The Karma system is currently undergoing maintenance (Monday, January 29, 2018).
The maintenance period has been extended to 8PM EST.
Karma Credits will not be available for redeeming during maintenance.
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 2016: 5.175, 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!
Right click to copy or hit: ctrl+c (cmd+c on mac)
Background: Randomized trials of web-based decision aids for prostate specific antigen (PSA) testing indicate that these interventions improve knowledge and reduce decisional conflict. However, we do...
Background: Randomized trials of web-based decision aids for prostate specific antigen (PSA) testing indicate that these interventions improve knowledge and reduce decisional conflict. However, we do not know about these tools’ impact on people who spontaneously use a PSA testing patient decision aid on the internet. Objective: 1) Determine the impact of publicly available web-based PSA Option Grid patient decision aids on preference shift, knowledge, and decisional conflict; 2) identify which frequently asked questions (FAQs) are associated with preference shift; 3) explore the possible relationships between these outcomes. Methods: Data were collected between January 1, 2016 and December 30, 2017. Users who accessed the online, interactive PSA Option Grid were provided with three options – have a PSA test, no PSA test, unsure. Users first declared their initial preference, completed five knowledge questions, and a four-item (yes or no) validated decisional conflict scale (SURE). Next, users were presented with ten FAQs and asked to identify their preference for each question based on the information provided. At the end, users declared their final preference and completed the same knowledge and decisional conflict questions. Paired sample t-tests were employed to compare before-and-after knowledge and decisional conflict scores. A multinomial regression analysis was conducted to determine which FAQs were associated with a shift in screening preference. Results: Of the 467 people who accessed the PSA Option Grid, 186 (40%) completed the interactive journey and associated surveys. After excluding 22 female users, we analyzed 164 responses. At completion, users shifted their preference to ‘not having the PSA test’ (26% vs 71%; P <.01), had higher levels of knowledge (68% vs 89%, P < .01), and lower decisional conflict (57% vs 11%, P < .01). Three FAQs were associated with preference shift: What does the test involve? If my PSA level is high, what are the chances that I have prostate cancer? What are the risks? No relationships were present between knowledge, decisional conflict, and preference shift. Conclusions: Unprompted use of the interactive PSA Option Grid leads to preference shift, increased knowledge, and reduced decisional conflict which confirms the ability of these tools to influence decision-making, even when used outside clinical encounters.
Background: Increasingly high levels of smartphone ownership and use pose the potential for addictive behaviors and negative health outcomes, particularly among younger populations. Previous methodolo...
Background: Increasingly high levels of smartphone ownership and use pose the potential for addictive behaviors and negative health outcomes, particularly among younger populations. Previous methodologies to understand mobile screen time have relied on self-report survey or ecological momentary assessment (EMA). Self-report is subject to bias and unreliability, while EMA can be burdensome to participants. New methodology is needed to advance the understanding of mobile screen time. Objective: The objective of this study was to test the feasibility of a novel methodology to record and evaluate mobile smartphone screen time and use: Battery Use Screenshot (BUS). Methods: The Battery Use Screenshot (BUS) approach, defined for this study as uploading a mobile phone screenshot of a specific page within a smartphone, was utilized within an online cross-sectional survey of adolescents aged 12 to 15 years old through the survey platform Qualtrics. Participants were asked to provide a screenshot of their battery use page, a feature within smartphones, to upload within the online survey. Feasibility was assessed by smartphone ownership and response rate to BUS upload request. Data availability was evaluated as applications (apps) per BUS, completeness of data within screenshot, and five most used applications based on battery use percentage. Results: Among those surveyed, 309 (26.7%) indicated ownership of their smartphone. A total of 105 screenshots were evaluated. For data availability, screenshots contained an average of 10.2 (SD=2.0) apps per screenshot and over half (55%) had complete data available. The most common apps included safari and home/lock screen. Conclusions: Findings describe BUS as a novel approach for real-time data collection focused on mobile smartphone screen time among young adolescents. Though feasibility showed some challenges in upload capacity of young teens, data availability was generally strong across this large data set. This data from screenshots have the potential to provide key insights into precise mobile smartphone screen use and time spent per mobile application. Future studies could explore the use of the BUS methodology to correlate mobile smartphone screen time with health outcomes.
Background: In health-related, web-based information search, people should choose objectively correct information, but they are often misguided by confirmation bias–the tendency to select and evalua...
Background: In health-related, web-based information search, people should choose objectively correct information, but they are often misguided by confirmation bias–the tendency to select and evaluate information in line with their prior attitudes. They are also misguided by dubious information, not taking source credibility into account properly. Objective: We test whether people are prone to confirmation bias in mental health-related information search, particularly (1) if high confidence worsens confirmation bias, (2) if social tags are an appropriate interface to circumvent the influence of prior attitudes, and (3) if people successfully distinguish high and low source credibility. Moreover, we describe attitudes towards the efficacy of the treatment of depression with antidepressants and psychotherapy. Methods: 520 participants of a representative sample of the German population were recruited on an online platform of a panel company. 250 (48%) completed the fully automated, randomized, controlled web-based study, which was accessible online from November 14th to November 18th 2014, until at least 250 participants completed the survey. Participants provided prior attitudes about antidepressants and psychotherapy. We manipulated (1) confidence by having participants recall situations in which they were confident or doubtful. Next, participants searched for blog posts about the treatment of depression, with social tag clouds differing in (2) tag popularity–either psychotherapy or antidepressant tags were more popular. Finally, we manipulated (3) source credibility with banners indicating high or low expertise of the tagging community, and we measured tag- and blog post selection, and treatment efficacy ratings after navigation. Results: We observed a tendency to rate psychotherapy (mean = 5.24, SD = 1.10) as more effective than antidepressants (mean = 4.61, SD = 1.19; t(225) = 9.71, P < .001, d = .56.). Tag popularity predicted the proportion of selected antidepressant tags (beta = 0.44, SE = .11, P < .001), and blog posts (beta = 0.46, SE = .11, P < .001). We could not replicate the confidence manipulation (t(224) < 1, P = .78). Participants did not attend to source credibility on banners (t(224) = 1.67, P = .10). When confidence was low (-1 SD), participants selected more blog posts consistent with prior attitudes (beta = -0.26, SE = 0.05, P < .001). Moreover, when confidence was low (-1 SD) and source credibility was high (+1 SD), the efficacy ratings of attitude consistent treatments increased (beta = 0.34, SE = 0.13, P = .01). Conclusions: We found correlational support for defense motivation account underlying confirmation bias in the mental health-related search context. That is, participants did not tend to select objectively correct, but information that supported their prior attitudes.
Background: Patients with hypothyroidism report poor health-related quality of life despite having undergone thyroid hormone replacement therapy (THRT). Understanding patient concerns regarding levoth...
Background: Patients with hypothyroidism report poor health-related quality of life despite having undergone thyroid hormone replacement therapy (THRT). Understanding patient concerns regarding levothyroxine can help improve the treatment outcomes of thyroid hormone replacement therapy. Objective: This study aimed to (1) identify the distinctive themes in patient concerns regarding THRT; to (2) determine whether patients have unique primary medication concerns specific to demographics, and to (3) determine the predictability of primary medication concerns on patient treatment satisfaction. Methods: We collected patient reviews from WebMD (1,037 reviews about generic levothyroxine and 1,075 reviews about the brand version) posted between September 1, 2007 and January 30, 2017. We used natural language processing (NLP) to identify the themes of medication concerns. Multiple regression analyses were conducted in order to examine the predictability of the primary medication concerns on patient treatment satisfaction. Results: NLP of the patient reviews of levothyroxine posted on a social networking site produced six distinctive themes of patient medication concerns related to levothyroxine treatment: ‘How to take the drug,’ ‘treatment initiation,’ ‘dose adjustment,’ ‘symptoms of pain,’ ‘generic substitutability’ and ‘appearance.’ Patients had different primary medication concerns unique to their gender, age, and treatment duration. Furthermore, treatment satisfaction on levothyroxine depended on what primary medication concerns the patient had. Conclusions: NLP of text content available on social networks could identify different themes of patient medication concerns that can be incorporated into tailored medication counseling to improve patient treatment satisfaction.
Background: Advanced lung cancer patients often have chronic lung disease with reduced exercise capacities and various symptoms leading to altered quality of life (QoL). No studies have assessed pulmo...
Background: Advanced lung cancer patients often have chronic lung disease with reduced exercise capacities and various symptoms leading to altered quality of life (QoL). No studies have assessed pulmonary rehabilitation (PR) employing a mobile application and an Internet of Things device in advanced lung cancer patients undergoing chemotherapy. Objective: We determined the feasibility and efficacy of a smartphone application-based PR on exercise capacity, symptom management, and QoL in these patients. Methods: A total of 100 patients were recruited in a prospective, single-arm intervention study using smartphone application-based PR program for 12 weeks. Exercise capacity (6-minute walking distance, 6MWD), QoL, symptom scale scores, and distress indexes were investigated. Results: Ninety patients completed the PR program. The most common cause of dropout was hospitalization due to cancer progression. After PR, there was significant improvement in the 6MWD; 380.1 ± 74.1 m at baseline, 429.1 ± 58.6 m at 6-weeks (P < .001), and 448.1 ± 50.0 m at 12-weeks (P < .001). However, the dyspnea scale score showed no significant improvement in the patients overall, but there was a trend for improvement in those with a stable tumor response (P = .065). Role (P = .02), emotional (P < .001), and social functioning (P = .002) scale scores showed significant improvement after PR. Symptom scale scores for fatigue (P < .001), anorexia (P = .047), and diarrhea (P = .01) also showed significant improvement. There was significant improvement in depression (P = .048) and anxiety (P = .01), while there was no significant change in QoL (P = .063) and severity of pain (P = .24). Conclusions: Smartphone application-based PR represents an effective and feasible program to improve exercise capacity, and to manage symptoms and distress in patients with advanced lung cancer, undergoing chemotherapy.
Background: Caring for individuals with chronic conditions is labor intensive, requiring ongoing appointments, treatments, and support. The growing number of individuals with chronic conditions makes...
Background: Caring for individuals with chronic conditions is labor intensive, requiring ongoing appointments, treatments, and support. The growing number of individuals with chronic conditions makes this current support model unsustainably burdensome on health care systems globally. Mobile health (mHealth) technologies are increasingly being used throughout health care to facilitate communication, track disease, and provide educational support to patients. Such technologies show promise, yet they are not being utilized to their full extent within US health care systems. Objective: The purpose of this study was to examine the utilization of staff and costs of a remote monitoring care model in persons with and without a chronic condition. Methods: At Dartmouth-Hitchcock Health, 2,894 employees volunteered to monitor their health, transmit data for analysis, and communicate digitally with a care team. Volunteers received Bluetooth-connected consumer-grade devices that were paired to a smartphone application that facilitated digital communication with nursing and health behavior change staff. Health data were collected, automatically analyzed, and generated behavioral support communications based on those analyses. Care support staff were automatically alerted according to purpose-developed algorithms. In a subgroup of participants and matched controls, we used difference-in-difference techniques to examine changes in per-capita expenditures. Results: Participants averaged 41 years of age; 73% (n = 2,104) were female and 13% (n = 376) had at least one chronic condition. On average, each month, participants submitted 23 vital sign measurements, engaged in 1.96 conversations, and received 0.25 automated messages. Persons with chronic conditions accounted for 40% of all staff conversations, with higher per-capita conversation rates for all shifts compared to those without chronic conditions (P<.001). Additionally, persons with chronic conditions engaged nursing staff more than those without chronic conditions (1.40 & 0.19 per-capita conversations, respectively, P<.001). When compared to the same period in the prior year, per-capita healthcare expenditures for persons with chronic conditions dropped by 15% (P=.06) more than did those for matched controls. Conclusions: The technology-based chronic condition management care model was frequently used and demonstrated the potential for cost savings among participants with chronic conditions. While further studies are necessary, this model appears to be a promising solution to efficiently provide patients with personalized care, when and where they need it.