JMIR Publications

Journal of Medical Internet Research

The leading peer-reviewed journal for health and healthcare in the Internet age.

JMIR's Thomson Reuter Impact Factor of 4.7 for 2013


Thomson Reuters has published the Journal Citation Reports (JCR) with its Journal Impact Factors for 2015. The Journal Impact Factor 2015 is defined as the number of citations in 2014 to the citations to articles published in the previous 2 years (2012-2013), divided by the number of articles published during that time. The Journal Impact Factor is a metric of excellence for a journal, it is not an article-level metric.

The Impact Factor is an increasingly controversial metric due its frequent misuse, e.g. administrators comparing the "raw" impact factor score across disciplines. This disadvantages journals in smaller disciplines such as medical informatics, which traditionally have less citations than for example multidisciplinary or general medicine journals. As one innovation, Thomson Reuters is now ranking journals by quartile (Q1, Q2, Q3, Q4), within their discipline.

While we at JMIR discourage obsession over the journal impact factor (in particular if abused as proxy to assess the quality of individual articles), our ranking in the JCR is an important validation that even as small open access publisher we can compete with journals published by publishing giants.

JMIR continues to be ranked in the first quartile (Q1) in both of it's disciplines, medical informatics (Q1) and health services research (Q1).

However, even these category-specific rankings are sometimes questionable, in particular for multidisciplinary journals such as JMIR which fit into more than the categories selected by the JCR editors. Moreover, the current JCR categories sometimes lump together journals which do not belong together, for example statistics journals are part of the medical informatics category, and oddly enough, the journal Statistical Methods in Medical Research is now suddenly the top-ranked journal in the medical informatics category.

It may therefore make more sense to compare JMIR against other leading multidisciplinary open access journals, as shown below. However, once again, the impact factor should not be the only determining factor when submitting an article. The journal scope and audience (who reads the journal) are equally important if one wants to maximize impact and influence of an article on key stakeholders and researchers, which is not measurable by citations (perhaps better measured with social media uptake and altmetrics).

We continue to encourage our authors to consider the full range of JMIR journals when submitting an article and consider the scope of the journal and the topic of the article.

Quiz: Which of the following #openaccess journals has the highest impact factor:

1) PloS One,

2) PeerJ,


4) BMJ Open,

5) JMIR 

(scroll down for the answer)

Journal Quartile (in their category)   Impact Factor 2015
1. JMIR Q1, Q1 3.428
2. PloS One Q1 3.234
3. BMJ Open Q2 2.271
4. PeerJ Q1 2.112
5. BMC Med Inform Med Dec Mk Q2 1.830

Beyond the Journal Impact Factor

Authors care (and should care) about other metrics/ratings such as author satisfaction with reviews and turnaround times, as for example evaluated by SciRevJMIR is ranked highly here as well (compare for example against PlosOne ratings).

scirev ranking of JMIR vs PlosOne

Other metrics to look at are the twimpact factor (social media impact) as well as post-publication dissemination activies by the publisher (JMIR is using TrendMD to promote published articles across other publishers such as BMJ and the JAMA network).

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Recent Articles:

  • Food tracker from the Connected Wellness Platform.
This image was created by authors.

    Health Coaching Reduces HbA1c in Type 2 Diabetic Patients From a Lower-Socioeconomic Status Community: A Randomized Controlled Trial


    Background: Adoptions of health behaviors are crucial for maintaining good health after type 2 diabetes mellitus (T2DM) diagnoses. However, adherence to glucoregulating behaviors like regular exercise and balanced diet can be challenging, especially for people living in lower-socioeconomic status (SES) communities. Providing cost-effective interventions that improve self-management is important for improving quality of life and the sustainability of health care systems. Objective: To evaluate a health coach intervention with and without the use of mobile phones to support health behavior change in patients with type 2 diabetes. Methods: In this noninferiority, pragmatic randomized controlled trial (RCT), patients from two primary care health centers in Toronto, Canada, with type 2 diabetes and a glycated hemoglobin/hemoglobin A1c (HbA1c) level of ≥7.3% (56.3 mmol/mol) were randomized to receive 6 months of health coaching with or without mobile phone monitoring support. We hypothesized that both approaches would result in significant HbA1c reductions, although health coaching with mobile phone monitoring would result in significantly larger effects. Participants were evaluated at baseline, 3 months, and 6 months. The primary outcome was the change in HbA1c from baseline to 6 months (difference between and within groups). Other outcomes included weight, waist circumference, body mass index (BMI), satisfaction with life, depression and anxiety (Hospital Anxiety and Depression Scale [HADS]), positive and negative affect (Positive and Negative Affect Schedule [PANAS]), and quality of life (Short Form Health Survey-12 [SF-12]). Results: A total of 138 patients were randomized and 7 were excluded for a substudy; of the remaining 131, 67 were allocated to the intervention group and 64 to the control group. Primary outcome data were available for 97 participants (74.0%). While both groups reduced their HbA1c levels, there were no significant between-group differences in change of HbA1c at 6 months using intention-to-treat (last observation carried forward [LOCF]) (P=.48) or per-protocol (P=.83) principles. However, the intervention group did achieve an accelerated HbA1c reduction, leading to a significant between-group difference at 3 months (P=.03). This difference was reduced at the 6-month follow-up as the control group continued to improve, achieving a reduction of 0.81% (8.9 mmol/mol) (P=.001) compared with a reduction of 0.84% (9.2 mmol/mol)(P=.001) in the intervention group. Intervention group participants also had significant decreases in weight (P=.006) and waist circumference (P=.01) while controls did not. Both groups reported improvements in mood, satisfaction with life, and quality of life. Conclusions: Health coaching with and without access to mobile technology appeared to improve glucoregulation and mental health in a lower-SES, T2DM population. The accelerated improvement in the mobile phone group suggests the connectivity provided may more quickly improve adoption and adherence to health behaviors within a clinical diabetes management program. Overall, health coaching in primary care appears to lead to significant benefits for patients from lower-SES communities with poorly controlled type 2 diabetes. Trial Registration: NCT02036892; (Archived by WebCite at

  • Image courtesy of stockimages (Stock photo - Image ID: 100111228) at with the URL

    A Computerized Lifestyle Application to Promote Multiple Health Behaviors at the Workplace: Testing Its Behavioral and Psychological Effects


    Background: Preventive health behaviors, such as regular physical activity and healthy nutrition, are recommended to maintain employability and to facilitate the health of employees. Theory-based workplace health promotion needs to include psychological constructs and consider the motivational readiness (so-called stages of change) of employees. According to the stages, people can be grouped as nonintenders (not motivated to change and not performing the goal behavior), intenders (decided to adopt the goal behavior but not started yet), or actors (performing the goal behavior already). The tailoring to these stages can be done computer based and should make workplace health promotion more effective. Objective: It was tested whether a parsimonious computer-based health promotion program implemented at the workplace was effective in terms of lifestyle changes and psychological outcomes as well as body weight. We hypothesized that the stage-matched intervention would outperform the one-size-fits-all active control condition (standard care intervention). Methods: In a randomized controlled trial, a total of 1269 employees were recruited by a trained research assistant at their workplace during a routine medical examination. After excluding noneligible employees, 560 completed Time 1 (T1), and 384 also completed Time 2 (T2), achieving a retention rate of 68.6%. Two fully automated computer-based treatments were adopted: (1) an active control condition with information about benefits of exercise and healthy nutrition (n=52), or (2) a stage-matched multiple-behavior intervention that provided different psychological treatments to 9 subgroups, addressing stages of change (nonintenders, intenders, and actors per behavior; n=332). Baseline assessments (T1) on behavior, psychological constructs, and body weight were repeated after 4 weeks (T2). Results: The stage-matched intervention outperformed the active control condition for lifestyle changes containing physical activity and nutrition (χ21=3.5; P=.04, for N=384) as well as psychological variables (physical activity intention, P=.04; nutrition intention, P=.03; nutrition planning, P=.02; and general social support to live healthily, P=.01). When predicting a healthy lifestyle at follow-up, baseline lifestyle (odds ratio, OR, 2.25, 95% CI 1.73-2.92; P<.01) and the intervention (OR 1.96, 95% CI 1.00-3.82; P=.05) were found to be significant predictors. Physical activity planning mediated the effect of the intervention on the adoption of an overall healthy lifestyle (consisting of activity and nutrition, R2adj=.08; P<.01), indicating that if the stage-matched intervention increased planning, the adoption of a healthy lifestyle was more likely. Conclusions: Matching an intervention to the motivational readiness of employees can make a health promotion program effective. Employees’ motivation, planning, social support, and lifestyle can be supported by a stage-matched intervention that focuses on both physical activity and healthy nutrition. Occupational settings provide a potential to implement parsimonious computer-based health promotion programs and to facilitate multiple behavior change.

  • E-cigarette dialogue (CC0 Public Domain; Free for commercial use/No attribution required).

    What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques


    Background: The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people’s experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. Objective: This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people’s experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. Methods: In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. Results: In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. Conclusions: This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a “hobbyist culture” that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design.

  • The copyright for this image is owned by Sascha Ludwig.

    Moving Knowledge Acquisition From the Lecture Hall to the Student Home: A Prospective Intervention Study


    Background: Podcasts are popular with medical students, but the impact of podcast use on learning outcomes in undergraduate medical education has not been studied in detail. Objective: Our aim was to assess the impact of podcasts accompanied by quiz questions and lecture attendance on short- and medium-term knowledge retention. Methods: Students enrolled for a cardio-respiratory teaching module were asked to prepare for 10 specific lectures by watching podcasts and submitting answers to related quiz questions before attending live lectures. Performance on the same questions was assessed in a surprise test and a retention test. Results: Watching podcasts and submitting answers to quiz questions (versus no podcast/quiz use) was associated with significantly better test performance in all items in the surprise test and 7 items in the retention test. Lecture attendance (versus no attendance) was associated with higher test performance in 3 items and 1 item, respectively. In a linear regression analysis adjusted for age, gender, and overall performance levels, both podcast/quiz use and lecture attendance were significant predictors of student performance. However, the variance explained by podcast/quiz use was greater than the variance explained by lecture attendance in the surprise test (38.7% vs 2.2%) and retention test (19.1% vs 4.0%). Conclusions: When used in conjunction with quiz questions, podcasts have the potential to foster knowledge acquisition and retention over and above the effect of live lectures.

  • (cc) Walthouwer et al, CC-BY-SA 2.0, please cite as (

    Use and Effectiveness of a Video- and Text-Driven Web-Based Computer-Tailored Intervention: Randomized Controlled Trial


    Background: Many Web-based computer-tailored interventions are characterized by high dropout rates, which limit their potential impact. Objective: This study had 4 aims: (1) examining if the use of a Web-based computer-tailored obesity prevention intervention can be increased by using videos as the delivery format, (2) examining if the delivery of intervention content via participants’ preferred delivery format can increase intervention use, (3) examining if intervention effects are moderated by intervention use and matching or mismatching intervention delivery format preference, (4) and identifying which sociodemographic factors and intervention appreciation variables predict intervention use. Methods: Data were used from a randomized controlled study into the efficacy of a video and text version of a Web-based computer-tailored obesity prevention intervention consisting of a baseline measurement and a 6-month follow-up measurement. The intervention consisted of 6 weekly sessions and could be used for 3 months. ANCOVAs were conducted to assess differences in use between the video and text version and between participants allocated to a matching and mismatching intervention delivery format. Potential moderation by intervention use and matching/mismatching delivery format on self-reported body mass index (BMI), physical activity, and energy intake was examined using regression analyses with interaction terms. Finally, regression analysis was performed to assess determinants of intervention use. Results: In total, 1419 participants completed the baseline questionnaire (follow-up response=71.53%, 1015/1419). Intervention use declined rapidly over time; the first 2 intervention sessions were completed by approximately half of the participants and only 10.9% (104/956) of the study population completed all 6 sessions of the intervention. There were no significant differences in use between the video and text version. Intervention use was significantly higher among participants who were allocated to an intervention condition that matched their preferred intervention delivery format. There were no significant interaction terms for any of the outcome variables; a match and more intervention use did not result in better intervention effects. Participants with a high BMI and participants who felt involved and supported by the intervention were more likely to use the intervention more often. Conclusions: Video delivery of tailored feedback does not increase the use of Web-based computer-tailored interventions. However, intervention use can potentially be increased by delivering intervention content via participants’ preferred intervention delivery format and creating feelings of relatedness. Because more intervention use was not associated with better intervention outcomes, more research is needed to examine the optimum number of intervention sessions in terms of maximizing use and effects. Trial Registration: Nederlands Trial Register: NTR3501; (Archived by WebCite at

  • Table of contents image.

Content providers: CDC/ Amanda Mills, ID# 13763, 

Copyright Restrictions:None - This image is in the public domain and thus free of any copyright restrictions.

    Designing and Testing an Inventory for Measuring Social Media Competency of Certified Health Education Specialists


    Background: Social media can promote healthy behaviors by facilitating engagement and collaboration among health professionals and the public. Thus, social media is quickly becoming a vital tool for health promotion. While guidelines and trainings exist for public health professionals, there are currently no standardized measures to assess individual social media competency among Certified Health Education Specialists (CHES) and Master Certified Health Education Specialists (MCHES). Objective: The aim of this study was to design, develop, and test the Social Media Competency Inventory (SMCI) for CHES and MCHES. Methods: The SMCI was designed in three sequential phases: (1) Conceptualization and Domain Specifications, (2) Item Development, and (3) Inventory Testing and Finalization. Phase 1 consisted of a literature review, concept operationalization, and expert reviews. Phase 2 involved an expert panel (n=4) review, think-aloud sessions with a small representative sample of CHES/MCHES (n=10), a pilot test (n=36), and classical test theory analyses to develop the initial version of the SMCI. Phase 3 included a field test of the SMCI with a random sample of CHES and MCHES (n=353), factor and Rasch analyses, and development of SMCI administration and interpretation guidelines. Results: Six constructs adapted from the unified theory of acceptance and use of technology and the integrated behavioral model were identified for assessing social media competency: (1) Social Media Self-Efficacy, (2) Social Media Experience, (3) Effort Expectancy, (4) Performance Expectancy, (5) Facilitating Conditions, and (6) Social Influence. The initial item pool included 148 items. After the pilot test, 16 items were removed or revised because of low item discrimination (r<.30), high interitem correlations (Ρ>.90), or based on feedback received from pilot participants. During the psychometric analysis of the field test data, 52 items were removed due to low discrimination, evidence of content redundancy, low R-squared value, or poor item infit or outfit. Psychometric analyses of the data revealed acceptable reliability evidence for the following scales: Social Media Self-Efficacy (alpha=.98, item reliability=.98, item separation=6.76), Social Media Experience (alpha=.98, item reliability=.98, item separation=6.24), Effort Expectancy(alpha =.74, item reliability=.95, item separation=4.15), Performance Expectancy (alpha =.81, item reliability=.99, item separation=10.09), Facilitating Conditions (alpha =.66, item reliability=.99, item separation=16.04), and Social Influence (alpha =.66, item reliability=.93, item separation=3.77). There was some evidence of local dependence among the scales, with several observed residual correlations above |.20|. Conclusions: Through the multistage instrument-development process, sufficient reliability and validity evidence was collected in support of the purpose and intended use of the SMCI. The SMCI can be used to assess the readiness of health education specialists to effectively use social media for health promotion research and practice. Future research should explore associations across constructs within the SMCI and evaluate the ability of SMCI scores to predict social media use and performance among CHES and MCHES.

  • (cc) Hu et al. CC-BY-SA 2.0, please cite as (

    Online Prediction of Health Care Utilization in the Next Six Months Based on Electronic Health Record Information: A Cohort and Validation Study


    Background: The increasing rate of health care expenditures in the United States has placed a significant burden on the nation’s economy. Predicting future health care utilization of patients can provide useful information to better understand and manage overall health care deliveries and clinical resource allocation. Objective: This study developed an electronic medical record (EMR)-based online risk model predictive of resource utilization for patients in Maine in the next 6 months across all payers, all diseases, and all demographic groups. Methods: In the HealthInfoNet, Maine’s health information exchange (HIE), a retrospective cohort of 1,273,114 patients was constructed with the preceding 12-month EMR. Each patient’s next 6-month (between January 1, 2013 and June 30, 2013) health care resource utilization was retrospectively scored ranging from 0 to 100 and a decision tree–based predictive model was developed. Our model was later integrated in the Maine HIE population exploration system to allow a prospective validation analysis of 1,358,153 patients by forecasting their next 6-month risk of resource utilization between July 1, 2013 and December 31, 2013. Results: Prospectively predicted risks, on either an individual level or a population (per 1000 patients) level, were consistent with the next 6-month resource utilization distributions and the clinical patterns at the population level. Results demonstrated the strong correlation between its care resource utilization and our risk scores, supporting the effectiveness of our model. With the online population risk monitoring enterprise dashboards, the effectiveness of the predictive algorithm has been validated by clinicians and caregivers in the State of Maine. Conclusions: The model and associated online applications were designed for tracking the evolving nature of total population risk, in a longitudinal manner, for health care resource utilization. It will enable more effective care management strategies driving improved patient outcomes.

  • What's wrong with me?

    The Diagnostic Validity and Reliability of an Internet-Based Clinical Assessment Program for Mental Disorders


    Background: Internet-based assessment has the potential to assist with the diagnosis of mental health disorders and overcome the barriers associated with traditional services (eg, cost, stigma, distance). Further to existing online screening programs available, there is an opportunity to deliver more comprehensive and accurate diagnostic tools to supplement the assessment and treatment of mental health disorders. Objective: The aim was to evaluate the diagnostic criterion validity and test-retest reliability of the electronic Psychological Assessment System (e-PASS), an online, self-report, multidisorder, clinical assessment and referral system. Methods: Participants were 616 adults residing in Australia, recruited online, and representing prospective e-PASS users. Following e-PASS completion, 158 participants underwent a telephone-administered structured clinical interview and 39 participants repeated the e-PASS within 25 days of initial completion. Results: With structured clinical interview results serving as the gold standard, diagnostic agreement with the e-PASS varied considerably from fair (eg, generalized anxiety disorder: κ=.37) to strong (eg, panic disorder: κ=.62). Although the e-PASS’ sensitivity also varied (0.43-0.86) the specificity was generally high (0.68-1.00). The e-PASS sensitivity generally improved when reducing the e-PASS threshold to a subclinical result. Test-retest reliability ranged from moderate (eg, specific phobia: κ=.54) to substantial (eg, bulimia nervosa: κ=.87). Conclusions: The e-PASS produces reliable diagnostic results and performs generally well in excluding mental disorders, although at the expense of sensitivity. For screening purposes, the e-PASS subclinical result generally appears better than a clinical result as a diagnostic indicator. Further development and evaluation is needed to support the use of online diagnostic assessment programs for mental disorders. Trial Registration: Australian and New Zealand Clinical Trials Registry ACTRN121611000704998; (Archived by WebCite at

  • Response to “Twitter-Based Journal Clubs: Some Additional Facts and Clarifications”


    This is a letter to the editor in response to Topf et al. We describe the rationale behind much of the analytical methods following publication of our original article " Globalization of Continuing Professional Development by Journal Clubs via Microblogging: A Systematic Review". Further, we provide some updated results following a subsequent review.

  • The contribution of 8

    Twitter-Based Journal Clubs: Additional Facts and Clarifications


    We read the recently published paper on globalization of continuing professional development by Roberts et al with great interest. The authors should be congratulated on their idea as well as their execution of this novel way of evaluating and describing Twitter-based journal clubs. We would like to add to their article by providing some additional advantages and features of a Twitter-based online journal club to provide the reader with a more complete appreciation of their educational potential.

  • Image by Nathan Wineinger.

    How Consumers and Physicians View New Medical Technology: Comparative Survey


    Background: As a result of the digital revolution coming to medicine, a number of new tools are becoming available and are starting to be introduced in clinical practice. Objective: We aim to assess health care professional and consumer attitudes toward new medical technology including smartphones, genetic testing, privacy, and patient-accessible electronic health records. Methods: We performed a survey with 1406 health care providers and 1102 consumer responders. Results: Consumers who completed the survey were more likely to prefer new technologies for a medical diagnosis (437/1102, 39.66%) compared with providers (194/1406, 13.80%; P<.001), with more providers (393/1406, 27.95%) than consumers (175/1102, 15.88%) reporting feeling uneasy about using technology for a diagnosis. Both providers and consumers supported genetic testing for various purposes, with providers (1234/1406, 87.77%) being significantly more likely than consumers (806/1102, 73.14%) to support genetic testing when planning to have a baby (P<.001). Similarly, 91.68% (1289/1406) of providers and 81.22% (895/1102) of consumers supported diagnosing problems in a fetus (P<.001). Among providers, 90.33% (1270/1406) were concerned that patients would experience anxiety after accessing health records, and 81.95% (1149/1406) felt it would lead to requests for unnecessary medical evaluations, but only 34.30% (378/1102; P<.001) and 24.59% (271/1102; P<.001) of consumers expressed the same concerns, respectively. Physicians (137/827, 16.6%) reported less concern about the use of technology for diagnosis compared to medical students (21/235, 8.9%; P=.03) and also more frequently felt that patients owned their medical record (323/827, 39.1%; and 30/235, 12.8%, respectively; P<.001). Conclusions: Consumers and health professionals differ significantly and broadly in their views of emerging medical technology, with more enthusiasm and support expressed by consumers.

  • Sleepy phone.

    Adherence to Technology-Mediated Insomnia Treatment: A Meta-Analysis, Interviews, and Focus Groups


    Background: Several technologies have been proposed to support the reduction of insomnia complaints. A user-centered assessment of these technologies could provide insight into underlying factors related to treatment adherence. Objective: Gaining insight into adherence to technology-mediated insomnia treatment as a solid base for improving those adherence rates by applying adherence-enhancing strategies. Methods: Adherence to technology-mediated sleep products was studied in three ways. First, a meta-analysis was performed to investigate adherence rates in technology-mediated insomnia therapy. Several databases were queried for technology-mediated insomnia treatments. After inclusion and exclusion steps, data from 18 studies were retrieved and aggregated to find an average adherence rate. Next, 15 semistructured interviews about sleep-support technologies were conducted to investigate perceived adherence. Lastly, several scenarios were written about the usage of a virtual sleep coach that could support adherence rates. The scenarios were discussed in six different focus groups consisting of potential users (n=15), sleep experts (n=7), and coaches (n=9). Results: From the meta-analysis, average treatment adherence appeared to be approximately 52% (95% CI 43%-61%) for technology-mediated insomnia treatments. This means that, on average, half of the treatment exercises were not executed, suggesting there is a substantial need for adherence and room for improvement in this area. However, the users in the interviews believed they adhered quite well to their sleep products. Users mentioned relying on personal commitment (ie, willpower) for therapy adherence. Participants of the focus groups reconfirmed their belief in the effectiveness of personal commitment, which they regarded as more effective than adherence-enhancing strategies. Conclusions: Although adherence rates for insomnia interventions indicate extensive room for improvement, users might not consider adherence to be a problem; they believe willpower to be an effective adherence strategy. A virtual coach should be able to cope with this “adherence bias” and persuade users to accept adherence-enhancing strategies, such as reminders, compliments, and community building.

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    Open Peer Review Period: Oct 2, 2015 - Nov 27, 2015

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    Date Submitted: Sep 28, 2015

    Open Peer Review Period: Sep 28, 2015 - Nov 23, 2015

    Background: More and more users turn to the web, including the emerging social media sites, seeking cancer-related information. Little is known, however, about their needs for cancer information that...

    Background: More and more users turn to the web, including the emerging social media sites, seeking cancer-related information. Little is known, however, about their needs for cancer information that are discussed in social questioning & answering (social Q&A) sites, a form of social medial where users can ask and answer one another about a variety of topics in everyday life. Objective: The current study investigates people’s cancer information needs presented in the form of questions in a social Q&A site by analyzing cancer-related questions posted on Yahoo! Answers, the most popular social Q&A site in North America. Methods: A total of 81,434 questions were randomly collected from Yahoo! Answers. We analyzed the questions using text mining techniques, focusing on extracting terms related to cancer information needs. We further classified terms into layers of information contexts based on an existing layered model of context for consumers’ health information searching in order to interpret the contexts associated with the terms. Results: A total of 534 terms were identified as meaningful concepts related to cancer information seeking. These terms were further classified into six layers, namely: demographic, cognitive, affective, social, situational, and technical layer. Together, these layers demonstrate that askers provide rich information about their personal characteristics, their understanding of their problems, emotions, social relationships, life situations, as well as information sources in their questions. Conclusions: Our results confirmed prior studies on cancer information seeking that people seek cancer information on a wide variety of topics. Further, it added to the existing literature in that people’s cancer information needs are multi-dimensional, indicated by different layers identified in their questions. The findings suggest that healthcare professionals and health information system developers should examine consumers’ cancer information needs in light of their specific demographic, cognitive, emotional, social, situational, and technical contexts.

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    Date Submitted: Sep 27, 2015

    Open Peer Review Period: Sep 28, 2015 - Nov 23, 2015

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    Date Submitted: Sep 28, 2015

    Open Peer Review Period: Sep 28, 2015 - Nov 23, 2015

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    Open Peer Review Period: Sep 26, 2015 - Nov 21, 2015

    Background: More than one third of college students who are overweight/obese are in need of weight loss programs tailored to college students. However, the availability and accessibility of these pro...

    Background: More than one third of college students who are overweight/obese are in need of weight loss programs tailored to college students. However, the availability and accessibility of these programs is unknown. Objective: This study examines availability and ease of access to weight loss programs for students on 10 universities with the largest undergraduate enrollment. Methods: Ten public universities with the largest student bodies (mean undergraduate enrollment: 41,122 ± 7,657 students). Websites of the universities were assessed to determine availability of weight loss programs. Services for high-risk health needs common to university campuses (i.e. alcohol and other drugs, victim services, sexual health, and eating disorders) were searched. Results: Three schools offered weight loss programming; however, none met predetermined criteria. Comparatively, all schools offered no-cost and continual enrollment programming for the other high-risk health needs. Conclusions: There are limited weight loss services available to undergraduate students compared with other university services. Collaboration between existing college health service providers is suggested for the delivery of appropriate programming for overweight/obese undergraduates wanting to lose weight.

  • Effectiveness of a Web-Based Computer-Tailored Multiple-Lifestyle Intervention for People at Risk for Cardiovascular Diseases: A Randomized Controlled Trial

    Date Submitted: Sep 20, 2015

    Open Peer Review Period: Sep 22, 2015 - Nov 17, 2015

    Background: Web-based computer-tailored interventions for multiple health behaviors can be an effective means to improve habituation of health behaviors in people at risk for cardiovascular diseases....

    Background: Web-based computer-tailored interventions for multiple health behaviors can be an effective means to improve habituation of health behaviors in people at risk for cardiovascular diseases. Yet, few randomized controlled trials have tested this assumption. Objective: The aim was to test an 8-week web-based computer-tailored intervention designed to improve habit strength for physical activity and fruit and vegetable consumption of people at risk for cardiovascular diseases. In a randomized controlled design, self-reported changes in perceived habit strength, self-efficacy and planning across different domains of physical activity as well as fruit and vegetable consumption were evaluated. Methods: This study is a web-based longitudinal study with a randomized controlled trial design involving an experimental group (n=403) and a waiting control group (n=387). Data collection was done in Germany and the Netherlands in 2013-2015. The intervention content was based on the Health Action Process Approach and involved personalized feedback on lifestyle behaviors that indicated whether participants complied with the guidelines for physical activity and fruit and vegetable consumption. There were three self-report web-based assessments: Baseline (T0, N=790), a posttest 8 weeks after baseline (T1, n=206) and a follow-up (T2, n=121) 3 months after baseline. Data analysis was done with analyses of variances in SPSS 22 and SPSS AMOS path analysis. Randomization into waiting control group and intervention group at T0 was successfully applied in terms of age (F(1, 789)<0.01, P=.962), body mass index (F(1, 789)=2.22, P=.137), baseline intentions (fruit and vegetable consumption: F(1, 789)=1.58, P=.209; physical activity: F(1, 789)<0.01, P=.928), baseline habit strength (fruit and vegetable consumption: F(1, 789)=1.72, P=.290; physical activity: F(1, 789)=2.53, P=.112), gender (chi²(1, 790)=1.21, P=.271) and country (chi²(1, 789)=1.07, P=.301). Results: Significant group by time interactions for the two dependent variables documented superior treatment effects for the intervention group, with substantially higher increases in self-reported habit strength for physical activity (F(1, 199)=7.71, P=.006, ES=.37) and fruit and vegetable consumption (F(1, 199)=7.71, P=.006, ES=.30) at posttest T1 for the experimental group. Mediation analyses yielded behavior-specific sequential mediator effects for T1 planning and T1 self-efficacy between the intervention and habit strength at follow-up T2 (fruit and vegetable consumption: B=0.19, 95% CI 0.05 to 0.14, P<.001; physical activity: B=0.09, 95% CI 0.14 to 0.25, P<.001). Conclusions: Our findings indicate the general effectiveness and practicability of web-based computer-tailored interventions to increase self-reported habit strength for physical activity and fruit and vegetable consumption. Self-efficacy and planning seem to play a major role in the mechanisms that facilitate the habituation of these behaviors and should actively be promoted in web-based interventions. The results need to be interpreted in view of the high dropout rates and medium effect sizes. However, a large number of people were reached and change in habit strength was achieved after 3 months. Clinical Trial: NCT01909349 and Nederlands Trial Register: NTR3706