the same level of perceived threat, PPE use was high whereas social distancing was rarely enacted. Identified influences represent targets for intervention strategies in response to future infectious disease outbreaks.

Effectiveness of a smartphone app (Drink Less) versus usual digital care for reducing alcohol consumption among increasing-and-higher-risk adult drinkers in the UK: a two-arm, parallel-group, double-blind, randomised controlled trial.

Background: Digital interventions, including apps and websites, can be effective for reducing alcohol consumption. However, many are not evidence- or theory-informed and have not been evaluated. We tested the effectiveness of the Drink Less app for reducing alcohol consumption compared with usual digital care in the UK. Methods: In this two-arm, parallel group, double-blind, randomised controlled trial, we enrolled increasing-and-higher-risk drinkers (AUDIT ≥ 8) in the UK, who were motivated to reduce their alcohol consumption and willing to use a digital intervention to do so, via online methods. Participants were randomly assigned (1:1), using an online algorithm, to receive a web link to download the Drink Less app (intervention) or to the NHS alcohol advice webpage (usual digital care). Researchers were masked to group allocation. Participants were followed up at one, three and six months. The primary outcome was self-reported weekly alcohol consumption at six months, adjusting for baseline consumption. The full analytic sample was used in most analyses, though missing data was treated in different ways. The primary, pre-registered intention-to-treat analysis assumed baseline-carried-forwards. Secondary pre-registered analyses also focused on the full analytic sample and used alternatives including multiple imputation and last observation carried forwards. This trial is registered with the ISRCTN registry, ISRCTN64052601. Findings: Between 07/13/2020 and 03/29/2022, 5602 people were randomly assigned to the Drink Less app (n = 2788) or comparator (n = 2814) groups. Six-month follow-up rates were 79% and 80%, respectively. The primary pre-registered conservative intention-to-treat approach assuming non-responders were drinking at baseline levels of consumption, found a non-significant greater reduction of 0.98 units in weekly alcohol consumption in the intervention group at 6-month follow-up (95% CI -2.67 to 0.70). The data were insensitive to detect the hypothesised effect (Bayes factor = 1.17). Data were not missing completely at random, with 6-month follow-up rates differing in terms of education, occupation, and income. We therefore conducted the pre-registered sensitivity analysis using multiple imputation, showing that the Drink Less app resulted in a 2.00-unit greater weekly reduction at 6-month follow-up compared with the NHS alcohol advice webpage (95% CI -3.76 to -0.24). Fewer than 0.1% of participants in both arms who responded to one, three or six-month follow-up reported adverse events linked to participation in the trial. Interpretation: The Drink Less app may be effective in reducing the alcohol consumption in increasing-and-higher-risk drinkers motivated to reduce their consumption. Funding: NIHR Public Health Research Programme.

One size doesn't fit all: methodological reflections in conducting community-based behavioural science research to tailor COVID-19 vaccination initiatives for public health priority populations.

BACKGROUND: Promoting the uptake of vaccination for infectious diseases such as COVID-19 remains a global challenge, necessitating collaborative efforts between public health units (PHUs) and communities. Applied behavioural science can play a crucial role in supporting PHUs' response by providing insights into human behaviour and informing tailored strategies to enhance vaccination uptake. Community engagement can help broaden the reach of behavioural science research by involving a more diverse range of populations and ensuring that strategies better represent the needs of specific communities. We developed and applied an approach to conducting community-based behavioural science research with ethnically and socioeconomically diverse populations to guide PHUs in tailoring their strategies to promote COVID-19 vaccination. This paper presents the community engagement methodology and the lessons learned in applying the methodology. METHODS: The community engagement methodology was developed based on integrated knowledge translation (iKT) and community-based participatory research (CBPR) principles. The study involved collaboration with PHUs and local communities in Ontario, Canada to identify priority groups for COVID-19 vaccination, understand factors influencing vaccine uptake and co-design strategies tailored to each community to promote vaccination. Community engagement was conducted across three large urban regions with individuals from Eastern European communities, African, Black, and Caribbean communities and low socioeconomic neighbourhoods. RESULTS: We developed and applied a seven-step methodology for conducting community-based behavioural science research: (1) aligning goals with system-level partners; (2) engaging with PHUs to understand priorities; (3) understanding community strengths and dynamics; (4) building relationships with each community; (5) establishing partnerships (community advisory groups); (6) involving community members in the research process; and (7) feeding back and interpreting research findings. Research partnerships were successfully established with members of prioritized communities, enabling recruitment of participants for theory-informed behavioural science interviews, interpretation of findings, and co-design of targeted recommendations for each PHU to improve COVID-19 vaccination uptake. Lessons learned include the importance of cultural sensitivity and awareness of sociopolitical context in tailoring community engagement, being agile to address the diverse and evolving priorities of PHUs, and building trust to achieve effective community engagement. CONCLUSION: Effective community engagement in behavioural science research can lead to more inclusive and representative research. The community engagement approach developed and applied in this study acknowledges the diversity of communities, recognizes the central role of PHUs, and can help in addressing complex public health challenges.

Methodological Insights on Recruitment and Retention From a Remote Randomized Controlled Trial Examining the Effectiveness of an Alcohol Reduction App: Descriptive Analysis Study.

BACKGROUND: Randomized controlled trials (RCTs) with no in-person contact (ie, remote) between researchers and participants offer savings in terms of cost and time but present unique challenges. OBJECTIVE: The goal of this study is to examine the differences between different forms of remote recruitment (eg, National Health Service [NHS] website, social media, and radio advertising) in the proportion of participants recruited, demographic diversity, follow-up rates, and cost. We also examine the cost per participant of sequential methods of follow-up (emails, phone calls, postal surveys, and postcards). Finally, our experience with broader issues around study advertising and participant deception is discussed. METHODS: We conducted a descriptive analysis of 5602 increasing-and-higher-risk drinkers (Alcohol Use Disorders Identification Test score ≥8), taking part in a 2-arm, parallel group, remote RCT with a 1:1 allocation, comparing the intervention (Drink Less app) with usual digital care (NHS alcohol advice web page). Participants were recruited between July 2020 and March 2022 and compensated with gift vouchers of up to £36 (a currency exchange rate of £1=US $1.26988 is applicable) for completing follow-up surveys, with 4 stages of follow-up: email reminders, phone calls, postal survey, and postcard. RESULTS: The three main recruitment methods were advertisements on (1) social media (2483/5602, 44.32%), (2) the NHS website (1961/5602, 35.01%), and (3) radio and newspapers (745/5602, 13.3%), with the remaining methods of recruitment accounting 7.37% (413/5602) of the sample. The overall recruitment cost per participant varied from £0 to £11.01. Costs were greater when recruiting participants who were men (£0-£28.85), from an ethnic minority group (£0-£303.81), and more disadvantaged (£0-£49.12). Targeted approaches were useful for recruiting more men but less useful in achieving diversity in ethnicity and socioeconomic status. Follow-up at 6 months was 79.58% (4458/5602). Of those who responded, 92.4% (4119/4458) responded by email. Each additional stage of follow-up resulted in an additional 2-3 percentage points of the overall sample being followed up, although phone calls, postal surveys, and postcards were more resource intensive than email reminders. CONCLUSIONS: For remote RCTs, researchers could benefit from using a range of recruitment methods and cost-targeted approaches to achieve demographic diversity. Automated emails with substantial financial incentives for prompt completion can achieve good follow-up rates, and sequential, offline follow-up options, such as phone calls and postal surveys, can further increase follow-up rates but are comparatively expensive. We also make broader recommendations focused on striking the right balance when designing remote RCTs. Careful planning, ongoing maintenance, and dynamic decision-making are required throughout a trial to balance the competing demands of participation among those eligible, deceptive participation among those who are not eligible, and ensuring no postrandomization bias is introduced by data-checking protocols.

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.

Using machine learning to extract information and predict outcomes from reports of randomised trials of smoking cessation interventions in the Human Behaviour-Change Project.

Background  Using reports of randomised trials of smoking cessation interventions as a test case, this study aimed to develop and evaluate machine learning (ML) algorithms for extracting information from study reports and predicting outcomes as part of the Human Behaviour-Change Project. It is the first of two linked papers, with the second paper reporting on further development of a prediction system. Methods  Researchers manually annotated 70 items of information ('entities') in 512 reports of randomised trials of smoking cessation interventions covering intervention content and delivery, population, setting, outcome and study methodology using the Behaviour Change Intervention Ontology. These entities were used to train ML algorithms to extract the information automatically. The information extraction ML algorithm involved a named-entity recognition system using the 'FLAIR' framework. The manually annotated intervention, population, setting and study entities were used to develop a deep-learning algorithm using multiple layers of long-short-term-memory (LSTM) components to predict smoking cessation outcomes. Results  The F1 evaluation score, derived from the false positive and false negative rates (range 0-1), for the information extraction algorithm averaged 0.42 across different types of entity (SD=0.22, range 0.05-0.88) compared with an average human annotator's score of 0.75 (SD=0.15, range 0.38-1.00). The algorithm for assigning entities to study arms ( e.g., intervention or control) was not successful. This initial ML outcome prediction algorithm did not outperform prediction based just on the mean outcome value or a linear regression model. Conclusions  While some success was achieved in using ML to extract information from reports of randomised trials of smoking cessation interventions, we identified major challenges that could be addressed by greater standardisation in the way that studies are reported. Outcome prediction from smoking cessation studies may benefit from development of novel algorithms, e.g., using ontological information to inform ML (as reported in the linked paper 3).

Developing and using ontologies in behavioural science: addressing issues raised.

Ontologies are ways of representing aspects of the world in terms of uniquely defined classes of 'entities' and relationships between them. They are widely used in biological science, data science and commerce because they provide clarity, consistency, and the ability to link information and data from different sources. Ontologies offer great promise as representational systems in behavioural science and could revolutionise descriptions of studies and findings, and the expression of models and theories. This paper discusses issues that have been raised about using ontologies in behavioural science and how these can be addressed. The issues arise partly from the way that ontologies represent information, which can be perceived as reductionist or simplistic, and partly from issues to do with their implementation. However, despite the simplicity of their structure, ontologies can represent complex entities that change over time, as well as their inter-relationships and highly nuanced information about them. Nevertheless, ontologies are only one of many ways of representing information and it is important to recognise when other forms are more efficient. With regard to implementation, it is important to build ontologies with involvement from the communities who will be using them. Far from constraining intellectual creativity, ontologies that are broadly-based can facilitate expression of nuance, comparison of findings and integration of different approaches and theories. Maintaining and updating ontologies remain significant challenges but can be achieved through establishing and coordinating communities of practice.