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Journal Description

The Journal of Medical Internet Research (JMIR), now in its 21st 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 2018: 4.945, ranked #1 out of 26 journals in the medical informatics category) 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 a 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 an open access journal, we are read by clinicians, allied health professionals, informal caregivers, and patients alike, and have (as with 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: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2020/3/e16769/; License: Licensed by JMIR.
    Web-based and Mobile Health...

    Mar 27, 2020

    Improving Self-Care in Patients With Coexisting Type 2 Diabetes and Hypertension by Technological Surrogate Nursing: Randomized Controlled Trial

    Authors List:

    Abstract:

    Background: Technological surrogate nursing (TSN) derives from the idea that nurse-caregiver substitutes can be created by technology to support chronic disease self-care. Objective: This paper begins by arguing that TSN is a useful and viable approach to chronic disease self-care. The analysis then focuses on the empirical research question of testing and demonstrating the effectiveness and safety of prototype TSN supplied to patients with the typical complex chronic disease of coexisting type 2 diabetes and hypertension. At the policy level, it is shown that the data allow for a calibration of TSN technology augmentation, which can be readily applied to health care management. Methods: A 24-week, parallel-group, randomized controlled trial (RCT) was designed and implemented among diabetic and hypertensive outpatients in two Hong Kong public hospitals. Participants were randomly assigned to an intervention group, supplied with a tablet-based TSN app prototype, or to a conventional self-managing control group. Primary indices—hemoglobin A1c, systolic blood pressure, and diastolic blood pressure—and secondary indices were measured at baseline and at 8, 12, 16, and 24 weeks after initiation, after which the data were applied to test TSN effectiveness and safety. Results: A total of 299 participating patients were randomized to the intervention group (n=151) or the control group (n=148). Statistically significant outcomes that directly indicated TSN effectiveness in terms of hemoglobin 1c were found in both groups but not with regard to systolic and diastolic blood pressure. These findings also offered indirect empirical support for TSN safety. Statistically significant comparative changes in these primary indices were not observed between the groups but were suggestive of an operational calibration of TSN technology augmentation. Statistically significant changes in secondary indices were obtained in one or both groups, but not between the groups. Conclusions: The RCT’s strong behavioral basis, as well as the importance of safety and effectiveness when complex chronic illness is proximately self-managed by layperson patients, prompted the formulation of the empirical joint hypothesis that TSN would improve patient self-care while satisfying the condition of patient self-safety. Statistical and decision analysis applied to the experimental outcomes offered support for this hypothesis. Policy relevance of the research is demonstrated by the derivation of a data-grounded operational calibration of TSN technology augmentation with ready application to health care management. Trial Registration: ClinicalTrials.gov NCT02799953; https://clinicaltrials.gov/ct2/show/NCT02799953

  • Source: FlickR; Copyright: Ted Eytan; URL: https://www.flickr.com/photos/taedc/26124262157; License: Creative Commons Attribution + ShareAlike (CC-BY-SA).
    Infodemiology and Infoveillance

    Mar 27, 2020

    Characteristics of Gun Advertisements on Social Media: Systematic Search and Content Analysis of Twitter and YouTube Posts

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    Abstract:

    Background: Although gun violence has been identified as a major public health concern, the scope and significance of internet gun advertising is not known. Objective: This study aimed to quantify the characteristics of gun advertising on social media and to compare the reach of posts by manufacturers with those of influencers. Methods: Using a systematic search, we created a database of recent and popular Twitter and YouTube posts made public by major firearm manufacturers and influencers. From our sample of social media posts, we reviewed the content of the posts on the basis of 19 different characteristics, such as type of gun, presence of women, and military or police references. Our content analysis summarized statistical differences in the information conveyed in posts to compare advertising approaches across social media platforms. Results: Sample posts revealed that firearm manufacturers use social media to attract audiences to websites that sell firearms: 14.1% (131/928; ±2.9) of Twitter posts, 53.6% (228/425; ±6.2) of YouTube videos, and 89.5% (214/239; ±5.1) of YouTube influencer videos link to websites that facilitate sales. Advertisements included women in efforts to market handguns and pistols for the purpose of protection: videos with women included protection themes 2.5 times more often than videos without women. Top manufacturers of domestic firearms received 98 million channel views, compared with 6.1 billion channel views received by the top 12 YouTube influencers. Conclusions: Firearm companies use social media as an advertising platform to connect viewers to websites that sell guns. Gun manufacturers appropriate YouTube servers, video streaming services, and the work of YouTube influencers to reach large audiences to promote the widespread sale of consumer firearms. YouTube and Twitter subsidize gun advertising by offering server and streaming services at no cost to gun manufacturers, to the commercial benefit of Google and Twitter’s corporate ownership.

  • Source: The Authors/Placeit; Copyright: The Authors/Placeit; URL: http://www.jmir.org/2020/3/e15642/; License: Licensed by JMIR.
    Mobile Health (mhealth)

    Mar 27, 2020

    Social Comparison Features in Physical Activity Promotion Apps: Scoping Meta-Review

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    Abstract:

    Background: Smartphone apps promoting physical activity (PA) are abundant, but few produce substantial and sustained behavior change. Although many PA apps purport to induce users to compare themselves with others (by invoking social comparison processes), improvements in PA and other health behaviors are inconsistent. Existing literature suggests that social comparison may motivate PA for some people under some circumstances. However, 2 aspects of work that apply social comparison theory to PA apps remain unclear: (1) how comparison processes have been operationalized or harnessed in existing PA apps and (2) whether incorporating sources of variability in response to comparison have been used to tailor comparison features of apps, which could improve their effectiveness for promoting PA. Objective: The aim of this meta-review was to summarize existing systematic, quantitative, and narrative reviews of behavior change techniques in PA apps, with an emphasis on social comparison features, to examine how social comparison is operationalized and implemented. Methods: We searched PubMed, Web of Science, and PsycINFO for reviews of PA smartphone apps. Of the 3743 initial articles returned, 26 reviews met the inclusion criteria. Two independent raters extracted the data from these reviews, including the definition of social comparison used to categorize app features, the percentage of apps categorized as inducing comparison, specific features intended to induce comparison, and any mention of tailoring comparison features. For reference, these data were also extracted for related processes (such as behavioral modeling, norm referencing, and social networking). Results: Of the included review articles, 31% (8/26) categorized app features as prompting social comparison. The majority of these employed Abraham and Michie’s earliest definition of comparison, which differs from versions in later iterations of the same taxonomy. Very few reviews specified what dimension users were expected to compare (eg, steps, physical fitness) or which features of the apps were used to induce comparison (eg, leaderboards, message boards). No review referenced tailoring of comparison features. In contrast, 54% (14/26) reviews categorized features for prompting behavioral modeling and 31% (8/26) referenced tailoring app features for users’ personal goals or preferences. Conclusions: The heterogeneity across reviews of PA apps and the absence of relevant information (eg, about dimensions or features relevant for comparison) create confusion about how to best harness social comparison to increase PA and its effectiveness in future research. No evidence was found that important findings from the broader social comparison literature (eg, that people have differing preferences for and responses to social comparison information) have been incorporated in the design of existing PA apps. Greater integration of the mobile health (mHealth) and social comparison literatures may improve the effectiveness of PA apps, thereby increasing the public health impact of these mHealth tools.

  • Source: Pexels; Copyright: bongkarn thanyakij; URL: https://www.pexels.com/photo/person-in-brown-long-sleeve-shirt-using-macbook-pro-3740874/; License: Licensed by JMIR.
    Personal Health Records,...

    Mar 27, 2020

    The Impact of Portal Satisfaction on Portal Use and Health-Seeking Behavior: Structural Equation Analysis

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    Abstract:

    Background: Our study addresses a gap in the modern information systems (IS) use literature by investigating factors that explain patient portal satisfaction (SWP) and perceptions about health-seeking behavior (HSB). A novel feature of our study is the incorporation of actual portal use data rather than the perceptions of use intention, which prevails in the modern IS literature. Objective: This study aimed to empirically validate factors that influence SWP as an influencing agent on portal use and HSB. Our population segment was comprised of college students with active patient portal accounts. Methods: Using web-based survey data from a population of portal users (n=1142) in a university health center, we proposed a theoretical model that adapts constructs from the Technology Acceptance Model by Davis, the revised Technology Adoption Model by Venkatesh, the Unified Theory of the Acceptance and Use of Technology 2, and the Health Belief Model by Rosenstock et al. We validated our model using structural equation modeling techniques. Results: Our model explained nearly 65% of the variance in SWP (R2=0.6499), nearly 33% of the variance in portal use (R2=0.3250), and 29% of the variance in HSB (R2=0.2900). Statistically significant antecedents of SWP included social influence (beta=.160, t499=6.145), habit (beta=.114, t499=4.89), facilitating conditions (beta=.062, t499=2.401), effort expectancy (beta=.311, t499=11.149), and performance expectancy (beta=.359, t499=11.588). SWP influenced HSB (beta=.505, t499=19.705) and portal use (beta=.050, t499=2.031). We did not find a statistically significant association between portal use and HSB (beta=.015, t499=0.513). Perceived severity significantly influenced HSB (beta=.129, t499=4.675) but not portal use (beta=.012, t499=.488). Conclusions: Understanding the importance of SWP and the role it plays in influencing HSB may point to future technology design considerations for information technology developers and health care providers. We extend current Expectancy Confirmation Theory research by finding a positive association between SWP and portal use.

  • Call Button on nursing home bed. Source: iStock by Getty Images; Copyright: SallyLL; URL: https://www.istockphoto.com/photo/call-button-on-hospital-or-nursing-home-bed-gm471151529-8553311; License: Licensed by the authors.
    Human Factors and Usability Case Studies

    Mar 27, 2020

    Use of Notification and Communication Technology (Call Light Systems) in Nursing Homes: Observational Study

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    Abstract:

    Background: The call light system is one of the major communication technologies that link nursing home staff to the needs of residents. By providing residents the ability to request assistance, the system becomes an indispensable resource for patient-focused health care. However, little is known about how call light systems are being used in nursing homes and how the system contributes to safety and quality of care for seniors. Objective: This study aimed to understand the experiences of nursing home staff who use call light systems and to uncover usability issues and challenges associated with the implemented systems. Methods: A mix of 150 hours of hypothetico-deductive (unstructured) task analysis and 90 hours of standard procedure (structured) task analysis was conducted in 4 different nursing homes. The data collected included insights into the nursing home’s work system and the process of locating and responding to call lights. Results: The data showed that the highest alarm rate is before and after mealtimes. The staff exceeded the administration’s expectations of time to respond 50% of the time. In addition, the staff canceled 10.0% (20/201) of call lights and did not immediately assist residents because of high workload. Furthermore, the staff forgot to come back to assist residents over 3% of the time. Usability issues such as broken parts, lack of feedback, lack of prioritization, and low or no discriminability also contributed to the long response time. More than 8% of the time, residents notified the staff about call lights after they waited for a long time, and eventually, these residents were left unattended. Conclusions: Nursing homes that are still using old call light systems risk the continuation of usability issues that can affect the performance of the staff and contribute to declining staff and resident outcomes. By incorporating feedback from nurses, nursing home management will better understand the influence that the perceptions and usability of technology have on the quality of health care for their residents. In this study, it has been observed that the call light system is perceived to be an important factor affecting the outcomes of the care process and satisfaction of both residents and staff as well as the staff’s performance. It is important to recognize that communication and notification technology contributes to the challenges the staff faced during their work, making their working conditions more difficult and challenging.

  • Source: Freepik; Copyright: jannoon028; URL: https://www.freepik.com/free-photo/top-view-doctor-using-laptop-clipboard_977808.htm#page=1&query=data%20medicine&position=7; License: Licensed by JMIR.
    Policy and Policy Proposals

    Mar 27, 2020

    The Value of Data: Applying a Public Value Model to the English National Health Service

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    Abstract:

    Research and innovation in biomedicine and health care increasingly depend on electronic data. The emergence of data-driven technologies and associated digital transformations has focused attention on the value of such data. Despite the broad consensus of the value of health data, there is less consensus on the basis for that value; thus, the nature and extent of health data value remain unclear. Much of the existing literature presupposes that the value of data is to be understood primarily in financial terms, and assumes that a single financial value can be assigned. We here argue that the value of a dataset is instead relational; that is, the value depends on who wants to use it and for what purposes. Moreover, data are valued for both nonfinancial and financial reasons. Thus, it may be more accurate to discuss the values (plural) of a dataset rather than the singular value. This plurality of values opens up an important set of questions about how health data should be valued for the purposes of public policy. We argue that public value models provide a useful approach in this regard. According to public value theory, public value is created, or captured, to the extent that public sector institutions further their democratically established goals, and their impact on improving the lives of citizens. This article outlines how adopting such an approach might be operationalized within existing health care systems such as the English National Health Service, with particular focus on actionable conclusions.

  • Source: iStock; Copyright: diego_cervo; URL: https://www.istockphoto.com/photo/woman-typing-phone-message-on-social-network-at-night-gm598912704-102768231?clarity=false; License: Licensed by the authors.
    Mobile Health (mhealth)

    Mar 26, 2020

    Text Messaging as a Screening Tool for Depression and Related Conditions in Underserved, Predominantly Minority Safety Net Primary Care Patients: Validity Study

    Authors List:

    Abstract:

    Background: SMS text messaging is an inexpensive, private, and scalable technology-mediated assessment mode that can alleviate many barriers faced by the safety net population to receive depression screening. Some existing studies suggest that technology-mediated assessment encourages self-disclosure of sensitive health information such as depressive symptoms while other studies show the opposite effect. Objective: This study aimed to evaluate the validity of using SMS text messaging to screen depression and related conditions, including anxiety and functional disability, in a low-income, culturally diverse safety net primary care population. Methods: This study used a randomized design with 4 study groups that permuted the order of SMS text messaging and the gold standard interview (INTW) assessment. The participants for this study were recruited from the participants of the prior Diabetes-Depression Care-management Adoption Trial (DCAT). Depression was screened by using the 2-item and 8-item Patient Health Questionnaire (PHQ-2 and PHQ-8, respectively). Anxiety was screened by using the 2-item Generalized Anxiety Disorder scale (GAD-2), and functional disability was assessed by using the Sheehan Disability Scale (SDS). Participants chose to take up the assessment in English or Spanish. Internal consistency and test-retest reliability were evaluated by using Cronbach alpha and intraclass correlation coefficient (ICC), respectively. Concordance was evaluated by using an ICC, a kappa statistic, an area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. A regression analysis was conducted to examine the association between the participant characteristics and the differences in the scores between the SMS text messaging and INTW assessment modes. Results: Overall, 206 participants (average age 57.1 [SD 9.18] years; females: 119/206, 57.8%) were enrolled. All measurements except the SMS text messaging–assessed PHQ-2 showed Cronbach alpha values ≥.70, indicating acceptable to good internal consistency. All measurements except the INTW-assessed SDS had ICC values ≥0.75, indicating good to excellent test-retest reliability. For concordance, the PHQ-8 had an ICC of 0.73 and AUROC of 0.93, indicating good concordance. The kappa statistic, sensitivity, and specificity for major depression (PHQ-8 ≥8) were 0.43, 0.60, and 0.86, respectively. The concordance of the shorter PHQ-2, GAD-2, and SDS scales was poor to fair. The regression analysis revealed that a higher level of personal depression stigma was associated with reporting higher SMS text messaging–assessed PHQ-8 and GAD-2 scores than the INTW-assessed scores. The analysis also determined that the differences in the scores were associated with marital status and personality traits. Conclusions: Depression screening conducted using the longer PHQ-8 scale via SMS text messaging demonstrated good internal consistency, test-retest reliability, and concordance with the gold standard INTW assessment mode. However, care must be taken when deploying shorter scales via SMS text messaging. Further regression analysis supported that a technology-mediated assessment, such as SMS text messaging, may create a private space with less pressure from the personal depression stigma and therefore encourage self-disclosure of depressive symptoms. Trial Registration: ClinicalTrials.gov NCT01781013; https://clinicaltrials.gov/ct2/show/NCT01781013

  • Source: Image created by Giovanni Spitale on behalf of DIPEx Switzerland; Copyright: DIPEx Switzerland; URL: http://www.jmir.org/2020/3/e15772/; License: Licensed by the authors.
    Consumer & Patient Education and...

    Mar 26, 2020

    Risks and Benefits of Web-Based Patient Narratives: Systematic Review

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    Abstract:

    Background: Patient narratives are illustrative, individual accounts of patients’ experiences with certain health conditions. Web-based patient narratives have become widely available on the internet and in social media, as part of electronically available patient decision aids or Web-based databases. In recent years, the role of patient narratives as a source of information, insight, and support for both health care users and providers has increasingly been emphasized. Although the potential impact of patient stories has high immediate plausibility, it is of interest to know if this impact can be captured in quantitative studies. Objective: This review aimed to evaluate whether research-generated Web-based patient narratives have quantifiable risks or benefits for (potential) patients, relatives, or health care professionals. Methods: We searched the following databases from August 2017 to March 2019: Medical Literature Analysis and Retrieval System Online, PsycInfo, Sociological Abstracts, Web of Science, and EMBASE. Titles and abstracts of the retrieved studies were reviewed and assessed for the inclusion and exclusion criteria. Papers were included if they studied the following: (1) (potential) patients, relatives, or health care professionals; (2) the effects of Web-based patient narratives that were generated scientifically (eg, through qualitative research methods); and (3) were quantitative studies. Furthermore, 2 authors independently performed an assessment of the quality of the included studies using a validated checklist. Results: Of 4226 documents, 17 studies met the inclusion criteria. The studies investigated 10 different sources of Web-based patient narratives. Sample sizes ranged from 23 to 2458. The mean score of the quality assessment was 82.6 (range 61-100). Effects regarding five different purposes were identified as follows: provide information, engage, model behavior, persuade, and comfort. We found positive effects in every category and negative effects in one category (persuade). Several of the reported effects are rather small or were identified under specific experimental conditions. Conclusions: Patient narratives seem to be a promising means to support users in improving their understanding of certain health conditions and possibly to provide emotional support and have an impact on behavioral changes. There is limited evidence for beneficial effects on some outcomes. However, narratives are characterized by considerable heterogeneity and the investigated outcomes are hardly comparable with each other, which makes the overall judgment difficult. As there are numerous possible measures and purposes of narratives, quantifying the impact of Web-based patient narratives remains a challenge. Future research is needed to define the optimal standards for quantitative approaches to narrative-based interventions.

  • Source: The Authors / Placeit; Copyright: The Authors / Placeit; URL: http://www.jmir.org/2020/3/e15312/; License: Licensed by JMIR.
    Web-based and Mobile Health...

    Mar 26, 2020

    Brief Web-Based Intervention for Depression: Randomized Controlled Trial on Behavioral Activation

    Authors List:

    Abstract:

    Background: Web-based interventions have been shown to be effective for the treatment of depression. However, interventions are often complex and include a variety of elements, making it difficult to identify the most effective component(s). Objective: The aim of this pilot study was to shed light on mechanisms in the online treatment of depression by comparing a single-module, fully automated intervention for depression (internet-based behavioral activation [iBA]) to a nonoverlapping active control intervention and a nonactive control group. Methods: We assessed 104 people with at least mild depressive symptoms (Patient Health Questionnaire-9, >4) via the internet at baseline (t0) and 2 weeks (t1) and 4 weeks (t2) later. After the t0 assessment, participants were randomly allocated to one of three groups: (1) iBA (n=37), (2) active control using a brief internet-based mindfulness intervention (iMBI, n=32), or (3) care as usual (CAU, n=35). The primary outcome was improvement in depressive symptoms, as measured using the Patient Health Questionnaire-9. Secondary parameters included changes in activity, dysfunctional attitudes, and quality of life Results: While groups did not differ regarding the change in depression from t0 to t1 (ηp2=.007, P=.746) or t0 to t2 (ηp2=.008, P=.735), iBA was associated with a larger decrease in dysfunctional attitudes from t0 to t2 in comparison to CAU (ηp2=.053, P=.04) and a larger increase in activity from t0 to t1 than the pooled control groups (ηp2=.060, P=.02). A change in depression from t0 to t2 was mediated by a change in activity from t0 to t1. At t1, 22% (6/27) of the participants in the iBA group and 12% (3/25) of the participants in the iMBI group indicated that they did not use the intervention. Conclusions: Although we did not find support for the short-term efficacy of the single-module iBA regarding depression, long-term effects are still conceivable, potentially initiated by changes in secondary outcomes. Future studies should use a longer intervention and follow-up interval. Trial Registration: DKRS (#DRKS00011562)

  • Source: The Authors/ Placeit; Copyright: The Authors/ Placeit; URL: http://www.jmir.org/2020/3/e12689/; License: Licensed by JMIR.
    Patient-Reported Outcome Measures...

    Mar 25, 2020

    Utilizing Digital Health to Collect Electronic Patient-Reported Outcomes in Prostate Cancer: Single-Arm Pilot Trial

    Authors List:

    Abstract:

    Background: Measuring patient-reported outcomes (PROs) requires an individual’s perspective on their symptoms, functional status, and quality of life. Digital health enables remote electronic PRO (ePRO) assessments as a clinical decision support tool to facilitate meaningful provider interactions and personalized treatment. Objective: This study explored the feasibility and acceptability of collecting ePROs using validated health-related quality of life (HRQoL) questionnaires for prostate cancer. Methods: Using Apple ResearchKit software, the Strength Through Insight app was created with content from validated HRQoL tools 26-item Expanded Prostate Cancer Index Composite (EPIC) or EPIC for Clinical Practice and 8-item Functional Assessment of Cancer Therapy Advanced Prostate Symptom Index. In a single-arm pilot study with patients receiving prostate cancer treatment at Thomas Jefferson University Hospital and affiliates, participants were recruited, and instructed to download Strength Through Insight and complete ePROs once a week over 12 weeks. A mixed methods approach, including qualitative pre- and poststudy interviews, was used to evaluate the feasibility and acceptability of Strength Through Insight for the collection and care management of cancer treatment. Results: Thirty patients consented to the study; 1 patient failed to complete any of the questionnaires and was left out of the analysis of the intervention. Moreover, 86% (25/29) reached satisfactory questionnaire completion (defined as completion of 60% of weekly questions over 12 weeks). The lower bound of the exact one-sided 95% CI was 71%, exceeding the 70% feasibility threshold. Most participants self-identified with having a high digital literacy level (defined as the ability to use, understand, evaluate, and analyze information from multiple formats from a variety of digital sources), and only a few participants identified with having a low digital literacy level (defined as only having the ability to gather information on the Web). Interviews were thematically analyzed to reveal the following: (1) value of emotional support and wellness in cancer treatment, (2) rise of social patient advocacy in online patient communities and networks, (3) patient concerns over privacy, and (4) desire for personalized engagement tools. Conclusions: Strength Through Insight was demonstrated as a feasible and acceptable method of data collection for ePROs. A high compliance rate confirmed the app as a reliable tool for patients with localized and advanced prostate cancer. Nearly all participants reported that using the smartphone app is easier than or equivalent to the traditional paper-and-pen approach, providing evidence of acceptability and support for the use of remote PRO monitoring. This study expands on current research involving the value of digital health, as a social and behavioral science, augmented with technology, can begin to contribute to population health management, as it shapes psychographic segmentation by demographic, socioeconomic, health condition, or behavioral factors to group patients by their distinct personalities and motivations, which influence their choices. Trial Registration: ClinicalTrials.gov NC03197948; http://clinicaltrials.gov/ct2/show/NC03197948

  • Source: barnimages.com/Flickr (http://www.flickr.com/photos/133488379@N08/32577635901); Copyright: barnimages.com; URL: https://barnimages.com/?s=Girl+is+browsing+Pinterest+on+her+MacBook+Air; License: Creative Commons Attribution (CC-BY).
    Web-based and Mobile Health...

    Mar 24, 2020

    The Association of Therapeutic Alliance With Long-Term Outcome in a Guided Internet Intervention for Depression: Secondary Analysis From a Randomized Control...

    Authors List:

    Abstract:

    Background: Therapeutic alliance has been well established as a robust predictor of face-to-face psychotherapy outcomes. Although initial evidence positioned alliance as a relevant predictor of internet intervention success, some conceptual and methodological concerns were raised regarding the methods and instruments used to measure the alliance in internet interventions and its association with outcomes. Objective: The aim of this study was to explore the alliance-outcome association in a guided internet intervention using a measure of alliance especially developed for and adapted to guided internet interventions, showing evidence of good psychometric properties. Methods: A sample of 223 adult participants with moderate depression received an internet intervention (ie, Deprexis) and email support. They completed the Working Alliance Inventory for Guided Internet Intervention (WAI-I) and a measure of treatment satisfaction at treatment termination and measures of depression severity and well-being at termination and 3- and 9-month follow-ups. For data analysis, we used two-level hierarchical linear modeling that included two subscales of the WAI-I (ie, tasks and goals agreement with the program and bond with the supporting therapist) as predictors of the estimated values of the outcome variables at the end of follow-up and their rate of change during the follow-up period. The same models were also used controlling for the effect of patient satisfaction with treatment. Results: We found significant effects of the tasks and goals subscale of the WAI-I on the estimated values of residual depressive symptoms (γ02=−1.74, standard error [SE]=0.40, 95% CI −2.52 to −0.96, t206=−4.37, P<.001) and patient well-being (γ02=3.10, SE=1.14, 95% CI 0.87-5.33, t198=2.72, P=.007) at the end of follow-up. A greater score in this subscale was related to lower levels of residual depressive symptoms and a higher level of well-being. However, there were no significant effects of the tasks and goals subscale on the rate of change in these variables during follow-up (depressive symptoms, P=.48; patient well-being, P=.26). The effects of the bond subscale were also nonsignificant when predicting the estimated values of depressive symptoms and well-being at the end of follow-up and the rate of change during that period (depressive symptoms, P=.08; patient well-being, P=.68). Conclusions: The results of this study point out the importance of attuning internet interventions to patients’ expectations and preferences in order to enhance their agreement with the tasks and goals of the treatment. Thus, the results support the notion that responsiveness to a patient’s individual needs is crucial also in internet interventions. Nevertheless, these findings need to be replicated to establish if they can be generalized to different diagnostic groups, internet interventions, and supporting formats.

  • Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2020/3/e17004/; License: Licensed by JMIR.
    New Methods

    Mar 24, 2020

    Hackathons as Stepping Stones in Health Care Innovation: Case Study With Systematic Recommendations

    Authors List:

    Abstract:

    Background: Until recently, developing health technologies was time-consuming and expensive, and often involved patients, doctors, and other health care professionals only as passive recipients of the end product. So far, users have been minimally involved in the ideation and creation stages of digital health technologies. In order to best address users’ unmet needs, a transdisciplinary and user-led approach, involving cocreation and direct user feedback, is required. In this context, hackathon events have become increasingly popular in generating enthusiasm for user-centered innovation. Objective: This case study describes preparatory steps and the performance of a health hackathon directly involving patients and health care professionals at all stages. Feasibility and outcomes were assessed, leading to the development of systematic recommendations for future hackathons as a vehicle for bottom-up innovation in health care. Methods: A 2-day hackathon was conducted in February 2017 in Berlin, Germany. Data were collected through a field study. Collected field notes were subsequently discussed in 15 informal meetings among the research team. Experiences of conducting two further hackathons in December 2017 and November 2018 were included. Results: In total, 30 participants took part, with 63% (19/30) of participants between 25 and 34 years of age, 30% (9/30) between 35 and 44 years of age, and 7% (2/30) younger than 25 years of age. A total of 43% (13/30) of the participants were female. The participation rate of medical experts, including patients and health care professionals, was 30% (9/30). Five multidisciplinary teams were formed and each tackled a specific health care problem. All presented projects were apps: a chatbot for skin cancer recognition, an augmented reality exposure-based therapy (eg, for arachnophobia), an app for medical neighborhood connectivity, a doctor appointment platform, and a self-care app for people suffering from depression. Patients and health care professionals initiated all of the projects. Conducting the hackathon resulted in significant growth of the digital health community of Berlin and was followed up by larger hackathons. Systematic recommendations for conducting cost-efficient hackathons (n≤30) were developed, including aspects of community building, stakeholder engagement, mentoring, themes, announcements, follow-up, and timing for each step. Conclusions: This study shows that hackathons are effective in bringing innovation to health care and are more cost- and time-efficient and potentially more sustainable than traditional medical device and digital product development. Our systematic recommendations can be useful to other individuals and organizations that want to establish user-led innovation in academic hospitals by conducting transdisciplinary hackathons.

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    Date Submitted: Mar 27, 2020

    Open Peer Review Period: Mar 27, 2020 - Apr 6, 2020

    Background: Along with the spread of novel coronavirus disease (COVID-19), internet hospitals in China were engaged in the epidemic prevention and control, offering epidemic-related online services an...

    Background: Along with the spread of novel coronavirus disease (COVID-19), internet hospitals in China were engaged in the epidemic prevention and control, offering epidemic-related online services and medical supports to the public. Objective: To explore the role of internet hospitals during the prevention and control of COVID-19 in China. Methods: Online epidemic-related consultations from multicenter internet hospitals in China during the epidemic of COVID-19 were collected. The counselees were described and classified into seven type groups. Symptoms were recorded and compared with reported COVID-19 patients. Hypochondriacal suspicion and offline-visit motivation were detected within each counselees’ group to evaluate the social panic of the epidemic along with the consequent medical seeking behaviors. The counselees’ motivation and the doctors’ recommendation for offline visit were compared. Risk factors affecting the counselees’ tendency of hypochondriacal suspicion and offline visit were explored by logistic regression models. The epidemic prevention and control measures based on internet hospitals were listed and the corresponding effects were discussed. Results: 4913 consultations were enrolled for analysis with the median age of the counselees 28 years (inter-quartile range: 22-33 years). There were 104(2.1%) healthy counselees, 147(3.0%) hypochondriacal counselees, 34(0.7%) exposed counselees, 853(17.4%) mildly suspicious counselees, 42(0.9%) moderately suspicious counselees, 3550(72.3%) highly suspicious counselees and 183(3.7%) severely suspicious counselees. 94.2% counselees had epidemic-related symptoms with a distribution similar to those of COVID-19. The hypochondriacal suspicion mode (44.1%) was common. The counselees’ motivation and the doctors’ recommendation for offline visit were inconsistent (P<0.001) with Cohen Kappa score 0.039, indicating irrational medical-seeking behaviors. Adult counselees (OR=1.816, P<0.001) with epidemiological exposure (OR= 7.568, P<0.001), shortness of breath (OR=1.440, P=0.001), diarrhea (OR=1.272, P=0.04) and unrelated symptoms (OR=1.509, P<0.001) were more likely to have hypochondriacal suspicion. Counselees with severe illnesses (OR= 2.303, P<0.001), fever (OR= 1.660, P<0.001), epidemiological exposure history (OR=1.440, P=0.012) and hypochondriacal suspicion (OR= 4.826, P<0.001) were more likely to attempt for offline visit. Re-attended counselees (OR=0.545, P=0.002) were less motivated to go to the offline clinic. Conclusions: Internet hospitals can serve different types of epidemic counselees, offer essential medical supports to the public during COVID-19, further release the social panic, promote social distancing, enhance the public’s ability of self-protection, correct irrational medical seeking behaviors, reduce the chance of nosocomial cross infection, facilitate epidemiological screening, thus play an important role on preventing and controlling COVID-19.

  • Towards Detecting Infection Incidences in People with Type 1 Diabetes Using Self-Recorded Data: A Novel Framework for a Digital Infectious Disease Detection Mechanism

    Date Submitted: Mar 26, 2020

    Open Peer Review Period: Mar 26, 2020 - Apr 3, 2020

    Background: Type 1 diabetes mellitus is a blood glucose (BG) metabolic disorder, which is caused by deficiencies of insulin secretion from pancreatic cells. The relationship between infection incident...

    Background: Type 1 diabetes mellitus is a blood glucose (BG) metabolic disorder, which is caused by deficiencies of insulin secretion from pancreatic cells. The relationship between infection incidents and elevated BG levels has been known for a long time. People with diabetes often experience prolonged episodes of elevated BG levels as a result of infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings on how to use such self-recorded data as a secondary source of information for other purposes, such as self-management related decision support during infection incidences and digital infectious disease detection system. Objective: The aim of the study is to demonstrate how people with type 1 diabetes can assist in detecting infectious diseases outbreak. Furthermore, to shade light upon the possibility of assisting the individual during such an incident. Specifically, we aim to retrospectively analyze the effect of infection incidences, such as influenza (flu), and light and mild common cold without fever, in order to identify key parameters that can effectively be used as potential indicators (events) of infection incidences. Moreover, the paper presents a general framework of a proposed digital infectious disease detection system based on self-recorded data from people with type 1 diabetes. Methods: We retrospectively analyzed high precision self-recorded data of 10 patient years captured within the longitudinal records of 3 people with type 1 diabetes. Getting such a rich and big dataset from large number of participants are extremely expensive and difficult to acquire, if not impossible. The participants were 2 males and 1 female with an average age of 34 (13.2) years. The dataset incorporates BG levels (Self-monitoring of blood glucose (SMBG) and continuous glucose measurement (CGM)), insulin (bolus and basal), diet (carbohydrate in grams) and self-reported events of illness. All the participants were free from any other health complications and other diseases during these years. Five normal patient years without any infection incidences and five patient years each with at least one or more cases of self-reported acute-infection incidences were analyzed and compared. We investigated the temporal evolution and probability distribution of BG levels, injected insulin, carbohydrate intake, and insulin to carbohydrate ratio within a specified timeframe (weekly, daily and hourly). For the daily and hourly timeframes, a moving average filter and non-parametric density estimation techniques, kernel density estimator, were used to analyze the data trend and distribution respectively, before, during, and after the infection incidences. The pre-infection, infection, and post-infection week analysis were carried out on raw dataset based on the week’s daily mean and standard deviation of BG levels, and daily sum and standard deviation of insulin and carbohydrate. A statistical boxplot was used to depict the comparison during pre-infection, infection, and post-infection week. All the experiments were carried out using Matlab 2018a. Results: Our analysis demonstrated that upon infection incidences, there is a dramatic shift in the operating point of the individual BG dynamics in all the timeframes (weekly, daily and hourly), which clearly violate the usual norm of BG dynamics. During regular/normal situations, BG levels usually lower when there is a significant increase in insulin injection and reduction in carbohydrate consumption. However, in all of the individual’s infection cases as opposed to the regular/normal days, there were prolonged period with elevated BG levels despite injecting higher amounts of insulin and reduced amount of carbohydrate consumption. For instance, in all the infection week on average, BG levels were elevated by 6.1% and 16%, insulin (bolus) were increased by 42% and 39.3%, carbohydrate consumption were reduced by 19% and 28.1%, and insulin to carbohydrate ratio were raised by 108.7% as compared to pre-infection and post-infection week respectively. Conclusions: We presented a novel approach on how to use self-recorded data from people with type 1 diabetes to develop an infection detection system. The analysis revealed that despite tight BG management regimes, BG levels were still elevated during the infection period, demonstrating the significant effect of infection on BG dynamics. Throughout the infection period, BG levels were elevated despite injecting higher amount of insulin and consuming lower amount of carbohydrate. The changes might be subjected to hormonal changes in the body as a result of infection incidences. However, the magnitude of the impact on BG dynamics could be correlated with different factors such as degree and severity of infection, type of pathogens, associated hormones involved and others. These changes are quite significant anomalies as compared to the regular/normal days, where BG levels lower with increased insulin injection and reduced carbohydrate consumption, and therefore, can be detected with appropriate individualized computational models, i.e., algorithms that span from prediction models to anomalies detection algorithms. Generally, we foresee that these findings can benefit the efforts towards building the next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

  • The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database

    Date Submitted: Mar 25, 2020

    Open Peer Review Period: Mar 25, 2020 - May 20, 2020

    Background: At the beginning of the artificial intelligence (A.I.) era, the expectations are high, and experts foresee that A.I. shows potential for diagnosing, managing and treating a wide variety of...

    Background: At the beginning of the artificial intelligence (A.I.) era, the expectations are high, and experts foresee that A.I. shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of A.I. in daily clinical practice are numerous, especially regarding the regulation of these technologies. Objective: Therefore, we provide an insight into the currently available A.I.-based medical devices and algorithms that have been approved by the U.S. Food & Drugs Administration (FDA). We aimed to raise awareness about the importance of regulatory bodies, clearly stating whether a medical device is A.I.-based or not. Methods: Cross-checking and validating all approvals, we identified 64 A.I.-based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any A.I.-related expressions in the official FDA announcement. Results: The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%) and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine / General Practice respectively. Conclusions: We launched the first comprehensive and open access database of strictly A.I.-based medical technologies that have been approved by the FDA. The database will be constantly updated. 

  • Text processing for detection of fungal ocular involvement in critical care patients: A cross-sectional study

    Date Submitted: Mar 23, 2020

    Open Peer Review Period: Mar 23, 2020 - May 18, 2020

    Background: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved...

    Background: Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved anti-fungal therapies, with multiple studies reporting only a few cases over several years. However, manual retrospective record review to detect cases is time-consuming. Objective: To determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data. Methods: We queried microbiology data from 46,467 critical care patients over a twelve-year period (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status, etc.) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient’s hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was documentation of any fungal ocular involvement. Results: 265 patients had culture-proven fungemia, with Candida albicans (43%) and Candida glabrata (28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 41%. Seven patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%. Conclusions: MIMIC-III contained no cases of ocular involvement among fungemic patients. This supports prior studies reporting low rates of ocular involvement in fungemia. Additionally, it demonstrates an application of natural language processing to expedite review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical exam findings that are documented within clinical notes.

  • Do doctors care? Validating a tool for the assessment of health information security in Spanish-speaking countries

    Date Submitted: Mar 16, 2020

    Open Peer Review Period: Mar 20, 2020 - May 20, 2020

    Background: Healthcare has increased its use of information technology over the last few years. A trend followed higher usage of Electronic Health Record in low-and-middle-income countries where docto...

    Background: Healthcare has increased its use of information technology over the last few years. A trend followed higher usage of Electronic Health Record in low-and-middle-income countries where doctors use non-medical applications and websites for healthcare-related tasks. Information security awareness and practices are essential to reduce the risk of breaches. Objective: To assess the internal reliability of the Spanish translation of three areas of the Human Aspects of Information Security Questionnaire (HAIS-Q), and to assess the knowledge, attitudes, and practices of medical doctors around information security. Methods: This is a cross-sectional descriptive study designed as a questionnaire-based. We used focus areas (Password management, social media use, and mobile devices use) from the Human Aspects of Information Security Questionnaire (HAIS-Q). Medical doctors in Ecuador answered an online survey between December 2017 and January 2018. Results: A total of 434 health professionals (response rate: 0.65) completed all the questions in our study. Scores were 37.4 (SD 5.9) for Password Management, 35.4 (SD 5.0) for Social Media Use and 35.9 (SD 5.7) for Mobile Devices. Cronbach’s alpha coefficient (α) was 0.78 (95% CI: 0.75, 0.81) for password management, 0.73 (95%CI: 0.69, 0.77) for mobile devices and 0.77 (95% CI: 0.73, 0.78) for Social Media Use. Conclusions: Our study shows that three components of the Spanish translation of the HAIS-Q questionnaire were internally reliable when applied in medical doctors. Medical doctors with eagerness to receive infosec training scored higher in social media use and mobile device use categories.

  • Prevailing outcome themes reported by people with degenerative cervical myelopathy: findings from a semi-structured interview

    Date Submitted: Mar 19, 2020

    Open Peer Review Period: Mar 19, 2020 - May 14, 2020

    Background: Degenerative cervical myelopathy (DCM) arises when arthritic changes of the cervical spine cause compression and a progressive injury to the spinal cord. It is common and potentially disab...

    Background: Degenerative cervical myelopathy (DCM) arises when arthritic changes of the cervical spine cause compression and a progressive injury to the spinal cord. It is common and potentially disabling. People with DCM (PwCM) to have amongst the lowest quality of life scores (SF-36) of chronic disease, although the drivers for this are not entirely understood. DCM research faces a number of challenges, including the heterogenous reporting of study data. The AO Spine RECODE-DCM project is an international consensus process that aims improve research efficiency through formation of a core outcome set (COS). A key part of COS development process is organizing outcomes into domains which represent key aspects of the disease. Objective: To facilitate this, we sought to qualitatively explore the context and impact of patient reported outcomes in DCM on people with DCM (PwCM) and their supporters. The aim was to improve understanding of patient perspective and assist the organisation of outcomes into domains for the consensus process. Methods: A single focus group was hosted by Myelopathy.org, a charity and support group for PwCM. The 40 minute session was audio-recorded and transcribed verbatim. Data was familiarized and 2 authors performed data coding independently. Codes were grouped into themes and a thematic analysis was performed guided by Braun & Clarke’s six-phase approach. The themes were subsequently reviewed with an independent PwCM stakeholder (ES), assisting in the process of capturing the true context and importance of themes. Results: Five PwCM (three men and two women) and three supporters (all women) participated. The average PwCM age was 53 and the median mJOA was 11 (±IQR 2), indicating these PwCM had moderate to severe DCM. 54 codes were grouped into 10 themes that captured the impact of DCM on PwCM and their supporters. These themes included: acceptance of symptoms, anticipatory anxiety, coping mechanisms/resilience, feelings of helplessness, financial consequences, lack of recognition, mental health impact, loss of life control, social reclusiveness/isolation and social stigma Conclusions: This is the first study to undertake qualitative analysis of PwCM perspectives. It has highlighted a number of prevailing themes currently unmeasured in clinical research or care. The determinants of low quality of life in DCM are currently unknown, and these findings provide a novel and so far, unique perspective. These perspectives will be used to inform the development of a core outcome set for DCM which is inclusive of all relevant stakeholders, including PwCM.

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