JMIR Publications

Journal of Medical Internet Research

The leading peer-reviewed journal for digital medicine, and health & healthcare in the Internet age.

JMIR's Thomson Reuter Impact Factor of 5.175 for 2016
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  • A group of smokers in Holland. Source: Flickr; Copyright: RosiePosieTosie; URL: https://flic.kr/p/ntFdzt; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    A Review of the Theoretical Basis, Effects, and Cost Effectiveness of Online Smoking Cessation Interventions in the Netherlands: A Mixed-Methods Approach

    Abstract:

    Background: Tobacco smoking is a worldwide public health problem. In 2015, 26.3% of the Dutch population aged 18 years and older smoked, 74.4% of them daily. More and more people have access to the Internet worldwide; approximately 94% of the Dutch population have online access. Internet-based smoking cessation interventions (online cessation interventions) provide an opportunity to tackle the scourge of tobacco. Objective: The goal of this paper was to provide an overview of online cessation interventions in the Netherlands, while exploring their effectivity, cost effectiveness, and theoretical basis. Methods: A mixed-methods approach was used to identify Dutch online cessation interventions, using (1) a scientific literature search, (2) a grey literature search, and (3) expert input. For the scientific literature, the Cochrane review was used and updated by two independent researchers (n=651 identified studies), screening titles, abstracts, and then full-text studies between 2013 and 2016 (CENTRAL, MEDLINE, and EMBASE). For the grey literature, the researchers conducted a Google search (n=100 websites), screening for titles and first pages. Including expert input, this resulted in six interventions identified in the scientific literature and 39 interventions via the grey literature. Extracted data included effectiveness, cost effectiveness, theoretical factors, and behavior change techniques used. Results: Overall, many interventions (45 identified) were offered. Of the 45 that we identified, only six that were included in trials provided data on effectiveness. Four of these were shown to be effective and cost effective. In the scientific literature, 83% (5/6) of these interventions included changing attitudes, providing social support, increasing self-efficacy, motivating smokers to make concrete action plans to prepare their attempts to quit and to cope with challenges, supporting identity change and advising on changing routines, coping, and medication use. In all, 50% (3/6) of the interventions included a reward for abstinence. Interventions identified in the grey literature were less consistent, with inclusion of each theoretical factor ranging from 31% to 67% and of each behavior change technique ranging from 28% to 54%. Conclusions: Although the Internet may provide the opportunity to offer various smoking cessation programs, the user is left bewildered as far as efficacy is concerned, as most of these data are not available nor offered to the smokers. Clear regulations about the effectiveness of these interventions need to be devised to avoid disappointment and failed quitting attempts. Thus, there is a need for policy regulations to regulate the proliferation of these interventions and to foster their quality in the Netherlands.

  • Two smartphones - one patient phone surrounded by diabetes tools, and a HCP phone in a physician's pocket. Visualizing the communication between them and the data collection. Source: Image created by the authors; Copyright: Heidi Holmen; URL: https://www.hioa.no/eng/employee/heidiho; License: Creative Commons Attribution (CC-BY).

    Tailored Communication Within Mobile Apps for Diabetes Self-Management: A Systematic Review

    Abstract:

    Background: The prevalence of diabetes is increasing and with the requirements for self-management and risk of late complications, it remains a challenge for the individual and society. Patients can benefit from support from health care personnel in their self-management, and the traditional communication between patients and health care personnel is changing. Smartphones and apps offer a unique platform for communication, but apps with integrated health care personnel communication based on patient data are yet to be investigated to provide evidence of possible effects. Objective: Our goal was to systematically review studies that aimed to evaluate integrated communication within mobile apps for tailored feedback between patients with diabetes and health care personnel in terms of (1) study characteristics, (2) functions, (3) study outcomes, (4) effects, and (5) methodological quality. Methods: A systematic literature search was conducted following our International Prospective Register of Systematic Reviews (PROSPERO) protocol, searching for apps with integrated communication for persons with diabetes tested in a controlled trial in the period 2008 to 2016. We searched the databases PubMed, Medical Literature Analysis and Retrieval System Online (MEDLINE), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Central, Excerpta Medica database (EMBASE), ClinicalTrials.gov, and the World Health Organization (WHO) International Clinical Trials Registry Platform. The search was closed in September 2016. Reference lists of primary articles and review papers were assessed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and we applied the Cochrane risk of bias tool to assess methodological quality. Results: We identified 2822 citations and after duplicate removal, we assessed 1128 citations. A total of 6 papers were included in this systematic review, reporting on data from 431 persons participating in small trials of short duration. The integrated communication features were mostly individualized as written non–real-time feedback. The number of functions varied from 2 to 9, and blood glucose tracking was the most common. HbA1c was the most common primary outcome, but the remaining reported outcomes were not standardized and comparable. Because of both the heterogeneity of the included trials and the poor methodological quality of the studies, a meta-analysis was not possible. A statistically significant improvement in the primary measure of outcome was found in 3 of the 6 included studies, of which 2 were HbA1c and 1 was mean daytime ambulatory blood pressure. Participants in the included trials reported positive usability or feasibility postintervention in 5 out of 6 trials. The overall methodological quality of the trials was, however, scored as an uncertain risk of bias. Conclusions: This systematic review highlights the need for more trials of higher methodological quality. Few studies offer an integrated function for communication and feedback from health care personnel, and the research field represents an area of heterogeneity with few studies of highly rigorous methodological quality. This, in combination with a low number of participants and a short follow-up, is making it difficult to provide reliable evidence of effects for stakeholders.

  • Source: Unsplash; Copyright: Helloquence; URL: https://unsplash.com/search/computer?photo=5fNmWej4tAA; License: Licensed by JMIR.

    The Effectiveness of Information Technology-Supported Shared Care for Patients With Chronic Disease: A Systematic Review

    Abstract:

    Background: In patients with chronic disease, many health care professionals are involved during treatment and follow-up. This leads to fragmentation that in turn may lead to suboptimal care. Shared care is a means to improve the integration of care delivered by various providers, specifically primary care physicians (PCPs) and specialty care professionals, for patients with chronic disease. The use of information technology (IT) in this field seems promising. Objective: Our aim was to systematically review the literature regarding the effectiveness of IT-supported shared care interventions in chronic disease in terms of provider or professional, process, health or clinical and financial outcomes. Additionally, our aim was to provide an inventory of the IT applications' characteristics that support such interventions. Methods: PubMed, Scopus, and EMBASE were searched from 2006 to 2015 to identify relevant studies using search terms related to shared care, chronic disease, and IT. Eligible studies were in the English language, and the randomized controlled trials (RCTs), controlled trials, or single group pre-post studies used reported on the effects of IT-supported shared care in patients with chronic disease and cancer. The interventions had to involve providers from both primary and specialty health care. Intervention and IT characteristics and effectiveness—in terms of provider or professional (proximal), process (intermediate), health or clinical and financial (distal) outcomes—were extracted. Risk of bias of (cluster) RCTs was assessed using the Cochrane tool. Results: The initial search yielded 4167 results. Thirteen publications were used, including 11 (cluster) RCTs, a controlled trial, and a pre-post feasibility study. Four main categories of IT applications were identified: (1) electronic decision support tools, (2) electronic platform with a call-center, (3) electronic health records, and (4) electronic communication applications. Positive effects were found for decision support-based interventions on financial and health outcomes, such as physical activity. Electronic health record use improved PCP visits and reduced rehospitalization. Electronic platform use resulted in fewer readmissions and better clinical outcomes—for example, in terms of body mass index (BMI) and dyspnea. The use of electronic communication applications using text-based information transfer between professionals had a positive effect on the number of PCPs contacting hospitals, PCPs’ satisfaction, and confidence. Conclusions: IT-supported shared care can improve proximal outcomes, such as confidence and satisfaction of PCPs, especially in using electronic communication applications. Positive effects on intermediate and distal outcomes were also reported but were mixed. Surprisingly, few studies were found that substantiated these anticipated benefits. Studies showed a large heterogeneity in the included populations, outcome measures, and IT applications used. Therefore, a firm conclusion cannot be drawn. As IT applications are developed and implemented rapidly, evidence is needed to test the specific added value of IT in shared care interventions. This is expected to require innovative research methods.

  • Young people using social media. Source: Wikimedia Commons; Copyright: Tom Sulcer; URL: https://commons.wikimedia.org/wiki/File:Young_people_texting_on_smartphones_using_thumbs.JPG; License: Public Domain (CC0).

    What Motivates Young Adults to Talk About Physical Activity on Social Network Sites?

    Abstract:

    Background: Electronic word-of-mouth on social network sites has been used successfully in marketing. In social marketing, electronic word-of-mouth about products as health behaviors has the potential to be more effective and reach more young adults than health education through traditional mass media. However, little is known about what motivates people to actively initiate electronic word-of-mouth about health behaviors on their personal pages or profiles on social network sites, thus potentially reaching all their contacts on those sites. Objective: This study filled the gap by applying a marketing theoretical model to explore the factors associated with electronic word-of-mouth on social network sites about leisure-time physical activity. Methods: A Web survey link was sent to undergraduate students at one of the Midwestern universities and 439 of them completed the survey. Results: The average age of the 439 participants was 19 years (SD=1 year, range: 18-24). Results suggested that emotional engagement with leisure-time physical activity (ie, affective involvement in leisure-time physical activity) predicted providing relevant opinions or information on social network sites. Social network site users who perceived stronger ties with all their contacts were more likely to provide and seek leisure-time physical activity opinions and information. People who provided leisure-time physical activity opinions and information were more likely to seek opinions and information, and people who forwarded information about leisure-time physical activity were more likely to chat about it. Conclusions: This study shed light on the application of the electronic word-of-mouth theoretical framework in promoting health behaviors. The findings can also guide the development of future social marketing interventions using social network sites to promote leisure-time physical activity.

  • Internet Cafe in Idfo, Aswan, Egypt. Source: Flickr; Copyright: Arrano; URL: https://www.flickr.com/photos/arrano/2583867012/; License: Creative Commons Attribution + Noncommercial + NoDerivatives (CC-BY-NC-ND).

    What Predicts Online Health Information-Seeking Behavior Among Egyptian Adults? A Cross-Sectional Study

    Abstract:

    Background: Over the last decade, the Internet has become an important source of health-related information for a wide range of users worldwide. Yet, little is known about the personal characteristics of Egyptian Internet users who search for online health information (OHI). Objective: The aim of the study was to identify the personal characteristics of Egyptian OHI seekers and to determine any associations between their personal characteristics and their health information-seeking behavior. Methods:  This cross-sectional questionnaire study was conducted from June to October 2015. A Web-based questionnaire was sent to Egyptian users aged 18 years and older (N=1400) of a popular Arabic-language health information website. The questionnaire included (1) demographic characteristics; (2) self-reported general health status; and (3) OHI-seeking behavior that included frequency of use, different topics sought, and self-reported impact of obtained OHI on health behaviors. Data were analyzed using descriptive statistics and multiple regression analysis. Results: A total of 490 participants completed the electronic questionnaire with a response rate equivalent to 35.0% (490/1400). Regarding personal characteristics, 57.1% (280/490) of participants were females, 63.4% (311/490) had a university level qualification, and 37.1% (182/490) had a chronic health problem. The most commonly sought OHI by the participants was nutrition-related. Results of the multiple regression analysis showed that 31.0% of the variance in frequency of seeking OHI among Egyptian adults can be predicted by personal characteristics. Participants who sought OHI more frequently were likely to be female, of younger age, had higher education levels, and good self-reported general health. Conclusions: Our results provide insights into personal characteristics and OHI-seeking behaviors of Egyptian OHI users. This will contribute to better recognize their needs, highlight ways to increase the availability of appropriate OHI, and may lead to the provision of tools allowing Egyptian OHI users to navigate to the highest-quality health information.

  • Opening of the Seventh Annual Conference of Digital Medicine & Medical 3D Printing, Medical Robots and Big Data Based Intelligent Medical Techniques, which was sponsored by the Digital Medical Branch of the Chinese Medical Association, April 2017. Source: Digital Chinese Medicine; URL: http://dcmhi.com/news/editors-chief-digital-chinese-medicine-gave-keynote-speeches-seventh-national-digital-medicine; License: Fair use/fair dealings.

    The Gap in Medical Informatics and Continuing Education Between the United States and China: A Comparison of Conferences in 2016

    Abstract:

    Background: China launched its second health reform in 2010 with considerable investments in medical informatics (MI). However, to the best of our knowledge, research on the outcomes of this ambitious undertaking has been limited. Objective: Our aim was to understand the development of MI and the state of continuing education in China and the United States from the perspective of conferences. Methods: We conducted a quantitative and qualitative analysis of four MI conferences in China and two in the United States: China Medical Information Association Annual Symposium (CMIAAS), China Hospital Information Network Annual Conference (CHINC), China Health Information Technology Exchange Annual Conference (CHITEC), China Annual Proceeding of Medical Informatics (CPMI) versus the American Medical Informatics Association (AMIA) and Healthcare Information and Management Systems Society (HIMSS). The scale, composition, and regional distribution of attendees, topics, and research fields for each conference were summarized and compared. Results: CMIAAS and CPMI are mainstream academic conferences, while CHINC and CHITEC are industry conferences in China. Compared to HIMSS 2016, the meeting duration of CHITEC was 3 versus 5 days, the number of conference sessions was 132 versus 950+, the number of attendees was 5000 versus 40,000+, the number of vendors was 152 versus 1400+, the number of subforums was 12 versus 230, the number of preconference education symposiums and workshops was 0 versus 12, and the duration of preconference educational symposiums and workshops was 0 versus 1 day. Compared to AMIA, the meeting duration of Chinese CMIAAS was 2 versus 5 days, the number of conference sessions was 42 versus 110, the number of attendees was 200 versus 2500+, the number of vendors was 5 versus 75+, and the number of subforums was 4 versus 10. The number of preconference tutorials and working groups was 0 versus 29, and the duration of tutorials and working group was 0 versus 1.5 days. Conclusions: Given the size of the Chinese economy and the substantial investment in MI, the output in terms of conferences remains low. The impact of conferences on continuing education to professionals is not significant. Chinese researchers and professionals should approach MI with greater rigor, including validated research methods, formal training, and effective continuing education, in order to utilize knowledge gained by other countries and to expand collaboration.

  • Source: FreeDigitalPhotos.net; Copyright: nokhoog_buchachon; URL: http://www.freedigitalphotos.net/images/Communications_and_n_g263-Hand_Pressing_A_Button_p64593.html; License: Licensed by the authors.

    Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet

    Abstract:

    Background: In question answering (QA) system development, question classification is crucial for identifying information needs and improving the accuracy of returned answers. Although the questions are domain-specific, they are asked by non-professionals, making the question classification task more challenging. Objective: This study aimed to classify health care–related questions posted by the general public (Chinese speakers) on the Internet. Methods: A topic-based classification schema for health-related questions was built by manually annotating randomly selected questions. The Kappa statistic was used to measure the interrater reliability of multiple annotation results. Using the above corpus, we developed a machine-learning method to automatically classify these questions into one of the following six classes: Condition Management, Healthy Lifestyle, Diagnosis, Health Provider Choice, Treatment, and Epidemiology. Results: The consumer health question schema was developed with a four-hierarchical-level of specificity, comprising 48 quaternary categories and 35 annotation rules. The 2000 sample questions were coded with 2000 major codes and 607 minor codes. Using natural language processing techniques, we expressed the Chinese questions as a set of lexical, grammatical, and semantic features. Furthermore, the effective features were selected to improve the question classification performance. From the 6-category classification results, we achieved an average precision of 91.41%, recall of 89.62%, and F1 score of 90.24%. Conclusions: In this study, we developed an automatic method to classify questions related to Chinese health care posted by the general public. It enables Artificial Intelligence (AI) agents to understand Internet users’ information needs on health care.

  • Screenshot of the Volunteer Science hosted web interface for retinal photo grading. Source: Image created by the authors; Copyright: The authors; URL: https://volunteerscience.com; License: Fair use/fair dealings.

    Improving Consensus Scoring of Crowdsourced Data Using the Rasch Model: Development and Refinement of a Diagnostic Instrument

    Abstract:

    Background: Diabetic retinopathy (DR) is a leading cause of vision loss in working age individuals worldwide. While screening is effective and cost effective, it remains underutilized, and novel methods are needed to increase detection of DR. This clinical validation study compared diagnostic gradings of retinal fundus photographs provided by volunteers on the Amazon Mechanical Turk (AMT) crowdsourcing marketplace with expert-provided gold-standard grading and explored whether determination of the consensus of crowdsourced classifications could be improved beyond a simple majority vote (MV) using regression methods. Objective: The aim of our study was to determine whether regression methods could be used to improve the consensus grading of data collected by crowdsourcing. Methods: A total of 1200 retinal images of individuals with diabetes mellitus from the Messidor public dataset were posted to AMT. Eligible crowdsourcing workers had at least 500 previously approved tasks with an approval rating of 99% across their prior submitted work. A total of 10 workers were recruited to classify each image as normal or abnormal. If half or more workers judged the image to be abnormal, the MV consensus grade was recorded as abnormal. Rasch analysis was then used to calculate worker ability scores in a random 50% training set, which were then used as weights in a regression model in the remaining 50% test set to determine if a more accurate consensus could be devised. Outcomes of interest were the percent correctly classified images, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) for the consensus grade as compared with the expert grading provided with the dataset. Results: Using MV grading, the consensus was correct in 75.5% of images (906/1200), with 75.5% sensitivity, 75.5% specificity, and an AUROC of 0.75 (95% CI 0.73-0.78). A logistic regression model using Rasch-weighted individual scores generated an AUROC of 0.91 (95% CI 0.88-0.93) compared with 0.89 (95% CI 0.86-92) for a model using unweighted scores (chi-square P value<.001). Setting a diagnostic cut-point to optimize sensitivity at 90%, 77.5% (465/600) were graded correctly, with 90.3% sensitivity, 68.5% specificity, and an AUROC of 0.79 (95% CI 0.76-0.83). Conclusions: Crowdsourced interpretations of retinal images provide rapid and accurate results as compared with a gold-standard grading. Creating a logistic regression model using Rasch analysis to weight crowdsourced classifications by worker ability improves accuracy of aggregated grades as compared with simple majority vote.

  • Source: Unsplash; Copyright: Bench Accounting; URL: https://unsplash.com/@benchaccounting?photo=8D2k7a3wMKQ; License: Licensed by JMIR.

    Trust and Credibility in Web-Based Health Information: A Review and Agenda for Future Research

    Abstract:

    Background: Internet sources are becoming increasingly important in seeking health information, such that they may have a significant effect on health care decisions and outcomes. Hence, given the wide range of different sources of Web-based health information (WHI) from different organizations and individuals, it is important to understand how information seekers evaluate and select the sources that they use, and more specifically, how they assess their credibility and trustworthiness. Objective: The aim of this study was to review empirical studies on trust and credibility in the use of WHI. The article seeks to present a profile of the research conducted on trust and credibility in WHI seeking, to identify the factors that impact judgments of trustworthiness and credibility, and to explore the role of demographic factors affecting trust formation. On this basis, it aimed to identify the gaps in current knowledge and to propose an agenda for future research. Methods: A systematic literature review was conducted. Searches were conducted using a variety of combinations of the terms WHI, trust, credibility, and their variants in four multi-disciplinary and four health-oriented databases. Articles selected were published in English from 2000 onwards; this process generated 3827 unique records. After the application of the exclusion criteria, 73 were analyzed fully. Results: Interest in this topic has persisted over the last 15 years, with articles being published in medicine, social science, and computer science and originating mostly from the United States and the United Kingdom. Documents in the final dataset fell into 3 categories: (1) those using trust or credibility as a dependent variable, (2) those using trust or credibility as an independent variable, and (3) studies of the demographic factors that influence the role of trust or credibility in WHI seeking. There is a consensus that website design, clear layout, interactive features, and the authority of the owner have a positive effect on trust or credibility, whereas advertising has a negative effect. With regard to content features, authority of the author, ease of use, and content have a positive effect on trust or credibility formation. Demographic factors influencing trust formation are age, gender, and perceived health status. Conclusions: There is considerable scope for further research. This includes increased clarity of the interaction between the variables associated with health information seeking, increased consistency on the measurement of trust and credibility, a greater focus on specific WHI sources, and enhanced understanding of the impact of demographic variables on trust and credibility judgments.

  • Source: The Authors; Copyright: Christopher Fairburn; URL: http://www.jmir.org/2017/6/e214/; License: Creative Commons Attribution (CC-BY).

    Scaling Up Psychological Treatments: A Countrywide Test of the Online Training of Therapists

    Abstract:

    Background: A major barrier to the widespread dissemination of psychological treatments is the way that therapists are trained. The current method is not scalable. Objective: Our objective was to conduct a proof-of-concept study of Web-centered training, a scalable online method for training therapists. Methods: The Irish Health Service Executive identified mental health professionals across the country whom it wanted to be trained in a specific psychological treatment for eating disorders. These therapists were given access to a Web-centered training program in transdiagnostic cognitive behavior therapy for eating disorders. The training was accompanied by a scalable form of support consisting of brief encouraging telephone calls from a nonspecialist. The trainee therapists completed a validated measure of therapist competence before and after the training. Results: Of 102 therapists who embarked upon the training program, 86 (84.3%) completed it. There was a substantial increase in their competence scores following the training (mean difference 5.84, 95% Cl –6.62 to –5.05; P<.001) with 42.5% (34/80) scoring above a predetermined cut-point indicative of a good level of competence. Conclusions: Web-centered training proved feasible and acceptable and resulted in a marked increase in therapist competence scores. If these findings are replicated, Web-centered training would provide a means of simultaneously training large numbers of geographically dispersed trainees at low cost, thereby overcoming a major obstacle to the widespread dissemination of psychological treatments.

  • Source: The Authors and Placeit.net (montage); Copyright: The Authors; URL: http://www.jmir.org/2017/6/e212/; License: Creative Commons Attribution (CC-BY).

    MyGeneFriends: A Social Network Linking Genes, Genetic Diseases, and Researchers

    Abstract:

    Background: The constant and massive increase of biological data offers unprecedented opportunities to decipher the function and evolution of genes and their roles in human diseases. However, the multiplicity of sources and flow of data mean that efficient access to useful information and knowledge production has become a major challenge. This challenge can be addressed by taking inspiration from Web 2.0 and particularly social networks, which are at the forefront of big data exploration and human-data interaction. Objective: MyGeneFriends is a Web platform inspired by social networks, devoted to genetic disease analysis, and organized around three types of proactive agents: genes, humans, and genetic diseases. The aim of this study was to improve exploration and exploitation of biological, postgenomic era big data. Methods: MyGeneFriends leverages conventions popularized by top social networks (Facebook, LinkedIn, etc), such as networks of friends, profile pages, friendship recommendations, affinity scores, news feeds, content recommendation, and data visualization. Results: MyGeneFriends provides simple and intuitive interactions with data through evaluation and visualization of connections (friendships) between genes, humans, and diseases. The platform suggests new friends and publications and allows agents to follow the activity of their friends. It dynamically personalizes information depending on the user’s specific interests and provides an efficient way to share information with collaborators. Furthermore, the user’s behavior itself generates new information that constitutes an added value integrated in the network, which can be used to discover new connections between biological agents. Conclusions: We have developed MyGeneFriends, a Web platform leveraging conventions from popular social networks to redefine the relationship between humans and biological big data and improve human processing of biomedical data. MyGeneFriends is available at lbgi.fr/mygenefriends.

  • Winter scene in Linköping, the capital of Östergötland county, Sweden. Source: Wikimedia Commons; Copyright: Johan Samuelsson; URL: https://commons.wikimedia.org/wiki/File%3AJulmarknad_Gamla_Link%C3%B6ping.jpg; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design

    Abstract:

    Background: Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic “big data” from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. Objective: The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. Methods: An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. Results: The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000). Conclusions: Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified.

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  • Comparison of the Effects of Exercise Therapy between Digital Video Disc and Web-site-based Interventions in Patients with Hip Osteoarthritis

    Date Submitted: Jun 23, 2017

    Open Peer Review Period: Jun 23, 2017 - Aug 18, 2017

    Background: Prevalence of developmental hip dysplasia is high in Japan, and morbidity associated with hip osteoarthritis has been reported as 1.0-4.3%. It is estimated that this figure will rise as th...

    Background: Prevalence of developmental hip dysplasia is high in Japan, and morbidity associated with hip osteoarthritis has been reported as 1.0-4.3%. It is estimated that this figure will rise as the aging population grows. Exercise therapy has been proven effective to treat certain aspects of hip osteoarthritis. Moreover, therapy provided via DVDs and web sites, allows patients to exercise in the comfort of their own homes. However, no studies have evaluated the effectiveness of visual instructions in patients with hip disorders thus far. Objective: This study aimed to compare the effectiveness of exercise therapy administered via DVDs and that administered via web sites. Methods: We developed a six-step progressive exercise therapy program for patients with hip osteoarthritis, which included three kinds each of Open-Kinetic-Chain and Closed-Kinetic-Chain exercises. The exercise menu was designed such that patients could reach Step 6 in 3 months by advancing to a new step every 2 weeks, with 30 to 40 min of exercise daily. Once the program was developed, exercise DVDs were produced using easily comprehendible pictures, videos and relaxing music. The DVD users were shown the number of times each exercise should be performed, in order to promote exercise continuity. In addition to the six-step exercise program, our web site was enabled to count the number of exercises performed by each patient and was accessible via the internet at any time. We instructed the patients to start at an appropriate level and step up every two weeks unless they experienced pain, when they were asked to stop temporarily. Patients with hip osteoarthritis for whom surgery was not advised were enrolled by one university hospital. Clinical symptoms and hip function were quantified using the Japanese Orthopedic Association Hip Disease Evaluation Questionnaire (JHEQ) and the Oxford Hip Score (OHS). Quality of life was measured using the SF-8 Health Survey, and self-efficacy for continued exercise was measured using the General Self-Efficacy Scale (GSES). Questionnaires were completed pre-intervention and after 6 months. Results: At 6-month follow-up, 10 DVD users (1 male, 9 female; mean age 51.3 [SD=16.1] years), and 18 web-site users (2 male, 16 female; mean age 52.3 [SD=10.4] years) were reachable. Small effect was observed for JHEQ-pain, SF-8 physical component summary (PCS) and SF-8 mental component summary in the DVD group, and for OHS, SF-8 (PCS) and GSES in the web-site group. Although we could not confirm a significant improvement, most parameters tended to improve over the 6 months. Conclusions: When comparing the effectiveness of exercise therapy between our DVD and web site, we found that, while both groups tended to improve in physical function, only the web-site group showed enhanced self-efficacy.

  • Transforming literature on people’s health experience s into a co-design workshop card-tool to inspire interaction

    Date Submitted: Jun 22, 2017

    Open Peer Review Period: Jun 23, 2017 - Aug 18, 2017

    Background: Patient experiences are an essential focus when designing healthcare services, as they are linked to patient outcomes, safety, clinical effectiveness and more meaningful health interaction...

    Background: Patient experiences are an essential focus when designing healthcare services, as they are linked to patient outcomes, safety, clinical effectiveness and more meaningful health interactions. A wealth of peer-reviewed data exists in the current literature that can help with understanding peoples’ experiences of health and health care services. Yet, health improvement teams are unable to find practical ways to use it and may therefore overlook its value. Objective: This study explored how the existing healthcare experience literature can be utilised in healthcare design. A card-tool was developed that can be used in healthcare collaborative design (co-design) workshops to make existing literature accessible and thereby enable understanding of health experiences, trigger discussion and facilitate human-centered healthcare improvements. Methods: Qualitative research, exploring the experience of living with diabetes and preventing diabetic eye disease, was gathered through a review of the literature. The findings were analysed through a process of affinity diagramming to identify insights into the health experience. These insights were developed into a card-tool, the Health Experience Insight Cards: Living with Diabetes edition that was used in a co-design workshop with participants who had relevant professional experience to discuss the future prevention of diabetic eye-disease. Results: The review identified papers 13 papers that fit the selection criteria. These were analysed to develop the Health Experience Insight Cards, Living with Diabetes Edition. Six Participants used the cards, in a co-design workshop. Analysis of the workshop identified three types of interaction that resulted from playing the cards in the design-game: (a) applying the insight from the card to the character/story, (b) discussing real life and (c) discussing experiences. Conclusions: A method was developed to transform patient experience literature into Health Experience Insight Cards. The method aids understanding of experiences, facilitates discussion and enables groups to work towards improving healthcare from a human-centred perspective.

  • Evaluation of the effectiveness of mhealth applications in self-care management of chronic lower back pain

    Date Submitted: Jun 21, 2017

    Open Peer Review Period: Jun 22, 2017 - Aug 17, 2017

    Background: Reviews of patient-targeted smartphone applications for pain management [22] showed that despite the large availability of applications for pain tracking, self-management, and exercise tra...

    Background: Reviews of patient-targeted smartphone applications for pain management [22] showed that despite the large availability of applications for pain tracking, self-management, and exercise training, the science of implementation of mHealth technologies and self-management of chronic conditions are important areas for further research [2, 23]. There has been little research regarding methods associated with continued user engagement, or the effectiveness of adherence to certain health platforms [6,14,24] for improving health outcomes among those living with chronic diseases. Objective: This study investigated the interaction of patients with various features of Limbr, a modular mHealth compliance enhancement intervention for self-management of Chronic Lower Back Pain (CLBP). Limbr is comprised of self-directed rehabilitation tutorial videos, personalizable, visual self-report tools, health coach support, and sensor-assisted passive tracking of activity levels. The Limbr program aims to promote adherence to the BackRx exercise rehabilitation regimen [21], increase engagement in self-directed management of pain (including pain, medication and exercise tracking) and improve self-reported outcomes of pain. Methods: We assessed CLBP patients’ adherence to (1) a 3-month, self-directed, rehabilitation program, and (2) user engagement in both self-reporting Activities of Daily Living (ADLs), medication, affect, and pain function, and frequency of messages to and from the health care coach. In addition, we tested the association between scores derived from our visual self-report method, YADL (an image based tool for tracking patient reported ADLs), and the well established Oswestry Disability Index (ODI) obtained from the Oswestry Low Back Pain Questionnaire. Participants were a convenience sample recruited through their clinician in New York, NY. 98 patients agreed to participate, of which, 35 patients completed the full 3 month intervention. In aggregate over 202 data points per patient were collected and analyzed. Results: Study results indicate that the Limbr mHealth intervention promoted engagement in patient self-monitoring and management of pain through use of the mobile applications. The 35 participants who completed the full three months of engagement demonstrated a sustained intensity of use of the Limbr system, with 65% of participants interacting at least once a week. Roughly 50% of participants perceived the visual self-reports as helpful in tracking pain-related ADLs, medication, and affect. In particular, about 60% of participants found YADL helpful in keeping track of ADLs. About two-thirds (71% and 67%) of the participants agreed that the daily notifications were beneficial in reminding them to complete the daily surveys and exercises respectively. 50% of the participants perceived the engagement features as highly motivating and very useful. It was also found that the Limbr self-assessment, YADL, is correlated to the ODI Index (P < .001). Conclusions: These results indicate that mHealth interventions that consist of visual self-reporting, personalized health coach messaging, weekly user updates and sensor-assisted reminders, can be very effective in promoting adherence to the self-management of chronic LBP and to a self-directed rehabilitation regimen. Clinical Trial: NCT03040310

  • e-Vita: Effect of integration of self-management web platforms on health status in COPD disease management in primary care

    Date Submitted: Jun 22, 2017

    Open Peer Review Period: Jun 22, 2017 - Aug 17, 2017

    Background: Worldwide nearly 3 million people die from chronic obstructive pulmonary disease (COPD) every year. Integrated disease management (IDM) improves quality of life for COPD patients and can r...

    Background: Worldwide nearly 3 million people die from chronic obstructive pulmonary disease (COPD) every year. Integrated disease management (IDM) improves quality of life for COPD patients and can reduce hospitalization. Self-management of COPD through eHealth is an effective method to improve IDM and clinical outcomes. Objective: The objective of this implementation study was to investigate the effect of three chronic obstructive pulmonary disease (COPD) eHealth programs applied in primary care on health status. The e-Vita COPD study compares different levels of integration of web-based self-management platforms in IDM in three primary care settings. Patients’ health status is examined using the Clinical COPD Questionnaire (CCQ). Methods: The parallel cohort design includes i) three levels of integration in IDM (groups 1, 2, 3) and ii) randomization of two levels of personal assistance for patients (group A high assistance, group B low assistance). Interrupted time series (ITS) design was used to collect CCQ data at multiple time points (before/after intervention) and multilevel linear regression modelling was used to analyse CCQ data. Results: Of the 702 invited patients, 215 (31%) registered to a platform. Of these, 82 participated in group 1 (high integration IDM), 36 in group 1A (high assistance), and 46 in group 1B (low assistance); 96 participated in group 2 (medium integration IDM), 44 in group 2A (high assistance) and 52 in group 2B (low assistance); also, 37 participated in group 3 (no integration IDM). In the total group, no significant difference was found in change in CCQ-trend (p=0.334) before (-0.47% per month) and after the intervention (-0.084% per month). Also, no significant difference was found in CCQ changes before vs. after the intervention between the groups with high vs. low personal assistance. In all subgroups, there was no significant change in the CCQ trend before and after the intervention (group 1A p=0.237; 1B p=0.991; 2A p=0.120; 2B p=0.166; group 3 p=0.945). Conclusions: The e-Vita eHealth-supported COPD programs had no beneficial impact on the health status of COPD patients. Also, no differences were found between the patient groups receiving different levels of personal assistance. Clinical Trial: NTR4098 (31072013)

  • A Predictive Cognitive Model for Implementation Intention and Reminder Effects on Behavior Change in a Mobile Health System

    Date Submitted: Jun 21, 2017

    Open Peer Review Period: Jun 21, 2017 - Jun 30, 2017

    Background: Implementation intentions are mental representations of simple plans to translate goal intentions into behavior under specific conditions. Studies show implementation intentions can produc...

    Background: Implementation intentions are mental representations of simple plans to translate goal intentions into behavior under specific conditions. Studies show implementation intentions can produce moderate to large improvements in behavioral goal achievement. Human associative memory mechanisms have been implicated in the processes by which implementation intentions produce effects. Based on the ACT-R theory of cognition, we hypothesized that the strength of implementation intention effect could be manipulated in predictable ways using reminders delivered by a mobile health (mHealth) application. Objective: The aim of this experiment was to manipulate the effects of implementation intentions on daily behavioral goal success in ways predicted by the ACT-R theory concerning concerning mHealth reminder scheduling. Methods: An incomplete factorial design was used in this mHealth study. All participants were asked to choose a healthy behavior goal associated with Eat Slowly, Walking, or Eating More Vegetables, and were asked to set implementation intentions. N = 66 adult participants were in the study for 28 days. Participants were stratified by Self-Efficacy and assigned to one of two Reminder conditions: Reminders-presented versus Reminders-absent. Self-Efficacy and Reminder conditions were crossed. Nested within the Reminders-presented condition was a crossing of Frequency of reminders sent (High, Low) by Distribution of reminders sent (Distributed, Massed). Participants in the Low Frequency condition got 7 reminders over 28 days; those in the High Frequency condition were sent 14. Participants in the Distributed conditions were sent reminders at uniform intervals. Participants in the Massed Distribution conditions were sent reminders in clusters. Results: There was a significant overall effect of reminders on achieving a daily behavioral goal (coefficient = 2.018, SE = 0.572, odds ratio = 7.52, 95% CI [0.9037, 3.2594], P < .001). As predicted by ACT-R, using default theoretical parameters, there was an interaction of reminder Frequency by Distribution on daily goal success (coefficient = 0.7994, SE = 0.2215, odds ratio = 2.2242, 95% CI [0.3656, 1.2341], P < .001). The total number of times a reminder was acknowledged as received by a participant had a marginal effect on daily goal success (coefficient = 0.0694, SE = 0.0410, odds ratio = 1.0717, 95% CI [-0.01116, 0.1505], P = 0.09) and the time since acknowledging receipt of a reminder was highly significant (coefficient = -0.0490, SE -= 0.0104, odds ratio, 95% CI [-0.0700, -0.2852], P < .001). A dual system ACT-R mathematical model was fit to individuals’ daily goal successes and reminder acknowledgments: A goal-striving system dependent on declarative memory and a habit-forming system that acquires automatic procedures for performance of behavioral goals. Conclusions: Computational cognitive theory, such as ACT-R, can be used to make precise quantitative predictions concerning daily health behavior goal success in response to implementation intentions and the dosing schedules of reminders.

  • Applying Social Network Analysis to Understand the Percentages of Keywords within Abstracts of Journals: A System Review of Three Journals

    Date Submitted: Jun 20, 2017

    Open Peer Review Period: Jun 20, 2017 - Aug 15, 2017

    Background: Academic literature suggests keywords that are retrieved from a paper’s title and abstract represent important concepts in that study. The percentage of keywords within an abstract (PKWA...

    Background: Academic literature suggests keywords that are retrieved from a paper’s title and abstract represent important concepts in that study. The percentage of keywords within an abstract (PKWA) is required to investigate. Objective: To compare the PKWA in journals of medical informatics and the keyword network relationship in order to develop a self-examining policy for the journal. Methods: Selecting 5,985 abstracts and their corresponding keywords in three journals (JMIR, JAMIA, and BMC Med Inform Decis Mak.) published between 1995 to 2017(April) on the US National Library of Medicine National Institutes of Health (Pubmed.org), we computed the PKWA for each journal by using MS Excel modules and compared the percentage differences across journals and years via a two-way ANOVA. Social network analysis (SNA) was performed to explore the relations of keywords in journals. Results: The PKWA are 48.81, 41.59, and 56.84 for the three journals, respectively. A statistically significant difference (p < 0.05) is found in the percentages among journals selected. In contrast, no differences (p> 0.05) are found (1) between years (2016 and 2017) and (2) in interaction effects between journals and years. Three journals display significantly different patterns in network keywords and major cohesion measures. Conclusions: It is required to apply the computer module when inspecting whether keywords are within abstracts. The cohesion measure provides journal editors with a method of examining keywords within an abstract for a paper under review.

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