Search

Purpose This paper reports expert opinion on e-health intervention characteristics that enable effective communication of characteristics across the diverse field of e-health interventions. The paper presents a visualization tool to support communication of the defining characteristics. Methods An initial list of e-health intervention characteristics was developed through an iterative process of item generation and discussion among the 12 authors. The list was distributed to 123 experts in the field, who were emailed an invitation to assess and rank the items. Participants were asked to evaluate these characteristics in three separate ways. Results A total of 50 responses were received for a response rate of 40.7%. Six respondents who reported having little or no expertise in e-health research were removed from the dataset. Our results suggest that 10 specific intervention characteristics were consistently supported as of central importance by the panel of 44 e-intervention experts. The weight and perceived relevance of individual items differed between experts; oftentimes, this difference is a result of the individual theoretical perspective and/or behavioral target of interest. Conclusions The first iteration of the visualization of salient characteristics represents an ambitious effort to develop a tool that will support communication of the defining characteristics for e-health interventions aimed to assist e-health developers and researchers to communicate the key characteristics of their interventions in a standardized manner that facilitates dialog.

PURPOSE: This paper reports expert opinion on e-health intervention characteristics that enable effective communication of characteristics across the diverse field of e-health interventions. The paper presents a visualization tool to support communication of the defining characteristics.METHODS: An initial list of e-health intervention characteristics was developed through an iterative process of item generation and discussion among the 12 authors. The list was distributed to 123 experts in the field, who were emailed an invitation to assess and rank the items. Participants were asked to evaluate these characteristics in three separate ways.RESULTS: A total of 50 responses were received for a response rate of 40.7%. Six respondents who reported having little or no expertise in e-health research were removed from the dataset. Our results suggest that 10 specific intervention characteristics were consistently supported as of central importance by the panel of 44 e-intervention experts. The weight and perceived relevance of individual items differed between experts; oftentimes, this difference is a result of the individual theoretical perspective and/or behavioral target of interest.CONCLUSIONS: The first iteration of the visualization of salient characteristics represents an ambitious effort to develop a tool that will support communication of the defining characteristics for e-health interventions aimed to assist e-health developers and researchers to communicate the key characteristics of their interventions in a standardized manner that facilitates dialog.

The Drug Use Disorders Identification Test (DUDIT) was developed for problematic substance use screening, and for a more detailed assessment of problematic use, the Drug Use Disorders Identification Test-Extended (DUDIT-E) was additionally developed.Examining the psychometric properties of DUDIT and DUIT-E across diverse settings in populations of young drug users.We examined the psychometric characteristics of these instruments across various settings in populations of young substance users differing in substance use severity and treatment status. Data were collected from three clinically relevant groups (n = 259) as well as a control sample of college students (n = 109).Reliability analyses indicated good internal consistency for both instruments; high intraclass correlations further indicated good test-retest reliability. Differences among study groups were significant on the DUDIT scale and all DUDIT-E subscales (p0.01), with the target groups exhibiting higher scores compared to controls. A two-factor solution was identified for the factor structure of DUDIT.The Hungarian version of DUDIT and DUDIT-E can effectively identify substance use problems among young users.

Purpose This observational study examined user characteristics, intervention use patterns, and variables associated with reductions in alcohol consumption for anonymous Internet help-seekers using a Web-based self-help program. Method A Web-based cognitive behavioral therapy (CBT) program with eight modules delivered over 10 weeks was offered to participants with at least hazardous use of alcohol according to the Alcohol Use Disorders Identification Test (AUDIT) (n = 4165). At baseline and 10-week follow-up, participants completed the Timeline-followback (TLFB), AUDIT, Drug Use Disorders Identification Test (DUDIT), Hospital Anxiety and Depression Scale (HADS), EuroQol-5 dimension (EQ-5D), World Health Organization Quality of Life Scale-abbreviated version (WHOQOL-BREF), Readiness to Change Questionnaire (RCQ), and Readiness Ruler. Follow-up completers and non-completers were compared at baseline, and follow-up completer outcomes were reported. Predictors of change in drinking behavior were evaluated at follow-up. Results Registered users were 41.88 years old on average (SD = 12.36), and 52 % were women; the mean baseline number of drinks during the past week was 27.27 (SD = 17.92) with 62 % in the AUDIT category of probable dependence and only 7 % having low-risk consumption according to public health guidelines. At follow-up (n = 1043), 53 % showed a clinically significant change to a lower level of alcohol use (χ2 = 254.403, p

Purpose University students in a study on estimated blood alcohol concentration (eBAC) feedback apps were offered participation in a second study, if reporting continued excessive consumption at 6-week follow-up. This study evaluated the effects on excessive alcohol consumption of offering access to an additional skills training app. Method A total of 186 students with excessive alcohol consumption were randomized to an intervention group or a wait list group. Both groups completed online follow-ups regarding alcohol consumption after 6 and 12 weeks. Wait list participants were given access to the intervention at 6-week follow-up. Assessment-only controls (n = 144) with excessive alcohol consumption from the ongoing study were used for comparison. Results The proportion of participants with excessive alcohol consumption declined in both intervention and wait list groups compared to controls at first (p