The Check Your Cannabis Screener: A New Online Personalized Feedback Tool

This brief report describes the development and first year of use of an Internet-based screener for Cannabis users. Two versions of the Check Your Cannabis screener were compared, one linked to an already established harm reduction website for young Cannabis users (as an exercise called “Check how I compare with others,” on www.WhatsWithWeed.ca) and the other a standalone version (www.CheckYourCannabis.net). The What’s With Weed version attracted ten times more users and had a significantly younger audience as compared to the standalone version, underlining the benefits of targeting a website to a specific audience and linking to websites with already established reputations. Further work is needed to establish any impact on actual Cannabis use from taking the Check Your Cannabis screener

Relationships of the Psychological Influence of Food and Barriers to Lifestyle Change to Weight and Utilization of Online Weight Loss Tools

Abstract: Introduction: The psychological influence of food (PFS) and perceived barriers to lifestyle change (PBLC) were considered as predictors of body mass index and website tool utilization (TU) in an online weight loss program. Materials and Methodology: An archival analysis of all (N = 1361) overweight/obese (BMI M = 31.6 + 6.24 kg/m 2), adult (M = 42.0 + 10.72 years) users (82.4 % female) of an evidence-based, multidisciplinary Internet weight loss program was performed. Predictor variables included: PFS and PBLC, age, and longest maintained weight loss in relation to 1) BMI 2

Demographic and indication-specific characteristics have limited association with social network engagement: evidence from 24,954 members of four health care support groups

Background: Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities. Objective: The aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables. Methods: Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables. Results: The DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R2 values (.013-.086) with limited statistically significant demographic and indication-specific variables. Conclusions: Very few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities

Targeting medication non-adherence behavior in selected autoimmune diseases: a systematic approach to digital health program development

Background 29 autoimmune diseases, including Rheumatoid Arthritis, gout, Crohn’s Disease, and Systematic Lupus Erythematosus affect 7.6-9.4% of the population. While effective therapy is available, many patients do not follow treatment or use medications as directed. Digital health and Web 2.0 interventions have demonstrated much promise in increasing medication and treatment adherence, but to date many Internet tools have proven disappointing. In fact, most digital interventions continue to suffer from high attrition in patient populations, are burdensome for healthcare professionals, and have relatively short life spans. Objective Digital health tools have traditionally centered on the transformation of existing interventions (such as diaries, trackers, stage-based or cognitive behavioral therapy programs, coupons, or symptom checklists) to electronic format. Advanced digital interventions have also incorporated attributes of Web 2.0 such as social networking, text messaging, and the use of video. Despite these efforts, there has not been little measurable impact in non-adherence for illnesses that require medical interventions, and research must look to other strategies or development methodologies. As a first step in investigating the feasibility of developing such a tool, the objective of the current study is to systematically rate factors of non-adherence that have been reported in past research studies. Methods Grounded Theory, recognized as a rigorous method that facilitates the emergence of new themes through systematic analysis, data collection and coding, was used to analyze quantitative, qualitative and mixed method studies addressing the following autoimmune diseases: Rheumatoid Arthritis, gout, Crohn’s Disease, Systematic Lupus Erythematosus, and inflammatory bowel disease. Studies were only included if they contained primary data addressing the relationship with non-adherence. Results Out of the 27 studies, four non-modifiable and 11 modifiable risk factors were discovered. Over one third of articles identified the following risk factors as common contributors to medication non-adherence (percent of studies reporting): patients not understanding treatment (44%), side effects (41%), age (37%), dose regimen (33%), and perceived medication ineffectiveness (33%). An unanticipated finding that emerged was the need for risk stratification tools (81%) with patient-centric approaches (67%). Conclusions This study systematically identifies and categorizes medication non-adherence risk factors in select autoimmune diseases. Findings indicate that patients understanding of their disease and the role of medication are paramount. An unexpected finding was that the majority of research articles called for the creation of tailored, patient-centric interventions that dispel personal misconceptions about disease, pharmacotherapy, and how the body responds to treatment. To our knowledge, these interventions do not yet exist in digital format. Rather than adopting a systems level approach, digital health programs should focus on cohorts with heterogeneous needs, and develop tailored interventions based on individual non-adherence patterns

Who are superusers of Digital Health Social Networks?

Digital Health Social Networks (DHSNs), otherwise known as online support groups or peerto- peer patient groups, have been in existence since the mid-1980s. However, they have only recently been recognized as important tools in healthcare. This dissertation focuses on superusers, a subset of DHSN participants who create the majority of content, and who are essential to the health and vibrancy of a network. The three essays in this dissertation assess the feasibility of quantitatively identifying superusers though theoretical models rooted in econometrics and graph theory. The data sources are four, long-standing DHSNs. Two of the four DHSNs focus on mental health (depressive disorder and panic disorder), and the remaining two on addictions (problem drinking and smoking cessation). The first essay examines associations between demographic characteristics, indication severity, and posting behaviour. The second investigates whether the distribution frequency in the four DHSNs follows properties of power laws. The third explores the feasibility of applying the Gini coefficient to measure DHSN inequality. This dissertation has two main contributions to theory and practice. The first is that superusers cannot be predicted through demographic or indication-specific characteristics. The second is that graph theory can be used to detect and track superusers in real time. Collectively, the three essays contain unique insights into DHSN utility and function. These insights, and related metrics, can be leveraged by researchers, moderators, managers, and funders to quantify the growth, stagnation, or decline of their networks

Methodological issues in the evaluation of Internet-based interventions for problem drinking

Introduction and Aims: In recent years, there has been an increase in the number of Internet-based interventions (IBI) for alcohol problems and other addictive behaviours. However, it is risky to assume interventions that have been found to work in face-to-face modalities can be translated into IBI that are equally effective. Design and Methods: Using selected examples from the published works, this paper will identify some of the special considerations that are relevant to the evaluation of IBI. In addition, methodological issues found in the ongoing development and evaluation of the Check Your Drinking screener (http://www.CheckYourDrinking.net), an IBI for problem drinkers, will be discussed. Results: There have been several randomised control trials with promising results. A primary limitation of much of the research conducted to date is concerns regarding the generalisability of the findings. Discussion and Conclusions: Caution should be taken in assuming that the IBI, which have been found to work in tightly controlled efficacy trials, will display similar levels of effectiveness when used in 'naturalistic' settings (i.e. not face-to-face in a research environment). Positive results from studies using a variety of different research designs will advance the potential for IBI, as a new means of helping problem drinkers reduce their alcohol consumption. Because of their accessibility and anonymity, IBI could facilitate a broad provision of treatment services at a population level

Who Are Superusers of Digital Health Social Networks?

Digital Health Social Networks (DHSNs), otherwise known as online support groups or peer-topeer patient groups, have been in existence since the mid-1980s. However, they have only recently been recognized as important tools in healthcare. This dissertation focuses on superusers, a subset of DHSN participants who create the majority of content, and who are essential to the health and vibrancy of a network. The three essays in this dissertation assess the feasibility of quantitatively identifying superusers though theoretical models rooted in econometrics and graph theory. The data sources are four, long-standing DHSNs. Two of the four DHSNs focus on mental health (depressive disorder and panic disorder), and the remaining two on addictions (problem drinking and smoking cessation). The first essay examines associations between demographic characteristics, indication severity, and posting behaviour. The second investigates whether the distribution frequency in the four DHSNs follows properties of power laws. The third explores the feasibility of applying the Gini coefficient to measure DHSN inequality. This dissertation has two main contributions to theory and practice. The first is that superusers cannot be predicted through demographic or indication-specific characteristics. The second is that graph theory can be used to detect and track superusers in real time. Collectively, the three essays contain unique insights into DHSN utility and function. These insights, and related metrics, can be leveraged by researchers, moderators, managers, and funders to quantify the growth, stagnation, or decline of their networks