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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 20th year, is the pioneer open access eHealth journal and is the flagship journal of JMIR Publications. It is the leading digital health journal globally in terms of quality/visibility (Impact Factor 2017: 4.671, ranked #1 out of 22 journals) and in terms of size (number of papers published). The journal focuses on emerging technologies, medical devices, apps, engineering, and informatics applications for patient education, prevention, population health and clinical care. As leading high-impact journal in its' disciplines (health informatics and health services research), it is selective, but it is now complemented by almost 30 specialty JMIR sister journals, which have a broader scope. Peer-review reports are portable across JMIR journals and papers can be transferred, so authors save time by not having to resubmit a paper to different journals. 

As open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as all JMIR journals) a focus on readable and applied science reporting the design and evaluation of health innovations and emerging technologies. We publish original research, viewpoints, and reviews (both literature reviews and medical device/technology/app reviews).

We are also a leader in participatory and open science approaches, and offer the option to publish new submissions immediately as preprints, which receive DOIs for immediate citation (eg, in grant proposals), and for open peer-review purposes. We also invite patients to participate (eg, as peer-reviewers) and have patient representatives on editorial boards.

Be a widely cited leader in the digitial health revolution and submit your paper today!

 

Recent Articles:

  • Source: Pexels; Copyright: Rawpixel.com; URL: https://www.pexels.com/photo/shallow-focus-photography-of-a-woman-in-green-top-wearing-white-coat-1056556/; License: Licensed by the authors.

    Information and Communication Technologies Interest, Access, and Use: Cross-Sectional Survey of a Community Sample of Urban, Predominantly Black Women

    Abstract:

    Background: Information and communication technologies (ICT) offer the potential for delivering health care interventions to low socioeconomic populations who often face barriers in accessing health care. However, most studies on ICT for health education and interventions have been conducted in clinical settings. Objective: The aim of this study was to examine access to and use of mobile phones and computers, as well as interest in, using ICT for receipt of behavioral health information among a community sample of urban, predominately black, women with low socioeconomic status. Methods: Participants (N=220) were recruited from hair salons and social service centers and completed audio-computer assisted self-interviews. Results: The majority of the participants (212/220, 96.3%) reported use of a cell phone at least weekly, of which 89.1% (189/212) used smartphones and 62.3% (137/220) reported computer use at least weekly. Of the women included in the study, 51.9% (107/206) reported using a cell phone and 39.4% (74/188) reported using a computer to access health and/or safety information at least weekly. Approximately half of the women expressed an interest in receiving information about stress management (51%-56%) or alcohol and health (45%-46%) via ICT. Smartphone ownership was associated with younger age (odds ratio [OR] 0.92, 95% CI 0.87-0.97) and employment (OR 5.12, 95% CI 1.05-24.95). Accessing health and safety information weekly by phone was associated with younger age (OR 0.96, 95% CI 0.94-0.99) and inversely associated with higher income (OR 0.42, 95% CI 0.20-0.92). Conclusions: Our findings suggest that ICT use, particularly smartphone use, is pervasive among predominantly black women with low socioeconomic status in urban, nonclinical settings. These results show that ICT is a promising modality for delivering health information to this population. Further exploration of the acceptability, feasibility, and effectiveness of using ICT to disseminate behavioral health education and intervention is warranted.

  • Source: Flickr.com; Copyright: Intel Free Press; URL: https://www.flickr.com/photos/intelfreepress/7897620020; License: Creative Commons Attribution (CC-BY).

    Social Media Use in Interventions for Diabetes: Rapid Evidence-Based Review

    Abstract:

    Background: Health authorities recommend educating diabetic patients and their families and initiating measures aimed at improving self-management, promoting a positive behavior change, and reducing the risk of complications. Social media could provide valid channel to intervene in and deliver diabetes education. However, it is not well known whether the use of these channels in such interventions can help improve the patients’ outcomes. Objective: The objective of our study was to review and describe the current existing evidence on the use of social media in interventions targeting people affected with diabetes. Methods: A search was conducted across 4 databases (PubMed, Scopus, EMBASE, and Cochrane Library).The quality of the evidence of the included primary studies was graded according to the Grading of Recommendations Assessment, Development and Evaluation criteria, and the risk of bias of systematic reviews was assessed by drawing on AMSTAR (A MeaSurement Tool to Assess systematic Reviews) guidelines. The outcomes reported by these studies were extracted and analyzed. Results: We included 20 moderate- and high-quality studies in the review: 17 primary studies and 3 systematic reviews. Of the 16 publications evaluating the effect on glycated hemoglobin (HbA1c) of the interventions using social media, 13 reported significant reductions in HbA1c values. The 5 studies that measured satisfaction with the interventions using social media found positive effects. We found mixed evidence regarding the effect of interventions using social media on health-related quality of life (2 publications found positive effects and 3 found no differences) and on diabetes knowledge or empowerment (2 studies reported improvements and 2 reported no significant changes). Conclusions: There is very little good-quality evidence on the use of social media in interventions aimed at helping people with diabetes. However, the use of these channels is mostly linked to benefits on patients’ outcomes. Public health institutions, clinicians, and other stakeholders who aim at improving the knowledge of diabetic patients could consider the use of social media in their interventions.

  • Source: Freepik.com; Copyright: Freepik.com; URL: https://www.freepik.com/free-photo/crop-man-working-with-laptop_1363923.htm#term=businessman%20laptop&page=1&position=16; License: Licensed by JMIR.

    Differences in the Effect of Internet-Based Cognitive Behavioral Therapy for Improving Nonclinical Depressive Symptoms Among Workers by Time Preference:...

    Abstract:

    Background: Previous randomized controlled trials (RCTs) have shown a significant intervention effect of internet-based computerized cognitive behavioral therapy (iCBT) on improving nonclinical depressive symptoms among healthy workers and community residents in a primary prevention setting. Time preference is one’s relative valuation for having a reward (eg, money) at present than at a later date. Time preference may affect the effectiveness of cognitive behavioral therapy. Objective: This RCT aimed to test the difference of intervention effect of an iCBT program on improving nonclinical depressive symptoms between two subgroups classified post-hoc on the basis of time preference among workers in Japan. Methods: All workers in one corporate group (approximate n=20,000) were recruited. Participants who fulfilled the inclusion criteria were randomly allocated to either intervention or control groups. Participants in the intervention group completed 6 weekly lessons and homework assignments within the iCBT program. The Beck Depression Inventory-II (BDI-II) and Kessler’s Psychological Distress Scale (K6) measures were obtained at baseline and 3-, 6-, and 12-month follow-ups. Two subgroups were defined by the median of time preference score at baseline. Results: Only few (835/20,000, 4.2%) workers completed the baseline survey. Of the 835 participants, 706 who fulfilled the inclusion criteria were randomly allocated to the intervention or control group. Participants who selected irrational time preference options were excluded (21 and 18 participants in the intervention and control groups, respectively). A three-way interaction (group [intervention/control] × time [baseline/follow-up] × time preference [higher/lower]) effect of iCBT was significant for BDI-II (t1147.42=2.33, P=.02) and K6 (t1254.04=2.51, P=.01) at the 3-month follow-up, with a greater effect of the iCBT in the group with higher time preference. No significant three-way interaction was found at the 6- and 12-month follow-ups. Conclusions: The effects of the iCBT were greater for the group with higher time preference at the shorter follow-up, but it was leveled off later. Workers with higher time preference may change their cognition or behavior more quickly, but these changes may not persist. Trial Registration: UMIN Clinical Trials Registry UMIN000014146; https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi? recptno=R000016466 (Archived by WebCite at http://www.webcitation.org/70o2rNk2V)

  • Restroom placard. Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2018/8/e251/; License: Creative Commons Attribution (CC-BY).

    Internet of Things Buttons for Real-Time Notifications in Hospital Operations: Proposal for Hospital Implementation

    Abstract:

    Background: Hospital staff frequently performs the same process hundreds to thousands of times a day. Customizable Internet of Things buttons are small, wirelessly-enabled devices that trigger specific actions with the press of an integrated button and have the potential to automate some of these repetitive tasks. In addition, IoT buttons generate logs of triggered events that can be used for future process improvements. Although Internet of Things buttons have seen some success as consumer products, little has been reported on their application in hospital systems. Objective: We discuss potential hospital applications categorized by the intended user group (patient or hospital staff). In addition, we examine key technological considerations, including network connectivity, security, and button management systems. Methods: In order to meaningfully deploy Internet of Things buttons in a hospital system, we propose an implementation framework grounded in the Plan-Do-Study-Act method. Results: We plan to deploy Internet of Things buttons within our hospital system to deliver real-time notifications in public-facing tasks such as restroom cleanliness and critical supply restocking. We expect results from this pilot in the next year. Conclusions: Overall, Internet of Things buttons have significant promise; future rigorous evaluations are needed to determine the impact of Internet of Things buttons in real-world health care settings.

  • Source: Pixabay.com; Copyright: StockSnap; URL: https://pixabay.com/en/people-woman-hands-phone-text-2586977/; License: Public Domain (CC0).

    Using Mobile Apps to Assess and Treat Depression in Hispanic and Latino Populations: Fully Remote Randomized Clinical Trial

    Abstract:

    Background: Most people with mental health disorders fail to receive timely access to adequate care. US Hispanic/Latino individuals are particularly underrepresented in mental health care and are historically a very difficult population to recruit into clinical trials; however, they have increasing access to mobile technology, with over 75% owning a smartphone. This technology has the potential to overcome known barriers to accessing and utilizing traditional assessment and treatment approaches. Objective: This study aimed to compare recruitment and engagement in a fully remote trial of individuals with depression who either self-identify as Hispanic/Latino or not. A secondary aim was to assess treatment outcomes in these individuals using three different self-guided mobile apps: iPST (based on evidence-based therapeutic principles from problem-solving therapy, PST), Project Evolution (EVO; a cognitive training app based on cognitive neuroscience principles), and health tips (a health information app that served as an information control). Methods: We recruited Spanish and English speaking participants through social media platforms, internet-based advertisements, and traditional fliers in select locations in each state across the United States. Assessment and self-guided treatment was conducted on each participant's smartphone or tablet. We enrolled 389 Hispanic/Latino and 637 non-Hispanic/Latino adults with mild to moderate depression as determined by Patient Health Questionnaire-9 (PHQ-9) score≥5 or related functional impairment. Participants were first asked about their preferences among the three apps and then randomized to their top two choices. Outcomes were depressive symptom severity (measured using PHQ-9) and functional impairment (assessed with Sheehan Disability Scale), collected over 3 months. Engagement in the study was assessed based on the number of times participants completed active surveys. Results: We screened 4502 participants and enrolled 1040 participants from throughout the United States over 6 months, yielding a sample of 348 active users. Long-term engagement surfaced as a key issue among Hispanic/Latino participants, who dropped from the study 2 weeks earlier than their non-Hispanic/Latino counterparts (P<.02). No significant differences were observed for treatment outcomes between those identifying as Hispanic/Latino or not. Although depressive symptoms improved (beta=–2.66, P=.006) over the treatment course, outcomes did not vary by treatment app. Conclusions: Fully remote mobile-based studies can attract a diverse participant pool including people from traditionally underserved communities in mental health care and research (here, Hispanic/Latino individuals). However, keeping participants engaged in this type of “low-touch” research study remains challenging. Hispanic/Latino populations may be less willing to use mobile apps for assessing and managing depression. Future research endeavors should use a user-centered design to determine the role of mobile apps in the assessment and treatment of depression for this population, app features they would be interested in using, and strategies for long-term engagement. Trial Registration: Clinicaltrials.gov NCT01808976; https://clinicaltrials.gov/ct2/show/NCT01808976 (Archived by WebCite at http://www.webcitation.org/70xI3ILkz)

  • Wechat interface of a tertiary referral hospital. Source: Image created by the authors; Copyright: The Authors; URL: http://www.jmir.org/2018/8/e249/; License: Creative Commons Attribution (CC-BY).

    Social Media Landscape of the Tertiary Referral Hospitals in China: Observational Descriptive Study

    Abstract:

    Background: Social media has penetrated all walks of life. Chinese health care institutions are increasingly utilizing social media to connect with their patients for better health service delivery. Current research has focused heavily on the use of social media in developed countries, with few studies exploring its usage in the context of developing countries, such as China. Tertiary hospitals in China are usually located in city centers, and they serve as medical hubs for multiple regions, with comprehensive and specialized medical care being provided. These hospitals are assumed to be the pioneers in creating official social media accounts to connect with their patients due to the fact that they appear to have more resources to support this innovative approach to communication and health care education. Objective: The objective of our study was to examine China’s best tertiary hospitals, as recognized by The National Health Commission of the People’s Republic of China (NHCPRC), and to map out the landscape of current social media usage by hospitals when engaging with patients. Methods: We examined the best 705 tertiary hospitals in China by collecting and analyzing data regarding their usage of popular Chinese social media apps Sina Weibo and WeChat. The specific data included (1) hospital characteristics (ie, time since established, number of beds, hospital type, and regions or localities) and (2) status of social media usage regarding two of the most popular local social media platforms in China (ie, time of initiation, number of followers, and number of tweets or posts). We further used a logistic regression model to test the association between hospital characteristics and social media adoption. Results: Of all, 76.2% (537/705) tertiary referral hospitals have created official accounts on either Sina Weibo or WeChat, with the latter being more popular among the two. In addition, our study suggests that larger and newer hospitals with greater resources are more likely to adopt social media, while hospital type and affiliation with universities are not significant predictors of social media adoption among hospitals. Conclusions: Our study demonstrated that hospitals are more inclined to use WeChat. The move by hospitals from Sina Weibo to WeChat indicates that patients are not satisfied by mere communication and that they now place more value on health service delivery. Meanwhile, utilizing social media requires comprehensive thinking from the hospital side. Once adopted, hospitals are encouraged to implement specific rules regarding social media usage. In the future, a long journey still lies ahead for hospitals in terms of operating their official social media accounts.

  • Source: iStock by Getty Images; Copyright: AlexRaths; URL: https://www.istockphoto.com/photo/laboratory-gm135018895-18443917?clarity=false; License: Creative Commons Attribution (CC-BY).

    Cloud Computing for Infectious Disease Surveillance and Control: Development and Evaluation of a Hospital Automated Laboratory Reporting System

    Abstract:

    Background: Outbreaks of several serious infectious diseases have occurred in recent years. In response, to mitigate public health risks, countries worldwide have dedicated efforts to establish an information system for effective disease monitoring, risk assessment, and early warning management for international disease outbreaks. A cloud computing framework can effectively provide the required hardware resources and information access and exchange to conveniently connect information related to infectious diseases and develop a cross-system surveillance and control system for infectious diseases. Objective: The objective of our study was to develop a Hospital Automated Laboratory Reporting (HALR) system based on such a framework and evaluate its effectiveness. Methods: We collected data for 6 months and analyzed the cases reported within this period by the HALR and the Web-based Notifiable Disease Reporting (WebNDR) systems. Furthermore, system evaluation indicators were gathered, including those evaluating sensitivity and specificity. Results: The HALR system reported 15 pathogens and 5174 cases, and the WebNDR system reported 34 cases. In a comparison of the two systems, sensitivity was 100% and specificity varied according to the reported pathogens. In particular, the specificity for Streptococcus pneumoniae, Mycobacterium tuberculosis complex, and hepatitis C virus were 99.8%, 96.6%, and 97.4%, respectively. However, the specificity for influenza virus and hepatitis B virus were only 79.9% and 47.1%, respectively. After the reported data were integrated with patients’ diagnostic results in their electronic medical records (EMRs), the specificity for influenza virus and hepatitis B virus increased to 89.2% and 99.1%, respectively. Conclusions: The HALR system can provide early reporting of specified pathogens according to test results, allowing for early detection of outbreaks and providing trends in infectious disease data. The results of this study show that the sensitivity and specificity of early disease detection can be increased by integrating the reported data in the HALR system with the cases’ clinical information (eg, diagnostic results) in EMRs, thereby enhancing the control and prevention of infectious diseases.

  • The OCD Stop program. Source: Image created by the authors; Copyright: The Authors; URL: http://www.jmir.org/2018/8/e242/; License: Creative Commons Attribution (CC-BY).

    Therapist-Assisted Internet-Based Cognitive Behavioral Therapy Versus Progressive Relaxation in Obsessive-Compulsive Disorder: Randomized Controlled Trial

    Abstract:

    Background: Obsessive-compulsive disorder (OCD) is a highly disabling psychological disorder with a chronic course if left untreated. Cognitive behavioral therapy (CBT) has been shown to be an effective treatment, but access to face-to-face CBT is not always possible. Internet-based CBT (iCBT) has become an increasingly viable option. However, no study has compared iCBT to an analogous control condition using a randomized controlled trial (RCT). Objective: A 2-armed RCT was used to compare a therapist-assisted 12-module iCBT to an analogous active attention control condition (therapist-assisted internet-based standard progressive relaxation training, iPRT) in adult OCD. This paper reports pre-post findings for OCD symptom severity. Method: In total, 179 participants (117 females, 65.7%) were randomized (stratified by gender) into iCBT or iPRT. The iCBT intervention included psychoeducation, mood and behavioral management, exposure and response prevention (ERP), cognitive therapy, and relapse prevention; the iPRT intervention included psychoeducation and relaxation techniques as a way of managing OCD-related anxiety but did not incorporate ERP or other CBT elements. Both treatments included audiovisual content, case stories, demonstrations of techniques, downloadable audio content and worksheets, and expert commentary. All participants received 1 weekly email, with a maximum 15-minute preparation time per client from a remote therapist trained in e-therapy. Emails aimed to monitor progress, provide support and encouragement, and assist in individualizing the treatment. Participants were assessed for baseline and posttreatment OCD severity with the telephone-administered clinician-rated Yale-Brown Obsessive-Compulsive Scale and other measures by assessors who were blinded to treatment allocation. Results: No pretreatment differences were found between the 2 conditions. Intention-to-treat analysis revealed significant pre-post improvements in OCD symptom severity for both conditions (P<.001). However, relative to iPRT, iCBT showed significantly greater symptom severity improvement (P=.001); Cohen d for iCBT was 1.05 (95% CI 0.72-1.37), whereas for iPRT it was 0.48 (95% CI 0.22-0.73). The iCBT condition was superior in regard to reliable improvement (25/51, 49% vs 16/55, 29%; P=.04) and clinically significant pre-post-treatment changes (17/51, 33% vs 6/55, 11%; P=.005). Those undertaking iCBT post completion of iPRT showed further significant symptom amelioration (P<.001), although the sequential treatment was no more efficacious than iCBT alone (P=.63). Conclusion: This study is the first to compare a therapist-assisted iCBT program for OCD to an analogous active attention control condition using iPRT. Our findings demonstrate the large magnitude effect of iCBT for OCD; interestingly, iPRT was also moderately efficacious, albeit significantly less so than the iCBT intervention. The findings are compared to previous internet-based and face-to-face CBT treatment programs for OCD. Future directions for technology-enhanced programs for the treatment of OCD are outlined. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12611000321943; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336704 (Archived by WebCite at http://www.webcitation.org/70ovUiOmd)

  • Electronic health record functionality-level adoption among US hospitals using the Electronic Medical Record Adoption Model maturation stages (2014-2035 years are forecasted using the Bass model; vertical-axis represents the number of hospitals). Source: Figure 2 from http://www.jmir.org/2018/8/e10458; Copyright: the authors; License: Creative Commons Attribution (CC-BY).

    Forecasting the Maturation of Electronic Health Record Functions Among US Hospitals: Retrospective Analysis and Predictive Model

    Abstract:

    Background: The Meaningful Use (MU) program has promoted electronic health record adoption among US hospitals. Studies have shown that electronic health record adoption has been slower than desired in certain types of hospitals; but generally, the overall adoption rate has increased among hospitals. However, these studies have neither evaluated the adoption of advanced functionalities of electronic health records (beyond MU) nor forecasted electronic health record maturation over an extended period in a holistic fashion. Additional research is needed to prospectively assess US hospitals’ electronic health record technology adoption and advancement patterns. Objective: This study forecasts the maturation of electronic health record functionality adoption among US hospitals through 2035. Methods: The Healthcare Information and Management Systems Society (HIMSS) Analytics’ Electronic Medical Record Adoption Model (EMRAM) dataset was used to track historic uptakes of various electronic health record functionalities considered critical to improving health care quality and efficiency in hospitals. The Bass model was used to predict the technological diffusion rates for repeated electronic health record adoptions where upgrades undergo rapid technological improvements. The forecast used EMRAM data from 2006 to 2014 to estimate adoption levels to the year 2035. Results: In 2014, over 5400 hospitals completed HIMSS’ annual EMRAM survey (86%+ of total US hospitals). In 2006, the majority of the US hospitals were in EMRAM Stages 0, 1, and 2. By 2014, most hospitals had achieved Stages 3, 4, and 5. The overall technology diffusion model (ie, the Bass model) reached an adjusted R-squared of .91. The final forecast depicted differing trends for each of the EMRAM stages. In 2006, the first year of observation, peaks of Stages 0 and 1 were shown as electronic health record adoption predates HIMSS’ EMRAM. By 2007, Stage 2 reached its peak. Stage 3 reached its full height by 2011, while Stage 4 peaked by 2014. The first three stages created a graph that exhibits the expected “S-curve” for technology diffusion, with inflection point being the peak diffusion rate. This forecast indicates that Stage 5 should peak by 2019 and Stage 6 by 2026. Although this forecast extends to the year 2035, no peak was readily observed for Stage 7. Overall, most hospitals will achieve Stages 5, 6, or 7 of EMRAM by 2020; however, a considerable number of hospitals will not achieve Stage 7 by 2035. Conclusions: We forecasted the adoption of electronic health record capabilities from a paper-based environment (Stage 0) to an environment where only electronic information is used to document and direct care delivery (Stage 7). According to our forecasts, the majority of hospitals will not reach Stage 7 until 2035, absent major policy changes or leaps in technological capabilities. These results indicate that US hospitals are decades away from fully implementing sophisticated decision support applications and interoperability functionalities in electronic health records as defined by EMRAM’s Stage 7.

  • Pain apps that measure pain using animations and illustrations rather than words and numbers. Source: Image created by the Authors; Copyright: University of Pittsburgh; URL: http://www.jmir.org/2018/8/e10056/; License: Licensed by JMIR.

    Abstract Animations for the Communication and Assessment of Pain in Adults: Cross-Sectional Feasibility Study

    Abstract:

    Background: Pain is the most common physical symptom requiring medical care, yet the current methods for assessing pain are sorely inadequate. Pain assessment tools can be either too simplistic or take too long to complete to be useful for point-of-care diagnosis and treatment. Objective: The aim was to develop and test Painimation, a novel tool that uses graphic visualizations and animations instead of words or numeric scales to assess pain quality, intensity, and course. This study examines the utility of abstract animations as a measure of pain. Methods: Painimation was evaluated in a chronic pain medicine clinic. Eligible patients were receiving treatment for pain and reported pain more days than not for at least 3 months. Using a tablet computer, participating patients completed the Painimation instrument, the McGill Pain Questionnaire (MPQ), and the PainDETECT questionnaire for neuropathic symptoms. Results: Participants (N=170) completed Painimation and indicated it was useful for describing their pain (mean 4.1, SE 0.1 out of 5 on a usefulness scale), and 130 of 162 participants (80.2%) agreed or strongly agreed that they would use Painimation to communicate with their providers. Animations selected corresponded with pain adjectives endorsed on the MPQ. Further, selection of the electrifying animation was associated with self-reported neuropathic pain (r=.16, P=.03), similar to the association between neuropathic pain and PainDETECT (r=.17, P=.03). Painimation was associated with PainDETECT (r=.35, P<.001). Conclusions: Using animations may be a faster and more patient-centered method for assessing pain and is not limited by age, literacy level, or language; however, more data are needed to assess the validity of this approach. To establish the validity of using abstract animations (“painimations”) for communicating and assessing pain, apps and other digital tools using painimations will need to be tested longitudinally across a larger pain population and also within specific, more homogenous pain conditions.

  • Source: Flickr; Copyright: How I See Life; URL: https://www.flickr.com/photos/mrsbluff/3368014757; License: Creative Commons Attribution + NoDerivatives (CC-BY-ND).

    Improving Consumer Understanding of Medical Text: Development and Validation of a New SubSimplify Algorithm to Automatically Generate Term Explanations in...

    Abstract:

    Background: While health literacy is important for people to maintain good health and manage diseases, medical educational texts are often written beyond the reading level of the average individual. To mitigate this disconnect, text simplification research provides methods to increase readability and, therefore, comprehension. One method of text simplification is to isolate particularly difficult terms within a document and replace them with easier synonyms (lexical simplification) or an explanation in plain language (semantic simplification). Unfortunately, existing dictionaries are seldom complete, and consequently, resources for many difficult terms are unavailable. This is the case for English and Spanish resources. Objective: Our objective was to automatically generate explanations for difficult terms in both English and Spanish when they are not covered by existing resources. The system we present combines existing resources for explanation generation using a novel algorithm (SubSimplify) to create additional explanations. Methods: SubSimplify uses word-level parsing techniques and specialized medical affix dictionaries to identify the morphological units of a term and then source their definitions. While the underlying resources are different, SubSimplify applies the same principles in both languages. To evaluate our approach, we used term familiarity to identify difficult terms in English and Spanish and then generated explanations for them. For each language, we extracted 400 difficult terms from two different article types (General and Medical topics) balanced for frequency. For English terms, we compared SubSimplify’s explanation with the explanations from the Consumer Health Vocabulary, WordNet Synonyms and Summaries, as well as Word Embedding Vector (WEV) synonyms. For Spanish terms, we compared the explanation to WordNet Summaries and WEV Embedding synonyms. We evaluated quality, coverage, and usefulness for the simplification provided for each term. Quality is the average score from two subject experts on a 1-4 Likert scale (two per language) for the synonyms or explanations provided by the source. Coverage is the number of terms for which a source could provide an explanation. Usefulness is the same expert score, however, with a 0 assigned when no explanations or synonyms were available for a term. Results: SubSimplify resulted in quality scores of 1.64 for English (P<.001) and 1.49 for Spanish (P<.001), which were lower than those of existing resources (Consumer Health Vocabulary [CHV]=2.81). However, in coverage, SubSimplify outperforms all existing written resources, increasing the coverage from 53.0% to 80.5% in English and from 20.8% to 90.8% in Spanish (P<.001). This result means that the usefulness score of SubSimplify (1.32; P<.001) is greater than that of most existing resources (eg, CHV=0.169). Conclusions: Our approach is intended as an additional resource to existing, manually created resources. It greatly increases the number of difficult terms for which an easier alternative can be made available, resulting in greater actual usefulness.

  • Source: The Authors; Copyright: Lutz Siemer; URL: http://www.jmir.org/2018/8/e246/; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).

    Blended Smoking Cessation Treatment: Exploring Measurement, Levels, and Predictors of Adherence

    Abstract:

    Background: Blended face-to-face and Web-based treatment is a promising way to deliver cognitive behavioral therapy. Since adherence has been shown to be a measure for treatment’s acceptability and a determinant for treatment’s effectiveness, in this study, we explored adherence to a new blended smoking cessation treatment (BSCT). Objective: The objective of our study was to (1) develop an adequate method to measure adherence to BSCT; (2) define an adequate degree of adherence to be used as a threshold for being adherent; (3) estimate adherence to BSCT; and (4) explore the possible predictors of adherence to BSCT. Methods: The data of patients (N=75) were analyzed to trace adherence to BSCT delivered at an outpatient smoking cessation clinic. In total, 18 patient activities (eg, using a Web-based smoking diary tool or responding to counselors’ messages) were selected to measure adherence; the degree of adherence per patient was compared with quitting success. The minimum degree of adherence of patients who reported abstinence was examined to define a threshold for the detection of adherent patients. The number of adherent patients was calculated for each of the 18 selected activities; the degree of adherence over the course of the treatment was displayed; and the number of patients who were adherent was analyzed. The relationship between adherence and 33 person-, smoking-, and health-related characteristics was examined. Results: The method for measuring adherence was found to be adequate as adherence to BSCT correlated with self-reported abstinence (P=.03). Patients reporting abstinence adhered to at least 61% of BSCT. Adherence declined over the course of the treatment; the percentage of adherent patients per treatment activity ranged from 82% at the start of the treatment to 11%-19% at the final-third of BSCT; applying a 61% threshold, 18% of the patients were classified as adherent. Marital status and social modeling were the best independent predictors of adherence. Patients having a partner had 11-times higher odds of being adherent (OR [odds ratio]=11.3; CI: 1.33-98.99; P=.03). For social modeling, graded from 0 (=partner and friends are not smoking) to 8 (=both partner and nearly all friends are smoking), each unit increase was associated with 28% lower odds of being adherent (OR=0.72; CI: 0.55-0.94; P=.02). Conclusions: The current study is the first to explore adherence to a blended face-to-face and Web-based treatment (BSCT) based on a substantial group of patients. It revealed a rather low adherence rate to BSCT. The method for measuring adherence to BSCT could be considered adequate because the expected dose-response relationship between adherence and quitting could be verified. Furthermore, this study revealed that marital status and social modeling were independent predictors of adherence. Trial Registration: Netherlands Trial Registry NTR5113; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5113 (Archived by WebCite at http://www.webcitation.org/71BAPwER8).

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    Date Submitted: Aug 12, 2018

    Open Peer Review Period: Aug 14, 2018 - Oct 9, 2018

    Background: Clinical trials are key to advancing evidence-based medical research. The medical research literature has identified the impact and risks of publication bias in clinical trials. Selective...

    Background: Clinical trials are key to advancing evidence-based medical research. The medical research literature has identified the impact and risks of publication bias in clinical trials. Selective publication for positive outcomes or non-publication of negative results could lead to misdirect subsequent research, justify further research, and result in literature reviews lean towards positive outcomes. Digital health randomized clinical trials face specific challenges, including high attrition rate, usability issues, and insufficient prior formative research. These challenges may become contributing factors to non-publication of trials results. To our knowledge, there exists no study that has analyzed and reported the characteristics of non-publication rates within the domain of digital health trials. Objective: The primary research objective was to examine the prevalence and characteristics of non-published digital health randomized clinical trials, including eHealth, mHealth and telehealth clinical trials, registered in ClinicalTrials.gov Methods: To identify digital health trials, a list of 47 search terms and phrases was developed through an iterative process and applied to the “Title”, “Interventions” and “Outcome Measures” fields of registered clinical trials with completion dates between April 1st, 2010 and April 1st, 2013. The search was based on the full dataset exported from the ClinlicalTrials.gov database with 265,657 registered clinical trials entries downloaded on February 10th, 2018, to allow for up to nearly 5 years for the publication of the study after trial completion. To identify publications related to the results of the trials, we extracted the complete registered randomized clinical trials content from the ClinicalTrials.gov website in XML format and identified relevant publications through a comprehensive approach that included an automated as well as a manual publication identification process. Results: In total, 6717 articles matched the priori search terms and phrases, of which 803 trials matched our latest completion date criteria. 556 randomized trials were included in this study after screening. We found that 150 (27%) of all included trials remain unpublished five years after the trials’ completion data. In bivariate analyses, statistically significant differences in trial characteristics between published and unpublished trials were found for the intervention target condition (cancer having the largest non-publication rate at 45%, while addiction/smoking cessation trials having the lowest non-publication rate at 16%), country (US at 33% vs non US at 18%), trial size (small trials at 52%, larger trials at 30%), clinical trial phases, and recruitment. In multivariate analyses, trial characteristics differences between published and unpublished trials remained statistically significant for the intervention target condition, country, trial size, trial phases and recruitment, with the odds of publication for non-US based trials being significant and 2.8 (CI:1.690-4.758) times more likely to be published compared to the reference group of the US based trials. Conclusions: In the realm of digital health, non-publication of registered clinical trials results is prevalent at 27%, which is lower than published non-publication rates in other fields. There are substantial differences in publication rates between US versus non-US based trials and whether, or not, the trials were funded by industry sponsors. Further research is required to define further determinants and reasons for non-publication, and more importantly to articulate the impact and risk of publication bias in the field of digital health clinical trials.

  • Effect of serial anthropometric measurement`s and motivational text messages on weight reduction amongst workers: Pilot Randomised Controlled Trial

    Date Submitted: Aug 12, 2018

    Open Peer Review Period: Aug 14, 2018 - Oct 9, 2018

    Background: Obesity is an endemic problem with significant health and financial consequences. Text messaging has been shown to be a simple and effective method of facilitating weight reduction. Additi...

    Background: Obesity is an endemic problem with significant health and financial consequences. Text messaging has been shown to be a simple and effective method of facilitating weight reduction. Additionally, waist-to-hip ratio has emerged as a significant anthropometric measure. However, few studies examined the effect of serial anthropometric self-measurement combined with text messaging. Objective: The primary aim was to assess whether an eight-week program, consisting of weekly serial self-measurements of waist and hip circumference, combined with motivational text messages, could reduce waist-to-hip ratio (WHR) among Australian workers. Methods: This was a community-based, participant-blinded, staggered-entry, parallel group study. Adult workers with access to mobile phones were eligible and recruited through an open access online survey. A balanced, block randomisation was used to assign participants to receive intervention or control messages for eight weeks. Outcome data was self-assessed through an online survey. Results: Sixty participants were randomised with 30 participants allocated to each a control and an intervention group. There was no significant change in WHR (P= .43) and all secondary outcome measures did not differ between the intervention group and control group at the end of the eight-week intervention. Both groups, however, showed a significant decrease in burnout over time (mean (SE): pre 4.80 (0.39) vs. post 3.36 (0.46) P=.004). The intervention uptake followed a downward trend. Peak participant replies to weekly self-measurements were received in week three (14/23 (61%)), and the least in week eight (8/23 (35%)). No harms were found to be resulting from this study. Conclusions: This study is an innovative pilot trial using text messaging and serial anthropometric measurements in weight management. No change was detected in waist-to-hip ratios in Australian workers over 8 weeks, therefore it was unable to be concluded that the intervention affected the primary outcome. However, these results should be interpreted in the context of limited sample size and decreasing intervention uptake over the course of the study. This pilot trial is useful for informing and contributing to the design of future studies and the growing body of literature on serial self-measurements combined with text messaging. Clinical Trial: The trial is registered at ANZCTR.org.au, number: ACTRN12616001496404.

  • Smartphone Apps Targeting Medication Adherence: Quality Assessment and Content Analysis of User Reviews

    Date Submitted: Aug 11, 2018

    Open Peer Review Period: Aug 14, 2018 - Oct 9, 2018

    Background: With the accessibility and widespread use of mobile phones, smartphone apps targeting medication adherence may be useful tools to help patients take medications as prescribed. Objective: O...

    Background: With the accessibility and widespread use of mobile phones, smartphone apps targeting medication adherence may be useful tools to help patients take medications as prescribed. Objective: Our objectives were to: 1) characterize and assess smartphone medication adherence apps guided by a conceptual framework on the focus of adherence interventions; and 2) conduct a content analysis of online reviews to explore users’ perspectives and experiences with smartphone medication adherence apps. Methods: We searched for smartphone medication adherence apps using keyword searches in Apple and Android operating systems. We characterized all apps in terms of number of downloads, ratings, languages, cost, and disease target. We categorized apps according to four key features of: 1) alerting to take medication; 2) tracking medication taking; 3) reminding to refill/indicating amount of medication left; and 4) storing medication information. We then selected representative apps from each operating system for detailed quality assessment and user testing. We also downloaded online reviews for these selected apps and conducted a qualitative content analysis using an inductive approach involving steps of initial open coding, construction of categories, and abstraction into themes. Results: We identified 704 apps (443 from Apple and 261 from Android), the majority of which having one (37.2% of Apple, 41.4% of Android) or two (38.1% of Apple, 31.4% of Android) features. Quality assessment and user testing of 20 selected apps revealed apps varied in quality and commonly focus on behavioural strategies to enhance medication adherence through alerts, reminders, and logs. A total of 1,323 eligible online reviews from these 20 selected apps were analyzed and the following themes emerged: 1) features and functions appreciated by users, which included the ability to set-up customized medication regimen details and reminders, monitor other health information (e.g. vitals, supplements, manage multiple people/pets), support health care visits (e.g. having a list of medications and necessary health information in one app); 2) negative user experiences which captured technical difficulties (glitches, confusing app navigation, poor interoperability), dosage schedule and reminder setup inflexibility; and 3) desired functions and features related to optimization of information input, improvement of reminders and upgrading app performance (better synchronization/backup of data and interoperability). Conclusions: A tremendous amount of smartphone medication adherence apps are currently available. The majority of apps have features representing a behavioural approach to intervention. Findings of the content analysis offer mostly positive feedback as well as insight into current limitations and improvements that could be addressed in current and future medication adherence apps.

  • Adoption of digital health innovations: perspectives from a stakeholder workshop

    Date Submitted: Aug 11, 2018

    Open Peer Review Period: Aug 14, 2018 - Oct 9, 2018

    Background: There are various complex reasons that influence sustainable adoption of innovations in healthcare systems. Low adoption can be caused by a lack of support from one or more stakeholders an...

    Background: There are various complex reasons that influence sustainable adoption of innovations in healthcare systems. Low adoption can be caused by a lack of support from one or more stakeholders and their needs and expectations are not always considered or aligned. Objective: To identify stakeholders’ perceptions on barriers and facilitators towards the sustainable adoption of digital health innovations. Methods: A stakeholder workshop was attended by twelve participants with a range of backgrounds on 25th August 2017, including people representing the views from patients, carers, local hospitals, pharmacy retailers, health insurers, health services researchers, engineers, and technology and pharmaceutical companies in Switzerland. Based on adoption of innovation frameworks, we asked participants to interview each other about three factors influencing the adoption of digitally-delivered health interventions: 1) facilitators and barriers in the external system; 2) needs and expectations of stakeholders; and 3) safety, quality, and usability of innovations. The worksheets and videos generated from the workshop were qualitatively analyzed and summarized. Results: Facilitators for adoption mentioned were high levels of income and education, and digital health being a high priority to stakeholders. Main common interests of different stakeholders were patient satisfaction and job protection. Healthcare spending was a misaligned interest; whilst some stakeholders were keen on spending more to obtain or provide the highest quality of care, others were focused on reducing healthcare spending to provide cost-effective services. Switzerland’s diversity and complexity in terms of the organisation with 26 cantons (administrative divisions) were barriers as this made it harder to ensure interoperability of interventions. A culture of innovation was considered a push factor, but adoption was inhibited by persistent paper-based systems, a fear of change, and unwillingness to share data. The sustainability of interventions can be promoted by making them patient-centered, meaning that patients should be involved throughout their development. Conclusions: Promoting sustainable adoption of digital health remains challenging despite various push factors being in place. Barriers related to fragmentation, patient-centeredness, data security, privacy, trust, and job security need to be addressed. A strength is that people from a wide range of backgrounds attended the workshop. A limitation is that the findings are focused on the macro level. In-depth case studies of specific issues need to be conducted in different settings. Clinical Trial: NA

  • Primary care physician perspectives on patient apps for type 2 diabetes self-management that link to primary care: An interview study to identify factors to support physician engagement

    Date Submitted: Aug 12, 2018

    Open Peer Review Period: Aug 14, 2018 - Oct 9, 2018

    Background: The health burden of type 2 diabetes can be mitigated by engaging in two key aspects of diabetes care: self-management and regular contact with health professionals. There is a clear benef...

    Background: The health burden of type 2 diabetes can be mitigated by engaging in two key aspects of diabetes care: self-management and regular contact with health professionals. There is a clear benefit to integrating these two aspects of care into a single clinical tool, and as smartphone ownership increases the ‘app’ becomes a more feasible platform. However, the effectiveness of online health interventions is contingent on uptake by health care providers, which is typically low. There has been little research that focuses specifically on barriers and facilitators to health care provider uptake for interventions that link self-management apps to the user’s primary care physician (PCP). Objective: This study aimed to explore PCPs’ attitudes towards a proposed self-management app for patients with diabetes that would link to primary care services. Methods: 25 semi-structured interviews explored PCP attitudes towards a proposed diabetes app. The interview schedule discussed potential features that would link in with the patient’s primary care services. Interviews were audio-recorded, transcribed and coded using Framework Analysis to ensure rigor. Results: Our analysis indicated that PCP attitudes towards the app were underpinned by perceived roles of: (1) diabetes self-management; (2) face-to-face care; and (3) the anticipated burden of new technologies in their practice. Theme 1 explored PCPs’ perceptions about how an app could foster patient independence for self-management behaviours, but could also increase responsibility and liability for the PCP. Theme 2 identified beliefs underpinning a commonly expressed preference for face-to-face care. PCPs perceived information was more motivating, better understood, and presented with greater empathy when delivered face-to-face, rather than online. Theme 3 described how most PCPs anticipated an initial increase in workload whilst learning to use a new clinical tool. Some PCPs accepted this burden on the basis that the change was inevitable as healthcare became more integrated. Others reported potential benefits were outweighed by effort to implement the app. This study also identified how app features can be positively framed, highlighting potential benefits for PCPs to maximise PCP engagement, buy-in and uptake. For example, PCPs were more positive when they perceived that an app could facilitate communication and motivation between consultations, would focus on building capacity for patient independence, and would reinforce rather than replace in-person care. They were also more positive about app features that were automated, integrated with existing software, flexible for different patients and included secondary benefits such as improved documentation. Conclusions: This study provided insight into PCP attitudes towards a diabetes app integrated with primary care services. This was observed as more than a technological change; PCPs were concerned about changes in workload, their role in self-management, and the nature of consultations. This research highlighted potential facilitators and barriers to engaging PCPs in the implementation process.

  • Skin Cancer Classification using Convolutional Neural Networks: Systematic Review

    Date Submitted: Aug 14, 2018

    Open Peer Review Period: Aug 14, 2018 - Aug 23, 2018

    Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable life-saving and fast diagnoses, even outsid...

    Background: State-of-the-art classifiers based on convolutional neural networks (CNNs) generally outperform the diagnosis of dermatologists and could enable life-saving and fast diagnoses, even outside the hospital via installation on mobile devices. To our knowledge, at present, there is no review of the current work in this research area. Objective: This study presents the first systematic review of the state-of-the-art research on classifying skin lesions with CNNs. We limit our review to skin lesion classifiers. In particular, methods that apply a CNN only for segmentation or for the classification of dermoscopic patterns are not considered here. Furthermore, this study discusses why the comparability of the presented procedures is very difficult and which challenges must be addressed in the future. Methods: We searched the Google Scholar, PubMed, Medline, Science Direct, and Web of Science databases for systematic reviews and original research articles published in English. Only papers that reported sufficient scientific proceedings are included in this review. Results: We found 13 papers that classified skin lesions using CNNs. In principle, classification methods can be differentiated according to three principles. Approaches that use a CNN already trained by means of another large data set and then optimize its parameters to the classification of skin lesions are both the most common methods as well as display the best performance with the currently available limited data sets. Conclusions: CNNs display a high performance as state-of-the-art skin lesion classifiers. Unfortunately, it is difficult to compare different classification methods because some approaches use non-public data sets for training and/or testing, thereby making reproducibility difficult.

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