Evolution Health Systems Inc

Toronto, Canada

Evolution Health Systems Inc

Toronto, Canada
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Van Mierlo T.,University of Reading | Van Mierlo T.,Evolution Health Systems Inc | Li X.,University of Toronto | Hyatt D.,University of Toronto | Ching A.T.,University of Toronto
Journal of Medical Internet Research | Year: 2017

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.

Van Mierlo T.,Evolution Health Systems Inc. | Fournier R.,Evolution Health Systems Inc. | Jean-Charles A.,Societe Canadienne du Cancer | Hovington J.,Societe Canadienne du Cancer | And 2 more authors.
PLoS ONE | Year: 2014

Introduction: For many organizations, limited budgets and phased funding restrict the development of digital health tools. This problem is often exacerbated by the ever-increasing sophistication of technology and costs related to programming and maintenance. Traditional development methods tend to be costly and inflexible and not client centered. The purpose of this study is to analyze the use of Agile software development and outcomes of a three-phase mHealth program designed to help young adult Quebecers quit smoking. Methods: In Phase I, literature reviews, focus groups, interviews, and behavior change theory were used in the adaption and re-launch of an existing evidence-based mHealth platform. Based on analysis of user comments and utilization data from Phase I, the second phase expanded the service to allow participants to live text-chat with counselors. Phase II evaluation led to the third and current phase, in which algorithms were introduced to target pregnant smokers, substance users, students, full-time workers, those affected by mood disorders and chronic disease. Results: Data collected throughout the three phases indicate that the incremental evolution of the intervention has led to increasing numbers of smokers being enrolled while making functional enhancements. In Phase I (240 days) 182 smokers registered with the service. 51% (n = 94) were male and 61.5% (n = 112) were between the ages of 18-24. In Phase II (300 days), 994 smokers registered with the service. 51% (n = 508) were male and 41% (n = 403) were between the ages of 18-24. At 174 days to date 873 smokers have registered in the third phase. 44% (n = 388) were male and 24% (n = 212) were between the ages of 18-24. Conclusions: Emerging technologies in behavioral science show potential, but do not have defined best practices for application development. In phased-based projects with limited funding, Agile appears to be a viable approach to building and expanding digital tools. © 2014 van Mierlo et al.

Van Mierlo T.,Evolution Health Systems Inc. | Voci S.,Center for Addiction and Mental Health | Lee S.,Canadian Cancer Society | Fournier R.,Evolution Health Systems Inc. | And 2 more authors.
Journal of Medical Internet Research | Year: 2012

Background: Online social networks are popular components of behavior-change websites. Research has identified the participation of certain network members who assume leadership roles by providing support, advice, and direction to other members. In the literature, these individuals have been variously defined as key players, posters, active users, or caretakers. Despite their identification, very little research has been conducted on the contributions or demographic characteristics of this population. For this study, we collectively categorized key players, posters, active users, and caretakers as superusers. Objectives: To analyze data from two large but distinct Web-assisted tobacco interventions (WATI) to help gain insight into superuser demographic characteristics and how they use social networks. Methods: We extracted cross-sectional data sets containing posting behaviors and demographic characteristics from a free, publicly funded program (the Canadian Cancer Society's Smokers' Helpline Online: SHO), and a free, privately run program (StopSmokingCenter.net: SSC). Results: Within the reporting period (SHO: June 26, 2008 to October 12, 2010; SSC: May 17, 2007 to October 12, 2010), 21,128 individuals registered for the SHO and 11,418 registered for the SSC. Within the same period, 1670 (7.90%) registrants made at least one post in the SHO social network, and 1627 (14.25%) registrants made at least one post in the SSC social network. SHO and SSC superusers accounted for 0.4% (n = 95) and 1.1% (n = 124) of all registrants, and 5.7% (95/1670) and 7.62% (124/1627) of all social network participants, and contributed to 34.78% (29,422/84,599) and 46.22% (61,820/133,753) of social network content, respectively. Despite vast differences in promotion and group management rules, and contrary to the beliefs of group moderators, there were no statistically significant differences in demographic characteristics between the two superuser groups. Conclusions: To our knowledge, this is the first study that compared demographic characteristics and posting behavior from two separate eHealth social networks. Despite vast differences in promotional efforts and management styles, both WATI attracted superusers with similar characteristics. As superusers drive network traffic, organizations promoting or supporting WATI should dedicate resources to encourage superuser participation. Further research regarding member dynamics and optimization of social networks for health care purposes is required. © Trevor van Mierlo, Sabrina Voci, Sharon Lee, Rachel Fournier, Peter Selby.

Selby P.,Center for Addiction and Mental Health | Selby P.,University of Toronto | Selby P.,Ontario Tobacco Research Unit | Van Mierlo T.,University of Toronto | And 5 more authors.
Journal of Medical Internet Research | Year: 2010

Background: Both intratreatment and extratreatment social support are associated with increased rates of smoking cessation.Internet-based social support groups have the capability of connecting widely dispersed groups of people trying to quit smoking, making social support available 24 hours a day, seven days a week, at minimal cost. However, to date there has been little research to guide developmentof this particular feature of Web-assisted tobacco interventions (WATIs).Objective: Our objectives were to compare the characteristics of smokers who post in an online smoking cessation support group withsmokers who do not post, conduct a qualitative analysis of discussion board content, and determine the time it takes for new users to receive feedback from existing members or moderators.Methods: Data were collected from StopSmokingCenter.net version 5.0, a WATI equipped with an online social support network moderated by trained program health educators that was operational from November 6, 2004, to May 15, 2007. Demographic and smoking characteristics for both users and nonusers of the online social support network were analyzed, and qualitative analyses were conducted to explore themes in message content. Posting patterns and their frequency were also analyzed. Results:During the study period, 16,764 individuals registered; of these, 70% (11,723) reported being American. The mean age of registrants was 38.9 years and 65% (10,965) were female. The mean number of cigarettes smoked was 20.6 per day. The mean score for the 41% (6849) of users who completed the Fagerström Test for Nicotine Dependence was 5.6. Of all registered members, 15% (2562) made at least one post in the online social support network; 25% of first posts received a response from another member within 12 minutes, 50% within 29 minutes. The most frequent first posts were from recent quitters who were struggling with their quit attempts, and most responses were from members who had quit for a month or more. Differences in demographic and smoking characteristics between members who posted on the support group board at least once and those who did not post were statistically but not clinically significant. Conclusions: Peer responses to new users were rapid, indicating that online social support networks may be particularly beneficial to smokers requiring more immediate assistance with their cessation attempt. his function may be especially advantageous for relapse prevention. Accessing this kind of rapid in-person support from a professional would take an inordinate amount of time and money. Further research regarding the effectiveness of WATIs with online social support networks is required to better understand the contribution of this feature to cessation, for both active users (posters) and passive users ("lurkers") alike.

Cunningham J.A.,Center for Addiction and Mental Health | Cunningham J.A.,University of Toronto | Wild T.C.,University of Alberta | Cordingley J.,Center for Addiction and Mental Health | And 2 more authors.
Alcohol and Alcoholism | Year: 2010

Aims: To examine the impact of a web-based personalized feedback intervention, the Check Your Drinking (CYD; www.CheckYourDrinking.net) screener at 12-month follow-up. Methods: Respondents (N = 185) were recruited from a general population telephone survey of Ontario, Canadian adults (≥18 years) by asking risky drinkers if they were willing to help develop and evaluate Internet-based interventions for drinkers. Those randomly assigned to the intervention condition were provided with the web address and a unique password to a study-specific copy of the CYD. Respondents assigned to the control condition were sent a written description of the different components of the CYD and asked how useful they thought each of the components might be. Respondents were followed up at 3, 6 and 12 months. Results: By the 12-month follow-up, the impact of the intervention previously reported at 3 and 6months of CYD on problem drinkers' alcohol consumption was no longer apparent (P > 0.05). Conclusions: Recognizing that many people with alcohol concerns will never seek treatment, recent years have seen an increase in efforts to find ways to take treatment to problem drinkers. The CYD is one such intervention that has a demonstrated effect on reducing alcohol consumption in the short term (i.e. 6 months). Other more intensive Internet-based interventions or interventions via other modalities may enhance this positive outcome over the short and long term among problem drinkers who would be otherwise unlikely to access treatment for their alcohol concerns. www.ClinicalTrials.gov registration #NCT00367575. © The Author 2010. Published by Oxford University Press on behalf of the Medical Council on Alcohol. All rights reserved.

Binks M.,Duke University | Binks M.,Binks Behavioral Health PLLC | Van Mierlo T.,Evolution Health Systems Inc. | Van Mierlo T.,University of Toronto
Journal of Medical Internet Research | Year: 2010

Background The Internet holds promise for the delivery of evidence-based weight loss treatment to underserved populations. However, most studies do not reflect the more naturalistic and common ad libitum, or freely at will, use of the Internet. Randomized clinical trials, for example, typically include at least some direct contact with participants and often have restrictive selection criteria. There is a paucity of research examining utilization patterns of online weight loss programs, particularly in the rapidly expanding direct-to-consumer arena. Objectives To examine self-reported characteristics (age, body mass index [BMI], gender), behaviors, and Internet site utilization patterns of a sample of users of a direct-to-consumer ad libitum Internet weight loss program. Methods This study is based on analysis of archival data from the initial 15 weeks of an ongoing, free, evidence-based, direct-to-consumer Internet weight loss program, the Healthy Weight Center, which included standard information about nutrition, fitness, and behavioral strategies; monitoring tools; and moderated support group message boards. Participants encountered the program through self-directed Internet searches and anonymously registered to utilize the site. Self-reported user characteristics and electronically tracked utilization data were extracted from existing program data, compiled, and examined. Pearson correlations were computed to examine the association of program utilization with age and BMI. One-way analysis of variance (ANOVA) was used for gender comparisons. Results We examined data from the first 204 adult users of the program who were classified as either overweight (BMI 25 to < 30 kg/m2) or obese (BMI > 30 kg/m2). The mean age of participants was 42.0 years (SD 11.7), 81.9% (167/204) were women, and mean BMI was 32.01 kg/m2 (SD 6.26). The percent of participants who used program tools was as follows: 13.7%, meal planner; 10.8%, nutrition lookup: 17.6%, activity log; 14.2%, journal; and 22.1%, weight tracker. Participants also used the following educational resources: nutrition, 13.2%; fitness, 6.4%; and behavioral, 7.4%. Of the personal self-assessments available through the program, 57.8% of participants assessed personal barriers, and 50.5% assessed relationship with food. Only 7.8% used the support group message boards. No significant associations between site utilization and age, gender, or BMI were found. Reasons for wanting to lose weight were: health, 87%; appearance, 74%; mobility, 44%; doctor recommendation, 23%; and spouse/friend suggested, 12%. The age participants reported first becoming overweight was young adulthood, 31%; late adulthood, 28%; childhood, 22%; adolescence, 17%; and as a toddler, 3%. Self-perceived factors contributing to weight gain were lack of exercise for 70% of participants, emotions for 62%, overeating for 61%, and slow metabolism for 33%. Conclusions Internet weight loss programs reach many people who cannot access traditional treatment. However, users appear not to be optimally utilizing key aspects of the weight loss intervention, such as education, monitoring, and support. This study provides insight into the patterns of ad libitum use of an online weight loss program across multiple treatment-related domains in a naturalistic Internet environment.

Van Mierlo T.,Evolution Health Systems Inc. | Van Mierlo T.,University of Reading | Fournier R.,Evolution Health Systems Inc. | Fournier R.,University of Reading | Ingham M.,Janssen Scientific Affairs LLC
PLoS ONE | Year: 2015

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 nonadherence patterns. Copyright: © 2015 van Mierlo et al.

Van Mierlo T.,University of Reading | Van Mierlo T.,Evolution Health Systems Inc | Hyatt D.,University of Toronto | Ching A.T.,University of Toronto
Journal of Medical Internet Research | Year: 2015

Background: Social networks are common in digital health. A new stream of research is beginning to investigate the mechanisms of digital health social networks (DHSNs), how they are structured, how they function, and how their growth can be nurtured and managed. DHSNs increase in value when additional content is added, and the structure of networks may resemble the characteristics of power laws. Power laws are contrary to traditional Gaussian averages in that they demonstrate correlated phenomena. Objectives: The objective of this study is to investigate whether the distribution frequency in four DHSNs can be characterized as following a power law. A second objective is to describe the method used to determine the comparison. Methods: Data from four DHSNs-Alcohol Help Center (AHC), Depression Center (DC), Panic Center (PC), and Stop Smoking Center (SSC)-were compared to power law distributions. To assist future researchers and managers, the 5-step methodology used to analyze and compare datasets is described. Results: All four DHSNs were found to have right-skewed distributions, indicating the data were not normally distributed. When power trend lines were added to each frequency distribution, R2 values indicated that, to a very high degree, the variance in post frequencies can be explained by actor rank (AHC .962, DC .975, PC .969, SSC .95). Spearman correlations provided further indication of the strength and statistical significance of the relationship (AHC .987. DC .967, PC .983, SSC .993, P<.001). Conclusions: This is the first study to investigate power distributions across multiple DHSNs, each addressing a unique condition. Results indicate that despite vast differences in theme, content, and length of existence, DHSNs follow properties of power laws. The structure of DHSNs is important as it gives insight to researchers and managers into the nature and mechanisms of network functionality. The 5-step process undertaken to compare actor contribution patterns can be replicated in networks that are managed by other organizations, and we conjecture that patterns observed in this study could be found in other DHSNs. Future research should analyze network growth over time and examine the characteristics and survival rates of superusers.

Van Mierlo T.,University of Reading | Van Mierlo T.,Evolution Health Systems Inc
Journal of Medical Internet Research | Year: 2014

Background: In recent years, cyberculture has informally reported a phenomenon named the 1% rule, or 90-9-1 principle, which seeks to explain participatory patterns and network effects within Internet communities. The rule states that 90% of actors observe and do not participate, 9% contribute sparingly, and 1% of actors create the vast majority of new content. This 90%, 9%, and 1% are also known as Lurkers, Contributors, and Superusers, respectively. To date, very little empirical research has been conducted to verify the 1% rule. Objective: The 1% rule is widely accepted in digital marketing. Our goal was to determine if the 1% rule applies to moderated Digital Health Social Networks (DHSNs) designed to facilitate behavior change. Methods: To help gain insight into participatory patterns, descriptive data were extracted from four long-standing DHSNs: the AlcoholHelpCenter, DepressionCenter, PanicCenter, and StopSmokingCenter sites. Results: During the study period, 63,990 actors created 578,349 posts. Less than 25% of actors made one or more posts. The applicability of the 1% rule was confirmed as Lurkers, Contributors, and Superusers accounted for a weighted average of 1.3% (n=4668), 24.0% (n=88,732), and 74.7% (n=276,034) of content. Conclusions: The 1% rule was consistent across the four DHSNs. As social network sustainability requires fresh content and timely interactions, these results are important for organizations actively promoting and managing Internet communities. Superusers generate the vast majority of traffic and create value, so their recruitment and retention is imperative for long-term success. Although Lurkers may benefit from observing interactions between Superusers and Contributors, they generate limited or no network value. The results of this study indicate that DHSNs may be optimized to produce network effects, positive externalities, and bandwagon effects. Further research in the development and expansion of DHSNs is required.

PubMed | Evolution Health Systems Inc.
Type: Journal Article | Journal: JMIR serious games | Year: 2016

Health care literature supports the development of accessible interventions that integrate behavioral economics, wearable devices, principles of evidence-based behavior change, and community support. However, there are limited real-world examples of large scale, population-based, member-driven reward platforms. Subsequently, a paucity of outcome data exists and health economic effects remain largely theoretical. To complicate matters, an emerging area of research is defining the role of Superusers, the small percentage of unusually engaged digital health participants who may influence other members.The objective of this preliminary study is to analyze descriptive data from GOODcoins, a self-guided, free-to-consumer engagement and rewards platform incentivizing walking, running and cycling. Registered members accessed the GOODcoins platform through PCs, tablets or mobile devices, and had the opportunity to sync wearables to track activity. Following registration, members were encouraged to join gamified group challenges and compare their progress with that of others. As members met challenge targets, they were rewarded with GOODcoins, which could be redeemed for planet- or people-friendly products.Outcome data were obtained from the GOODcoins custom SQL database. The reporting period was December 1, 2014 to May 1, 2015. Descriptive self-report data were analyzed using MySQL and MS Excel.The study period includes data from 1298 users who were connected to an exercise tracking device. Females consisted of 52.6% (n=683) of the study population, 33.7% (n=438) were between the ages of 20-29, and 24.8% (n=322) were between the ages of 30-39. 77.5% (n=1006) of connected and active members met daily-recommended physical activity guidelines of 30 minutes, with a total daily average activity of 107 minutes (95% CI 90, 124). Of all connected and active users, 96.1% (n=1248) listed walking as their primary activity. For members who exchanged GOODcoins, the mean balance was 4,000 (95% CI 3850, 4150) at time of redemption, and 50.4% (n=61) of exchanges were for fitness or outdoor products, while 4.1% (n=5) were for food-related items. Participants were most likely to complete challenges when rewards were between 201-300 GOODcoins.The purpose of this study is to form a baseline for future research. Overall, results indicate that challenges and incentives may be effective for connected and active members, and may play a role in achieving daily-recommended activity guidelines. Registrants were typically younger, walking was the primary activity, and rewards were mainly exchanged for fitness or outdoor products. Remaining to be determined is whether members were already physically active at time of registration and are representative of healthy adherers, or were previously inactive and were incentivized to change their behavior. As challenges are gamified, there is an opportunity to investigate the role of superusers and healthy adherers, impacts on behavioral norms, and how cooperative games and incentives can be leveraged across stratified populations. Study limitations and future research agendas are discussed.

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