Published on 17.02.17 in Vol 19, No 2 (2017): February
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.
J Med Internet Res 2017;19(2):e40
Digital health social networks (DHSNs), otherwise known as discussion forums or peer-to-peer support groups, are in abundance [- ]. Although the efficacy of these networks is still being evaluated, the consensus is that social support and knowledge sharing increase patient education, enhance self-management, and decrease burden on existing health services [ - ].
In an era of increasing health costs [, ], an aging population [ - ], and an annual US $300 billion adherence problem [ - ], DHSNs are beginning to play an important role in improving the delivery of North American health services [ , ].
As we increasingly rely on technology to help us look after our health, management science is playing a greater role in using data to measure efficiencies [- ]. In the case of DHSNs, analysis is now turning to mechanisms that drive growth, help attain sustainability, and generate positive network externalities.
Research on Social Network Structure, Growth, and Sustainability
As a discipline, social network theory (SNT) maps social capital and the strength of relationships in networks. Within a network, nodes are individual actors, and ties are the relationships between nodes. For decades, disciplines such as economics, political science, public health, marketing, and finance have analyzed real world relationships within networks of actors [- ]. These studies typically leverage graph theory, sociograms, or stochastic models to examine relationships [ - ].
Recently, SNT has shifted toward the topology of scale-free networks. This stream of research investigates whether network growth is random, if networks evolve, follow encoded and organized principles [- ], and if taxonomies of actors naturally exist [ - ].
Three Fundamentals of Digital Health Social Networks
In the context of this study, actors are DHSN registrants who have created, at minimum, 1 post. From this perspective, 3 fundamental principles guide network growth.
The first is the network’s total number of posts. In most DHSNs, actor posts remain on the network, and each new post adds to the quantitative size and value of the community. Whether actors passively read, actively respond to, or agree or disagree with new content, the quantitative value of the network n increased with each new post by n +1. In management and economics literature this is referred to as positive network externalities .
Second is the number of actors in the network. If a network contains x actors, potential connections between actors is x (x−1). The greater the number of actors, the greater the potential for network expansion and the generation of new externalities. This has been illustrated in the study of networks in demand-side economies, where the value of a product or service is directly related to the number of others who use it [, ].
Third, the mathematical relationship between these 2 quantities (positive network externalities and number of actors) represents a power law [- ]. Marketing experts have observed this phenomenon and have intuitively referred to it as the 1% rule or the 90-9-1 principle [ , ]. Both concepts are related to the Pareto principle [ ], and applied empirically, they have shown to be intrinsic to social network structure [ - ].
Monitoring nodes and ties, and monitoring topologies are important considerations for those who manage social networks. However, these tasks are retrospective as they examine a network’s past state. Methods to drive future growth and promote individual agency are required. As the creation of externalities governs the success of a network, it would be helpful to profile actors who create value by generating externalities .
The 4 interventions in this study [- ] contained self-guided interactive behavior change treatment programs based on state-of-the-art best practice, and have been examined extensively in the literature [ - ]. A component of each of the interventions is a DHSN moderated by trained and paid employees. All posts are reviewed and approved by a moderator, and any post that does not address the indication is permanently removed. Moderators can also instantaneously communicate with all actors. outlines each program’s theoretical constructs and evidence base.
|Theoretical construct||Problem drinking||Depressive disorder||Panic disorder||Smoking cessation|
|Brief intervention ||X||X||X||X|
|Cognitive behavioral therapy ||X||X|
|Harm reduction ||X||X|
|Health belief model ||X||X||X||X|
|Motivational interviewing ||X||X||X||X|
|Normative feedback ||X||X|
|Social cognitive theory ||X||X||X||X|
|Structured relapse prevention ||X|
|Targeting and tailoring ||X||X|
|Transtheoretical model ||X|
outlines intervention launch dates, data acquisition dates, number of registrants, number of actors, total posts, and number of subjects used in analysis from their intervention DHSN inception until December 31, 2015.
|Social network||Social network launch date||Data acquisition date||Number of days active||Number of subjects registered in program||Number of actors, n (%)||Number of actor postsa||Number of subjects in analysis, n (%)a|
|Dec 26, 2005||Dec 31, 2015||3658||5049||1085 (21.49)||16,231||4784 (94.75)|
|Feb 6, 2003||Dec 31, 2015||4712||11,675||2065 (17.69)||20,516||1958 (16.77)|
|Panic disorder||January 23, 2002||Dec 31, 2015||591||9783||3579 (36.58)||61,743||6151 (62.87)|
|Sep 26, 2001||Dec 31, 2015||5210||52,396||8452 (16.13)||521,997||12,061 (23.01)|
|Total||n/ab||n/a||18,671||78,903||15,181 (19.24)||620,487||25,178 (31.91)|
|Mean||n/a||n/a||4688||19,726||3795 (19.24)||155,122||6239 (31.63)|
aModerator posts removed.
bn/a: not applicable.
Data Collected at Registration
Demographic characteristics (age, gender, highest level of education obtained, current occupation), and indication-specific details () were collected at registration. Program registration and participation were free; however, consenting to the use of personal data for research purposes was a requirement.
|Problem drinking||Average drinks per day||Drop-down menu 0-30+|
|Program goal: cut down, stop, unsure||Likert scale|
|Depressive disorder||Depression rating over past 2 weeks||Likert scale 0-10|
|Level of distress over past 2 weeks||Likert scale 0-10|
|Level of interference over past 2 weeks||Likert scale 0-10|
|Tried cognitive behavior therapy in the past||Yes or no|
|Currently being treated||Yes or no|
|Using program with health care professional||Yes or no|
|Panic disorder||Number of attacks over past 2 weeks||Drop-down menu 0-51+|
|Average fear rating during attack||Likert scale 0-10|
|Attack interference with average daily life||Drop-down menu 0-4|
|Attack causing avoidance||Drop-down menu 0-4|
|Tried cognitive behavior therapy in the past||Yes/No|
|Use of program with health care professional||Yes/No|
|Smoking cessation||Smoking patterns: ≥ 1 cigarette per day, occasional smoker, recently quit||Drop-down menu|
|Last cigarette: >24 hours, <24 hours||Radio button|
|Cigarettes per day||Drop-down menu 0-100+|
|Total years smoked||Drop-down menu 0-75+|
|Minutes to first cigarette: >60, 31-60, 6-31, ≤5||Drop-down menu|
|Past year quit attempts > 24 hours||Drop-down menu 0-10+|
|Number of cohabitant smokers||Drop-down menu 0-10+|
|Fagerstrom dependency score (very low, low, moderate, high, very high)||Internal calculation|
As a first step in profiling actors based on characteristics, and to investigate the feasibility of developing predictive models that identify superusers in real time, the objective of this study was to examine the association between number of posts and actor demographic and indication-specific variables inputted at registration.
Data were extracted from the custom SQL DHSN databases of the 4 digital health interventions. As they contained full data sets, samples totaling 24,954 registrants and 3285 actors were used in the analysis ().
A total of 5 models were developed to explore whether posting behavior was associated with demographics characteristics and indication-specific severity amongst all registrants ().
|1||ProblemDrinkingPostsAllRegistrants = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5DrinksPerDay + β6Goal + ϵ|
|2||DepressiveDisorderPostsAllRegistrants = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5Rating + β6Distress + β7Interference + β8CBT + β9Treated + β10Professional + ϵ|
|3||PanicDisorderPostsAllRegistrants = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5Attacks + β6Fear + β7Interference + β8Avoidance + β9CBT + β10Professional + ϵ|
|4||SmokingCessationPostsAllRegistrants = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5Patterns + β6LastCigarette + β7CigarettesPerDay + β8YearsSmoked + β9FirstCigarette+ β10PastQuits + β11CohabitantSmokers + β12FagerstromScore + ϵ|
|5||TotalPostsAllRegistrants = β0+ β1Age + β2Gender + β3Education + β4Occupation + ϵ|
Another 5 additional regression models were developed to explore whether posting behavior was associated with demographics characteristics and indication-severity amongst actors ().
|6||ProblemDrinkingPostsActors = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5DrinksperDay + β6Goal + ϵ|
|7||DepressiveDisorderPostsActors = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5Rating + β6Distress + β7Interference + β8CBT + β9Treated + β10Professional + ϵ|
|8||PanicDisorderPostsActors = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5Attacks + β6Fear + β7Interference + β8Avoidance + β9CBT + β10Professional + ϵ|
|9||SmokingCessationPostsActors = β0+ β1Age + β2Gender + β3Education + β4Occupation + β5Patterns + β6Last Cigarette + β7CigarettesPerDay + β8YearsSmoked + β9FirstCigarette + β10PastQuits + β11CohabitantSmokers + β12FagerstromScore + ϵ|
|10||TotalPostsActors = β0+ β1Age + β2Gender + β3Education + β4Occupation + ϵ|
Dummy variables were created for categorical data, with 1 dummy variable excluded during regressions. Analyses were performed with Stata version 13 (Stata Corp LLP, College Station, TX, USA).
As outlined in previous research conducted on the 4 DHSNs, the number of posts per actor is right skewed, indicating the presence of a power law . Negative binomial regression was employed as the method of analysis for 3 reasons. First, the dependent variable in our model, number of observations, is counted as integers only. Second, negative binomial regression can capture the skewness of the data. Third, Poisson distribution requires the mean and the variance of the model to be identical and in each of the models, the hypothesis of equidispersion is rejected.
All data collection policies and procedures adhered to international privacy guidelines [- ] and were in accordance with the Helsinki Declaration of 1975, as revised in 2008 [ ]. The study was consistent with the University Research Ethics Committee procedures at Henley Business School, University of Reading, and was exempt from full review.
All 5 models had low R2 values (seeand ).
Regression Analysis: Demographic Variables
A total of 4 independent demographic variables were included in each of the 10 models ().
In 9 of the models, age was positively and significantly associated with number of posts (beta range =.13-.4). This means that as age of registrants increased, number of posts increased marginally.
Education was positively and significantly associated to the number of posts in 6 models (beta range =.082-.315). This means that within these 6 models, number of posts increases by less than 1 with every unit increase in education category.
Gender was negatively and significantly associated number of posts in 4 models (beta range =−.766 to −.272). This means that within these 4 models, number of posts decreased by less than 1 with male registrants.
Registrants had the option of selecting from 1 of 12 occupations. Compared with registrants who indicated that they were full-time students, occupation was positively associated with number of posts in 14 cases (beta range =.377-5.301), and negatively associated with number of posts in 19 cases (beta range =-2.609 to -.587).
The variable occupation not listed was selected with the greatest frequency 60% (6/10), and was positively and significantly associated to the number of posts in 4 of these 6 models (beta range =.488-.703), but negatively and significantly associated to the number of posts in 2 of these 6 models (beta range =−1.314 to −.945).
|Independent variable||Model 1 |
beta (P value)
|Model 2 |
beta (P value)
|Model 3 |
beta (P value)
|Model 4 |
beta (P value)
|Model 5 |
beta (P value)
|Model 6 |
beta (P value)
|Model 7 |
beta (P value)
|Model 8 |
beta (P value)
|Model 9 |
beta (P value)
|Model 10 |
beta (P value)
|Full-time student (reference)|
|Stay at home mom or dad|
|Teacher or professor||−2.348 |
|Administrative, financial or clerical sales or service||.519 |
|Technologist or technical occupation||.532 |
|Farming, forestry, fishing or mining||1.016 |
|.400 (.04)||3.793 |
|Trades, transport or equipment operator||−1.564 |
|Processing, manufacturing or utilities||−.846 |
|Unemployed at present or on work leave||.479 |
|Professional services (eg, certified accountant, lawyer, doctor)||−.856 |
|Occupation not listed||.703 |
|−.945 (.001)||.647 |
Regression Analysis: Indication-Specific Variables
In total, 10 indication-specific variables were tested for their association with posting behavior in the 2 addiction health interventions ().
Problem Drinking Intervention
In the problem drinking intervention, registrants had the option of selecting 1 of the 3 program goals. Compared with registrants who indicated that they wanted to cut down, quit drinking was positively and significantly associated with the number of posts in model 2 (beta=.463, P=.02). The option not sure was negatively and significantly associated with the number of posts in model 2 (beta=−. 460, P=.02) and model 7 (beta=−.509, P=.001).
|Independent Variables||Model 2 |
beta (P value)
|Model 7 |
beta (P value)
|Model 5 |
beta (P value)
|Model 10 |
beta (P value)
|Cut down (reference)||n/aa||n/a|
|Quit drinking||.463 |
|Not sure||−.460 |
|≥ one cigarette per day, occasional smoker, recently quit||n/a||n/a||.278 |
|Last cigarette: >24 hours, <24 hours||n/a||n/a||.534 |
|Cigarettes per day||n/a||n/a|
|Total years smoked||n/a||n/a||.040 |
|Minutes to first cigarette: >60, 31-60, 6-31, ≤5||n/a||n/a||.705 |
|Past year quit attempts > 24 hours||n/a||n/a||−.048 |
|Number of cohabitant smokers||n/a||n/a|
|Fagerstrom dependency score (very low, low, moderate, high, very high)||n/a||n/a||0.657 |
an/a: not applicable.
Smoking Cessation Intervention
In model 5, increased cigarette consumption (smoking patterns) (beta=.278, P=.001) and having a cigarette within the past 24 hours (last cigarette) were positively and significantly associated with posting behavior (beta=.534, P=.002).
In both models, increases in total years smoked (beta=.040, P<.001; beta=.025, P=.001), decreases in minutes to first cigarette (beta=.705, P=.002; beta=.625, P<.001), and higher Fagerstrom dependency scores (beta=.657, P=.001; beta=.651, P<.001) were positively and significantly associated with posting behavior. Having a greater number of quit attempts was negatively and significantly associated with posting (beta = −.048, P=.02; −.054, P=.001).
Regression Analysis: Indication-Specific Variables in Two Mental Health Interventions
Ten indication-specific variables were tested for their association with posting behavior in the 2 mental health interventions. Whether a participant had tried cognitive behavior therapy in the past and was using of the program with a health care professional were asked in both mental health interventions ().
Past Cognitive Behavior Therapy Experience
In models 3, 4, and 9 posting behavior was positively and significantly associated with experience with CBT (beta= .851, P=.01; beta=1.118, P<.001; beta=.870, P<.001).
|Independent variables||Model 3 |
|Model 8 |
|Model 4 |
|Model 9 |
|Depression rating past 2 weeks (0-10)||n/aa||n/a|
|Level of distress past 2 weeks (0-10)||n/a||n/a|
|Level of interference past 2 weeks (0-10)||n/a||n/a|
|Currently being treated||n/a||n/a|
|Tried cognitive behavior therapy in the past||.851 |
|Number of attacks over past 2 weeks |
Using program with a health care professional
|Average fear rating during attack||n/a||n/a||−.099 |
|Attack interference in average daily life||n/a||n/a||.406 |
|Attack causing avoidance||n/a||n/a|
an/a: not applicable.
In the depression interventions, other than past CBT experience, there were no statistically significant associations with posting behavior.
Panic Disorder Intervention
In the panic disorder intervention, attacks interfering in average daily life were positively and significantly associated with posting behavior (beta=.406, P<.001; beta=.224, P=.01). In model 4, increases in number of attacks over the past 2 weeks were positively and significantly associated with posting (beta=.054, P=.03), and in model 9 average fear rating during an attack was negatively and significantly associated with posting (beta=−.099, P=.01).
Despite observable statistically significant results in demographic and indication-specific data, all regressions had low R2 values, and their impact on superuser behavior was minimal. As mentioned previously, all models fail to explain the variance of the dependent variables.
Based on the results in 4 of the 10 models, females tend to post more than males. However, these results should be interpreted with caution as the impact was minimal (beta range=−.766 to −.272) and only statistically significant in all subject models. These results also do not confirm the gender of superusers.
Increased posting with age was positively and statistically significant in 9 of the 10 models, although the increase is negligible and should be interpreted with caution (beta range=.130-.400). For example, the analysis did not consider whether addiction treatment for smoking cessation, or if treatment for mental health issues, also coincides with age.
Although the impact is minimal, increased education was related to increases in posting behavior in 6 of the 10 models (beta range=.082-.315). The issue of education level and use of medical resources has a rich history in the literature and is nonconclusive. For example, one might assume that actors with higher levels should have better knowledge seeking skills and make limited use of DHSNs, or conversely, that actors with lower education levels and fewer formal resources would use DHSNs with greater intensity.
A recent qualitative review on factors affecting therapeutic compliance found the effect of education level to be equivocal . While some studies found that patients with higher levels of education might have higher compliance, others found that patients with lower levels of education or no formal education were more compliant. The authors concluded that education level was not a good predictor of therapeutic compliance, and our findings reflect this in regards to education being associated with posting.
In the smoking cessation intervention, inexperienced quitters who have smoked longer, have increased dependency, and have recently quit, tend to post more. This supports past research indicating that the intervention’s DHSN primarily acts as a relapse prevention tool for new quitters [, ]. If this finding is true it highlights the importance of detecting and supporting superusers as they primarily respond to, and support, new users.
It was interesting to note that experience with cognitive behavior therapy was associated with posting behavior in 3 of the 4 mental health models, though this impact was minimal (beta range=.851-1.118).
The results of this study suggest that demographic or indication-specific variables have limited association with the creation of externalities in DHSNs. What, if anything, may be associated with posting behavior? If superusers are key to the growth and sustainability of DHSNs, how can they be detected?
The real-time assessment of phenotype, or observable traits resulting from the interaction of an individual in an environment, have recently been recognized as key to the next frontier of medicine . Phenotypes differ from demographic and indication-specific data as they give insight on behavior. Although traditionally difficult to detect, some phenotypes are now being recognized through big data analysis.
For example, a recent study identified the ability to use natural language processing to detect phenotypes in electronic health records . Another study found that an individual’s personal attitudes including use of addictive substances, happiness, and sexual orientation can be detected through Facebook likes [ ], and Instagram photos and Twitter feeds have been shown to contain predictive markers of depression [ , ].
DHSN content may contain rich sources of phenotypes as an post or an actor’s profile may include avatars, images, badges or awards for participation, likes or other semiotic indicators of support from other members, or links to specific outside resources. Post content may be mined for specific keywords, phrases, or even tone. Time of post, time between posts, response to specific types of content or members, or other time-based interactions may also be indicative of specific behavior. Recent health care informatics research has also identified a relationship between increased systems use and outcomes, and a variety of unique system measures that may help categorize behaviors .
A challenge is that even if phenotypes can be predicted, risk-stratifying behavior may prove difficult. However, the medication adherence literature, which generally classifies patients as full compliers, partial compliers, or noncompliers may give insights on categorizing behavior similar to nonadherence  and research is beginning to investigate indication-specific factors that categorize patients and their motivations [ - ]. Future research into adherence to DHSNs might also consider the feasibility of stratifying actors according to real-time behavior.
In some respects, the low R2 values in the models and lack of statistically significant variables in this study expose the limitations of big data. Popular belief holds that large data sets of survey data will contain insights and intelligence that have been previously unobtainable [- ], and the promise of big data is so compelling that laymen are being encouraged to experiment with sophisticated techniques that previously required a high degree of training [ ]. Whereas increased knowledge and interdisciplinary training and collaboration are certainly positive, as in this study, results from the analysis of large datasets pertaining to specific demographic characteristics or indication-specific variables may, at best, illustrate the complexity of predicting human behavior.
Strengths and Limitations
The results of this study are from “real world” social networks and the main strengths are the longevity of the DHSNs, the number of posts, the 4 separate indications, and that 2 of the social networks in the study were focused on mental health, and the remaining 2 on addictions.
Ideally, data from this study would be derived from a randomized controlled experiment. However, it would be difficult, if not impossible, to recruit a study population and execute a study in a similar sample. We are not aware of any other study in the health care literature with such an extensive and complete dataset, and as such, results should be interpreted accordingly.
A strength and limitation is that the populations analyzed are self-selecting populations that actively sought help. In the context of this study it was helpful to have datasets of active and engaged participants. However, these results may not be indicative of populations of patients in health plans, hospital networks, or mass public health campaigns.
A limitation to this study is that demographic and indication-specific data was self-report. Self-report data is common in digital health studies, and the consensus is that data from subjects is at least as reliable as pencil-and-paper questionnaires [- ]. However, due to the anonymous nature and nonrandomization of study subjects, results should be interpreted with caution.
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 methods and models that may identify individuals who promote DHSN growth.
Conflicts of Interest
Trevor van Mierlo is the CEO & Founder of Evolution Health Systems. Evolution Health owns and manages digital health interventions, including the applications analyzed in this study.
Multimedia Appendix 1PPTX File, 2MB
- Eysenbach G, Powell J, Englesakis M, Rizo C, Stern A. Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. Br Med J 2004 May 15;328(7449):1166 [FREE Full text] [CrossRef] [Medline]
- Jadad AR, Enkin MW, Glouberman S, Groff P, Stern A. Are virtual communities good for our health? Br Med J 2006 Apr 22;332(7547):925-926 [FREE Full text] [CrossRef] [Medline]
- Ziebland S, Wyke S. Health and illness in a connected world: how might sharing experiences on the internet affect people's health? Milbank Q 2012 Jun;90(2):219-249 [FREE Full text] [CrossRef] [Medline]
- Balatsoukas P, Kennedy CM, Buchan I, Powell J, Ainsworth J. The role of social network technologies in online health promotion: a narrative review of theoretical and empirical factors influencing intervention effectiveness. J Med Internet Res 2015;17(6):e141 [FREE Full text] [CrossRef] [Medline]
- Pew Research Center. Washington, DC; 2016 Nov 11. Social Media Fact Sheet URL: http://www.pewinternet.org/fact-sheets/social-networking-fact-sheet/ [accessed 2017-01-23] [WebCite Cache]
- Cole J, Watkins C, Kleine D. Health advice from Internet discussion forums: how bad is dangerous? J Med Internet Res 2016 Jan 06;18(1):e4 [FREE Full text] [CrossRef] [Medline]
- Myneni S, Cobb N, Cohen T. In pursuit of theoretical ground in behavior change support systems: analysis of peer-to-peer communication in a health-related online community. J Med Internet Res 2016 Feb 02;18(2):e28. [CrossRef] [Medline]
- Robinson TN, Walters PA. Health-net: an interactive computer network for campus health promotion. J Am Coll Health 1986 Jun;34(6):284-285. [CrossRef] [Medline]
- Bender JL, Jimenez-Marroquin MC, Ferris LE, Katz J, Jadad AR. Online communities for breast cancer survivors: a review and analysis of their characteristics and levels of use. Support Care Cancer 2013 May;21(5):1253-1263. [CrossRef] [Medline]
- Brennan PF, Moore SM, Smyth KA. The effects of a special computer network on caregivers of persons with Alzheimer's disease. Nurs Res 1995;44(3):166-172. [Medline]
- Cobb NK, Graham AL, Byron MJ, Niaura RS, Abrams DB, Workshop P. Online social networks and smoking cessation: a scientific research agenda. J Med Internet Res 2011;13(4):e119 [FREE Full text] [CrossRef] [Medline]
- Ploderer B, Smith W, Howard S, Pearce J, Borland R. Patterns of support in an online community for smoking cessation. In: C&T '13. 2013 Presented at: Proceedings of the 6th International Conference on Communities and Technologies; June 29 - July 02, 2013; Munich, Germany p. 26-35.
- Takahashi Y, Uchida C, Miyaki K, Sakai M, Shimbo T, Nakayama T. Potential benefits and harms of a peer support social network service on the internet for people with depressive tendencies: qualitative content analysis and social network analysis. J Med Internet Res 2009;11(3):e29 [FREE Full text] [CrossRef] [Medline]
- Wright K. Social support within an on-line cancer community: an assessment of emotional support, perceptions of advantages and disadvantages, and motives for using the community from a communication perspective. J Appl Commun Res 2002 Jan;30(3):195-209. [CrossRef]
- Wicks P, Keininger DL, Massagli MP, de la Loge C, Brownstein C, Isojärvi J, et al. Perceived benefits of sharing health data between people with epilepsy on an online platform. Epilepsy Behav 2012 Jan;23(1):16-23 [FREE Full text] [CrossRef] [Medline]
- Conrad P, Bandini J, Vasquez A. Illness and the Internet: from private to public experience. Health (London) 2016 Jan;20(1):22-32. [CrossRef] [Medline]
- Patton M. Forbes. 2015 Jun 29. Health Care Costs Rise Faster Than Inflation URL: http://www.forbes.com/sites/mikepatton/2015/06/29/u-s-health-care-costs-rise-faster-than-inflation/ [accessed 2017-01-25] [WebCite Cache]
- Woolhandler S, Campbell T, Himmelstein DU. Costs of health care administration in the United States and Canada. N Engl J Med 2003 Aug 21;349(8):768-775. [CrossRef] [Medline]
- Schneider EL. The Aging of America. J Am Med Assoc 1990 May 02;263(17):2335. [CrossRef]
- Zweifel P, Felder S, Meiers M. Ageing of population and health care expenditure: a red herring? Health Econ 1999 Sep;8(6):485-496. [Medline]
- Getzen TE. Population aging and the growth of health expenditures. J Gerontol 1992 May;47(3):S98-104. [Medline]
- de Meijer C, Wouterse B, Polder J, Koopmanschap M. The effect of population aging on health expenditure growth: a critical review. Eur J Ageing 2013 May 15;10(4):353-361. [CrossRef]
- Chisholm-Burns MA, Spivey CA. The 'cost' of medication nonadherence: consequences we cannot afford to accept. J Am Pharm Assoc (2003) 2012;52(6):823-826. [CrossRef] [Medline]
- DiMatteo MR. Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research. Med Care 2004 Mar;42(3):200-209. [Medline]
- Viswanathan M, Golin CE, Jones CD, Ashok M, Blalock SJ, Wines RC, et al. Interventions to improve adherence to self-administered medications for chronic diseases in the United States: a systematic review. Ann Intern Med 2012 Dec 4;157(11):785-795. [CrossRef] [Medline]
- Forissier T, Firlik K. Capgemini. 2012. Estimated Annual Pharmaceutical Revenue Loss Due to Medication Non-Adherence URL: https://www.capgemini.com/resource-file-access/resource/pdf/Estimated_Annual_Pharmaceutical_Revenue_Loss_Due_to_Medication_Non-Adherence.pdf [accessed 2017-01-25] [WebCite Cache]
- Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health Aff (Millwood) 2008;27(3):759-769 [FREE Full text] [CrossRef] [Medline]
- Glasgow RE, McKay HG, Piette JD, Reynolds KD. The RE-AIM framework for evaluating interventions: what can it tell us about approaches to chronic illness management? Patient Educ Couns 2001 Aug;44(2):119-127. [Medline]
- Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006 May 16;144(10):742-752. [Medline]
- Holden RJ, Karsh B. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010 Feb;43(1):159-172 [FREE Full text] [CrossRef] [Medline]
- Harrison MI, Koppel R, Bar-Lev S. Unintended consequences of information technologies in health care--an interactive sociotechnical analysis. J Am Med Inform Assoc 2007;14(5):542-549 [FREE Full text] [CrossRef] [Medline]
- Emirbayer M, Goodwin J. Network analysis, culture, and the problem of agency. AJS 1994;99(6):1411-1454.
- Grabher G, Stark D. Organizing diversity: evolutionary theory, network analysis and postsocialism. Reg Stud 1997 Jul;31(5):533-544. [CrossRef]
- Leibenstein H. Bandwagon, snob, and veblen effects in the theory of consumers' demand. Q J Econ 1950;64(2):183-207.
- Luke DA, Harris JK. Network analysis in public health: history, methods, and applications. Annu Rev Public Health 2007;28:69-93. [CrossRef] [Medline]
- Safranek R. The Emerging Role of Social Media in Political and Regime Change. In: ProQuest Discover Guides. Ann Arbor: ProQuest; 2012:1-14.
- Uzzi B. The sources and consequences of embeddedness for the economic performance of organizations: the network effect. Am Sociol Rev 1996;61(4):674-698.
- Newman M. The structure and function of complex networks. SIAM Rev 2003 Jan;45(2):167-256. [CrossRef]
- Robins G, Pattison P, Kalish Y, Lusher D. An introduction to exponential random graph (p*) models for social networks. Soc Networks 2007 May;29(2):173-191. [CrossRef]
- Snijders TA, van de Bunt GG, Steglich CE. Introduction to stochastic actor-based models for network dynamics. Soc Networks 2010 Jan;32(1):44-60. [CrossRef]
- Albert R, Barabási A. Statistical mechanics of complex networks. Rev Mod Phys 2002 Jan 30;74(1):47-97. [CrossRef]
- Carron-Arthur B, Cunningham JA, Griffiths KM. Describing the distribution of engagement in an Internet support group by post frequency: a comparison of the 90-9-1 Principle and Zipf's Law. Internet Interv 2014 Oct;1(4):165-168. [CrossRef]
- Kossinets G, Watts DJ. Empirical analysis of an evolving social network. Science 2006 Jan 6;311(5757):88-90 [FREE Full text] [CrossRef] [Medline]
- van MT, Hyatt D, Ching AT. Mapping power law distributions in digital health social networks: methods, interpretations, and practical implications. J Med Internet Res 2015;17(6):e160 [FREE Full text] [CrossRef] [Medline]
- van Mierlo T, Hyatt D, Ching AT. Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks. Netw Model Anal Health Inform Bioinform 2016;5(1):32 [FREE Full text] [CrossRef] [Medline]
- Su W. Integrating and Mining Virtual Communities Across Multiple Online Social Networks: Concepts, Approaches and Challenges. In: IEEE Computer Society Press. 2014 Presented at: The Fourth International Conference on Digital Information and Communication Technology and its Applications; May 6, 2014; Bangkok p. 199-204. [CrossRef]
- Carron-Arthur B, Ali K, Cunningham JA, Griffiths KM. From help-seekers to influential users: a systematic review of participation styles in online health communities. J Med Internet Res 2015 Dec 01;17(12):e271 [FREE Full text] [CrossRef] [Medline]
- Cunningham JA, van Mierlo T, Fournier R. An online support group for problem drinkers: AlcoholHelpCenter.net. Patient Educ Couns 2008 Feb;70(2):193-198. [CrossRef] [Medline]
- Jones R, Sharkey S, Smithson J, Ford T, Emmens T, Hewis E, et al. Using metrics to describe the participative stances of members within discussion forums. J Med Internet Res 2011;13(1):e3 [FREE Full text] [CrossRef] [Medline]
- van Mierlo T, Voci S, Lee S, Fournier R, Selby P. Superusers in social networks for smoking cessation: analysis of demographic characteristics and posting behavior from the Canadian cancer society's smokers' helpline online and StopSmokingCenter.net. J Med Internet Res 2012;14(3):e66 [FREE Full text] [CrossRef] [Medline]
- Zhao K, Yen J, Greer G, Qiu B, Mitra P, Portier K. Finding influential users of online health communities: a new metric based on sentiment influence. J Am Med Inform Assoc 2014 Oct;21(e2):e212-e218. [CrossRef] [Medline]
- Baye MR. Advanced Topics in Business Strategy. In: Managerial EconomicsBusiness Strategy. 7 ed. New York, NY: The McGraw-Hill Companies Inc; 2010.
- Katz ML, Shapiro C. Network externalities, competition, and compatibility. Am Econ Rev 1985;75(3):424-440.
- Lin K, Lu H. Why people use social networking sites: an empirical study integrating network externalities and motivation theory. Comput Human Behav 2011 May;27(3):1152-1161. [CrossRef]
- Andriani P, McKelvey B. Beyond Gaussian averages: redirecting international business and management research toward extreme events and power laws. J Int Bus Stud 2007 Oct 18;38(7):1212-1230. [CrossRef]
- Newman M. Power laws, Pareto distributions and Zipf's law. Contemp Phys 2005 Sep;46(5):323-351. [CrossRef]
- Gabaix X. Power laws in Economics and Finance. Annu Rev Econ 2009 Sep 02;1(1):255-294. [CrossRef]
- Nielsen J. NNGroup. Fremont, CA: Nielson Normal Group; 2009 Oct 09. The 90-9-1 Rule for Participation Inequality: Lurkers vs. Contributors in Internet Communities URL: https://www.nngroup.com/articles/participation-inequality/ [accessed 2017-01-25] [WebCite Cache]
- Arthur C. TheGuardian.: Guardian News and Media Ltd; 2006. What is the 1% rule? URL: https://www.theguardian.com/technology/2006/jul/20/guardianweeklytechnologysection2 [accessed 2017-01-25] [WebCite Cache]
- Sanders R. The Pareto principle: its use and abuse. Jnl of Product & Brand Mgt 1992 Jun;1(2):37-40. [CrossRef]
- Buntain C, Golbeck J. Identifying social roles in reddit using network structure. In: WWW '14 Companion. 2014 Presented at: The 23rd International Conference on World Wide Web; April 07 - 11, 2014; Seoul, Korea p. 615-620. [CrossRef]
- Graham T, Wright S. Discursive equality and everyday talk online: the impact of “Superparticipants”. J Comput-Mediat Comm 2013 May 20;19(3):625-642. [CrossRef]
- van Mierlo T. The 1% rule in four digital health social networks: an observational study. J Med Internet Res 2014;16(2):e33 [FREE Full text] [CrossRef]
- Carron-Arthur B, Reynolds J, Bennett K, Bennett A, Cunningham JA, Griffiths KM. Community structure of a mental health internet support group: modularity in user thread participation. JMIR Ment Health 2016;3(2):e20 [FREE Full text] [CrossRef]
- Alcoholhelpcenter. Toronto: Evolution Health Systems The Alcohol Help Center URL: http://www.alcoholhelpcenter.net/ [accessed 2017-01-24] [WebCite Cache]
- DepressionCenter. Toronto: Evolution Health Systems The Depression Center URL: http://www.DepressionCenter.net/ [accessed 2017-01-22] [WebCite Cache]
- PanicCenter. Toronto: Evolution Health Systems The Panic Center URL: http://www.PanicCenter.net/ [accessed 2017-01-24] [WebCite Cache]
- StopSmokingCenter. Toronto: Evolution Health The Stop Smoking Center URL: http://www.StopSmokingCenter.net/ [accessed 2017-01-24] [WebCite Cache]
- Cunningham J. Internet Evidence-Based Treatments. In: Miller PE, editor. Evidence-Based Addiction Treatment. Burlington: Academic Press; 2009:379-397.
- Cunningham JA. Comparison of two internet-based interventions for problem drinkers: randomized controlled trial. J Med Internet Res 2012 Aug 01;14(4):e107 [FREE Full text] [CrossRef] [Medline]
- Cunningham JA, Humphreys K, Kypri K, van Mierlo T. Formative evaluation and three-month follow-up of an online personalized assessment feedback intervention for problem drinkers. J Med Internet Res 2006 Apr 12;8(2):e5 [FREE Full text] [CrossRef] [Medline]
- Cunningham JA, Selby P, van Mierlo T. Integrated online services for smokers and drinkers? Use of the check your drinking assessment screener by participants of the Stop Smoking Center. Nicotine Tob Res 2006 Dec;8 Suppl 1:S21-S25. [Medline]
- Cunningham JA, Wild TC, Cordingley J, van Mierlo T, Humphreys K. A randomized controlled trial of an internet-based intervention for alcohol abusers. Addiction 2009 Dec;104(12):2023-2032 [FREE Full text] [CrossRef] [Medline]
- Cunningham JA, Wild TC, Cordingley J, van Mierlo MT, Humphreys K. Twelve-month follow-up results from a randomized controlled trial of a brief personalized feedback intervention for problem drinkers. Alcohol Alcohol 2010;45(3):258-262 [FREE Full text] [CrossRef] [Medline]
- Davis J. The Panic Center. Child Adolesc Ment Health 2007 Feb;12(1):49-50. [CrossRef]
- Doumas DM, McKinley LL, Book P. Evaluation of two Web-based alcohol interventions for mandated college students. J Subst Abuse Treat 2009 Jan;36(1):65-74. [CrossRef] [Medline]
- Farvolden P, Denisoff E, Selby P, Bagby RM, Rudy L. Usage and longitudinal effectiveness of a Web-based self-help cognitive behavioral therapy program for panic disorder. J Med Internet Res 2005 Mar 26;7(1):e7 [FREE Full text] [CrossRef] [Medline]
- Farvolden P, McBride C, Bagby RM, Ravitz P. A Web-based screening instrument for depression and anxiety disorders in primary care. J Med Internet Res 2003;5(3):e23 [FREE Full text] [CrossRef] [Medline]
- McDonnell DD, Lee H, Kazinets G, Moskowitz JM. Online recruitment of targeted populations: lessons learned from a smoking cessation study among Korean Americans. Soc Mar Q 2010 Sep 26;16(3):2-22. [CrossRef]
- Pike KJ, Rabius V, McAlister A, Geiger A. American Cancer Society's QuitLink: randomized trial of Internet assistance. Nicotine Tob Res 2007 Mar;9(3):415-420. [CrossRef] [Medline]
- Rabius V, Pike KJ, Wiatrek D, McAlister AL. Comparing internet assistance for smoking cessation: 13-month follow-up of a six-arm randomized controlled trial. J Med Internet Res 2008 Nov 21;10(5):e45 [FREE Full text] [CrossRef] [Medline]
- Selby P, van Mierlo T, Voci SC, Parent D, Cunningham JA. Online social and professional support for smokers trying to quit: an exploration of first time posts from 2562 members. J Med Internet Res 2010 Aug 18;12(3):e34 [FREE Full text] [CrossRef] [Medline]
- van Mierlo T, Fournier R, Jean-Charles A, Hovington J, Ethier I, Selby P. I'll txt U if I have a problem: how the Société Canadienne du cancer in Quebec applied behavior-change theory, data mining and agile software development to help young adults quit smoking. PLoS One 2014;9(3):e91832 [FREE Full text] [CrossRef] [Medline]
- O'Donnell A, Wallace P, Kaner E. From efficacy to effectiveness and beyond: what next for brief interventions in primary care? Front Psychiatry 2014;5:113 [FREE Full text] [CrossRef] [Medline]
- Herbert J, Forman E. The Evolution of Cognitive Behavioral Therapy: the rise of psychological acceptancemindfullness. In: Acceptance and Mindfulness in Cognitive Behavior Therapy: Understanding and applying the new therapies. New York: Wiley; 2011:1-26.
- Deterding S. Gamification. Interactions 2012 Jul 01;19(4):14-17. [CrossRef]
- Erickson P, Butters J, Walko K, Butterill D, Reggie C, Fischer B, et al. CAMH. Toronto; 2002. CAMH and Harm Reduction: A Background Paper on its Meaning and Application for Substance Use Issues URL: http://www.camh.ca/en/hospital/about_camh/influencing_public_policy/public_policy_submissions/harm_reduction/Pages/harmreductionbackground.aspx [accessed 2017-01-25] [WebCite Cache]
- Janz NK, Becker MH. The Health Belief Model: a decade later. Health Educ Q 1984;11(1):1-47. [Medline]
- Miller W, Rollnick S. Motivational Interviewing: Preparing People to Change. 2nd ed. New York: The Guilford Press; 2002.
- Moreira MT, Smith LA, Foxcroft D. Social norms interventions to reduce alcohol misuse in university or college students. Cochrane Database Syst Rev 2009 Jul 08(3):CD006748. [CrossRef] [Medline]
- Bandura A. Social cognitive theory: an agentic perspective. Annu Rev Psychol 2001;52:1-26. [CrossRef] [Medline]
- Sanchez-Craig M. Drink Wise: How to quit drinking or cut down. A self-help book. Toronto: Addiction Research Foundation; 1995.
- Kreuter MW, Skinner CS. Tailoring: what's in a name? Health Educ Res 2000 Feb;15(1):1-4. [Medline]
- Prochaska JO, DiClemente CC. Transtheoretical therapy: toward a more integrative model of change. Psychotherapy Theory Research & Practice 1982;19(3):276-288. [CrossRef]
- EUR-Lex. 1995. Directive 95/46/EC on the protection of individuals with regard to the processing of personal data and on the free movement of such data URL: http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:31995L0046 [accessed 2017-01-25] [WebCite Cache]
- Laws-lois.justice.: Government of Canada; 2015 Jun 23. Personal Information Protection and Electronic Documents Act URL: http://laws-lois.justice.gc.ca/eng/acts/P-8.6/ [accessed 2017-01-25] [WebCite Cache]
- HHS.: Office for Civil Rights Headquarters; 2003 May. Summary of the HIPAA Privacy Rule URL: https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html?language=es [accessed 2017-01-25] [WebCite Cache]
- World Medical Association Declaration. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. J Am Med Assoc 2013 Nov 27;310(20):2191-2194. [CrossRef] [Medline]
- Jin J, Sklar GE, Min Sen Oh V, Chuen LS. Factors affecting therapeutic compliance: a review from the patient's perspective. Ther Clin Risk Manag 2008 Feb;4(1):269-286 [FREE Full text] [Medline]
- Ausiello D, Lipnick S. Real-time assessment of wellness and disease in daily life. Big Data 2015 Sep 01;3(3):203-208 [FREE Full text] [CrossRef] [Medline]
- Yu S, Liao KP, Shaw SY, Gainer VS, Churchill SE, Szolovits P, et al. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources. J Am Med Inform Assoc 2015 Sep;22(5):993-1000 [FREE Full text] [CrossRef] [Medline]
- Kosinski M, Stillwell D, Graepel T. Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci 2013 Mar 11;110(15):5802-5805 [FREE Full text] [CrossRef]
- Reece A, Danforth C. Cornell University Library. 2016. Instagram photos reveal predictive markers of depression URL: https://arxiv.org/abs/1608.03282 [accessed 2017-01-25] [WebCite Cache]
- Reece A, Reagan A, Lix K, Dodds P, Danforth C, Langer E. Cornell University Library. 2016 Aug 27. Forecasting the onset and course of mental illness with Twitter data URL: https://arxiv.org/abs/1608.07740 [accessed 2017-01-25] [WebCite Cache]
- Su W, Chih M. Is More eHealth System Use Better for Cancer PatientsFamily Caregivers? A Literature Review. 2016 Presented at: American Medical Informatics Association 2016 Annual Symposium; November 12-16, 2016; Chicago, IL. [CrossRef]
- Claxton AJ, Cramer J, Pierce C. A systematic review of the associations between dose regimens and medication compliance. Clinical Therapeutics 2001 Aug;23(8):1296-1310. [CrossRef]
- Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care 2004 May;27(5):1218-1224. [Medline]
- van Mierlo T, Fournier R, Ingham M. Targeting medication non-adherence behavior in selected autoimmune diseases: a systematic approach to digital health program development. PLoS One 2015;10(6):e0129364 [FREE Full text] [CrossRef] [Medline]
- Hill J. Adherence with drug therapy in the rheumatic diseases Part two: measuring and improving adherence. Musculoskeletal Care 2005;3(3):143-156. [CrossRef] [Medline]
- Julian LJ, Yelin E, Yazdany J, Panopalis P, Trupin L, Criswell LA, et al. Depression, medication adherence, and service utilization in systemic lupus erythematosus. Arthritis Rheum 2009 Feb 15;61(2):240-246 [FREE Full text] [CrossRef] [Medline]
- boyd D, Crawford K. Critical questions for big data. Inf Commun Soc 2012;15(5):662-679 [FREE Full text] [CrossRef]
- Poynter R. VisionCritical.: Vision Critical Communications Inc; 2013. Big Data successes and limitations: What researchers and marketers need to know URL: https://www.visioncritical.com/big-data-successes-and-limitations/ [accessed 2017-01-25] [WebCite Cache]
- Wu X, Zhu X, Wu GQ, Ding W. Data mining with big data. IEEE Trans Knowl Data Eng 2014 Jan;26(1):97-107. [CrossRef]
- Luo G. A review of automatic selection methods for machine learning algorithms and hyper-parameter values. Netw Model Anal Health Inform Bioinforma 2016;5(1):1-16. [CrossRef]
- Carlbring P, Brunt S, Bohman S, Austin D, Richards J, Öst L, et al. Internet vs. paper and pencil administration of questionnaires commonly used in panic/agoraphobia research. Comput Human Behav 2007 May;23(3):1421-1434. [CrossRef]
- Fanning J, McAuley E. A comparison of tablet computer and paper-based questionnaires in healthy aging research. JMIR Res Protoc 2014;3(3):e38 [FREE Full text] [CrossRef] [Medline]
- Hohwü L, Lyshol H, Gissler M, Jonsson SH, Petzold M, Obel C. Web-based versus traditional paper questionnaires: a mixed-mode survey with a Nordic perspective. J Med Internet Res 2013 Aug 26;15(8):e173 [FREE Full text] [CrossRef] [Medline]
- Riva G, Teruzzi T, Anolli L. The use of the internet in psychological research: comparison of online and offline questionnaires. Cyberpsychol Behav 2003 Feb;6(1):73-80. [CrossRef] [Medline]
- Smith AB, King M, Butow P, Olver I. A comparison of data quality and practicality of online versus postal questionnaires in a sample of testicular cancer survivors. Psychooncology 2013 Jan;22(1):233-237. [CrossRef] [Medline]
- Whitehead L. Methodological issues in Internet-mediated research: a randomized comparison of internet versus mailed questionnaires. J Med Internet Res 2011 Dec 04;13(4):e109 [FREE Full text] [CrossRef] [Medline]
- Wu RC, Thorpe K, Ross H, Micevski V, Marquez C, Straus SE. Comparing administration of questionnaires via the internet to pen-and-paper in patients with heart failure: randomized controlled trial. J Med Internet Res 2009 Feb 06;11(1):e3 [FREE Full text] [CrossRef] [Medline]
- Brigham J, Lessov-Schlaggar CN, Javitz HS, Krasnow RE, McElroy M, Swan GE. Test-retest reliability of web-based retrospective self-report of tobacco exposure and risk. J Med Internet Res 2009 Aug 11;11(3):e35 [FREE Full text] [CrossRef] [Medline]
|DHSN: Digital health social network|
Edited by G Eysenbach; submitted 07.07.16; peer-reviewed by B Carron-Arthur, A Graham, WC Su; comments to author 18.11.16; revised version received 31.12.16; accepted 13.01.17; published 17.02.17
©Trevor van Mierlo, Xinlong Li, Douglas Hyatt, Andrew T Ching. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.02.2017.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.