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
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Patient perspectives on sharing anonymised personal health data using a digital system for dynamic consent and research feedback: a qualitative study

Background: Electronic health records are widely acknowledged to provide an important opportunity to anonymise patient-level healthcare data and collate across populations to support research. Nonetheless, in the wake of public and policy concerns about security and inappropriate use of data, conventional approaches towards data governance may no longer be sufficient to respect and protect individual privacy. One proposed solution to improve transparency and public trust is known as Dynamic Consent which uses information technology to facilitate a more explicit and accessible opportunity to opt-out, whereby patients can tailor preferences about whom they share their data with, and can change their preferences reliably at any time. Furthermore, electronic systems provide opportunities for informing patients about data recipients and the results of research to which their data have contributed. Objective: To explore patient perspectives on the use of anonymised healthcare data for research purposes, and to evaluate patient perceptions of a dynamic consent model and electronic system to enable and implement on-going communication and collaboration between patients and researchers. Methods: Qualitative interviews and focus groups that included a video presentation explaining the re-use of anonymised electronic patient records for research. Slides and tablet devices were used to introduce the dynamic consent system for discussion. Thirty-five patients with chronic rheumatic disease with varying levels of illness and social deprivation, and five participants from a patient and public involvement health research network. Results: Patients were supportive of sharing their anonymised electronic patient record for research but noted a lack of transparency and awareness around the use of data, making it difficult to secure public trust. Whilst there were general concerns about detrimental consequences of data falling into the wrong hands, such as insurance companies, participants generally considered the altruistic benefits of sharing healthcare data outweighed the risks. Views were mostly positive about the use of an electronic interface to enable greater control over consent choices, although some patients were happy to share their data without further engagement. Participants were particularly enthusiastic about the system as a means of enabling feedback regarding data recipients and associated research results, noting that this would improve trust and public engagement in research. More than half of patients found the touchscreen interface easy to use although a significant minority, especially those with limited access to technology, expressed some trepidation and felt they may need support to use the system. Conclusions: Patients from a range of socioeconomic backgrounds viewed positively a digital system for dynamic consent and, in particular, feedback about data recipients and research results. Implementation of a system would require careful interface design and would need to be located within a robust data infrastructure, yet it has the potential to improve trust and engagement in electronic medical record research.

2015-07-04

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,

3) BMC MDM,

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:

  • Zombies Run - (c) Six to Start (fair Use).

    Apps for IMproving FITness and Increasing Physical Activity Among Young People: The AIMFIT Pragmatic Randomized Controlled Trial

    Abstract:

    Background: Given the global prevalence of insufficient physical activity (PA), effective interventions that attenuate age-related decline in PA levels are needed. Mobile phone interventions that positively affect health (mHealth) show promise; however, their impact on PA levels and fitness in young people is unclear and little is known about what makes a good mHealth app. Objective: The aim was to determine the effects of two commercially available smartphone apps (Zombies, Run and Get Running) on cardiorespiratory fitness and PA levels in insufficiently active healthy young people. A second aim was to identify the features of the app design that may contribute to improved fitness and PA levels. Methods: Apps for IMproving FITness (AIMFIT) was a 3-arm, parallel, randomized controlled trial conducted in Auckland, New Zealand. Participants were recruited through advertisements in electronic mailing lists, local newspapers, flyers posted in community locations, and presentations at schools. Eligible young people aged 14-17 years were allocated at random to 1 of 3 conditions: (1) use of an immersive app (Zombies, Run), (2) use of a nonimmersive app (Get Running), or (3) usual behavior (control). Both smartphone apps consisted of a fully automated 8-week training program designed to improve fitness and ability to run 5 km; however, the immersive app featured a game-themed design and narrative. Intention-to-treat analysis was performed using data collected face-to-face at baseline and 8 weeks, and all regression models were adjusted for baseline outcome value and gender. The primary outcome was cardiorespiratory fitness, objectively assessed as time to complete the 1-mile run/walk test at 8 weeks. Secondary outcomes were PA levels (accelerometry and self-reported), enjoyment, psychological need satisfaction, self-efficacy, and acceptability and usability of the apps. Results: A total of 51 participants were randomized to the immersive app intervention (n=17), nonimmersive app intervention (n=16), or the control group (n=18). The mean age of participants was 15.7 (SD 1.2) years; participants were mostly NZ Europeans (61%, 31/51) and 57% (29/51) were female. Overall retention rate was 96% (49/51). There was no significant intervention effect on the primary outcome using either of the apps. Compared to the control, time to complete the fitness test was –28.4 seconds shorter (95% CI –66.5 to 9.82, P=.20) for the immersive app group and –24.7 seconds (95% CI –63.5 to 14.2, P=.32) for the nonimmersive app group. No significant intervention effects were found for secondary outcomes. Conclusions: Although apps have the ability to increase reach at a low cost, our pragmatic approach using readily available commercial apps as a stand-alone instrument did not have a significant effect on fitness. However, interest in future use of PA apps is promising and highlights a potentially important role of these tools in a multifaceted approach to increase fitness, promote PA, and consequently reduce the adverse health outcomes associated with insufficient activity. Trial Registration: Australian New Zealand Clinical Trials Registry: ACTRN12613001030763; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12613001030763 (Archived by WebCite at http://www.webcitation.org/6aasfJVTJ).

  • Screenshot of http://www.gofor2and5.com.au.

    Who Uses the Internet as a Source of Nutrition and Dietary Information? An Australian Population Perspective

    Abstract:

    Background: The Internet contains a plethora of nutrition information. Health organizations are increasingly using the Internet to deliver population-wide health information and interventions. Effective interventions identify their target population and their needs; however, little is known about use of the Internet as a source of nutrition information. Objective: The aim was to assess the change in prevalence and demographic characteristics of Western Australian adults accessing the Internet as a source of nutrition information and identify specific information needs. Methods: Data were pooled from the Western Australian Department of Health’s 3-yearly Nutrition Monitoring Survey Series telephone survey between 1995 and 2012 of 7044 participants aged 18 to 64 years. Outcome variables were the main sources of nutrition information used in the last year and yes/no responses to 4 suggestions to what would make it easier to eat a healthy diet. Sociodemographic variables were collected. Results: The proportion of respondents using the Internet for nutrition information increased from <1% in 1995-2001 to 9.1% in 2004 and 33.7% in 2012. Compared to 2004, logistic regression showed that the odds of using the Internet for this information increased significantly in 2009 (OR 2.84, 95% CI 2.07-3.88) and 2012 (OR 5.20, 95% CI 3.86-7.02, P<.001). Respondents using the Internet as a source were more likely to be female (OR 1.30, 95% CI 1.05-1.60, P=.02), live in a metropolitan area (OR 1.26, 95% CI 1.03-1.54, P=.03), born in countries other than Australia/UK/Ireland (OR 1.41, 95% CI 1.07-1.85, P=.02), more educated (university: OR 2.46, 95% CI 1.77-3.42, P<.001), and were less likely to be older (55-64 years: OR 0.38, 95% CI 0.25-0.57, P<.001). The majority of respondents agreed the following information would assist them to make healthier choices: more ways to prepare healthy foods (72.0%, 95% CI 70.7-73.3), quicker ways to prepare healthy foods (79.0%, 95% CI 77.8-80.1), how to choose healthy foods (68.8%, 95% CI 67.5-70.1), and knowing more about cooking (54.7%, 95% CI 53.3-56.1). Those using the Internet for nutrition information were more likely than nonusers to want to know quicker ways to prepare healthy foods (83.0% vs 78.1%, P=.005) and information on choosing healthy foods (76.3% vs 67.3%, P<.001). Conclusions: Use of the Internet as a main source of nutrition information has grown rapidly since 2004; one-third of Western Australian adults reported using the Internet for this purpose in 2012. Information on preparing healthy foods (ideas, quicker ways), choosing ingredients, and knowing more about cooking would make it easier to eat a healthy diet. For Internet users, emphasis should be on quicker ways and choosing ingredients. These finding have implications for policy makers and practitioners and suggest that traditional health promotion tactics should continue to be used to reach the broader population.

  • Free for commercial use (http://iconbug.com/detail/icon/7705/folded-paper-twitter/).

    Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning

    Abstract:

    Background: Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public’s knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Objective: Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Methods: Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Results: Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Conclusions: Social media outlets like Twitter can uncover real-time snapshots of personal sentiment, knowledge, attitudes, and behavior that are not as accessible, at this scale, through any other offline platform. Using the vast data available through social media presents an opportunity for social science and public health methodologies to utilize computational methodologies to enhance and extend research and practice. This study was successful in automating a complex five-category manual content analysis of e-cigarette-related content on Twitter using machine learning techniques. The study details machine learning model specifications that provided the best accuracy for data related to e-cigarettes, as well as a replicable methodology to allow extension of these methods to additional topics.

  • Image source: Still image taken from video titled Lazy Girl No.6, by Maria Raquel Cochez. Licensed under cc-by-sa 3.0,  https://de.wikipedia.org/wiki/Datei:LazyGirl6.jpg.

    An Interactive Computer Session to Initiate Physical Activity in Sedentary Cardiac Patients: Randomized Controlled Trial

    Abstract:

    Background: Physical activity (PA) improves many facets of health. Despite this, the majority of American adults are insufficiently active. Adults who visit a physician complaining of chest pain and related cardiovascular symptoms are often referred for further testing. However, when this testing does not reveal an underlying disease or pathology, patients typically receive no additional standard care services. A PA intervention delivered within the clinic setting may be an effective strategy for improving the health of this population at a time when they may be motivated to take preventive action. Objective: Our aim was to determine the effectiveness of a tailored, computer-based, interactive personal action planning session to initiate PA among a group of sedentary cardiac patients following exercise treadmill testing (ETT). Methods: This study was part of a larger 2x2 randomized controlled trial to determine the impact of environmental and social-cognitive intervention approaches on the initiation and maintenance of weekly PA for patients post ETT. Participants who were referred to an ETT center but had a negative-test (ie, stress tests results indicated no apparent cardiac issues) were randomized to one of four treatment arms: (1) increased environmental accessibility to PA resources via the provision of a free voucher to a fitness facility in close proximity to their home or workplace (ENV), (2) a tailored social cognitive intervention (SC) using a “5 As”-based (ask, advise, assess, assist, and arrange) personal action planning tool, (3) combined intervention of both ENV and SC approaches (COMBO), or (4) a matched contact nutrition control (CON). Each intervention was delivered using a computer-based interactive session. A general linear model for repeated measures was conducted with change in PA behavior from baseline to 1-month post interactive computer session as the primary outcome. Results: Sedentary participants (n=452; 34.7% participation rate) without a gym membership (mean age 58.57 years; 59% female, 78% white, 12% black, 11% Hispanic) completed a baseline assessment and an interactive computer session. PA increased across the study sample (F1,441=30.03, P<.001). However, a time by condition interaction (F3,441=8.33, P<.001) followed by post hoc analyses indicated that SC participants exhibited a significant increase in weekly PA participation (mean 45.1, SD 10.2) compared to CON (mean -2.5, SD 10.8, P=.004) and ENV (mean 8.3, SD 8.1, P<.05). Additionally, COMBO participants exhibited a significant increase in weekly PA participation (mean 53.4, SD 8.9) compared to CON (P<.001) and ENV (P=.003) participants. There were no significant differences between ENV and CON or between SC and COMBO. Conclusions: A brief, computer-based, interactive personal action planning session may be an effective tool to initiate PA within a health care setting, in particular as part of the ETT system. Trial Registration: Clinicaltrials.gov NCT00432133, http://clinicaltrials.gov/ct2/show/NCT00432133 (Archived by WebCite at http://www.webcitation.org/6aa8X3mw1).

  • The FAMILY project.

    Using Information and Communication Technologies for Family Communication and Its Association With Family Well-Being in Hong Kong: FAMILY Project

    Abstract:

    Background: Family communication is central to the family and its functioning. It is a mutual process in which family members create, share, and regulate meaning. Advancement and proliferation of information and communication technologies (ICTs) continues to change methods of family communication. However, little is known about the use of different methods for family communication and the influence on family well-being. Objective: We investigated the sociodemographic factors associated with different methods of family communication and how they are associated with perceived family harmony, happiness, and health (3Hs) among Chinese adults in Hong Kong. Methods: Data came from a territory-wide probability-based telephone survey using the Family and Health Information Trend survey (FHInTs). Frequency of family communication using different methods (ie, face-to-face, phone, instant messaging [IM], social media sites, and email) were recoded and classified as frequent (always/sometimes) and nonfrequent (seldom/never) use. Family well-being was measured using 3 questions of perceived family harmony, happiness, and health with higher scores indicating better family well-being. Adjusted odds ratios for family communication methods by sociodemographic characteristics and adjusted beta coefficients for family well-being by communication methods were calculated. Results: A total of 1502 adults were surveyed. Face-to-face (94.85%, 1408/1484) was the most frequent means of communication followed by phone (78.08%, 796/1484), IM (53.64%, 796/1484), social media sites (17.60%, 261/1484), and email (13.39%, 198/1484). Younger age was associated with the use of phone, IM, and social media sites for family communication. Higher educational attainment was associated with more frequent use of all modes of communication, whereas higher family income was only significantly associated with more frequent use of IM and email (P=.001). Face-to-face (beta 0.65, 95% CI 0.33-0.97) and phone use (beta 0.20, 95% CI 0.02-0.38) for family communication were associated with significantly higher levels of perceived family well-being. Conclusions: Socioeconomic disparities in using these information and communication technologies (ICT) methods for family communication were observed. Although traditional methods remain as the main platform for family communication and were associated with better family well-being, a notable proportion of respondents are using new ICT methods, which were not associated with perceived family well-being. Because ICTs will continue to diversify modes of family communication, more research is needed to understand the impact of ICTs on family communication and well-being.

  • Contrast between paper and electronic diaty.

    More Than Telemonitoring: Health Provider Use and Nonuse of Life-Log Data in Irritable Bowel Syndrome and Weight Management

    Abstract:

    Background: The quantified self, self-monitoring or life-logging movement is a trend to incorporate technology into data acquisition on aspects of a person's daily life in terms of inputs (eg food consumed), states (eg mood), and performance (mental and physical). Consumer self-monitoring mobile phone apps have been widely studied and used to promote healthy behavior changes. Data collected through life-logging apps also have the potential to support clinical care. Objective: We sought to develop an in-depth understanding of providers’ facilitators and barriers to successfully integrating life-log data into their practices and creating better experiences. We specifically investigated three research questions: How do providers currently use patient-collected life-log data in clinical practice? What are provider concerns and needs with respect to this data? What are the constraints for providers to integrate this type of data into their workflows? Methods: We interviewed 21 health care providers—physicians, dietitians, a nurse practitioner, and a behavioral psychologist—who work with obese and irritable bowel syndrome patients. We transcribed and analyzed interviews according to thematic analysis and an affinity diagramming process. Results: Providers reported using self-monitoring data to enhance provider-patient communication, develop personalized treatment plans, and to motivate and educate patients, in addition to using them as diagnostic and adherence tools. However, limitations associated with current systems and workflows create barriers to regular and effective review of this data. These barriers include a lack of time to review detailed records, questions about providers' expertise to review it, and skepticism about additional benefits offered by reviewing data. Current self-monitoring tools also often lack flexibility, standardized formats, and mechanisms to share data with providers. Conclusions: Variations in provider needs affect tracking and reviewing needs. Systems to support diagnosis might require better reliability and resolution, while systems to support interaction should support collaborative reflection and communication. Automatic synthesis of data logs could help providers focus on educational goals while communication of contextual information might help providers better understand patient values. We also discuss how current mobile apps and provider systems do, and do not, support these goals, and future design opportunities to realize the potential benefits of using life-logging tools in clinical care.

  • Title image. Image credit: Shutterstock.

    Predictors of “Liking” Three Types of Health and Fitness-Related Content on Social Media: A Cross-Sectional Study

    Abstract:

    Background: Adolescence and young adulthood are key periods for developing norms related to health behaviors and body image, and social media can influence these norms. Social media is saturated with content related to dieting, fitness, and health. Health and fitness–related social media content has received significant media attention for often containing objectifying and inaccurate health messages. Limited research has identified problematic features of such content, including stigmatizing language around weight, portraying guilt-related messages regarding food, and praising thinness. However, no research has identified who is “liking” or “following” (ie, consuming) such content. Objective: This exploratory study aimed to identify demographics, mental health, and substance use–related behaviors that predicted consuming 3 types of health and fitness–related social media content—weight loss/fitness motivation pages (ie, “fitspiration”), detox/cleanse pages, and diet/fitness plan pages—among young social media users. Methods: Participants (N=1001; age: median 21.06, IQR 17.64-24.64; female: 723/1001, 72.23%) completed a cross-sectional 112-question online survey aimed at social media users aged between 15-29 years residing in Victoria, Australia. Logistic regression was used to determine which characteristics predicted consuming the 3 types of health and fitness–related social media content. Results: A total of 378 (37.76%) participants reported consuming at least 1 of the 3 types of health and fitness–related social media content: 308 (30.77%) fitspiration pages, 145 (14.49%) detox pages, and 235 (23.48%) diet/fitness plan pages. Of the health and fitness–related social media content consumers, 85.7% (324/378) identified as female and 44.8% (324/723) of all female participants consumed at least 1 type of health and fitness–related social media content. Predictors of consuming at least one type of health and fitness–related social media content in univariable analysis included female gender (OR 3.5, 95% CI 2.5-4.9, P<.001), being aged 15-17 years (OR 3.0, 95% CI 2.2-4.0, P<.001), residing outside a major city (OR 2.0, 95% CI 1.4-2.9, P<.001), having no post–high school education (OR 2.2, 95% CI 1.7-2.9, P<.001), being born in Australia (OR 2.0, 95% CI 1.2-3.2, P=.006), having a self-reported eating disorder (OR 2.4, 95% CI 1.5-3.9, P<.001), being a victim of bullying (OR 1.7, CI 1.3-2.3, P<.001), misusing detox/laxative teas or diet pills (OR 4.6, 95% CI 2.8-7.6, P<.001), never using illegal drugs (OR 1.6, 95% CI 1.2-2.0, P=.001), and not engaging in risky single occasion drinking on a weekly basis (OR 2.0, 95% CI 1.3-3.0, P=.003). Conclusions: Consumers of health and fitness–related social media content were predominantly teenaged girls. There is a need to ensure that this social media content portrays responsible health messages and to research further the role of fitspiration pages, detox pages, and diet/fitness plan pages in influencing body image and health behaviors.

  • Distribution of searches across organ systems. Each organ system name is followed by the percentage of queries related to that system. Each pie chart shows the distribution of searches for that organ system grouped by their term category, followed by the 10 most frequent and 10 least frequent terms searched for related to that system. Searches about diseases and symptoms (dark blue) dominated most systems. Searches about the endocrine system included a significant number of drug searches, followed by the cardiovascular system.

    Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance

    Abstract:

    Background: Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance. Objective: To analyze health care professionals’ information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource. Methods: Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert. Results: Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert. Conclusions: Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate.

  • This is a royalty free image by KROMKRATHOG (http://www.freedigitalphotos.net/images/modern-technology-business-concept-mobile-phones-with-tablet-com-photo-p193160).

    App Usage Factor: A Simple Metric to Compare the Population Impact of Mobile Medical Apps

    Abstract:

    Background: One factor when assessing the quality of mobile apps is quantifying the impact of a given app on a population. There is currently no metric which can be used to compare the population impact of a mobile app across different health care disciplines. Objective: The objective of this study is to create a novel metric to characterize the impact of a mobile app on a population. Methods: We developed the simple novel metric, app usage factor (AUF), defined as the logarithm of the product of the number of active users of a mobile app with the median number of daily uses of the app. The behavior of this metric was modeled using simulated modeling in Python, a general-purpose programming language. Three simulations were conducted to explore the temporal and numerical stability of our metric and a simulated app ecosystem model using a simulated dataset of 20,000 apps. Results: Simulations confirmed the metric was stable between predicted usage limits and remained stable at extremes of these limits. Analysis of a simulated dataset of 20,000 apps calculated an average value for the app usage factor of 4.90 (SD 0.78). A temporal simulation showed that the metric remained stable over time and suitable limits for its use were identified. Conclusions: A key component when assessing app risk and potential harm is understanding the potential population impact of each mobile app. Our metric has many potential uses for a wide range of stakeholders in the app ecosystem, including users, regulators, developers, and health care professionals. Furthermore, this metric forms part of the overall estimate of risk and potential for harm or benefit posed by a mobile medical app. We identify the merits and limitations of this metric, as well as potential avenues for future validation and research.

  • Photo by Gualberto107. Published on 01 March 2014 Stock photo - Image ID: 100240392; http://www.freedigitalphotos.net/images/blood-test-photo-p240392.

    “You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions

    Abstract:

    Background: Consumer health information technologies (HIT) that encourage self-tracking, such as diet and fitness tracking apps and disease journals, are attracting widespread interest among technology-oriented consumers (such as “quantified self” advocates), entrepreneurs, and the health care industry. Such electronic technologies could potentially benefit the growing population of patients with multiple chronic conditions (MCC). However, MCC is predominantly a condition of the elderly and disproportionately affects the less affluent, so it also seems possible that the barriers to use of consumer HIT would be particularly severe for this patient population. Objective: Our aim was to explore the perspectives of individuals with MCC using a semistructured interview study. Our research questions were (1) How do individuals with MCC track their own health and medical data? and (2) How do patients and providers perceive and use patient-tracked data? Methods: We used semistructured interviews with patients with multiple chronic diseases and providers with experience caring for such patients, as well as participation in a diabetes education group to triangulate emerging themes. Data were analyzed using grounded theory and thematic analysis. Recruitment and analysis took place iteratively until thematic saturation was reached. Results: Interviews were conducted with 22 patients and 7 health care providers. The patients had an average of 3.5 chronic conditions, including type 2 diabetes, heart disease, chronic pain, and depression, and had regular relationships with an average of 5 providers. Four major themes arose from the interviews: (1) tracking this data feels like work for many patients, (2) personal medical data for individuals with chronic conditions are not simply objective facts, but instead provoke strong positive and negative emotions, value judgments, and diverse interpretations, (3) patients track for different purposes, ranging from sense-making to self-management to reporting to the doctor, and (4) patients often notice that physicians trust technologically measured data such as lab reports over patients’ self-tracked data. Conclusions: Developers of consumer health information technologies for data tracking (such as diet and exercise apps or blood glucose logs) often assume patients have unlimited enthusiasm for tracking their own health data via technology. However, our findings potentially explain relatively low adoption of consumer HIT, as they suggest that patients with multiple chronic illnesses consider it work to track their own data, that the data can be emotionally charged, and that they may perceive that providers do not welcome it. Similar themes have been found in some individual chronic diseases but appeared more complex because patients often encountered “illness work” connected to multiple diseases simultaneously and frequently faced additional challenges from aging or difficult comorbidities such as chronic pain, depression, and anxiety. We suggest that to make a public health impact, consumer HIT developers should engage creatively with these pragmatic and emotional issues to reach an audience that is broader than technologically sophisticated early adopters. Novel technologies are likely to be successful only if they clearly reduce patient inconvenience and burden, helping them to accomplish their “illness work” more efficiently and effectively.

  • A-CHESS main menu.

    Successful Organizational Strategies to Sustain Use of A-CHESS: A Mobile Intervention for Individuals With Alcohol Use Disorders

    Abstract:

    Background: Mobile health (mHealth) services are growing in importance in health care research with the advancement of wireless networks, tablets, and mobile phone technologies. These technologies offer a wide range of applications that cover the spectrum of health care delivery. Although preliminary experiments in mHealth demonstrate promising results, more robust real-world evidence is needed for widespread adoption and sustainment of these technologies. Objective: Our aim was to identify the problems/challenges associated with sustained use of an mHealth addiction recovery support app and to determine strategies used by agencies that successfully sustained client use of A-CHESS. Methods: Qualitative inquiry assessed staff perceptions about organizational attributes and strategies associated with sustained use of the mobile app, A-CHESS. A total of 73 interviews of clinicians and administrators were conducted. The initial interviews (n=36) occurred at the implementation of A-CHESS. Follow-up interviews (n=37) occurred approximately 12 and 24 months later. A coding scheme was developed and Multiuser NVivo was used to manage and analyze the blinded interview data. Results: Successful strategies used by treatment providers to sustain A-CHESS included (1) strong leadership support, (2) use of client feedback reports to follow up on non-engaged clients, (3) identify passionate staff and incorporate A-CHESS discussions in weekly meetings, (4) develop A-CHESS guidelines related to client use, (5) establish internal work groups to engage clients, and (6) establish a financial strategy to sustain A-CHESS use. The study also identified attributes of A-CHESS that enhanced as well as inhibited its sustainability. Conclusions: Mobile apps can play an important role in health care delivery. However, providers will need to develop strategies for engaging both staff and patients in ongoing use of the apps. They will also need to rework business processes to accommodate the changes in communication frequency and style, learn to use app data for decision making, and identify financing mechanisms for supporting these changes.

  • Blue Button, the slogan, ‘Download My Data,’ the Blue Button Logo, and the Blue Button Combined Logo are registered service marks owned by the U.S. Department of Health and Human Services.

    Use of the Blue Button Online Tool for Sharing Health Information: Qualitative Interviews With Patients and Providers

    Abstract:

    Background: Information sharing between providers is critical for care coordination, especially in health systems such as the United States Department of Veterans Affairs (VA), where many patients also receive care from other health care organizations. Patients can facilitate this sharing by using the Blue Button, an online tool that promotes patients’ ability to view, print, and download their health records. Objective: The aim of this study was to characterize (1) patients’ use of Blue Button, an online information-sharing tool in VA’s patient portal, My HealtheVet, (2) information-sharing practices between VA and non-VA providers, and (3) how providers and patients use a printed Blue Button report during a clinical visit. Methods: Semistructured qualitative interviews were conducted with 34 VA patients, 10 VA providers, and 9 non-VA providers. Interviews focused on patients’ use of Blue Button, information-sharing practices between VA and non-VA providers, and how patients and providers use a printed Blue Button report during a clinical visit. Qualitative themes were identified through iterative rounds of coding starting with an a priori schema based on technology adoption theory. Results: Information sharing between VA and non-VA providers relied primarily on the patient. Patients most commonly used Blue Button to access and share VA laboratory results. Providers recognized the need for improved information sharing, valued the Blue Button printout, and expressed interest in a way to share information electronically across settings. Conclusions: Consumer-oriented technologies such as Blue Button can facilitate patients sharing health information with providers in other health care systems; however, more education is needed to inform patients of this use to facilitate care coordination. Additional research is needed to explore how personal health record documents, such as Blue Button reports, can be easily shared and incorporated into the clinical workflow of providers.

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  • Patient perspectives on sharing anonymised personal health data using a digital system for dynamic consent and research feedback: a qualitative study

    Date Submitted: Aug 18, 2015

    Open Peer Review Period: Aug 19, 2015 - Oct 14, 2015

    Background: Electronic health records are widely acknowledged to provide an important opportunity to anonymise patient-level healthcare data and collate across populations to support research. Noneth...

    Background: Electronic health records are widely acknowledged to provide an important opportunity to anonymise patient-level healthcare data and collate across populations to support research. Nonetheless, in the wake of public and policy concerns about security and inappropriate use of data, conventional approaches towards data governance may no longer be sufficient to respect and protect individual privacy. One proposed solution to improve transparency and public trust is known as Dynamic Consent which uses information technology to facilitate a more explicit and accessible opportunity to opt-out, whereby patients can tailor preferences about whom they share their data with, and can change their preferences reliably at any time. Furthermore, electronic systems provide opportunities for informing patients about data recipients and the results of research to which their data have contributed. Objective: To explore patient perspectives on the use of anonymised healthcare data for research purposes, and to evaluate patient perceptions of a dynamic consent model and electronic system to enable and implement on-going communication and collaboration between patients and researchers. Methods: Qualitative interviews and focus groups that included a video presentation explaining the re-use of anonymised electronic patient records for research. Slides and tablet devices were used to introduce the dynamic consent system for discussion. Thirty-five patients with chronic rheumatic disease with varying levels of illness and social deprivation, and five participants from a patient and public involvement health research network. Results: Patients were supportive of sharing their anonymised electronic patient record for research but noted a lack of transparency and awareness around the use of data, making it difficult to secure public trust. Whilst there were general concerns about detrimental consequences of data falling into the wrong hands, such as insurance companies, participants generally considered the altruistic benefits of sharing healthcare data outweighed the risks. Views were mostly positive about the use of an electronic interface to enable greater control over consent choices, although some patients were happy to share their data without further engagement. Participants were particularly enthusiastic about the system as a means of enabling feedback regarding data recipients and associated research results, noting that this would improve trust and public engagement in research. More than half of patients found the touchscreen interface easy to use although a significant minority, especially those with limited access to technology, expressed some trepidation and felt they may need support to use the system. Conclusions: Patients from a range of socioeconomic backgrounds viewed positively a digital system for dynamic consent and, in particular, feedback about data recipients and research results. Implementation of a system would require careful interface design and would need to be located within a robust data infrastructure, yet it has the potential to improve trust and engagement in electronic medical record research.

  • Understanding Online Health Groups for Depression: Social Network and Linguistic Perspectives

    Date Submitted: Aug 15, 2015

    Open Peer Review Period: Aug 17, 2015 - Oct 12, 2015

    Background: Mental health problems have become increasingly prevalent in the past decade. With the advance of Internet and Web 2.0 technologies, social media presents a novel platform for Web users to...

    Background: Mental health problems have become increasingly prevalent in the past decade. With the advance of Internet and Web 2.0 technologies, social media presents a novel platform for Web users to form online health groups to discuss health related issues and mutually help each other through anonymously revealing their mental conditions, sharing personal experiences, exchanging health information, and providing suggestions and support. These conversations in online health groups contain valuable information to facilitate the understanding of their mutual help behaviors and the mental health problems. Objective: We aim to characterize the conversations in a major online health group for Major Depressive Disorder (MDD) patients in a popular Chinese social media platform. In particular, we aim to understand how the Web users discuss depression related issues from the perspectives of the social networks formed by the members’ conversations and the linguistic patterns revealed by the language use. Methods: Social network analysis and linguistics analysis are employed to characterize the social structure and linguistic patterns, respectively. Furthermore, we integrate both perspectives to exploit the hidden relationships between them. Results: We find the heavy use of self-focus words and negative words. In general, group members use a slightly higher proportion of negative words than positive words and the negative sentiment peaks at late night. The social network of the MDD group for depression possesses small-world and scale-free properties, while with a much higher reciprocity ratio and clustering coefficient value, as compared to the networks of other online health groups, social media platforms, and classic network models. We did not identify strong correlations between most of the network properties and linguistic properties except the length of posts. However, interesting convergent trends are found in the relations between the network properties and a number of linguistic properties. Conclusions: (1) The language use of the MDD group members is different from those of other social media groups, indicating the different linguistic patterns of depressed people from general Web users. (2) The social structure of the MDD group is much stickier than those of other social media groups, indicating the high tendency of mutual communications in the MDD group. (3) The relationships between linguistic patterns and the social network properties of the MDD group are not strong, with convergent self-organizing behaviors.

  • Development and feasibility of a text-messaging and pedometer programme to promote physical activity in people at high risk of type 2 diabetes (PROPELS)

    Date Submitted: Aug 12, 2015

    Open Peer Review Period: Aug 17, 2015 - Oct 12, 2015

    Background: Mobile technologies for health (mHealth) represent a promising strategy for reducing type 2 diabetes (T2DM) risk. The PROPELS trial investigates whether structured group-based education al...

    Background: Mobile technologies for health (mHealth) represent a promising strategy for reducing type 2 diabetes (T2DM) risk. The PROPELS trial investigates whether structured group-based education alone or supplemented with a follow-on support programme combining self-monitoring with pedometers and tailored text-messaging is effective in promoting and maintaining physical activity (PA) among people at high risk of T2DM. Objective: This paper describes the iterative development of the PROPELS follow-on support programme and presents evidence on its acceptability and feasibility. Methods: We used a modified mHealth development framework with four phases: 1) conceptualisation of the follow-on support programme using theory and evidence; 2) formative research including focus groups (participants: n=15, aged 39-79 years); 3) pre-testing focus groups using a think aloud protocol (participants: n= 20, aged 52-78 years); and 4) piloting (participants: n= 11). Analysis was informed by the constant comparative approach, with findings from each phase informing subsequent phases. Results: The first three phases informed the structure, nature and content of the follow-on support programme, including the frequency of text-messages; the need for tailored content and two-way interaction; the importance of motivational messages based on encouragement and reinforcement of affective benefits (e.g., enjoyment), with minimal messages about weight and T2DM risk; and the need for appropriate language. The refined programme is personalised and tailored to the individual’s perceived confidence, previous activity levels and PA goals. The pilot phase indicated that the programme appeared to fit well with everyday routines and was easy to use, also by older adults. Conclusions: We developed a pragmatic, feasible and innovative text-messaging and pedometer programme based on evidence and behaviour change theory and grounded in the experiences, views and needs of people at high diabetes risk. A large scale trial is testing the effectiveness of this four-year programme over and above structured group education alone. Clinical Trial: ISRCTN83465245

  • Pro-anorexia and anti pro-anorexia communities on YouTube

    Date Submitted: Aug 6, 2015

    Open Peer Review Period: Aug 17, 2015 - Oct 12, 2015

    Background: Pro-anorexia communities exist online and encourage harmful weight-loss and weight-control practices, often by emotional content that enforces social ties within these communities. Little...

    Background: Pro-anorexia communities exist online and encourage harmful weight-loss and weight-control practices, often by emotional content that enforces social ties within these communities. Little is known about those user-generated online communities that directly oppose pro-anorexia communities. Objective: To study emotional reactions to pro-anorexia and anti pro-anorexia online communities on YouTube using sentiment analysis. Methods: Using the top 50 YouTube pro-anorexia and anti pro-anorexia user channels as a starting point, we gathered data on users, their videos and their commentators. 395 anorexia-videos and 12,161 comments were analyzed using positive and negative sentiments and ratings given by the viewers of the videos. The emotional information was automatically extracted with the automatic sentiment detection tool whose reliability was tested with human coders. Regression models were used to estimate the strength of sentiments. The models were controlled for the number of video views and comments, number of months the video had been on YouTube, the duration of the video, uploader’s activity as a video commentator and uploader’s country information. Results: The 395 videos had over 6 million views and comments by almost 8,000 people. Anti pro-anorexia video comments expressed more positive sentiments than those of pro-anorexia videos. These videos were also favored more by users and they had higher viewer rates. Negative sentiments were equally distributed in both pro-anorexia and anti-pro anorexia communities. Conclusions: Despite the pro-anorexia content being widespread on YouTube, videos promoting help for anorexia and opposing the pro-anorexia community were more popular, gaining more positive feedback and comments than the pro-anorexia videos. Thus, the anti pro-anorexia community acts as a counterforce on YouTube. Professionals working with young people should be aware of the social media dynamics and versatility of user-generated eating disorder content online.

  • Efficacy of a Web-based tailored intervention to reduce cannabis use in young people attending adult education centers in Quebec

    Date Submitted: Aug 5, 2015

    Open Peer Review Period: Aug 17, 2015 - Oct 12, 2015

    Background: Cannabis use is common in young adults and can be associated with different social and health issues including learning difficulties and school dropout. Web interventions are an increasing...

    Background: Cannabis use is common in young adults and can be associated with different social and health issues including learning difficulties and school dropout. Web interventions are an increasingly popular way to intervene with this population. Objective: The aim of this study was to evaluate the efficacy of a theory web-based intervention to help young people reduce or eliminate their cannabis use by developing a more positive intention to abstain. The intervention was implemented in seven adult education centers in Quebec. Methods: An experimental design was conducted to evaluate the efficacy of Web-based intervention to reduce or eliminate the cannabis use (primary outcome), which can be explained by a high intention (secondary outcome) to abstain from cannabis use. The participants were randomly assigned to either an experimental group (i.e., exposed to the Web-based intervention) or a control group (i.e., no intervention). Results: Among the 588 young adults recruited 343 reported using cannabis at least once in the previous year. At baseline, 26.6% of respondents reported having used cannabis every day in the previous month. After the intervention, participants in the experimental group were more likely to have reduced or stopped cannabis use compared to the control group (RP=1.78 IC: 1.21-2.70, P=.003). Also, intention to abstain was higher among those exposed to the intervention (RP=1.33 IC: 1.04-1.74 P=.025). Conclusions: This study shows that Web-based intervention developed on theoretical grounds, can be effective for reducing cannabis use among young people attending adult education centers.

  • Mental health smartphone apps: Evidence-based recommendations for future developments

    Date Submitted: Aug 4, 2015

    Open Peer Review Period: Aug 4, 2015 - Sep 29, 2015

    Background: The number of mental health apps (MHapps) developed and now available to smartphone users has proliferated over recent years. MHapps and other technology-based solutions have the potential...

    Background: The number of mental health apps (MHapps) developed and now available to smartphone users has proliferated over recent years. MHapps and other technology-based solutions have the potential to play an important part in the future of mental health care, yet there is no single guide for the development of evidence-based MHapps. Many currently available MHapps lack features that would greatly improve their functionality, or include features that are not optimized. Furthermore, MHapp developers rarely conduct or publish trial-based experimental validation of their apps, with a previous systematic review revealing a complete lack of trial-based evidence for many of the hundreds of MHapps available. Objective: To guide future MHapp development, a set of clear, practical, evidence-based recommendations is presented for MHapp developers to create better, more rigorous apps. Methods: A literature review was conducted, scrutinizing research across diverse research fields, including mental health interventions, preventative health, mobile health, and mobile app design. Results: Sixteen recommendations were formulated. Evidence for each recommendation is discussed, and guidance on how they might be integrated into the overall design of a Mhapp is offered. Each recommendation is rated on the basis of the strength of associated evidence. It is important to design a MHapp using a behavioral plan and interactive framework that encourages the user to engage with the app, so it may not be possible to incorporate all sixteen recommendations into a single MHapp. Conclusions: Randomized controlled trials are required to validate future MHapps and the principles upon which they are designed, and to further investigate the recommendations presented in this review. Effective MHapps are required to help prevent mental health problems and to ease the burden on health systems.

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