Open Access

Background: Common to most theory-based intervention approaches is the idea of supporting intentions to increase the probability of behavior change. This principle works only if (a) intentions can be explained by the hypothesized socio-cognitive constructs, and (b) people actually do what they intend to do. The overall aim of this thesis was to test these premises using two health behavior theories applied to reducing at-risk alcohol use. Method: The three papers underlying this thesis were based on data of the randomized controlled “Trial Of Proactive Alcohol interventions among job-Seekers” (TOPAS). A total of 1243 job-seekers with at-risk alcohol use were randomized to stage tailored intervention (ST), non-stage tailored intervention (NST), or control group. The ST participants (n = 426) were analyzed in paper 1. Paper 2 was based on the baseline and 3-month data provided by the NST participants (n = 433). Paper 3 was based on baseline, 3-, 6-, and 15-month data provided by the control and ST group not intending to change alcohol use (n = 629). Latent variable modeling was used to investigate the associations of social-cognitive constructs and intentional stages (paper 1), the extent to which intentions were translated into alcohol use (paper 2), and the different trajectories of alcohol use among people not intending to change as well as the ST effect on the trajectories (paper 3). Results: Persons in different intentional stages differed in the processes of change in which they engaged, in the importance placed by them on the pros and cons of alcohol use, and in the perceived ability to quit (ps < 0.01). The association between intentions and alcohol use was weak. The magnitude of this intention-behavior gap depended on the extent to which normative expectations have changed over time (p < 0.01) and was reduced when controlling for the mediating effect of temporal stability of intentions. The gap was also present among people not intending to change: Even without intervention, 35% of the persons reduced the amount of alcohol use after 15 months (p < 0.05) and 2% achieved abstinence. Persons with heavier drinking (33%) and persons with low but frequent use (30%) did not change. Persons with frequent alcohol use seem to benefit less from ST than those with occasional use, although differences were not statistically significant. Conclusions: Intentions can be quite well explained by the hypothesized socio-cognitive constructs. In a sample of persons who were, as a whole, little motivated to change, the precision of how well intentions predict subsequent alcohol use was modest though. Time and socio-contextual influences should be considered.

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.

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.

Background In combination with systematic routine screening, brief alcohol interventions have the potential to promote population health. Little is known on the optimal screening interval. Therefore, this study pursued 2 research questions: (i) How stable are screening results for at‐risk drinking over 12 months? (ii) Can the transition from low‐risk to at‐risk drinking be predicted by gender, age, school education, employment, or past week alcohol use? Methods A sample of 831 adults (55% female; mean age = 30.8 years) from the general population was assessed 4 times over 12 months. The Alcohol Use Disorders Identification Test—Consumption was used to screen for at‐risk drinking each time. Participants were categorized either as low‐risk or at‐risk drinkers at baseline, 3, 6, and 12 months later. Stable and instable risk status trajectories were analyzed descriptively and graphically. Transitioning from low‐risk drinking at baseline to at‐risk drinking at any follow‐up was predicted using a logistic regression model. Results Consistent screening results over time were observed in 509 participants (61%). Of all baseline low‐risk drinkers, 113 (21%) received a positive screening result in 1 or more follow‐up assessments. Females (vs. males; OR = 1.66; 95% confidence intervals [95% CI] = 1.04; 2.64), 18‐ to 29‐year‐olds (vs. 30‐ to 45‐year‐olds; OR = 2.30; 95% CI = 1.26; 4.20), and those reporting 2 or more drinking days (vs. less than 2; OR = 3.11; 95% CI = 1.93; 5.01) and heavy episodic drinking (vs. none; OR = 2.35; 95% CI = 1.06; 5.20) in the week prior to the baseline assessment had increased odds for a transition to at‐risk drinking. Conclusions Our findings suggest that the widely used time frame of 1 year may be ambiguous regarding the screening for at‐risk alcohol use although generalizability may be limited due to higher‐educated people being overrepresented in our sample.