Dr Therese Fozard

INTRODUCTION: Incentive Sensitisation theory suggests wanting and liking are dissociable concepts, with wanting, but not liking typically increasing with repeated drug use. Wanting is associated with anticipation of reward, whereas liking relates to pleasure derived from consummatory behaviour. However, numerous studies have conceptualised liking as an anticipatory cognition. This study explores whether levels of nicotine dependence differentially effect wanting and liking responses to smoking-related cues, and whether anticipated and consummatory liking are equivalent, and dissociable from wanting. METHOD: Heavy (HS, mean = 16 cigarettes/day) and light non-daily (LS, mean = 2 cigarettes/day) smokers completed wanting and anticipated liking questionnaires pre-, immediately post-exposure to smoking-related and neutral cues and at session-end. Consummatory liking was measured post-session, immediately after smoking. RESULTS: Wanting and anticipated liking responses were comparable. Smoking-related cues increased wanting and anticipated liking compared to neutral cues. This effect was maintained until session-end. No baseline differences were seen between HS and LS on wanting or anticipated liking, however after cue exposure, and at session-end, HS reported greater drug wanting and anticipated liking than LS. Conversely, HS and LS did not differ on consummatory liking. Analyses confirmed the relationship between wanting and anticipated liking was significantly stronger than wanting and consummatory liking or anticipated and consummatory liking. CONCLUSIONS: Wanting and anticipated liking appear to be overlapping constructs assessing expectations of reward, that are dissociable from consummatory liking. Furthermore, heavier smoking increases drug wanting, but not smoking pleasure. Future attempts to dissociate these concepts should ensure liking is measured during/immediately after consumption.

BACKGROUND: Cannabis smoking and cigarette smoking often co-occur, yet limited research has investigated the potentially different role impulsivity may play when these behaviours occur in isolation, compared with in combination. AIMS: This study examined trait and behavioural impulsivity as a function of both cigarette and cannabis smoking. METHODS: Trait impulsivity (BIS-11) was compared between 44 non-smokers, 76 cigarette only, 47 cannabis only and 58 cannabis plus cigarette smokers. The effects of cigarette and cannabis smoking on behavioural impulsivity (stop-signal and information sampling tasks) were then assessed in 87 of these participants during a laboratory session. RESULTS: Trait impulsivity was significantly higher in cigarette smokers than non-smokers, irrespective of cannabis use, except for motor impulsivity, where cigarette smoking was only associated with elevated trait impulsivity in non-smokers of cannabis. Dimensions of trait impulsivity were significantly positively related to cigarette smoking frequency and nicotine dependence, but not to cannabis smoking frequency or dependence. Smoking cigarettes or cannabis was associated with significantly impaired reflection impulsivity relative to not smoking either substance. However, no additional increases in reflection impulsivity were observed in those who smoked both cigarettes and cannabis. No group differences in response inhibition were detected. CONCLUSIONS: Heightened trait impulsivity appears to be uniquely related to cigarette smoking, whilst the smoking of cigarettes or cannabis is associated with impairments in reflection impulsivity. Improved outcomes for treating cannabis dependence may result from encouraging concomitant cigarette smokers to cease using both drugs simultaneously in order to reduce heightened impulsivity and risk of relapse.

Background Accurate anthropometric measurement is important within epidemiological studies and clinical practice. Traditionally, self-reported weight is validated against in-person weight measurement. Objectives This study aimed to 1) determine the comparison of online self-reported weight against images of weight captured on scales in a young adult sample, 2) compare this across body mass index (BMI), gender, country, and age groups, and 3) explore demographics of those who did/did not provide a weight image. Methods Cross-sectional analysis of baseline data from a 12-mo longitudinal study of young adults in Australia and the UK was conducted. Data were collected by online survey via Prolific research recruitment platform. Self-reported weight and sociodemographics (for example, age, gender) were collected for the whole sample (n = 512), and images of weight for a subset (n = 311). Tests included Wilcoxon signed-rank test to evaluate differences between measures, Pearson correlation to explore the strength of the linear relationship, and Bland-Altman plots to evaluate agreement. Results Self-reported weight [median (interquartile range), 92.5 kg (76.7–112.0)] and image-captured weight [93.8 kg (78.8–112.8)] were significantly different (z = −6.76, P < 0.001), but strongly correlated (r = 0.983, P < 0.001). In the Bland-Altman plot [mean difference −0.99 kg (−10.83, 8.84)], most values were within limits of agreement (2 standard deviation). Correlations remained high across BMI, gender, country, and age groups (r > 0.870, P < 0.002). Participants with BMI in ranges 30–34.9 and 35–39.9 kg/m2 were less likely to provide an image. Conclusions This study demonstrates the method concordance of image-based collection methods with self-reported weight in online research.

Introduction Understanding the complexities of change in eating behaviours, mental health, well-being and weight is crucial to inform healthcare and service provision, particularly in light of the exacerbating effects of the COVID-19 pandemic. This study aims to address the need for more comprehensive cross-sectional and longitudinal evidence, by tracking eating behaviours, mental health, health related behaviours and weight over a 12-month period, in a sample of young adults (18–35 years) in the UK and Australia. Methods and analysis Online surveys administered via the Prolific online research platform will be used for data collection at baseline, 6 months and 12 months. The survey (approximately 45 min) measures demographics, the impact of COVID-19, body mass index (BMI), weight management and health service usage, eating behaviours, personality, mental health, and health-related behaviours. An optional substudy component at each time point aims to validate self-reported weight in the main survey through images. Study inclusion criteria are; aged 18–34 years at baseline, BMI ≥20 kg/m2, and residing in the UK or Australia. A target of 500 participants at baseline was set, recruited through Prolific, and with recruitment stratified by BMI, sex and country. The proposed analyses include creating static predictive models using baseline data (eg, using latent class analysis, factor analysis or similar), and mapping changes longitudinally (eg, using multivariate regressions). These analyses will enable changes in the study measures to be identified, as well as predictors and outcomes of change. Ethics and dissemination Ethical approval was granted by Leeds Beckett University, UK (reference number 86004) and the University of Newcastle, Australia (reference number H-2022–0110). Study findings will be disseminated through scientific journals, conferences, institute websites and social media, and briefings tailored to policy, practice and the public, with the intention to help inform the future development of health and well-being care and support for young adults across Australia and the UK.