Psychometric properties of the 11-item self-report Drug Use Disorders Identification Test (DUDIT) were evaluated in a sample of heavy drug users from prison, probation, and inpatient detoxification settings, and in a general Swedish population sample. In the drug user sample, the DUDIT predicted drug dependence with a sensitivity of 90% for both DSM-4 and ICD-10 and a respective specificity of 78 and 88%. Reliability according to Cronbach’s α coefficient was 0.80. In the population sample, 3.1% scored positive on the DUDIT; T-score values are suggested. The DUDIT screens effectively for drug-related problems in clinically selected groups and may prove useful in the context of public health surveys.

Face-to-face brief interventions for problem drinking are effective, but they have found limited implementation in routine care and the community. Internet-based interventions could overcome this treatment gap. We investigated effectiveness and moderators of treatment outcomes in internet-based interventions for adult problem drinking (iAIs). Systematic searches were performed in medical and psychological databases to 31 December 2016. A one-stage individual patient data meta-analysis (IPDMA) was conducted with a linear mixed model complete-case approach, using baseline and first follow-up data. The primary outcome measure was mean weekly alcohol consumption in standard units (SUs, 10 grams of ethanol). Secondary outcome was treatment response (TR), defined as less than 14/21 SUs for women/men weekly. Putative participant, intervention, and study moderators were included. Robustness was verified in three sensitivity analyses: a two-stage IPDMA, a one-stage IPDMA using multiple imputation, and a missing-not-at-random (MNAR) analysis. We obtained baseline data for 14,198 adult participants (19 randomised controlled trials [RCTs], mean age 40.7 [SD = 13.2], 47.6% women). Their baseline mean weekly alcohol consumption was 38.1 SUs (SD = 26.9). Most were regular problem drinkers (80.1%, SUs 44.7, SD = 26.4) and 19.9% (SUs 11.9, SD = 4.1) were binge-only drinkers. About one third were heavy drinkers, meaning that women/men consumed, respectively, more than 35/50 SUs of alcohol at baseline (34.2%, SUs 65.9, SD = 27.1). Post-intervention data were available for 8,095 participants. Compared with controls, iAI participants showed a greater mean weekly decrease at follow-up of 5.02 SUs (95% CI −7.57 to −2.48, p < 0.001) and a higher rate of TR (odds ratio [OR] 2.20, 95% CI 1.63–2.95, p < 0.001, number needed to treat [NNT] = 4.15, 95% CI 3.06–6.62). Persons above age 55 showed higher TR than their younger counterparts (OR = 1.66, 95% CI 1.21–2.27, p = 0.002). Drinking profiles were not significantly associated with treatment outcomes. Human-supported interventions were superior to fully automated ones on both outcome measures (comparative reduction: −6.78 SUs, 95% CI −12.11 to −1.45, p = 0.013; TR: OR = 2.23, 95% CI 1.22–4.08, p = 0.009). Participants treated in iAIs based on personalised normative feedback (PNF) alone were significantly less likely to sustain low-risk drinking at follow-up than those in iAIs based on integrated therapeutic principles (OR = 0.52, 95% CI 0.29–0.93, p = 0.029). The use of waitlist control in RCTs was associated with significantly better treatment outcomes than the use of other types of control (comparative reduction: −9.27 SUs, 95% CI −13.97 to −4.57, p < 0.001; TR: OR = 3.74, 95% CI 2.13–6.53, p < 0.001). The overall quality of the RCTs was high; a major limitation included high study dropout (43%). Sensitivity analyses confirmed the robustness of our primary analyses. To our knowledge, this is the first IPDMA on internet-based interventions that has shown them to be effective in curbing various patterns of adult problem drinking in both community and healthcare settings. Waitlist control may be conducive to inflation of treatment outcomes.

Brief interventions via the internet have been shown to reduce university students’ alcohol intake. This study tested two smartphone applications (apps) targeting drinking choices on party occasions, with the goal of reducing problematic alcohol intake among Swedish university students.

The KIDSCREEN-27 is a measure of child and adolescent quality of life (QoL), with excellent psychometric properties, available in child-report and parent-rating versions in 38 languages. This study provides child-reported and parent-rated norms for the KIDSCREEN-27 among Swedish 11–16 year-olds, as well as child-parent agreement. Sociodemographic correlates of self-reported wellbeing and parent-rated wellbeing were also measured. A random population sample consisting of 600 children aged 11–16, 100 per age group and one of their parents (N = 1200), were approached for response to self-reported and parent-rated versions of the KIDSCREEN-27. Parents were also asked about their education, employment status and their own QoL based on the 26-item WHOQOL-Bref. Based on the final sampling pool of 1158 persons, a 34.8% response rate of 403 individuals was obtained, including 175 child-parent pairs, 27 child singleton responders and 26 parent singletons. Gender and age differences for parent ratings and child-reported data were analyzed using t-tests and the Mann-Whitney U-test. Post-hoc Dunn tests were conducted for pairwise comparisons when the p-value for specific subscales was 0.05 or lower. Child-parent agreement was tested item-by-item, using the Prevalence- and Bias-Adjusted Kappa (PABAK) coefficient for ordinal data (PABAK-OS); dimensional and total score agreement was evaluated based on dichotomous cut-offs for lower well-being, using the PABAK and total, continuous scores were evaluated using Bland-Altman plots. Compared to European norms, Swedish children in this sample scored lower on Physical wellbeing (48.8 SE/49.94 EU) but higher on the other KIDSCREEN-27 dimensions: Psychological wellbeing (53.4/49.77), Parent relations and autonomy (55.1/49.99), Social Support and peers (54.1/49.94) and School (55.8/50.01). Older children self-reported lower wellbeing than younger children. No significant self-reported gender differences occurred and parent ratings showed no gender or age differences. Item-by-item child-parent agreement was slight for 14 items (51.9%), fair for 12 items (44.4%), and less than chance for one item (3.7%), but agreement on all dimensions as well as the total score was substantial according to the PABAK-OS. Visual interpretation of the Bland-Altman plot suggested that when children’s average wellbeing score was lower parents seemed to rate their children as having relatively higher total wellbeing, but as children’s average wellbeing score increased, parents tended to rate their children as having relatively lower total wellbeing. Children living with both parents had higher wellbeing than those who lived with only one parent. Results agreed with European findings that adolescent wellbeing decreases with age but contrasted with some prior Swedish research identifying better wellbeing for boys on all dimensions but Social support and peers. The study suggests the importance of considering children’s own reports and not only parental or other informant ratings. Future research should be conducted at regular intervals and encompass larger samples.

Objective The purpose of this article is to explore learners’ perceptions of using virtual patients in a behavioral medicine Massive Open Online Course (MOOCs) and thereby describe innovative ways of disseminating knowledge in health-related areas. Methods A 5-week MOOC on behavioral medicine was hosted on the edX platform. The authors developed two branched virtual patients consisting of video recordings of a live standardized patient, with multiple clinical decision points and narration unfolding depending on learners’ choices. Students interacted with the virtual patients to treat stress and sleep problems. Answers to the exit survey and participant comments from the discussion forum were analyzed qualitatively and quantitatively. Results In total, 19,236 participants enrolled in the MOOC, out of which 740 received the final certificate. The virtual patients were completed by 2317 and 1640 participants respectively. Among survey respondents (n = 442), 83.1% agreed that the virtual patient exercise was helpful. The qualitative analysis resulted in themes covering what it was like to work with the virtual patient, with subthemes on learner-centered education, emotions/eustress, game comparisons, what the participants learned, what surprised them, how confident participants felt about applying interventions in practice, suggestions for improvement, and previous experiences of virtual patients. Conclusions Students were enthusiastic about interacting with the virtual patients as a means to apply new knowledge about behavioral medicine interventions. The most common suggestion was to incorporate more interactive cases with various levels of complexity. Further research should include patient outcomes and focus on interprofessional aspects of learning with virtual patients in a MOOC.