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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 < 0.005). At month 24, neither smoking status nor number of cigarettes per day moderated the efficacy of PE (IRR = 0.69, 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.
Little is known about the (co-)occurrence of smoking, alcohol at-risk drinking, physical inactivity and overweight, and the motivation to change these behavioral health risk factors (HRFs) in older general hospital patients with cardiovascular disease. Between October and December 2016, all consecutively admitted patients aged 50 to 79 years were proactively recruited on 3 cardiology wards and asked to participate in a survey on HRFs and behavior change motivation. Of the eligible patients, 80.4% participated in the survey (n = 328). The mean age was 66.5 years (standard deviation 9.0), and 65.5% were male. At least 1 HRF was present in 91.8% (n = 280), at least 2 HRFs in 54.4% (n = 166), and 3 or 4 HRFs in 12.1% (n = 37) of participants. The proportion of older adults who contemplated or were changing or planning to change their behavior to meet health behavior recommendations ranged between 66.0% (smoking) and 93.2% (alcohol consumption). The results indicate a notable co-occurrence of behavioral HRFs in older patients with cardiovascular disease. The majority of older adults were at least considering changing the respective behavior. To prevent and treat diseases efficiently, hospitalization may be a suitable moment for systematic multiple HRF screening and intervention.
Background:
Social equity in the efficacy of behavior change intervention is much needed. While the efficacy of brief alcohol interventions (BAIs), including digital interventions, is well established, particularly in health care, the social equity of interventions has been sparsely investigated.
Objective:
We aim to investigate whether the efficacy of computer-based versus in-person delivered BAIs is moderated by the participants’ socioeconomic status (ie, to identify whether general hospital patients with low-level education and unemployed patients may benefit more or less from one or the other way of delivery compared to patients with higher levels of education and those that are employed).
Methods:
Patients with nondependent at-risk alcohol use were identified through systematic offline screening conducted on 13 general hospital wards. Patients were approached face-to-face and asked to respond to an app for self-assessment provided by a mobile device. In total, 961 (81% of eligible participants) were randomized and received their allocated intervention: computer-generated and individually tailored feedback letters (CO), in-person counseling by research staff trained in motivational interviewing (PE), or assessment only (AO). CO and PE were delivered on the ward and 1 and 3 months later, were based on the transtheoretical model of intentional behavior change and required the assessment of intervention data prior to each intervention. In CO, the generation of computer-based feedback was created automatically. The assessment of data and sending out feedback letters were assisted by the research staff. Of the CO and PE participants, 89% (345/387) and 83% (292/354) received at least two doses of intervention, and 72% (280/387) and 54% (191/354) received all three doses of intervention, respectively. The outcome was change in grams of pure alcohol per day after 6, 12, 18, and 24 months, with the latter being the primary time-point of interest. Follow-up interviewers were blinded. Study group interactions with education and employment status were tested as predictors of change in alcohol use using latent growth modeling.
Results:
The efficacy of CO and PE did not differ by level of education (P=.98). Employment status did not moderate CO efficacy (Ps≥.66). Up to month 12 and compared to employed participants, unemployed participants reported significantly greater drinking reductions following PE versus AO (incidence rate ratio 0.44, 95% CI 0.21-0.94; P=.03) and following PE versus CO (incidence rate ratio 0.48, 95% CI 0.24–0.96; P=.04). After 24 months, these differences were statistically nonsignificant (Ps≥.31).
Conclusions:
Computer-based and in-person BAI worked equally well independent of the patient’s level of education. Although findings indicate that in the short-term, unemployed persons may benefit more from BAI when delivered in-person rather than computer-based, the findings suggest that both BAIs have the potential to work well among participants with low socioeconomic status.