Developing content for a virtual reality scenario that motivates quit attempts in adult smokers: A focus group study with art-based methods.

Virtual reality (VR) could be used to deliver messages to smokers that encourages them to attempt quitting. For a VR smoking cessation intervention to be effective, the target population must find the content engaging, relevant, inoffensive, and compelling. Informed by health behaviour theory and narrative transportation theory, this study used focus groups combined with art-based methods (participant sketches) to inform the development of VR content that will appropriately address smokers' beliefs about quitting smoking. Data were analysed using reflexive thematic analysis. Four in-person focus groups (N = 21) were held between July and August 2023. Just under half the sample were from an ethnic minority (42.8%) and women (42.9%), and the mean age was 33.6 years (standard deviation = 15.9). More than half the sample had a low motivation to quit (61.0%). We developed six themes concerning: the VR content suggested by participants, the rationale behind it, its technological execution and potential widescale implementation. Many participants downplayed the health consequences of smoking, prioritising the immediate rewards of smoking over quitting's long-term benefits. Therefore, participants suggested content set in the future, showing the benefits of cessation or the negative consequences of continued smoking. Family members were recommended as supporting VR characters to increase the contents' emotional salience. Participants also suggested graphic content that would trigger anxiety about smoking, suggesting that fear appeals were welcome. Participants wanted a truly novel intervention- not a leaflet about smoking statistics presented through VR. Participants suggested healthcare locations (e.g., doctors' offices) for implementation, as home ownership of VR headsets is low. Also, this would make the VR appear more legitimate as a health intervention (rather than casual entertainment) and could complement in-person advice. Future research will refine the participant-generated ideas with experts in VR design and smoking cessation.

Mobile Health Interventions for Substance Use Disorders.

Substance use disorders (SUDs) have an enormous negative impact on individuals, families, and society as a whole. Most individuals with SUDs do not receive treatment because of the limited availability of treatment providers, costs, inflexible work schedules, required treatment-related time commitments, and other hurdles. A paradigm shift in the provision of SUD treatments is currently underway. Indeed, with rapid technological advances, novel mobile health (mHealth) interventions can now be downloaded and accessed by those that need them anytime and anywhere. Nevertheless, the development and evaluation process for mHealth interventions for SUDs is still in its infancy. This review provides a critical appraisal of the significant literature in the field of mHealth interventions for SUDs with a particular emphasis on interventions for understudied and underserved populations. We also discuss the mHealth intervention development process, intervention optimization, and important remaining questions.

Classification of Lapses in Smokers Attempting to Stop: A Supervised Machine Learning Approach Using Data From a Popular Smoking Cessation Smartphone App.

INTRODUCTION: Smoking lapses after the quit date often lead to full relapse. To inform the development of real time, tailored lapse prevention support, we used observational data from a popular smoking cessation app to develop supervised machine learning algorithms to distinguish lapse from non-lapse reports. AIMS AND METHODS: We used data from app users with ≥20 unprompted data entries, which included information about craving severity, mood, activity, social context, and lapse incidence. A series of group-level supervised machine learning algorithms (eg, Random Forest, XGBoost) were trained and tested. Their ability to classify lapses for out-of-sample (1) observations and (2) individuals were evaluated. Next, a series of individual-level and hybrid algorithms were trained and tested. RESULTS: Participants (N = 791) provided 37 002 data entries (7.6% lapses). The best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.969 (95% confidence interval [CI] = 0.961 to 0.978). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUC = 0.482-1.000). Individual-level algorithms could be constructed for 39/791 participants with sufficient data, with a median AUC of 0.938 (range: 0.518-1.000). Hybrid algorithms could be constructed for 184/791 participants and had a median AUC of 0.825 (range: 0.375-1.000). CONCLUSIONS: Using unprompted app data appeared feasible for constructing a high-performing group-level lapse classification algorithm but its performance was variable when applied to unseen individuals. Algorithms trained on each individual's dataset, in addition to hybrid algorithms trained on the group plus a proportion of each individual's data, had improved performance but could only be constructed for a minority of participants. IMPLICATIONS: This study used routinely collected data from a popular smartphone app to train and test a series of supervised machine learning algorithms to distinguish lapse from non-lapse events. Although a high-performing group-level algorithm was developed, it had variable performance when applied to new, unseen individuals. Individual-level and hybrid algorithms had somewhat greater performance but could not be constructed for all participants because of the lack of variability in the outcome measure. Triangulation of results with those from a prompted study design is recommended prior to intervention development, with real-world lapse prediction likely requiring a balance between unprompted and prompted app data.

Within-person associations between psychological and contextual factors and lapse incidence in smokers attempting to quit: A systematic review and meta-analysis of ecological momentary assessment studies.

AIMS: When attempting to stop smoking, discrete smoking events ('lapses') are strongly associated with a return to regular smoking ('relapse'). No study has yet pooled the psychological and contextual antecedents of lapse incidence, captured in ecological momentary assessment (EMA) studies. This systematic review and meta-analysis aimed to synthesize within-person psychological and contextual predictor-lapse associations in smokers attempting to quit. METHODS: We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science. A narrative synthesis and multi-level, random-effects meta-analyses were conducted, focusing on studies of adult, non-clinical populations attempting to stop smoking, with no restrictions on setting. Outcomes were the association between a psychological (e.g. stress, cravings) or contextual (e.g. cigarette availability) antecedent and smoking lapse incidence; definitions of 'lapse' and 'relapse'; the theoretical underpinning of EMA study designs; and the proportion of studies with pre-registered study protocols/analysis plans and open data. RESULTS: We included 61 studies, with 19 studies contributing ≥ 1 effect size(s) to the meta-analyses. We found positive relationships between lapse incidence and 'environmental and social cues' [k = 12, odds ratio (OR) = 4.53, 95% confidence interval (CI) = 2.02, 10.16, P = 0.001] and 'cravings' (k = 10, OR = 1.71, 95% CI = 1.34, 2.18, P < 0.001). 'Negative feeling states' was not significantly associated with lapse incidence (k = 16, OR = 1.10, 95% CI = 0.98, 1.24, P = 0.12). In the narrative synthesis, negative relationships with lapse incidence were found for 'behavioural regulation', 'motivation not to smoke' and 'beliefs about capabilities'; positive relationships with lapse incidence were found for 'positive feeling states' and 'positive outcome expectancies'. Although lapse definitions were comparable, relapse definitions varied widely across studies. Few studies explicitly drew upon psychological theory to inform EMA study designs. One of the included studies drew upon Open Science principles. CONCLUSIONS: In smokers attempting to stop, environmental and social cues and cravings appear to be key within-person antecedents of smoking lapse incidence. Due to low study quality, the confidence in these estimates is reduced.

Estimated Failure to Report Unsuccessful Quit Attempts by Type of Cessation Aid: A Population Survey of Smokers in England.

Introduction: It has been estimated that smokers tend to fail to report unsuccessful quit attempts that lasted a short time and occurred a longer time ago. However, it is unclear whether the failure to report unsuccessful quit attempts varies by the type of cessation aid used. Methods: A total of 5,892 smokers aged 16+ years who had made 1+ quit attempts in the past year were surveyed between January 2014 and December 2020 as part of the Smoking Toolkit Study. Respondents indicated when their most recent quit attempt started, how long it lasted, and which cessation aid(s) were used (e.g., unaided, varenicline, and behavioural support). The percentage failure to report for each cessation aid and 95% bootstrap confidence intervals (CIs) were estimated with an established method. Test for equality of proportions was performed to examine whether quit attempts lasting between one day and one week and that started >6 months ago failed to be reported at a different rate depending on the cessation aid used. Results: We estimated that after three months, 97% (95% CI = 96%-98%) of unaided quit attempts lasting less than one day, 80% (95% CI = 79%-81%) of those lasting between one day and one week, and 60% (95% CI = 59%-61%) of those lasting between one week and one month fail to be reported. Compared with unaided attempts, the estimated percentage failure to report quit attempts that lasted between one day and one week and that started >6 months ago was significantly lower for attempts involving behavioural support (92% of unaided attempts vs. 75% of attempts involving behavioural support, χ 2(1) = 9.29, p = 0.002). No other significant differences were detected. Conclusions: Smokers in England appear to fail to report a substantial proportion of unsuccessful quit attempts. This failure appears particularly prominent for attempts that last a short time or occurred longer ago and appears lower for attempts involving behavioural support compared with unaided attempts.

Understanding health behaviours in context: A systematic review and meta-analysis of ecological momentary assessment studies of five key health behaviours.

Ecological Momentary Assessment (EMA) involves repeated, real-time sampling of health behaviours in context. We present the state-of-knowledge in EMA research focused on five key health behaviours (physical activity and sedentary behaviour, dietary behaviour, alcohol consumption, tobacco smoking, sexual health), summarising theoretical (e.g., psychological and contextual predictors) and methodological aspects (e.g., study characteristics, EMA adherence). We searched Ovid MEDLINE, Embase, PsycINFO and Web of Science until February 2021. We included studies focused on any of the aforementioned health behaviours in adult, non-clinical populations that assessed ≥1 psychological/contextual predictor and reported a predictor-behaviour association. A narrative synthesis and random-effects meta-analyses of EMA adherence were conducted. We included 633 studies. The median study duration was 14 days. The most frequently assessed predictors were 'negative feeling states' (21%) and 'motivation and goals' (16.5%). The pooled percentage of EMA adherence was high at 81.4% (95% CI = 80.0%, 82.8%, k = 348) and did not differ by target behaviour but was somewhat higher in student (vs. general population) samples, when EMAs were delivered via mobile phones/smartphones (vs. handheld devices), and when event contingent (vs. fixed) sampling was used. This review showcases how the EMA method has been applied to improve understanding and prediction of health behaviours in context.

Exploring Users' Experiences With a Quick-Response Chatbot Within a Popular Smoking Cessation Smartphone App: Semistructured Interview Study.

BACKGROUND: Engagement with smartphone apps for smoking cessation tends to be low. Chatbots (ie, software that enables conversations with users) offer a promising means of increasing engagement. OBJECTIVE: We aimed to explore smokers' experiences with a quick-response chatbot (Quit Coach) implemented within a popular smoking cessation app and identify factors that influence users' engagement with Quit Coach. METHODS: In-depth, one-to-one, semistructured qualitative interviews were conducted with adult, past-year smokers who had voluntarily used Quit Coach in a recent smoking cessation attempt (5/14, 36%) and current smokers who agreed to download and use Quit Coach for a minimum of 2 weeks to support a new cessation attempt (9/14, 64%). Verbal reports were audio recorded, transcribed verbatim, and analyzed within a constructivist theoretical framework using inductive thematic analysis. RESULTS: A total of 3 high-order themes were generated to capture users' experiences and engagement with Quit Coach: anthropomorphism of and accountability to Quit Coach (ie, users ascribing human-like characteristics and thoughts to the chatbot, which helped foster a sense of accountability to it), Quit Coach's interaction style and format (eg, positive and motivational tone of voice and quick and easy-to-complete check-ins), and users' perceived need for support (ie, chatbot engagement was motivated by seeking distraction from cravings or support to maintain motivation to stay quit). CONCLUSIONS: Anthropomorphism of a quick-response chatbot implemented within a popular smoking cessation app appeared to be enabled by its interaction style and format and users' perceived need for support, which may have given rise to feelings of accountability and increased engagement.

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial.

BACKGROUND: Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. OBJECTIVE: In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. METHODS: Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. RESULTS: For iCanQuit, participants were clustered into 3 groups: "1-week users" (610/1069, 57.06%), "4-week users" (303/1069, 28.34%), and "26-week users" (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: "1-week users" (695/1064, 65.32%) and "3-week users" (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. CONCLUSIONS: Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

Associations between smoking to relieve stress, motivation to stop and quit attempts across the social spectrum: A population survey in England.

Smoking prevalence in several high-income countries is steadily declining but remains persistently high in 'lower' socioeconomic position (SEP) groups, contributing to inequities in morbidity and mortality. Smoking to relieve stress is a commonly endorsed motive for continued smoking; however, it remains unclear whether smoking to relieve stress has a negative impact on motivation to stop and future quit attempts and if so, whether associations are moderated by SEP. This was an observational study with cross-sectional and prospective survey data from the nationally representative Smoking Toolkit Study in England. A total of 1,135 adult smokers were surveyed at baseline, with 153 (13.5%) respondents followed up at 12 months. Respondents provided information on demographic, social and smoking characteristics. A series of multivariable logistic regression analyses was conducted. Bayes Factors (BFs) were calculated to explore non-significant associations. Smoking to relieve stress was commonly endorsed by respondents from both 'lower' (43.2% [95% CI = 39.4%, 47.0%]) and 'higher' (40.5% [95% CI = 35.9%, 45.1%]) SEP groups (p = 0.39). Smoking to relieve stress was associated with high motivation to stop at baseline (ORadj = 1.48, 95% CI = 1.03-2.12, p = 0.035) but not significantly with the odds of making a quit attempt at a 12-month follow-up, although the magnitude and direction of the effect was similar to that observed for high motivation to stop (ORadj = 1.49, 95% CI = 0.69-3.20, p = 0.3). Data were insensitive to detect moderation effects of SEP (BF = 0.90 and BF = 1.65, respectively). Smoking to relieve stress is a commonly endorsed motive and is associated with high motivation to stop but not significantly with the odds of making a quit attempt in the next 12 months, although the magnitude and direction of the effect was similar for both outcomes. There was no clear evidence of moderation by SEP, although data were insensitive to distinguish the alternative from the null hypothesis.

Changes in Cigarette Smoking and Vaping in Response to the COVID-19 Pandemic in the UK: Findings from Baseline and 12-Month Follow up of HEBECO Study.

This study investigated UK adults' changes in cigarette smoking and vaping during the COVID-19 pandemic and factors associated with any changes. Data were from an online longitudinal study. A self-selected sample (n = 332) of 228 smokers and 155 vapers (51 participants were both smokers and vapers) completed 5 surveys between April 2020 and June 2021. Participants self-reported data on sociodemographics, COVID-19-related, and smoking/vaping characteristics. During the 12 months of observations, among smokers, 45% self-reported a quit attempt (27.5% due to COVID-19-related reasons) since the onset of COVID-19 pandemic and the quit rate was 17.5%. At 12 months, 35.1% of continuing smokers (n = 174) reported smoking less and 37.9% the same, while 27.0% reported an increase in the number of cigarettes smoked/day. Among vapers, 25.0% self-reported a quit attempt (16.1% due to COVID-19-related reasons) and the quit rate was 18.1%. At 12 months, 47.7% of continuing vapers (n = 109) reported no change in the frequency of vaping/hour, while a similar proportion reported vaping less (27.5%) and more (24.8%). Motivation to quit smoking and being younger were associated with making a smoking quit attempt and smoking cessation. Being a cigarette smoker was associated with vaping cessation. Among a self-selected sample, COVID-19 stimulated more interest in reducing or quitting cigarette smoking than vaping.

Personality typologies of smokers and excessive drinkers: a cross-sectional survey of respondents in the BBC Lab UK Study.

Background: Several personality traits have been linked to addictive behaviours, including smoking and excessive drinking. We hypothesised that the combination of low conscientiousness, high extraversion and high neuroticism would be synergistically associated with smoking, excessive drinking and both behaviours combined. Methods: Respondents aged 16+ years ( N=363,454) were surveyed between 2009-2013 as part of the BBC Lab UK Study, with no restrictions on geographical location. Respondents provided information about sociodemographic characteristics, personality traits, and smoking and alcohol consumption. A series of multivariable logistic regression analyses were conducted. Results: No significant three-way but significant two-way interactive effects were observed. The association of high extraversion with smoking was more pronounced in those with high (vs. low) conscientiousness (OR adj=1.51, 95% CI=1.46, 1.56, p<.001; OR adj=1.38, 95% CI=1.35, 1.42, p<.001). The association of high extraversion with excessive drinking was more pronounced in those with low (vs. high) conscientiousness (OR adj=1.70, 95% CI=1.67, 1.74, p<.001; OR adj=1.60, 95% CI=1.56, 1.63, p<.001). The association of high extraversion with both behaviours combined was more pronounced in those with high (vs. low) conscientiousness (OR adj=1.74, 95% CI=1.65, 1.83, p<.001; OR adj=1.62, 95% CI= 1.56, 1.68, p<.001). Results remained largely robust in sensitivity analyses. Conclusions: In a large international survey, we identified two-way 'personality typologies' that are associated with greater odds of smoking, excessive drinking and both behaviours combined. The results may be useful for the tailoring of behaviour change interventions to at-risk individuals.

A pilot randomised trial of a brief virtual reality scenario in smokers unmotivated to quit: Assessing the feasibility of recruitment.

Individual-level interventions for smokers unmotivated to quit remain scarce and have had limited success. Little is known about the potential of virtual reality (VR) for delivering messaging to smokers unmotivated to quit. This pilot trial aimed to assess the feasibility of recruitment and acceptability of a brief, theory-informed VR scenario and estimate proximal quitting outcomes. Unmotivated smokers (recruited between February-August 2021) aged 18+ years who had access to, or were willing to receive via post, a VR headset were randomly assigned (1:1) using block randomisation to view the intervention (i.e., a hospital-based scenario with motivational stop smoking messaging) or a 'sham' VR scenario (i.e., a scenario about the human body without any smoking-specific messaging) with a researcher present via teleconferencing software. The primary outcome was feasibility of recruitment (i.e., achieving the target sample size of 60 participants within 3 months of recruitment). Secondary outcomes included acceptability (i.e., positive affective and cognitive attitudes), quitting self-efficacy and intention to stop smoking (i.e., clicking on a weblink with additional stop smoking information). We report point estimates and 95% confidence intervals (CIs). The study protocol was pre-registered (osf.io/95tus). A total of 60 participants were randomised within 6 months (intervention: n = 30; control: n = 30), 37 of whom were recruited within a 2-month period of active recruitment following an amendment to gift inexpensive (£7) cardboard VR headsets via post. The mean (SD) age of participants was 34.4 (12.1) years, with 46.7% identifying as female. The mean (SD) cigarettes smoked per day was 9.8 (7.2). The intervention (86.7%, 95% CI = 69.3%-96.2%) and control (93.3%, 95% CI = 77.9%-99.2%) scenarios were rated as acceptable. Quitting self-efficacy and intention to stop smoking in the intervention (13.3%, 95% CI = 3.7%-30.7%; 3.3%, 95% CI = 0.1%-17.2%) and control (26.7%, 95% CI = 12.3%-45.9%; 0%, 95% CI = 0%-11.6%) arm were comparable. The target sample size was not achieved within the feasibility window; however, an amendment to gift inexpensive headsets via post appeared feasible. The brief VR scenario appeared acceptable to smokers unmotivated to quit.

Technology-mediated just-in-time adaptive interventions (JITAIs) to reduce harmful substance use: a systematic review.

BACKGROUND AND AIMS: Lapse risk when trying to stop or reduce harmful substance use is idiosyncratic, dynamic and multi-factorial. Just-in-time adaptive interventions (JITAIs) aim to deliver tailored support at moments of need or opportunity. We aimed to synthesize evidence on decision points, tailoring variables, intervention options, decision rules, study designs, user engagement and effectiveness of technology-mediated JITAIs for reducing harmful substance use. METHODS: Systematic review of empirical studies of any design with a narrative synthesis. We searched Ovid MEDLINE, Embase, PsycINFO, Web of Science, the ACM Digital Library, the IEEE Digital Library, ClinicalTrials.gov, the ISRCTN register and dblp using terms related to substance use/mHealth/JITAIs. Outcomes were user engagement and intervention effectiveness. Study quality was assessed with the mHealth Evidence Reporting and Assessment checklist. FINDINGS: We included 17 reports of 14 unique studies, including two randomized controlled trials. JITAIs targeted alcohol (S = 7, n = 120 520), tobacco (S = 4, n = 187), cannabis (S = 2, n = 97) and a combination of alcohol and illicit substance use (S = 1, n = 63), and primarily relied on active measurement and static (i.e. time-invariant) decision rules to deliver support tailored to micro-scale changes in mood or urges. Two studies used data from prior participants and four drew upon theory to devise decision rules. Engagement with available JITAIs was moderate-to-high and evidence of effectiveness was mixed. Due to substantial heterogeneity in study designs and outcome variables assessed, no meta-analysis was performed. Many studies reported insufficient detail on JITAI infrastructure, content, development costs and data security. CONCLUSIONS: Current implementations of just-in-time adaptive interventions (JITAIs) for reducing harmful substance use rely on active measurement and static decision rules to deliver support tailored to micro-scale changes in mood or urges. Studies on JITAI effectiveness are lacking.

Establishing best practices in cancer online support groups: protocol for a realist review.

INTRODUCTION: Considerable observational evidence suggests that cancer online support groups reduce feelings of isolation, depression and anxiety, enhance coping and self-management, and lead to better informed patients. Other studies indicate that cancer online support groups can increase distress. Yet no studies theorise the complex, context-dependent mechanisms by which cancer online support groups generate their-sometimes contrasting-outcomes. METHODS AND ANALYSIS: Guided by an integrated knowledge translation approach and the strategy for patient-oriented research, we will conduct a realist review of cancer online support groups in partnership with stakeholders. We will follow Pawson's five steps and existing quality standards to develop a program theory that explains how cancer online support groups work, for whom and in what circumstances. The specific research questions will be: what positive and negative outcomes have been reported on cancer online support groups? What are the mechanisms that are associated with these outcomes, in which contexts and for whom? Through a rigorous review of relevant scientific and grey literature, as well as ongoing dialogue with stakeholders, a program theory will be developed to explain who benefits from cancer online support groups and who does not, what benefits they derive (or do not), and the factors that affect these outcomes. ETHICS AND DISSEMINATION: The use of secondary data for this review precludes the need for ethical approval. Dissemination will be informed by the knowledge-to-action framework and will consist of tailored knowledge products that are conceived of collaboratively with stakeholders. These will include peer-reviewed publications on how cancer online support groups can be optimised and best practice recommendations to maximise the benefits experienced by people with cancer. These traditional scientific outputs, along with their respective evidence summaries, will be amplified through strategic social media events hosted and promoted by knowledge users. PROSPERO REGISTRATION NUMBER: CRD42021250046.

Pilot randomised controlled trial of the Risk Acceptance Ladder (RAL) as a tool for targeting health communications.

BACKGROUND: Improving adherence to self-protective behaviours is a public health priority. We aimed to assess the potential effectiveness and ease of use of an online version of the Risk Acceptance Ladder (RAL) in promoting help-seeking for cigarette smoking, excessive alcohol consumption, insufficient physical activity, or low fruit and vegetable consumption. METHODS: 843 UK adults were recruited, of whom 602 engaged in at least one risky behaviour. Those with no immediate plans to change (n = 171) completed a behaviour specific RAL. Participants were randomised to one of two conditions; a short message congruent (on-target, n = 73) or incongruent (off-target, n = 98) with their RAL response. Performance of the RAL was assessed by participants' ability to select an applicable RAL item and reported ease of use of the RAL. Effectiveness was assessed by whether or not participants clicked a link to receive information about changing their target behaviour. RESULTS: Two thirds (68.9%, 95% CI = 61.8%-75.3%) of participants were able to select an applicable RAL item that corresponded to what they believed would need to change in order to alter their target behaviour, with 64.9% (95% CI = 57.5%-71.7%) reporting that it was easy to select one option. Compared with the off-target group, participants allocated to the on-target group had greater odds of clicking on the link to receive information (31.5% vs 19.4%; OR = 2.07, 95% CI = 1.01-4.26). CONCLUSION: The Risk Acceptance Ladder may have utility as a tool for tailoring messages to prompt initial steps to engaging in self-protective behaviours.

Systematic review of ecological momentary assessment (EMA) studies of five public health-related behaviours: review protocol.

INTRODUCTION: Ecological momentary assessment (EMA) involves repeated, real-time assessments of phenomena (eg, cognitions, emotions, behaviours) over a period of time in naturalistic settings. EMA is increasingly used to study both within-person and between-person processes. We will review EMA studies investigating key health behaviours and synthesise: (1) study characteristics (eg, frequency of assessments, adherence, incentives), (2) associations between psychological predictors and behaviours and (3) moderators of adherence to EMA protocols. METHODS AND ANALYSIS: This review will focus on EMA studies conducted across five public health behaviours in adult, non-clinical populations: movement behaviour (including physical activity and sedentary behaviour), dietary behaviour, alcohol consumption, tobacco smoking and preventive sexual health behaviours. Studies need to have assessed at least one psychological or contextual predictor of these behaviours. Studies reporting exclusively on physiological outcomes (eg, cortisol) or those not conducted under free-living conditions will be excluded. We will search OVID MEDLINE, Embase, PsycINFO and Web of Science using terms relevant to EMA and the selected health behaviours. Reference lists of existing systematic reviews of EMA studies will be hand searched. Identified articles will be screened by two reviewers. This review is expected to provide a comprehensive summary of EMA studies assessing psychological or contextual predictors of five public health behaviours. ETHICS AND DISSEMINATION: The results will be disseminated through peer-reviewed publications and presentations. Data from included studies will be made available to other researchers. No ethics are required. PROSPERO REGISTRATION NUMBER: CRD42020168314.

Associations between vaping and Covid-19: Cross-sectional findings from the HEBECO study.

AIMS: To explore i) associations between vaping and self-reported diagnosed/suspected Covid-19; ii) changes in vaping since Covid-19 and factors associated with these changes; iii) whether Covid-19 motivated current or recent ex-vapers to quit. METHODS: Cross-sectional online survey of 2791 UK adults recruited 30/04/2020-14/06/2020. Participants self-reported data on sociodemographic characteristics, diagnosed/suspected Covid-19, vaping status, changes in vaping and motivation to quit vaping since Covid-19. RESULTS: There were no differences in diagnosed/suspected Covid-19 between never, current and ex-vapers. Bayes factors indicated there was sufficient evidence to rule out small negative (protective) associations between vaping status and diagnosed/suspected Covid-19. Among current vapers (n = 397), 9.7 % (95 % CI 6.8-12.6 %) self-reported vaping less than usual since Covid-19, 42.0 % (37.2-46.9 %) self-reported vaping more, and 48.3 % (43.4-53.2 %) self-reported no change. In adjusted analyses, vaping less was associated with being female (aOR = 3.40, 95 % CI 1.73-6.71), not living with children (aOR = 4.93, 1.15-21.08) and concurrent smoking (aOR = 8.77, 3.04-25.64), while vaping more was associated with being younger (aOR = 5.26, 1.37-20.0), living alone (aOR = 2.08, 1.14-3.85), and diagnosed/suspected Covid-19 (aOR = 4.72, 2.60-8.62). Of current vapers, 32.2 % (95 % CI 27.5-36.8 %) were motivated to quit vaping since Covid-19, partly motivated by Covid-19, and 21.0 %, (10.5-31.4 %) of recent ex-vapers quit vaping due to Covid-19. CONCLUSIONS: Among UK adults, self-reported diagnosed/suspected Covid-19 was not associated with vaping status. Half of current vapers changed their vaping consumption since Covid-19, with the majority reporting an increase, and a minority was motivated to quit due to Covid-19. REGISTRATION: The analysis plan was pre-registered, and it is available at https://osf.io/6j8z3/.

Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis.

INTRODUCTION: Using WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation. METHODS: Participants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression. RESULTS: Three qualitatively different sequence clusters were identified. "Disengagers" (576/1,313) almost exclusively used the goal-setting feature. "Tryers" (375/1,313) used goal setting and two of the values- and acceptance-based components ("Be Aware," "Be Willing"). "Committers" (362/1,313) primarily used two of the values- and acceptance-based components ("Be Willing," "Be Inspired"), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003). DISCUSSION: WebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success. IMPLICATIONS: This case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.

COVID-19, smoking, vaping and quitting: a representative population survey in England.

AIMS: To estimate (1) associations between self-reported COVID-19, hand-washing, smoking status, e-cigarette use and nicotine replacement therapy (NRT) use and (2) the extent to which COVID-19 has prompted smoking and vaping quit attempts and more smoking inside the home. DESIGN: Cross-sectional household surveys. SETTING AND PARTICIPANTS: A representative sample of the population in England from April to May 2020. The sample included 3179 adults aged ≥ 18 years. MEASUREMENTS: Participants who reported that they definitely or thought they had coronavirus were classified as having self-reported COVID-19. Participants were asked how often they wash their hands after returning home, before preparing foods, before eating or before touching their face. They were also asked whether, due to COVID-19, they had (i) attempted to quit smoking, (ii) attempted to quit vaping and (iii) changed the amount they smoke inside the home. FINDINGS: Odds of self-reported COVID-19 were significantly greater among current smokers [20.9%, adjusted odds ratio (aOR) = 1.34, 95% confidence interval (CI) = 1.04-1.73] and long-term (> 1-year) ex-smokers (16.1%, aOR = 1.33, 95% CI = 1.05-1.68) compared with never smokers (14.5%). Recent (< 1-year) ex-smokers had non-significantly greater odds of self-reported COVID-19 (22.2%, aOR = 1.50, 95% CI = 0.85-2.53). Bayes factors indicated there was sufficient evidence to rule out large differences in self-reported COVID-19 by NRT use and medium differences by e-cigarette use. With the exception of hand-washing before face-touching, engagement in hand-washing behaviours was high (> 85%), regardless of nicotine use. A minority (12.2%) of quit attempts in the past 3 months were reportedly triggered by COVID-19, and approximately one in 10 current e-cigarette users reported attempting to quit vaping because of COVID-19. CONCLUSIONS: In England, current smokers and long-term ex-smokers appear to have higher odds of self-reported COVID-19 compared with never smokers in adjusted analyses, but there were no large differences between people who used nicotine replacement therapy or e-cigarettes. Engagement in hand-washing appears to be high, regardless of nicotine or tobacco use. A minority of past-year smokers and current e-cigarette users, respectively, report attempting to quit smoking/vaping due to COVID-19.