Cutting consumption without diluting the experience: Preferences for different tactics for reducing alcohol consumption among increasing-and-higher-risk drinkers based on drinking context.

Contexts in which people drink vary. Certain drinking contexts may be more amenable to change than others and the effectiveness of alcohol reduction tactics may differ across contexts. This study aimed to explore how helpful context-specific tactics for alcohol reduction were perceived as being amongst increasing-and-higher-risk drinkers. Using the Behaviour Change Technique Taxonomy, context-specific tactics to reduce alcohol consumption were developed by the research team and revised following consultation with experts in behaviour change. In four focus groups (two online, two in-person), N = 20 adult increasing-and-higher-risk drinkers in the UK discussed how helpful tactics developed for four drinking contexts would be: drinking at home alone (19 tactics), drinking at home with partner or family (21 tactics), in the pub with friends (23 tactics), and a meal out of the home (20 tactics). Transcripts were analysed using constant comparison methods. Participants endorsed four broad approaches to reducing alcohol consumption which encompassed all the individual tactics developed by the research team: Diluting and substituting drinks for those containing less alcohol (e.g. switching to soft drinks or no- or low-alcohol drinks); Reducing external pressure to drink (e.g. setting expectations in advance); Creating barriers to drinking (e.g. not buying alcohol to keep at home or storing it in less visible places), and Setting new habits (e.g. breaking old patterns and taking up new hobbies). Three cross-cutting themes influenced how applicable these approaches were to different drinking contexts. These were: Situational pressure, Drinking motives, and Financial motivation. Diluting and substituting drinks which enabled covert reduction and Reducing external pressure to drink were favoured in social drinking contexts. Diluting and substituting drinks which enabled participants to feel that they were having 'a treat' or which facilitated relaxation and Creating barriers to drinking were preferred at home. Interventions to reduce alcohol consumption should offer tactics tailored to individuals' drinking contexts and which account for context-specific individual and situational pressure to drink.

Supervised machine learning to predict smoking lapses from Ecological Momentary Assessments and sensor data: Implications for just-in-time adaptive intervention development.

Specific moments of lapse among smokers attempting to quit often lead to full relapse, which highlights a need for interventions that target lapses before they might occur, such as just-in-time adaptive interventions (JITAIs). To inform the decision points and tailoring variables of a lapse prevention JITAI, we trained and tested supervised machine learning algorithms that use Ecological Momentary Assessments (EMAs) and wearable sensor data of potential lapse triggers and lapse incidence. We aimed to identify a best-performing and feasible algorithm to take forwards in a JITAI. For 10 days, adult smokers attempting to quit were asked to complete 16 hourly EMAs/day assessing cravings, mood, activity, social context, physical context, and lapse incidence, and to wear a Fitbit Charge 4 during waking hours to passively collect data on steps and heart rate. A series of group-level supervised machine learning algorithms (e.g., Random Forest, XGBoost) were trained and tested, without and with the sensor data. Their ability to predict lapses for out-of-sample (i) observations and (ii) individuals were evaluated. Next, a series of individual-level and hybrid (i.e., group- and individual-level) algorithms were trained and tested. Participants (N = 38) responded to 6,124 EMAs (with 6.9% of responses reporting a lapse). Without sensor data, the best-performing group-level algorithm had an area under the receiver operating characteristic curve (AUC) of 0.899 (95% CI = 0.871-0.928). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.524-0.994; median AUC = 0.639). 15/38 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.855 (range: 0.451-1.000). Hybrid algorithms could be constructed for 25/38 participants, with a median AUC of 0.692 (range: 0.523 to 0.998). With sensor data, the best-performing group-level algorithm had an AUC of 0.952 (95% CI = 0.933-0.970). Its ability to classify lapses for out-of-sample individuals ranged from poor to excellent (AUCper person = 0.494-0.979; median AUC = 0.745). 11/30 participants had adequate data for individual-level algorithms to be constructed, with a median AUC of 0.983 (range: 0.549-1.000). Hybrid algorithms could be constructed for 20/30 participants, with a median AUC of 0.772 (range: 0.444 to 0.968). In conclusion, high-performing group-level lapse prediction algorithms without and with sensor data had variable performance when applied to out-of-sample individuals. Individual-level and hybrid algorithms could be constructed for a limited number of individuals but had improved performance, particularly when incorporating sensor data for participants with sufficient wear time. Feasibility constraints and the need to balance multiple success criteria in the JITAI development and implementation process are discussed.

Comparing identity, attitudes, and indicators of effectiveness in people who smoke, vape or use heated tobacco products: A cross-sectional study.

BACKGROUND: There is limited long-term and independent research on heated tobacco products (HTPs). We compared people who used HTPs with those who used nicotine vaping products (NVP) or cigarettes on smoker identity, indicators of effectiveness and, among NVP/HTP users, perceptions of these products. METHODS: Adults exclusive cigarette smokers (N = 45) and ex-smokers with medium/long-term (>3months) NVP (N = 46) or HTP use (N = 45) were recruited in London, UK. Participants completed a questionnaire assessing socio-demographics, smoking characteristics, smoker identity, dependence, intention to stop and attitudes towards HTP/NVP. RESULTS: In adjusted analysis, people who used cigarettes (Mean Difference (MD) = 1.4, 95%Confidence Intervals (CI) 0.7,2.0) and HTPs (MD = 0.8, 95%CI 0.1, 1.5) reported stronger smoker identities than those who used NVPs. Compared with smokers, HTP/NVP users had lower cravings for cigarettes (MD = 3.0, 95%CI 1.6, 4.3; MD = 3.1, 95%CI 1.9, 4.3, respectively), and higher intention to stop product use (MD = -0.8, 95%CI -1.7,-0.01; MD = -1.2, 95%CI -2.0, -0.3, respectively). People using HTPs or NVPs reported similar perceived product satisfaction (HTP:M = 3.4, 95%CI 2.8, 3.9; NVP:M = 3.0, 95%CI 2.5, 3.5), efficacy for smoking cessation (HTP:M = 4.5, 95%CI 4.2, 4.9; NVP:M = 4.6, 95%CI 4.3, 4.9) and safety (HTP:M = 2.1, 95%CI 2.0, 2.2; NVP:M = 2.0, 95%CI 1.8, 2.1). HTP users reported greater perceived addictiveness than NVPs (MD = 0.3, 95%CI 0.2, 0.6). CONCLUSIONS: HTP and NVP users perceived products to be similarly acceptable and effective suggesting that HTPs, like NVPs, may support smoking cessation. However, since HTP use appears to maintain a stronger smoker identity and perceived addiction, this may suggest a more limited role of HTP for a permanent transition away from cigarettes.