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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 2019: 5.03), ranking Q1 in the medical informatics category, and is also the largest journal in the field. The journal focuses on emerging technologies, medical devices, apps, engineering, telehealth 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, and which together receive over 6.000 submissions a year. 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 journal but can simply transfer it between 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: Freepik; Copyright: yanalya; URL: https://www.freepik.com/free-photo/portrait-man-lying-down-desk-near-laptop_1281115.htm; License: Licensed by JMIR.

    Effects of the COVID-19 Pandemic on Obsessive-Compulsive Symptoms Among University Students: Prospective Cohort Survey Study

    Abstract:

    Background: The COVID-19 pandemic is associated with common mental health problems. However, evidence for the association between fear of COVID-19 and obsessive-compulsive disorder (OCD) is limited. Objective: This study aimed to examine if fear of negative events affects Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores in the context of a COVID-19–fear-invoking environment. Methods: All participants were medical university students and voluntarily completed three surveys via smartphone or computer. Survey 1 was conducted on February 8, 2020, following a 2-week-long quarantine period without classes; survey 2 was conducted on March 25, 2020, when participants had been taking online courses for 2 weeks; and survey 3 was conducted on April 28, 2020, when no new cases had been reported for 2 weeks. The surveys comprised the Y-BOCS and the Zung Self-Rating Anxiety Scale (SAS); additional items included questions on demographics (age, gender, only child vs siblings, enrollment year, major), knowledge of COVID-19, and level of fear pertaining to COVID-19. Results: In survey 1, 11.3% of participants (1519/13,478) scored ≥16 on the Y-BOCS (defined as possible OCD). In surveys 2 and 3, 3.6% (305/8162) and 3.5% (305/8511) of participants had scores indicative of possible OCD, respectively. The Y-BOCS score, anxiety level, quarantine level, and intensity of fear were significantly lower at surveys 2 and 3 than at survey 1 (P<.001 for all). Compared to those with a lower Y-BOCS score (<16), participants with possible OCD expressed greater intensity of fear and had higher SAS standard scores (P<.001). The regression linear analysis indicated that intensity of fear was positively correlated to the rate of possible OCD and the average total scores for the Y-BOCS in each survey (P<.001 for all). Multiple regressions showed that those with a higher intensity of fear, a higher anxiety level, of male gender, with sibling(s), and majoring in a nonmedicine discipline had a greater chance of having a higher Y-BOCS score in all surveys. These results were redemonstrated in the 5827 participants who completed both surveys 1 and 2 and in the 4006 participants who completed all three surveys. Furthermore, in matched participants, the Y-BOCS score was negatively correlated to changes in intensity of fear (r=0.74 for survey 2, P<.001; r=0.63 for survey 3, P=.006). Conclusions: Our findings indicate that fear of COVID-19 was associated with a greater Y-BOCS score, suggesting that an environment (COVID-19 pandemic) × psychology (fear and/or anxiety) interaction might be involved in OCD and that a fear of negative events might play a role in the etiology of OCD.

  • Source: Pexels; Copyright: Andrea Piacquadio; URL: https://www.pexels.com/photo/serious-senior-man-in-formal-suit-working-on-laptop-at-workplace-3823494/; License: Licensed by JMIR.

    VA Video Connect for Clinical Care in Older Adults in a Rural State During the COVID-19 Pandemic: Cross-Sectional Study

    Abstract:

    Background: The COVID-19 pandemic has accelerated the need for telehealth at home. Although the Department of Veterans Affairs is a leading provider of telehealth, disparities may exist in reaching older veterans living in rural areas. VA Video Connect (VVC) is a video conferencing app that enables veterans to connect with their health care provider via a secure and private session. Objective: The aim of this study was to examine the capability and willingness of older veterans to participate in a VVC visit during the COVID-19 pandemic. Methods: A cross-sectional study was conducted on older veterans (N=118) at the Central Arkansas Veterans Healthcare System. Participants were interviewed over the phone and responses to the following items were recorded: availability of internet, email, and an electronic device with a camera; veterans’ willingness to complete an appointment via a VVC visit; and availability of assistance from a caregiver for those who were unable to participate in a VVC visit alone. Results: Participants’ mean age was 72.6 (SD 8.3) years, 92% (n=108) were male, 69% (n=81) were Caucasian, 30% (n=35) were African Americans, and 36% (n=42) lived in a rural location. The majority reported having access to the internet (n=93, 77%) and email service (n=83, 70%), but only 56% (n=67) had a camera-equipped device. Overall, 53% (n=63) were willing and capable of participating in a VVC visit. The availability of internet access was significantly lower in rural compared to nonrural participants (P=.045) and in those with or less than a high school education compared to those who pursued higher education (P=.02). Willingness to participate in the VVC visit was significantly lower in rural compared to nonrural participants (P=.03). Of the participants who reported they were able and willing to partake in a VVC visit (n=54), 65% (n=35) opted for VVC and 35% (n=19) preferred a phone visit. In total, 77% (n=27) of the scheduled VVC visits were successful. Conclusions: Despite advances in technology, and willingness on the part of health care systems, there are some lingering issues with capability and willingness to participate in video telehealth visits, particularly among older adults residing in rural areas.

  • Source: freepik; Copyright: rawpixel.com; URL: https://www.freepik.com/free-photo/messaging-with-friend_4103193.htm#page=2&query=text+message+smartphone&position=39; License: Licensed by JMIR.

    Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to Promote Physical Activity and a Healthy Diet: Viewpoint

    Abstract:

    Background: Chatbots empowered by artificial intelligence (AI) can increasingly engage in natural conversations and build relationships with users. Applying AI chatbots to lifestyle modification programs is one of the promising areas to develop cost-effective and feasible behavior interventions to promote physical activity and a healthy diet. Objective: The purposes of this perspective paper are to present a brief literature review of chatbot use in promoting physical activity and a healthy diet, describe the AI chatbot behavior change model our research team developed based on extensive interdisciplinary research, and discuss ethical principles and considerations. Methods: We conducted a preliminary search of studies reporting chatbots for improving physical activity and/or diet in four databases in July 2020. We summarized the characteristics of the chatbot studies and reviewed recent developments in human-AI communication research and innovations in natural language processing. Based on the identified gaps and opportunities, as well as our own clinical and research experience and findings, we propose an AI chatbot behavior change model. Results: Our review found a lack of understanding around theoretical guidance and practical recommendations on designing AI chatbots for lifestyle modification programs. The proposed AI chatbot behavior change model consists of the following four components to provide such guidance: (1) designing chatbot characteristics and understanding user background; (2) building relational capacity; (3) building persuasive conversational capacity; and (4) evaluating mechanisms and outcomes. The rationale and evidence supporting the design and evaluation choices for this model are presented in this paper. Conclusions: As AI chatbots become increasingly integrated into various digital communications, our proposed theoretical framework is the first step to conceptualize the scope of utilization in health behavior change domains and to synthesize all possible dimensions of chatbot features to inform intervention design and evaluation. There is a need for more interdisciplinary work to continue developing AI techniques to improve a chatbot’s relational and persuasive capacities to change physical activity and diet behaviors with strong ethical principles.

  • Periconception lifestyle counselling. Source: Image created by the Authors; Copyright: The Authors; URL: http://www.jmir.org/2020/9/e19378/; License: Creative Commons Attribution (CC-BY).

    Impact of a Blended Periconception Lifestyle Care Approach on Lifestyle Behaviors: Before-and-After Study

    Abstract:

    Background: Periconception lifestyle behaviors affect maternal, paternal, offspring, and transgenerational health outcomes. Previous research in other target populations has shown that personalized lifestyle interventions, in which face-to-face counseling and eHealth (“blended care”) are combined, may effectively target these lifestyle behaviors. Objective: We aimed to assess the effectiveness of a periconceptional lifestyle intervention on the improvement of specific lifestyle components. Methods: A blended periconception lifestyle care approach was developed, combining the outpatient lifestyle counseling service “Healthy Pregnancy” with the eHealth platform “Smarter Pregnancy” (www.smarterpregnancy.co.uk) in which lifestyle was coached for 24 weeks. All couples contemplating pregnancy or already pregnant (≤12 weeks of gestation) who visited the outpatient clinics of the Department of Obstetrics and Gynecology at the Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands, between June and December 2018, were invited to participate. We measured changes in lifestyle behaviors at weeks 12 and 24 compared with baseline. Generalized estimating equations were used to analyze the changes in lifestyle behaviors over time. Subgroup analyses were performed for women with obesity (BMI ≥30 kg/m2), women pregnant at the start of the intervention, and those participating as a couple. Results: A total of 539 women were screened for eligibility, and 450 women and 61 men received the blended periconception intervention. Among the participating women, 58.4% (263/450) were included in the preconception period. Moreover, 78.9% (403/511) of the included participants completed the online lifestyle coaching. At baseline, at least one poor lifestyle behavior was present in most women (379/450, 84.2%) and men (58/61, 95.1%). In the total group, median fruit intake increased from 1.8 to 2.2 pieces/day (P<.001) and median vegetable intake increased from 151 to 165 grams/day (P<.001) after 24 weeks of online coaching. The probability of taking folic acid supplementation among women increased from 0.97 to 1 (P<.001), and the probability of consuming alcohol and using tobacco in the total group decreased from 0.25 to 0.19 (P=.002) and from 0.20 to 0.15 (P=.63), respectively. Overall, the program showed the strongest effectiveness for participating couples. Particularly for vegetable and fruit intake, their consumption increased from 158 grams/day and 1.8 pieces/day at baseline to 190 grams/day and 2.7 pieces/day at the end of the intervention, respectively. Conclusions: We succeeded in including most participating women in the preconception period. A high compliance rate was achieved and users demonstrated improvements in several lifestyle components. The blended periconception lifestyle care approach seems to be an effective method to improve lifestyle behaviors. The next step is to further disseminate this approach and to perform a randomized trial to compare the use of blended care with the provision of only eHealth. Additionally, the clinical relevance of these results will need to be substantiated further.

  • Source: Pexels; Copyright: Matthias Zomer; URL: https://www.pexels.com/photo/action-adult-affection-eldery-339620/; License: Licensed by JMIR.

    A Personalized Health Monitoring System for Community-Dwelling Elderly People in Hong Kong: Design, Implementation, and Evaluation Study

    Abstract:

    Background: Telehealth is an effective means to assist existing health care systems, particularly for the current aging society. However, most extant telehealth systems employ individual data sources by offline data processing, which may not recognize health deterioration in a timely way. Objective: Our study objective was two-fold: to design and implement an integrated, personalized telehealth system on a community-based level; and to evaluate the system from the perspective of user acceptance. Methods: The system was designed to capture and record older adults’ health-related information (eg, daily activities, continuous vital signs, and gait behaviors) through multiple measuring tools. State-of-the-art data mining techniques can be integrated to detect statistically significant changes in daily records, based on which a decision support system could emit warnings to older adults, their family members, and their caregivers for appropriate interventions to prevent further health deterioration. A total of 45 older adults recruited from 3 elderly care centers in Hong Kong were instructed to use the system for 3 months. Exploratory data analysis was conducted to summarize the collected datasets. For system evaluation, we used a customized acceptance questionnaire to examine users’ attitudes, self-efficacy, perceived usefulness, perceived ease of use, and behavioral intention on the system. Results: A total of 179 follow-up sessions were conducted in the 3 elderly care centers. The results of exploratory data analysis showed some significant differences in the participants’ daily records and vital signs (eg, steps, body temperature, and systolic blood pressure) among the 3 centers. The participants perceived that using the system is a good idea (ie, attitude: mean 5.67, SD 1.06), comfortable (ie, self-efficacy: mean 4.92, SD 1.11), useful to improve their health (ie, perceived usefulness: mean 4.99, SD 0.91), and easy to use (ie, perceived ease of use: mean 4.99, SD 1.00). In general, the participants showed a positive intention to use the first version of our personalized telehealth system in their future health management (ie, behavioral intention: mean 4.45, SD 1.78). Conclusions: The proposed health monitoring system provides an example design for monitoring older adults’ health status based on multiple data sources, which can help develop reliable and accurate predictive analytics. The results can serve as a guideline for researchers and stakeholders (eg, policymakers, elderly care centers, and health care providers) who provide care for older adults through such a telehealth system.

  • Graphic of a patient and clinician using MyDiabetesPlan in the clinic. Source: Image created by MyDiabetesPlan team; Copyright: Catherine H Yu; License: Creative Commons Attribution (CC-BY).

    Impact of MyDiabetesPlan, a Web-Based Patient Decision Aid on Decisional Conflict, Diabetes Distress, Quality of Life, and Chronic Illness Care in Patients...

    Abstract:

    Background: Person-centered care is critical for delivering high-quality diabetes care. Shared decision making (SDM) is central to person-centered care, and in diabetes care, it can improve decision quality, patient knowledge, and patient risk perception. Delivery of person-centered care can be facilitated with the use of patient decision aids (PtDAs). We developed MyDiabetesPlan, an interactive SDM and goal-setting PtDA designed to help individualize care priorities and support an interprofessional approach to SDM. Objective: This study aims to assess the impact of MyDiabetesPlan on decisional conflict, diabetes distress, health-related quality of life, and patient assessment of chronic illness care at the individual patient level. Methods: A two-step, parallel, 10-site cluster randomized controlled trial (first step: provider-directed implementation only; second step: both provider- and patient-directed implementation 6 months later) was conducted. Participants were adults 18 years and older with diabetes and 2 other comorbidities at 10 family health teams (FHTs) in Southwestern Ontario. FHTs were randomly assigned to MyDiabetesPlan (n=5) or control (n=5) through a computer-generated algorithm. MyDiabetesPlan was integrated into intervention practices, and clinicians (first step) followed by patients (second step) were trained on its use. Control participants received static generic Diabetes Canada resources. Patients were not blinded. Participants completed validated questionnaires at baseline, 6 months, and 12 months. The primary outcome at the individual patient level was decisional conflict; secondary outcomes were diabetes distress, health-related quality of life, chronic illness care, and clinician intention to practice interprofessional SDM. Multilevel hierarchical regression models were used. Results: At the end of the study, the intervention group (5 clusters, n=111) had a modest reduction in total decisional conflicts compared with the control group (5 clusters, n=102; −3.5, 95% CI −7.4 to 0.42). Although there was no difference in diabetes distress or health-related quality of life, there was an increase in patient assessment of chronic illness care (0.7, 95% CI 0.4 to 1.0). Conclusions: Use of goal-setting decision aids modestly improved decision quality and chronic illness care but not quality of life. Our findings may be due to a gap between goal setting and attainment, suggesting a role for optimizing patient engagement and behavioral support. The next steps include clarifying the mechanisms by which decision aids impact outcomes and revising MyDiabetesPlan and its delivery. Trial Registration: ClinicalTrials.gov NCT02379078; https://clinicaltrials.gov/ct2/show/NCT02379078

  • Chinese factory workers took body temperature before entering the workplace during work resumption following the COVID-19 outbreak. Source: Seventh Affiliated Hospital, Sun Yat-sen University; Copyright: The authors; URL: http://www.jmir.org/2020/9/e22457/; License: Creative Commons Attribution (CC-BY).

    Self-Reported Compliance With Personal Preventive Measures Among Chinese Factory Workers at the Beginning of Work Resumption Following the COVID-19 Outbreak:...

    Abstract:

    Background: Maintaining compliance with personal preventive measures is important to achieve a balance of COVID-19 pandemic control and work resumption. Objective: The aim of this study was to investigate self-reported compliance with four personal measures to prevent COVID-19 among a sample of factory workers in Shenzhen, China, at the beginning of work resumption in China following the COVID-19 outbreak. These preventive measures included consistent wearing of face masks in public spaces (the workplace and other public settings); sanitizing hands using soap, liquid soap, or alcohol-based hand sanitizer after returning from public spaces or touching public installations and equipment; avoiding social and meal gatherings; and avoiding crowded places. Methods: The participants were adult factory workers who had resumed work in Shenzhen, China. A stratified two-stage cluster sampling design was used. We randomly selected 14 factories that had resumed work. All full-time employees aged ≥18 years who had resumed work in these factories were invited to complete a web-based survey. Out of 4158 workers who had resumed work in these factories, 3035 (73.0%) completed the web-based survey from March 1 to 14, 2020. Multilevel logistic regression models were fitted. Results: Among the 3035 participants, 2938 (96.8%) and 2996 (98.7%) reported always wearing a face mask in the workplace and in other public settings, respectively, in the past month. However, frequencies of self-reported sanitizing hands (2152/3035, 70.9%), avoiding social and meal gatherings (2225/3035, 73.3%), and avoiding crowded places (1997/3035, 65.8%) were relatively low. At the individual level, knowledge about COVID-19 (adjusted odds ratios [AORs] from 1.16, CI 1.10-1.24, to 1.29, CI 1.21-1.37), perceived risk (AORs from 0.58, CI 0.50-0.68, to 0.85, CI 0.72-0.99) and severity (AOR 1.05, CI 1.01-1.09, and AOR 1.07, CI 1.03-1.11) of COVID-19, perceived effectiveness of preventive measures by the individual (AORs from 1.05, CI 1.00-1.10, to 1.09, CI 1.04-1.13), organization (AOR 1.30, CI 1.20-1.41), and government (AORs from 1.14, CI 1.04-1.25, to 1.21, CI 1.02-1.42), perceived preparedness for a potential outbreak after work resumption (AORs from 1.10, CI 1.00-1.21, to 1.50, CI 1.36-1.64), and depressive symptoms (AORs from 0.93, CI 0.91-0.94, to 0.96, CI 0.92-0.99) were associated with self-reported compliance with at least one personal preventive measure. At the interpersonal level, exposure to COVID-19–specific information through official media channels (AOR 1.08, CI 1.04-1.11) and face-to-face communication (AOR 0.90, CI 0.83-0.98) were associated with self-reported sanitizing of hands. The number of preventive measures implemented in the workplace was positively associated with self-reported compliance with all four preventive measures (AORs from 1.30, CI 1.08-1.57, to 1.63, CI 1.45-1.84). Conclusions: Measures are needed to strengthen hand hygiene and physical distancing among factory workers to reduce transmission following work resumption. Future programs in workplaces should address these factors at multiple levels.

  • Source: Foter.com; Copyright: Foter.com; URL: https://foter.com/photo3/man-sitting-at-table-with-coffee-cup-using-digital-tablet/; License: Public Domain (CC0).

    Understanding the Community Risk Perceptions of the COVID-19 Outbreak in South Korea: Infodemiology Study

    Abstract:

    Background: South Korea is among the best-performing countries in tackling the coronavirus pandemic by using mass drive-through testing, face mask use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis. Objective: We attempt to explore patterns of community health risk perceptions of COVID-19 in South Korea using internet search data. Methods: Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19–related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access data set for the time period of December 5, 2019, to May 31, 2020. Time-lag correlations calculated by Spearman rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, and GT and NAVER RSVs in lag periods (of 1-3 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor of <5. Results: The numbers of COVID-19–related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a face mask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763-0.823; P<.001) and age groups ≤29 years (r=0.726-0.821; P<.001), 30-44 years (r=0.701-0.826; P<.001), and ≥50 years (r=0.706-0.725; P<.001). In terms of spatial distribution, internet search data were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704-0.804; P<.001) compared to those of desktop searches (r=0.705-0.717; P<.001), indicating changing behaviors in searching for online health information during the outbreak. These varied internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test–related information as being more important than disease-related knowledge. Meanwhile, younger, and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19–related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case–based model and potentially be used to predict epidemic curves. Conclusions: The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.

  • Source: Unsplash; Copyright: dole777; URL: https://unsplash.com/photos/EQSPI11rf68; License: Licensed by JMIR.

    Factors Engaging Users of Diabetes Social Media Channels on Facebook, Twitter, and Instagram: Observational Study

    Abstract:

    Background: Diabetes patient associations and diabetes-specific patient groups around the world are present on social media. Although active participation and engagement in these diabetes social media groups has been mostly linked to positive effects, very little is known about the content that is shared on these channels or the post features that engage their users the most. Objective: The objective of this study was to analyze (1) the content and features of posts shared over a 3-year period on 3 diabetes social media channels (Facebook, Twitter, and Instagram) of a diabetes association, and (2) users’ engagement with these posts (likes, comments, and shares). Methods: All social media posts published from the Norwegian Diabetes Association between January 1, 2017, and December 31, 2019, were extracted. Two independent reviewers classified the posts into 7 categories based on their content. The interrater reliability was calculated using Cohen kappa. Regression analyses were carried out to analyze the effects of content topic, social media channel, and post features on users’ engagement (likes, comments, and shares). Results: A total of 1449 messages were posted. Posts of interviews and personal stories received 111% more likes, 106% more comments, and 112% more shares than miscellaneous posts (all P<.001). Messages posted about awareness days and other celebrations were 41% more likely to receive likes than miscellaneous posts (P<.001). Conversely, posts on research and innovation received 31% less likes (P<.001), 35% less comments (P=.02), and 25% less shares (P=.03) than miscellaneous posts. Health education posts received 38% less comments (P=.003) but were shared 39% more than miscellaneous posts (P=.007). With regard to social media channel, Facebook and Instagram posts were both 35 times more likely than Twitter posts to receive likes, and 60 times and almost 10 times more likely to receive comments, respectively (P<.001). Compared to text-only posts, those with videos had 3 times greater chance of receiving likes, almost 4 times greater chance of receiving comments, and 2.5 times greater chance of being shared (all P<.001). Including both videos and emoji in posts increased the chances of receiving likes by almost 7 times (P<.001). Adding an emoji to posts increased their chances of receiving likes and being shared by 71% and 144%, respectively (P<.001). Conclusions: Diabetes social media users seem to be least engaged in posts with content topics that a priori could be linked to greater empowerment: research and innovation on diabetes, and health education. Diabetes social media groups, public health authorities, and other stakeholders interested in sharing research and innovation content and promoting health education on social media should consider including videos and emoji in their posts, and publish on popular and visual-based social media channels, such as Facebook and Instagram, to increase user engagement.

  • Source: freepik; Copyright: freepik; URL: https://www.freepik.com/free-photo/close-up-person-using-cellphone_3006108.htm#page=1&query=people%20using%20phone&position=13; License: Licensed by JMIR.

    Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis

    Abstract:

    Background: Chronic disease represents a large and growing burden to the health care system worldwide. One method of managing this burden is the use of app-based interventions; however attrition, defined as lack of patient use of the intervention, is an issue for these interventions. While many apps have been developed, there is some evidence that they have significant issues with sustained use, with up to 98% of people only using the app for a short time before dropping out and/or dropping use down to the point where the app is no longer effective at helping to manage disease. Objective: Our objectives are to systematically appraise and perform a meta-analysis on dropout rates in apps for chronic disease and to qualitatively synthesize possible reasons for these dropout rates that could be addressed in future interventions. Methods: MEDLINE (Medical Literature Analysis and Retrieval System Online), PubMed, Cochrane CENTRAL (Central Register of Controlled Trials), and Embase were searched from 2003 to the present to look at mobile health (mHealth) and attrition or dropout. Studies, either randomized controlled trials (RCTs) or observational trials, looking at chronic disease with measures of dropout were included. Meta-analysis of attrition rates was conducted in Stata, version 15.1 (StataCorp LLC). Included studies were also qualitatively synthesized to examine reasons for dropout and avenues for future research. Results: Of 833 studies identified in the literature search, 17 were included in the review and meta-analysis. Out of 17 studies, 9 (53%) were RCTs and 8 (47%) were observational trials, with both types covering a range of chronic diseases. The pooled dropout rate was 43% (95% CI 29-57), with observational studies having a higher dropout rate (49%, 95% CI 27-70) than RCTs in more controlled scenarios, which only had a 40% dropout rate (95% CI 16-63). The studies were extremely varied, which is represented statistically in the high degree of heterogeneity (I2>99%). Qualitative synthesis revealed a range of reasons relating to attrition from app-based interventions, including social, demographic, and behavioral factors that could be addressed. Conclusions: Dropout rates in mHealth interventions are high, but possible areas to minimize attrition exist. Reducing dropout rates will make these apps more effective for disease management in the long term. Trial Registration: International Prospective Register of Systematic Reviews (PROSPERO) CRD42019128737; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019128737

  • Source: Unsplash; Copyright: Brooke Cagle; URL: https://unsplash.com/photos/tLG2hcpITZE; License: Licensed by JMIR.

    The Influence of Three Modes of Human Support on Attrition and Adherence to a Web- and Mobile App–Based Mental Health Promotion Intervention in a...

    Abstract:

    Background: The escalating prevalence of mental health disorders necessitates a greater focus on web- and mobile app–based mental health promotion initiatives for nonclinical groups. However, knowledge is scant regarding the influence of human support on attrition and adherence and participant preferences for support in nonclinical settings. Objective: This study aimed to compare the influence of 3 modes of human support on attrition and adherence to a digital mental health intervention for a nonclinical cohort. It evaluated user preferences for support and assessed whether adherence and outcomes were enhanced when participants received their preferred support mode. Methods: Subjects participated in a 10-week digital mental health promotion intervention and were randomized into 3 comparative groups: standard group with automated emails (S), standard plus personalized SMS (S+pSMS), and standard plus weekly videoconferencing support (S+VCS). Adherence was measured by the number of video lessons viewed, points achieved for weekly experiential challenge activities, and the total number of weeks that participants recorded a score for challenges. In the postquestionnaire, participants ranked their preferred human support mode from 1 to 4 (S, S+pSMS, S+VCS, S+pSMS & VCS combined). Stratified analysis was conducted for those who received their first preference. Preintervention and postintervention questionnaires assessed well-being measures (ie, mental health, vitality, depression, anxiety, stress, life satisfaction, and flourishing). Results: Interested individuals (N=605) enrolled on a website and were randomized into 3 groups (S, n=201; S+pSMS, n=202; S+VCS, n=201). Prior to completing the prequestionnaire, a total of 24.3% (147/605) dropped out. Dropout attrition between groups was significantly different (P=.009): 21.9% (44/201) withdrew from the S group, 19.3% (39/202) from the S+pSMS group, and 31.6% (64/202) from the S+VCS group. The remaining 75.7% (458/605) registered and completed the prequestionnaire (S, n=157; S+pSMS, n=163; S+VCS, n=138). Of the registered participants, 30.1% (138/458) failed to complete the postquestionnaire (S, n=54; S+pSMS, n=49; S+VCS, n=35), but there were no between-group differences (P=.24). For the 69.9% (320/458; S, n=103; S+pSMS, n=114; S+VCS, n=103) who completed the postquestionnaire, no between-group differences in adherence were observed for mean number of videos watched (P=.42); mean challenge scores recorded (P=.71); or the number of weeks that challenge scores were logged (P=.66). A total of 56 participants (17.5%, 56/320) received their first preference in human support (S, n=22; S+pSMS, n=26; S+VCS, n=8). No differences were observed between those who received their first preference and those who did not with regard to video adherence (P=.91); challenge score adherence (P=.27); or any of the well-being measures including, mental health (P=.86), vitality (P=.98), depression (P=.09), anxiety (P=.64), stress (P=.55), life satisfaction (P=.50), and flourishing (P=.47). Conclusions: Early dropout attrition may have been influenced by dissatisfaction with the allocated support mode. Human support mode did not impact adherence to the intervention, and receiving the preferred support style did not result in greater adherence or better outcomes. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR): 12619001009101; http://www.anzctr.org.au/ACTRN12619001009101.aspx

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    Development of a Social Network for People Without a Diagnosis (RarePairs): Evaluation Study

    Abstract:

    Background: Diagnostic delay in rare disease (RD) is common, occasionally lasting up to more than 20 years. In attempting to reduce it, diagnostic support tools have been studied extensively. However, social platforms have not yet been used for systematic diagnostic support. This paper illustrates the development and prototypic application of a social network using scientifically developed questions to match individuals without a diagnosis. Objective: The study aimed to outline, create, and evaluate a prototype tool (a social network platform named RarePairs), helping patients with undiagnosed RDs to find individuals with similar symptoms. The prototype includes a matching algorithm, bringing together individuals with similar disease burden in the lead-up to diagnosis. Methods: We divided our project into 4 phases. In phase 1, we used known data and findings in the literature to understand and specify the context of use. In phase 2, we specified the user requirements. In phase 3, we designed a prototype based on the results of phases 1 and 2, as well as incorporating a state-of-the-art questionnaire with 53 items for recognizing an RD. Lastly, we evaluated this prototype with a data set of 973 questionnaires from individuals suffering from different RDs using 24 distance calculating methods. Results: Based on a step-by-step construction process, the digital patient platform prototype, RarePairs, was developed. In order to match individuals with similar experiences, it uses answer patterns generated by a specifically designed questionnaire (Q53). A total of 973 questionnaires answered by patients with RDs were used to construct and test an artificial intelligence (AI) algorithm like the k-nearest neighbor search. With this, we found matches for every single one of the 973 records. The cross-validation of those matches showed that the algorithm outperforms random matching significantly. Statistically, for every data set the algorithm found at least one other record (match) with the same diagnosis. Conclusions: Diagnostic delay is torturous for patients without a diagnosis. Shortening the delay is important for both doctors and patients. Diagnostic support using AI can be promoted differently. The prototype of the social media platform RarePairs might be a low-threshold patient platform, and proved suitable to match and connect different individuals with comparable symptoms. This exchange promoted through RarePairs might be used to speed up the diagnostic process. Further studies include its evaluation in a prospective setting and implementation of RarePairs as a mobile phone app.

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