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The leading peer-reviewed journal for digital medicine, and health & healthcare in the Internet age
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!
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Background: In addition to addiction and substance abuse, motivational interviewing (MI) is increasingly being integrated in treating other clinical issues such as mental health problems. Despite many...
Background: In addition to addiction and substance abuse, motivational interviewing (MI) is increasingly being integrated in treating other clinical issues such as mental health problems. Despite many technological adaptations of MI, most of them have focused on delivering the action-oriented treatment, leaving its relational component unexplored or vaguely described. This study intends an early design of a conversational sequence of both technical and relational components of MI for a mental health concern. Objective: This case study aims to design a conversational sequence for a brief motivational interview to be delivered by a Web-based text messaging application (“chatbot”) and investigate its conversational experience for stress management with graduate students. Methods: A brief conversational sequence was designed by incorporating both technical (change talk) and relational (O-A-R-S) components of MI, inspired by the summons-answer sequence by Schegloff. A Web-based text messaging app, Bonobot, was built as a research prototype to deliver the sequence in an online conversation. A total of 30 full-time graduate students who self-reported stress in regard of their school life were recruited for a survey of demographic information and perceived stress (PSS-10), and a semi-structured interview. Interviews were transcribed verbatim and analyzed by Braun and Clarke’s thematic method. Themes that reflect the process, impact of, and needs for the conversational experience are reported. Results: Participants had a high level of perceived stress (M=22.5, SD=5.0). Our findings include themes as follows: Evocative Questions and Clichéd Feedback; Self-Reflection and Potential Consolation; and Need for Information and Contextualized Feedback. Participants particularly favored the relay of change talk questions, but were less satisfied with the emotional responses that filled in-between. Change talk was a good means of reflecting on themselves, and some of Bonobot’s encouragements related to graduate school life were appreciated. Participants suggested the conversation provide informational support, as well as more personalized emotional feedback. Conclusions: A conversational sequence that incorporates technical and relational components of MI was presented in this case study. Participant feedback suggests sequencing change talk questions and emotional responses can facilitate a conversation for stress management, with change talk possibly offering a chance of self-reflection. More diversified sequences, along with more contextualized emotional feedback, should follow to offer better conversational experience and to confirm any empirical effect. Clinical Trial: n/a
Background: Depressive symptoms are common in people with type 2 diabetes and exacerbate disease burden through increased social and occupational impairment and greater morbidity and mortality. Effec...
Background: Depressive symptoms are common in people with type 2 diabetes and exacerbate disease burden through increased social and occupational impairment and greater morbidity and mortality. Effective depression treatments exist, however rates of depression screening in type 2 diabetes are variable, access to psychological support is characteristically low, and impact of treatment on daily functioning remains unclear. Web-based cognitive behaviour therapy (CBT) is easily accessible, private, non-stigmatising and a potential solution to reducing the substantial personal and public health impact of comorbid type 2 diabetes and depression. Objective: To evaluate the efficacy of the web-based CBT program, myCompass, for improving social and occupational functioning in a large community sample of people with type 2 diabetes and self-reported mild-to-moderate depressive symptoms. myCompass is an unguided, public health treatment program for common mental health problems. Impact of treatment on depressive symptoms, diabetes-related distress, anxiety symptoms and self-care behaviour was also examined. Methods: Participants with type 2 diabetes and mild-to-moderate depressive symptoms (N = 780) were recruited online and via community and general practice settings. Screening, consent and data collection were conducted online, and randomisation was to either myCompass (n = 391) for 8 weeks plus a 4-week tailing off period, or an active placebo intervention (n = 379). At baseline and post-intervention (3-months), participants completed the Work and Social Adjustment Scale (WSAS), the primary outcome measure. Secondary outcome measures included the Patient Health Questionnaire-9 item (PHQ-9), Diabetes Distress Scale (DDS), Generalised Anxiety Disorder Questionnaire-7 item (GAD-7) and items from the Self-Management Profile for Type 2 Diabetes (SMP-T2D). Results: At baseline, mean scores on several outcome measures, including the primary outcome of work and social functioning, were near to the normal range, despite a varied and extensive recruitment process. Of the 780 trial participants, 473 (61%) completed the post-intervention assessment. Intention-to-treat analyses revealed improvement in functioning, depression, anxiety, diabetes distress and healthy eating over time in both groups. Except for blood glucose monitoring and medication adherence, there were no specific between-group effects. Follow-up analyses suggested the outcomes did not depend on age, morbidity or treatment engagement. Conclusions: Improvement in social and occupational functioning and the secondary outcomes was generally no greater for myCompass users than users of the control program at 3 months post-intervention. These findings should be interpreted in light of near-normal mean baseline scores on several variables, the self-selected study sample and sample attrition. Further attention to factors influencing uptake and engagement with mental health treatments by people with type 2 diabetes, and the impact of illness comorbidity on patient conceptualisation and experience of mental health symptoms, is essential to reduce the burden of type 2 diabetes. Clinical Trial: ACTRN12615000931572
Background: Obesity is one of the largest drivers of healthcare spending, but nearly half of the population with obesity demonstrate suboptimal readiness for weight loss treatment. Black women are dis...
Background: Obesity is one of the largest drivers of healthcare spending, but nearly half of the population with obesity demonstrate suboptimal readiness for weight loss treatment. Black women are disproportionately likely to have both obesity and limited weight loss readiness. However, they have been shown to be receptive to strategies that prevent weight gain. Objective: This work evaluates the costs and cost-effectiveness of a digital weight gain prevention intervention (Shape) for Black women. Methods: A cost and cost-effectiveness analysis of Shape was conducted from the payer perspective. Costs included those of program delivery and were summarized by program element: self-monitoring, skills training, coaching, and administration. Effectiveness was measured in quality-adjusted life years (QALYs). The primary outcome was the incremental cost per QALY of Shape relative to usual care. Results: Shape cost an average of $758 per participant. The base-case model, in which quality of life benefits decay linearly to zero five years post intervention cessation, generated an incremental cost-effectiveness ratio (ICER) of $55,264/QALY. Probabilistic sensitivity analyses suggest an ICER below $50,000/QALY and $100,000/QALY in 39% and 98% of simulations, respectively. Results are highly sensitive to durability of benefits, rising to $165,730 if benefits end 6 months post intervention. Conclusions: Results suggest the Shape intervention is cost effective based on established benchmarks, indicating it can be part of a successful strategy to address the nation’s growing obesity epidemic in low-income at-risk communities. Clinical Trial: The trial was registered with the ClinicalTrials.gov database (NCT00938535)
Background: Our goal is to improve psychosocial and spiritual care outcomes for elderly patients with cancer by optimizing an intervention focused on dignity conservation tasks such as settling relati...
Background: Our goal is to improve psychosocial and spiritual care outcomes for elderly patients with cancer by optimizing an intervention focused on dignity conservation tasks such as settling relationships, sharing words of love, and preparing a legacy document. These tasks are central needs for elderly patients with cancer. Dignity Therapy (DT) has proven efficacy and can be led by nurses or chaplains, the two disciplines within palliative care that may be most available to provide DT. DT is well accepted by patients in studies, but not widely used; it remains unclear how best it can work in real life settings. Objective: We propose a randomized clinical trial whose aims are to: (1) compare usual palliative care for elderly patients with cancer and usual palliative care with DT groups for effects on: a) patient outcomes (dignity impact, existential tasks, and cancer prognosis awareness); and b) processes of delivering palliative spiritual care services (satisfaction and unmet spiritual needs); and (2) explore the influence of physical symptoms and spiritual distress on the dignity impact and existential tasks effects of usual palliative care and nurse- or chaplain-led DT. We hypothesize that, controlling for pretest scores, each of the DT groups will have higher scores on the dignity impact and existential tasks measures than the usual care group; each of the DT groups will have better peaceful awareness and treatment preference more consistent with their cancer prognosis than the usual care group. We also hypothesize that physical symptoms and spiritual distress will significantly affect intervention effects. Methods: We are conducting a 3-arm, pre/posttest, randomized, controlled 4-step, stepped-wedge design to compare the effects of usual outpatient palliative care and usual outpatient palliative care along with either nurse or chaplain led DT on patient outcomes (dignity impact, existential tasks, and cancer prognosis awareness). We will include 560 elderly patients with cancer from 6 outpatient palliative care services across the U.S. Using multilevel analysis with site, provider (nurse, chaplain), and time (step) included in the model, we will compare usual care and DT groups for effects on patient outcomes and spiritual care processes and determine the moderating effects of physical symptoms and spiritual distress Results: Results are expected in 4 years. Conclusions: This rigorous trial of DT will constitute a landmark step in palliative care and spiritual health services research. Clinical Trial: NCT03209440
Background: Search engines display helpline notices when people query for suicide-related information. Objective: Here we aim to examine if these notices and other information displayed in response to...
Background: Search engines display helpline notices when people query for suicide-related information. Objective: Here we aim to examine if these notices and other information displayed in response to suicide-related queries are correlated with subsequent searches for suicide prevention rather than harmful information. Methods: Anonymous suicide-related searches made to Bing and Google in the United States, the United Kingdom, Hong Kong, and Taiwan during ten months were extracted. Descriptive analyses and regression models were fit to the data to assess the correlation with observed behaviours. Results: Display of helpline notices was not associated with an observed change in click behaviour or future searches. Pages with higher rank, being neutral to suicide, and those shown among more anti-suicide pages were more likely to be clicked on. Having more anti-suicide webpages displayed was the only factor associated with further searches for suicide prevention information. Conclusions: Helpline notices are not associated with harm. If they cause positive change, it is small. This is possibly due to the variability in intent of users seeking suicide-related information. Nonetheless, helpline notice should be displayed but more efforts should be made to improve the visibility and ranking of suicide prevention webpages.
Background: Worldwide, the burden of chronic diseases is growing, necessitating novel approaches that complement and go beyond evidence-based medicine. In this respect a promising avenue is the second...
Background: Worldwide, the burden of chronic diseases is growing, necessitating novel approaches that complement and go beyond evidence-based medicine. In this respect a promising avenue is the secondary use of Electronic Health Records (EHR) data, where clinical data are analysed to conduct basic and clinical and translational research. Methods based on machine learning algorithms to process EHR are resulting in improved understanding of patients’ clinical trajectories and chronic disease risk prediction, creating a unique opportunity to derive previously unknown clinical insights. However, wealth of patients’ clinical history remains locked behind clinical narratives in free-form text. Consequently, unlocking the full potential of EHR data is contingent on development of Natural Language Processing (NLP) methods to automatically transform clinical text into structured clinical data that can be directly processed using machine learning algorithms. Objective: To provide a comprehensive overview of the development and uptake on NLP methods applied to free-text clinical notes related to chronic diseases, including investigation of challenges faced by NLP methodologies in understanding clinical narratives. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed and searches were conducted in 5 databases using “clinical notes”, “natural language processing” and “chronic disease” as keywords as well as their variations to maximise coverage of the articles. Results: Of the 2646 articles considered, 100 met the inclusion criteria. Review of the included papers resulted in identification of 42 chronic diseases, which were then further classified into 10 diseases categories using ICD-10. Majority of the studies focused on diseases of circulatory system (N=38) while endocrine and metabolic diseases were fewest (N=12). This was due to the structure of clinical records related to metabolic diseases that typically contain much more structured data than medical records for diseases of circulatory system, which focus more on unstructured data and consequently have seen a stronger focus of NLP. The review has shown that there is a significant increase in the use of machine learning methods compared to rule-based approaches, however deep learning methods remain emergent (N=3). Consequently, majority of works focus on classification of disease phenotype, while only a handful of papers concern the extraction of comorbidities from the free-text or the integration of clinical notes with structured data. There is a notable use of relatively simple methods, such as shallow classifiers (or combination with rule-based methods), due to the interpretability of predictions, which still represents a significant issue for more complex methods. Finally, scarcity of publicly available data may also have contributed to insufficient development of more advanced methods, such as extraction of word embeddings from clinical notes. Conclusions: Efforts are needed to improve (1) progression of clinical NLP methods from extraction towards understanding; (2) recognition of relations among entities, rather than entities in isolation; (3) temporal extraction to understand past, current and future clinical events; (4) exploitation of alternative sources of clinical knowledge; and (5) availability of large-scale, de-identified clinical corpora.