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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 pioneering 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 (e.g. in grant proposals), and for open peer-review purposes. We also invite patients to participate, e.g. 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: The increase in availability of patient data through consumer health wearable devices and smartphone sensors provides opportunities for mental health treatment beyond traditional self-repo...
Background: The increase in availability of patient data through consumer health wearable devices and smartphone sensors provides opportunities for mental health treatment beyond traditional self-report measurements. Previous studies have suggested that wearables can be effectively used to benefit the physical health of people with mental health issues but little research has explored the integration of wearable devices into mental health care. As such, early research is still necessary to address factors that might impact that integration including patient's motivations to use wearables and its subsequent data. Objective: We sought to gain an understanding of patients' motivations to use or not to use wearables devices during intensive treatment program for posttraumatic stress disorder (PTSD). During this treatment, they received a complementary Fitbit. We investigated the following research questions: How did the veterans in the intensive treatment program use their Fitbit? What are contributing motivators for the use and non-use of the Fitbit? Methods: We conducted semi-structured interviews with 13 veterans who completed an intensive treatment program for PTSD. We transcribed and analyzed interviews using thematic analysis. Results: We identified three major motivations for veterans to use the Fitbit during their time in the program: increase self-awareness, support social interactions, and give back to other veterans. We also identified three major reasons certain features of the Fitbit were not used: lack of clarity around the purpose of the Fitbit, lack of meaning in the Fitbit data, and challenges in the veteran-provider relationship. Conclusions: In order to integrate wearable data into mental health treatment programs, it is important to understand the patient perspectives and motivations. We also discuss how the military culture and PTSD may have contributed to our participant's behaviors and attitudes towards Fitbit usage. We conclude with possible approaches for integrating PGD into mental health treatment settings that may address the challenges we identified.
Background: Social media provides people with easy ways to communicate their attitudes and feelings to a wide audience. Many people unfortunately have negative associations and feelings about dental t...
Background: Social media provides people with easy ways to communicate their attitudes and feelings to a wide audience. Many people unfortunately have negative associations and feelings about dental treatment due to former painful experiences. Former research indicates that there might exist a pervasive and negative occupational stereotype related to dentists, and that this stereotype is expressed in many different venues, including in movies and in literature. Objective: This study investigates the language used in relation to dentists and medical doctors in the social media channel Twitter. The purpose is to compare the professions concerning the use of emotional words and pain-related words, which might underlie the pervasive negative stereotype identified in relation to dentists. We hypothesize that (A) tweets about dentists will have more negative emotion words than tweets about medical doctors, and that (B) pain related words are used more frequently in tweets about dentists than medical doctors. Methods: Twitter content (“tweets”) about dentists and medical doctors were collected scanning the keywords “dentist” and “doctor” using the Twitter API 140Dev. Word content of the selected tweets were analysed using the Linguistic Inquiry and Word Count software. The research hypotheses were investigated using non-parametric Wilcoxon-Mann-Whitney tests. Results: Over 2.3 million tweets were collected in total, of which about 1/3 contained the word “dentist” and about 2/3 contained the word “doctor”. Hypothesis A was supported as there were a higher proportion of negative words used in tweets about dentists than in tweets about medical doctors; W = 634925.00, p < .001. Similarly, tests showed a difference in proportions of anger words (W = 582087.00, p < .001), anxiety words (W = 660532.00, p < .001), and sadness words (W = 617011.00, p < .001), with higher proportions in tweets about dentists than tweets about doctors. Also, Hypothesis B was supported as there were a higher proportion of pain related words used in tweets about dentists than about doctors; W = 590139.00, p < .001. Conclusions: The results from this study support the existence of a negative stereotype for dentists among Twitter-users. The impact of expression of this stereotype on Twitter needs to be further explored with other study designs.
Background: In the United States, sudden infant death syndrome (SIDS) is the leading cause of death in infants aged 1 month to 1 year. Approximately 3,500 infants die from SIDS and sleep-related reaso...
Background: In the United States, sudden infant death syndrome (SIDS) is the leading cause of death in infants aged 1 month to 1 year. Approximately 3,500 infants die from SIDS and sleep-related reasons on a yearly basis. Unintentional sleep-related deaths and bed sharing, a known risk factor for SIDS, are on the rise. Furthermore, ethnic disparities exist among those most affected by SIDS. Despite public health campaigns, infant mortality persists. Given the popularity of social media, understanding social media conversations around SIDS and safe sleep may provide the medical and public health communities with the information needed to spread, reinforce, or counteract false information regarding SIDS and safe sleep. Objective: The purpose of this project was to study the social media conversation around SIDS to understand possible influences and guide health promotion efforts, public health research, and enable health professionals to engage in directed communication regarding this topic. Methods: This study used textual analytics to identify topics and extract meanings contained in unstructured textual data. Twitter messages were captured during the months of September, October, and November of 2017. Tweets and retweets were collected using NUVI software in conjunction with Twitter’s Search API using the keywords: "sids", "infant death syndrome", "sudden infant death syndrome”, and "safe sleep." This returned a total of 41,358 messages, which were analyzed using text-mining and social media monitoring software. Results: Multiple themes were identified, including: recommendations for safe sleep to prevent SIDS, safe sleep devices, the potential causes of SIDS, and how breastfeeding reduces SIDS. Compared to the months of September and November, October (Pregnancy and Infant Loss Awareness Month) demonstrated personal and specific stories of infant loss. The top influencers were news organizations, universities, and health-related organizations. Conclusions: This study offers valuable information regarding the public’s perception and opinions regarding SIDS and safe sleep. It highlights the contradicting information the public is exposed to regarding SIDS and the continued controversy of vaccines. This analysis also emphasizes the lack of public health organizations’ presence on Twitter compared to the influence of universities and news media organizations. It also demonstrates the prevalence of safe sleep products that are embedded in safe sleep messaging. These findings can assist providers in speaking about relevant topics when engaging in conversation about the prevention of SIDS and the promotion of safe sleep. Further, public health agencies and advocates should utilize social media and Twitter to better communicate accurate health information as well as continue to combat the spread of false information.
Background: Current interventions to support patients with medication adherence are generally resource-intensive and ineffective. Brief messages, such as those delivered via short message service (SMS...
Background: Current interventions to support patients with medication adherence are generally resource-intensive and ineffective. Brief messages, such as those delivered via short message service (SMS) systems, are increasingly used in digital health interventions to support adherence because they can be delivered on a wide-scale and at low cost. The content of messages is a crucial intervention feature for promoting behaviour change, but it is often unclear what the rationale is for chosen wording or any underlying mechanisms targeted for behavioural change. There is little guidance for developing and optimising brief message content for use in mobile-device delivered interventions. Objective: (1) To identify theoretical constructs (i.e. the targets that interventions aim to change) and behavioural strategies (i.e. features of intervention content) found to be associated with medication adherence in patients with type 2 diabetes. Additionally, (2) to map these onto a standard taxonomy for behaviour change techniques (BCTs) i.e. ‘active ingredients’ of interventions used to promote behavioural change, to produce an evidence-based set of approaches that have shown promise of improving adherence in previous studies and which could be further tested in digital-health interventions. Methods: A rapid systematic review of existing relevant systematic reviews was conducted. Medline and PsycINFO databases were searched from inception to 10th April 2017. Inclusion criteria: (A) systematic reviews of quantitative data if the studies reviewed (i) identified predictors of or correlates with medication adherence and/or evaluated medication adherence-enhancing interventions and (ii) included adult participants taking medication to manage a chronic physical health condition; and (B) systematic reviews of qualitative studies of experiences of medication adherence for adult participants with type 2 diabetes. Data were extracted on review characteristics and BCTs, theoretical constructs or behavioural strategies associated with improved medication adherence. Constructs and strategies were mapped onto the BCT v1 taxonomy. Results: A total of 1701 articles were identified; 25 systematic reviews (19 quantitative reviews, 3 qualitative reviews and 3 mixed-method reviews) were included. 21 theoretical constructs (e.g. self-efficacy) and 18 behavioural strategies (e.g. habit analysis) were identified in the included reviews. In total, 46 BCTs were identified as being related to medication adherence (e.g. habit formation, prompts/cues, information about health consequences). Conclusions: We have identified 46 promising BCTs related to medication adherence, upon which the content of brief messages delivered through mobile devices to improve adherence could be based. By using explicit systematic review methods and linking our findings to a standardised taxonomy of BCTs, we have described a novel approach for the development of SMS message content. Future brief message interventions that aim to support medication adherence could incorporate the BCTs identified in this review.
Background: The increasing digitalization of healthcare services with enhanced access to fast internet connections along with wide use of smartphones offers the opportunity to get health advice or tre...
Background: The increasing digitalization of healthcare services with enhanced access to fast internet connections along with wide use of smartphones offers the opportunity to get health advice or treatment remotely. As a service provider, it is important to consider how consumers can take full advantage of available services and how this can create an enabling environment. However, it is important to consider the digital context and the attributes of current and future users such as their readiness, i.e. knowledge, skills and attitudes, including trust and motivation. Objective: To evaluate how a combination of the e-Health Literacy Questionnaire (eHLQ) with selected dimensions from the Health Education Impact Questionnaire (heiQ) and the Health Literacy Questionnaire (HLQ) can be used together as one instrument to characterize an individual’s level of health technology readiness and explore how the generated data can be used to create health technology readiness profiles of potential users of health technologies and digital health services. Methods: The instrument as well as sociodemographic questions was administered to a population of 305 citizens with a recent cancer diagnosis referred to rehabilitation in a setting that plans to introduce various technologies to assist the individuals. Properties of the READHY instrument were evaluated using confirmatory factor analysis (CFA), convergent and discriminant validity analysis and exploratory factor analysis (EFA). To identify different health technology readiness profiles in the population, the data were further analyzed using hierarchical and k-means cluster analysis. Results: The CFA found a suitable fit for the 13 factors with only one cross loading of one item between two dimensions. The convergent and discriminant validity analysis revealed many factor correlations suggesting that, in this population, a more parsimonious model might be achieved. EFA pointed to five to six constructs based on aggregates of the existing dimensions. The results were not satisfactory, so an eight-factor CFA was performed resulting in a good fit with only one item cross loading between two dimensions. Cluster analysis showed that data from the READHY instrument can be clustered to create meaningful health technology readiness profiles of users. Conclusions: The 13 dimensions from heiQ, HLQ and eHLQ can be used in combination to describe user’s health technology readiness level and degree of enablement. Further studies in other populations are needed to understand whether the associations between dimensions are consistent and if the number of dimensions can be reduced.
Background: Electronic health records (EHR) have been widely proposed as a mechanism for improving health care quality. However, rigorous research on the impact of EHR on behavioral health service del...
Background: Electronic health records (EHR) have been widely proposed as a mechanism for improving health care quality. However, rigorous research on the impact of EHR on behavioral health service delivery is scant, especially for children and adolescents. Objective: The current study evaluated usability of an EHR developed to support implementation of the Wraparound care coordination model for youth with complex behavioral health needs, and impact of the EHR on service processes, fidelity, and proximal outcomes. Methods: Thirty-four Wraparound facilitators working in two programs in two states were randomized to either use the new EHR (n=19) or continue to implement Wraparound services as usual (SAU) using paper-based documentation (n=15). Assignment was unblended due to facilitators working in the same organizations. Key functions of the EHR included standard fields such as youth and family information, diagnoses, assessment data, and progress notes; as well as maintenance of a coordinated plan of care; progress measurement on strategies and services; communication among team members; and reporting on services, expenditures, and outcomes. All youth referred to services for 8 months (N=211) were eligible for the study. After excluding those who were ineligible (n=69) and who declined to participate (n=59), n=83 youth were enrolled in the study: n=49 in the EHR condition and n=34 in the SAU condition. Facilitators serving these youth and families and their supervisors completed measures of EHR usability and appropriateness, supervision processes and activities, work satisfaction, and use of and attitudes toward standardized assessments. Parents and caregivers completed measures related to fidelity and quality of behavioral health care, including Wraparound team climate, working alliance with providers, fidelity to the Wraparound model, and satisfaction with services. Results: EHR-assigned facilitators from both sites demonstrated robust use of the system. Facilitators in the EHR group reported spending significantly more time reviewing client progress (P = .03) in supervision, and less time overall sending reminders to youth/families (P = .04). A trend toward less time on administrative tasks (P = .098) in supervision was also found. Facilitators in both groups reported significantly increased use of measurement-based care strategies overall, which may reflect cross-group contamination (given that randomization of staff to the EHR occurred within agencies and supervisors supervised both types of staff). Although not significant at P < .05, there was a trend (P = .10) toward caregivers in the EHR group reporting poorer working alliance on one subscale focused on shared agreement on tasks. No other significant between-group differences were found. Conclusions: Results support the proposal that use of EHR systems can promote use of client progress data and promote efficiency; however, there was little evidence of any impact (positive or negative) on overall service quality, fidelity, or client satisfaction. The field of children’s behavioral health services would benefit from additional research on EHR systems using designs that include larger sample sizes and longer follow-up periods. Clinical Trial: Clinicaltrials.gov NCT02421874, https://clinicaltrials.gov/ct2/show/NCT02421874