The Role of Tobacco Smoking in the Efficacy of Brief Alcohol Intervention: Results from a Randomized Controlled Trial

This study investigated whether tobacco smoking affected outcomes of brief alcohol interventions (BAIs) in at-risk alcohol-drinking general hospital patients. Between 2011 and 2012 among patients aged 18–64 years, 961 patients were allocated to in-person counseling (PE), computer-based BAI containing computer-generated individual feedback letters (CO), and assessment only. PE and CO included contacts at baseline, 1, and 3 months. After 6, 12, 18, and 24 months, self-reported reduction of alcohol use per day was assessed as an outcome. By using latent growth curve models, self-reported smoking status, and number of cigarettes per day were tested as moderators. In PE and CO, alcohol use was reduced independently of smoking status (IRRs ≤ 0.61, ps 0.05) and CO (IRR = 0.85, ps > 0.05). Up to month 12, among persons smoking ≤ 19 cigarettes per day, the efficacy of CO increased with an increasing number of cigarettes (ps < 0.05). After 24 months, the efficacy of PE and CO that have been shown to reduce drinking did not differ by smoking status or number of cigarettes per day. Findings indicate that efficacy may differ by the number of cigarettes in the short term.Peer Reviewe

Do in-person and computer-based brief alcohol interventions reduce tobacco smoking among general hospital patients? Secondary outcomes from a randomized controlled trial

Abstract Background At-risk alcohol use and tobacco smoking often co-occur. We investigated whether brief alcohol interventions (BAIs) among general hospital patients with at-risk alcohol use may also reduce tobacco smoking over 2 years. We also investigated whether such effects vary by delivery mode; i.e. in-person versus computer-based BAI. Methods A proactively recruited sample of 961 general hospital patients with at-risk alcohol use aged 18 to 64 years was allocated to three BAI study groups: in-person BAI, computer-based BAI, and assessment only. In-person- and computer-based BAI included motivation-enhancing intervention contacts to reduce alcohol use at baseline and 1 and 3 months later. Follow-ups were conducted after 6, 12, 18 and 24 months. A two-part latent growth model, with self-reported smoking status (current smoking: yes/no) and number of cigarettes in smoking participants as outcomes, was estimated. Results Smoking participants in computer-based BAI smoked fewer cigarettes per day than those assigned to assessment only at month 6 (meannet change = − 0.02; 95% confidence interval = − 0.08–0.00). After 2 years, neither in-person- nor computer-based BAI significantly changed smoking status or number of cigarettes per day in comparison to assessment only or to each other (ps ≥ 0.23). Conclusions While computer-based BAI also resulted in short-term reductions of number of cigarettes in smoking participants, none of the two BAIs were sufficient to evoke spill-over effects on tobacco smoking over 2 years. For long-term smoking cessation effects, multibehavioural interventions simultaneously targeting tobacco smoking along with at-risk alcohol use may be more effective. Trial registration number: NCT01291693

Proactive automatised lifestyle intervention (PAL) in general hospital patients: study protocol of a single-group trial

IntroductionThe co-occurrence of health risk behaviours (HRBs, ie, tobacco smoking, at-risk alcohol use, insufficient physical activity and unhealthy diet) increases the risks of cancer, other chronic diseases and mortality more than additively; and applies to more than half of adult general populations. However, preventive measures that target all four HRBs and that reach the majority of the target populations, particularly those persons most in need and hard to reach are scarce. Electronic interventions may help to efficiently address multiple HRBs in healthcare patients. The aim is to investigate the acceptance of a proactive and brief electronic multiple behaviour change intervention among general hospital patients with regard to reach, retention, equity in reach and retention, satisfaction and changes in behaviour change motivation, HRBs and health.Methods and analysisA pre–post intervention study with four time points is conducted at a general hospital in Germany. All patients, aged 18–64 years, admitted to participating wards of five medical departments (internal medicine A and B, general surgery, trauma surgery, ear, nose and throat medicine) are systematically approached and invited to participate. Based on behaviour change theory and individual HRB profile, 175 participants receive individualised and motivation-enhancing computer-generated feedback at months 0, 1 and 3. Intervention reach and retention are determined by the proportion of participants among eligible patients and of participants who continue participation, respectively. Equity in reach and retention are measured with regard to school education and other sociodemographics. To investigate satisfaction with the intervention and subsequent changes, a 6-month follow-up is conducted. Descriptive statistics, multivariate regressions and latent growth modelling are applied.Ethics and disseminationThe local ethics commission and data safety appointee approved the study procedures. Results will be disseminated via publication in international scientific journals and presentations on scientific conferences.Trial registration numberNCT05365269

Investigating the Association Between the Co-Occurrence of Behavioral Health Risk Factors and Sick Days in General Hospital Patients

Objectives: To investigate the co-occurrence of 4 behavioral health risk factors (BHRFs), namely tobacco smoking, alcohol at-risk drinking, physical inactivity and unhealthy diet and their association with sick days prior to hospitalization in general hospital patients. Methods: Over 10 weeks (11/2020-04/2021), all 18-64-year-old patients admitted to internal medicine, general and trauma surgery, and otorhinolaryngology wards of a tertiary care hospital were systematically approached. Among 355 eligible patients, 278 (78.3%) participated, and 256 (72.1%) were analyzed. Three BHRF sum scores were determined, including current tobacco smoking, alcohol use, physical inactivity and 1 of 3 indicators of unhealthy diet. Associations between BHRF sum scores and sick days in the past 6 months were analyzed using multivariate zero-inflated negative binomial regressions. Results: Sixty-two percent reported multiple BHRFs (≥2). The BHRF sum score was related to the number of sick days if any (p = 0.009) with insufficient vegetable and fruit intake as diet indicator. Conclusion: The majority of patients disclosed multiple BHRFs. These were associated with sick days prior to admission. The findings support the need to implement interventions targeting multiple BHRFs in general hospitals