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Background: Despite the rapid adoption of genomic sequencing in clinical practice, clinical sequencing reports in electronic health record (EHR) systems are currently being written in unstructured formats such as PDF or free text. These formats hinder the implementation of a clinical decision support system and secondary research applications. Therefore, there is an urgent need to standardize genomic sequencing reports in EHR systems. Objective: To implement standardized machine-processable clinical sequencing reports in an EHR system, the ISO/TS 20428 international standard was developed for a structured template. This study aims to verify the actual use of the ISO/TS 20428 standard in clinical practice settings. Methods: Here, we describe the practical implementation of ISO/TS 20428 using Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) genomics implementation guidance to efficiently deliver required genomic sequencing results to clinicians through an EHR system. Results: We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. Conclusions: To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard based information system in a practical setting. This prototyping work proves that clinical genomics sequencing reporting data can be effectively implemented in an EHR system using the existing ISO/TS 20428 standard and FHIR resources.
The Journal of Medical Internet Research—the leading eHealth journal with an impact factor of 4.7 and ranked #1 in its category—is planning to publish a special issue on using technology to detect and combat research misconduct (Guest Editor: Ana Marusic).
The special issue will publish original research, viewpoint papers, and tutorials covering detection methods, prevention methods, and dissemination methods such as preprints and online peer-review reports, as well as ethical issues that arise from using new technologies.
Examples of topics we are interested in:
The role of preprints in creating new ethical issues and/or in detecting research misconduct
Plagiarism detection (software/algorithms)
Impact of web-based training programs for researchers
Technological methods to detect data inconsistencies or data fabrication
The role of large databases
Trial registration and registered reports
Automated peer-review or software that flags potential issues
Artificial intelligence applications for peer-review
Automated methods to decide on authorship or order of authors
Dissemination methods
Education and training of researchers using new media
Software to detect the inappropriate manipulation of image data
Software or algorithms to check the reported statistical results in articles for consistency
Transparency throughout the research process, including the availability of data and code
Analysis of how retracted papers are covered on the internet and in popular media
Blogs, websites, and other tools covering research misconduct or facilitating the discussion of suspicious publications and uncovering research misconduct
Databases of scientific misconduct
Post-publication peer-review, including new platforms and approaches allowing commenting on published research
Blockchain approaches
Detecting and preventing peer-review fraud, peer-reviewer identity management, etc
How to Submit
In step 1 of the submission process (http://www.jmir.org/author) select the section “Theme Issue 2019: Using Technology to Detect, Combat, and Prevent Research Misconduct” from the section drop-down box (see also: How do I submit to a theme issue?).
For this special issue, the submission fees are waived and the regular Article Processing Fee is discounted by 20%.
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.
Background: With the advancement in information technology and mobile internet, digital health interventions (DHIs) are improving the care of cardiovascular diseases (CVDs). The impact of DHIs on cost-effective management of CVDs has been examined using the decision analytic model–based health technology assessment approach. Objective: The aim of this study was to perform a systematic review of the decision analytic model–based studies evaluating the cost-effectiveness of DHIs on the management of CVDs. Methods: A literature review was conducted in Medline, Embase, Cumulative Index to Nursing and Allied Health Literature Complete, PsycINFO, Scopus, Web of Science, Center for Review and Dissemination, and Institute for IEEE Xplore between 2001 and 2018. Studies were included if the following criteria were met: (1) English articles, (2) DHIs that promoted or delivered clinical interventions and had an impact on patients’ cardiovascular conditions, (3) studies that were modeling works with health economic outcomes of DHIs for CVDs, (4) studies that had a comparative group for assessment, and (5) full economic evaluations including a cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis, and cost-consequence analysis. The primary outcome collected was the cost-effectiveness of the DHIs, presented by incremental cost per additional quality-adjusted life year (QALY). The quality of each included study was evaluated using the Consolidated Health Economic Evaluation Reporting Standards. Results: A total of 14 studies met the defined criteria and were included in the review. Among the included studies, heart failure (7/14, 50%) and stroke (4/14, 29%) were two of the most frequent CVDs that were managed by DHIs. A total of 9 (64%) studies were published between 2015 and 2018 and 5 (36%) published between 2011 and 2014. The time horizon was ≤1 year in 3 studies (21%), >1 year in 10 studies (71%), and 1 study (7%) did not declare the time frame. The types of devices or technologies used to deliver the health interventions were short message service (1/14, 7%), telephone support (1/14, 7%), mobile app (1/14, 7%), video conferencing system (5/14, 36%), digital transmission of physiologic data (telemonitoring; 5/14, 36%), and wearable medical device (1/14, 7%). The DHIs gained higher QALYs with cost saving in 43% (6/14) of studies and gained QALYs at a higher cost at acceptable incremental cost-effectiveness ratio (ICER) in 57% (8/14) of studies. The studies were classified as excellent (0/14, 0%), good (9/14, 64%), moderate (4/14, 29%), and low (1/14, 7%) quality. Conclusions: This study is the first systematic review of decision analytic model–based cost-effectiveness analyses of DHIs in the management of CVDs. Most of the identified studies were published recently, and the majority of the studies were good quality cost-effectiveness analyses with an adequate duration of time frame. All the included studies found the DHIs to be cost-effective.
Background: Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient work. Objective: This study aimed to characterize the different tasks and contexts in which context-aware systems for patient work were used as well as to assess any existing evidence about the impact of such systems on health-related process or outcome measures. Methods: A total of 6 databases (MEDLINE, EMBASE, CINAHL, ACM Digital, Web of Science, and Scopus) were scanned using a predefined search strategy. Studies were included in the review if they focused on patients with chronic conditions, involved the use of a context-aware system to support patients’ health-related activities, and reported the evaluation of the systems by the users. Studies were screened by independent reviewers, and a narrative synthesis of included studies was conducted. Results: The database search retrieved 1478 citations; 6 papers were included, all published from 2009 onwards. The majority of the papers were quasi-experimental and involved pilot and usability testing with a small number of users; there were no randomized controlled trials (RCTs) to evaluate the efficacy of a context-aware system. In the included studies, context was captured using sensors or self-reports, sometimes involving both. Most studies used a combination of sensor technology and mobile apps to deliver personalized feedback. A total of 3 studies examined the impact of interventions on health-related measures, showing positive results. Conclusions: The use of context-aware systems to support patient work is an emerging area of research. RCTs are needed to evaluate the effectiveness of context-aware systems in improving patient work, self-management practices, and health outcomes in chronic disease patients.
Background: Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span. Methods: We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities. Results: The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.
Background: Social media use is now ubiquitous, but the growth in social media communications has also made it a convenient digital platform for drug dealers selling controlled substances, opioids, and other illicit drugs. Previous studies and news investigations have reported the use of popular social media platforms as conduits for opioid sales. This study uses deep learning to detect illicit drug dealing on the image and video sharing platform Instagram. Objective: The aim of this study was to develop and evaluate a machine learning approach to detect Instagram posts related to illegal internet drug dealing. Methods: In this paper, we describe an approach to detect drug dealers by using a deep learning model on Instagram. We collected Instagram posts using a Web scraper between July 2018 and October 2018 and then compared our deep learning model against 3 different machine learning models (eg, random forest, decision tree, and support vector machine) to assess the performance and accuracy of the model. For our deep learning model, we used the long short-term memory unit in the recurrent neural network to learn the pattern of the text of drug dealing posts. We also manually annotated all posts collected to evaluate our model performance and to characterize drug selling conversations. Results: From the 12,857 posts we collected, we detected 1228 drug dealer posts comprising 267 unique users. We used cross-validation to evaluate the 4 models, with our deep learning model reaching 95% on F1 score and performing better than the other 3 models. We also found that by removing the hashtags in the text, the model had better performance. Detected posts contained hashtags related to several drugs, including the controlled substance Xanax (1078/1228, 87.78%), oxycodone/OxyContin (321/1228, 26.14%), and illicit drugs lysergic acid diethylamide (213/1228, 17.34%) and 3,4-methylenedioxy-methamphetamine (94/1228, 7.65%). We also observed the use of communication applications for suspected drug trading through user comments. Conclusions: Our approach using a combination of Web scraping and deep learning was able to detect illegal online drug sellers on Instagram, with high accuracy. Despite increased scrutiny by regulators and policymakers, the Instagram platform continues to host posts from drug dealers, in violation of federal law. Further action needs to be taken to ensure the safety of social media communities and help put an end to this illicit digital channel of sourcing.
Background: Consumer-generated health data (CGHD) are any clinically relevant data collected by patients or their carers (consumers) that may improve health care outcomes. Like patient experience measures, these data reflect the consumer perspective and is part of a patient-centric agenda. The use of CGHD is believed to enhance diagnosis, patient engagement, and thus foster an improved therapeutic partnership with health care providers. Objective: The aim of this study was to further identify how these data were used by consumers and how it influences engagement via a validated framework. In addition, carer data has not been explored for the purpose of engagement. Methods: Study 1 used interviews with CGHD-experienced patients, carers, and doctors to understand attitudes about data collection and use, developing an ontological framework. Study 2 was a pilot trial with carers (parents) of children undergoing laparoscopic appendectomy. For 10 days carers generated and emailed surgical site photographs to a tertiary children’s hospital. Subsequently, carers were interviewed about the engagement framework. In total, 60 interviews were analyzed using theme and content analysis. Results: This study validates a framework anchored in engagement literature, which categorizes CGHD engagement outcomes into 4 domains: physiological, cognitive, emotional, and behavioral. CGHD use is complex, interconnected, and can be organized into 10 themes within these 4 domains. Conclusions: CGHD can instigate an ecosystem of engagement and provide clinicians with an enhanced therapeutic relationship through an extended view into the patient’s world. In addition to clinical diagnosis and efficient use of health care resources, data offer another tool to manage consumers service experience, especially the emotions associated with the health care journey. Collection and use of data increases consumers sense of reassurance, improves communication with providers, and promotes greater personal responsibility, indicating an empowering consumer process. Finally, it can also improve confidence and satisfaction in the service.
Background: Distributed ledger technology (DLT) holds great potential to improve health information exchange. However, the immutable and transparent character of this technology may conflict with data privacy regulations and data processing best practices. Objective: The aim of this paper is to develop a proof-of-concept system for immutable, interoperable, and General Data Protection Regulation (GDPR)–compliant exchange of blood glucose data. Methods: Given that there is no ideal design for a DLT-based patient-provider data exchange solution, we proposed two different variations for our proof-of-concept system. One design was based purely on the public IOTA distributed ledger (a directed acyclic graph-based DLT) and the second used the same public IOTA ledger in combination with a private InterPlanetary File System (IPFS) cluster. Both designs were assessed according to (1) data reversal risk, (2) data linkability risks, (3) processing time, (4) file size compatibility, and (5) overall system complexity. Results: The public IOTA design slightly increased the risk of personal data linkability, had an overall low processing time (requiring mean 6.1, SD 1.9 seconds to upload one blood glucose data sample into the DLT), and was relatively simple to implement. The combination of the public IOTA with a private IPFS cluster minimized both reversal and linkability risks, allowed for the exchange of large files (3 months of blood glucose data were uploaded into the DLT in mean 38.1, SD 13.4 seconds), but involved a relatively higher setup complexity. Conclusions: For the specific use case of blood glucose explored in this study, both designs presented a suitable performance in enabling the interoperable exchange of data between patients and providers. Additionally, both systems were designed considering the latest guidelines on personal data processing, thereby maximizing the alignment with recent GDPR requirements. For future works, these results suggest that the conflict between DLT and data privacy regulations can be addressed if careful considerations are made regarding the use case and the design of the data exchange system.
Background: Chronotype is the propensity for a person to sleep at a particular time during 24 hours. It is largely regulated by the circadian clock but constrained by work obligations to a specific sleep schedule. The discrepancy between biological and social time can be described as social jetlag (SJL), which is highly prevalent in modern society and associated with health problems. SJL and chronotypes have been widely studied in Western countries but have never been described in China. Objective: We characterized the chronotypes and SJL in mainland China objectively by analyzing a database of Chinese sleep-wake pattern recorded by up-to-date wearable devices. Methods: We analyzed 71,176 anonymous Chinese people who were continuously recorded by wearable devices for at least one week between April and July in 2017. Chronotypes were assessed (N=49,573) by the adjusted mid-point of sleep on free days (MSFsc). Early, intermediate, and late chronotypes were defined by arbitrary cut-offs of MSFsc <3 hours, between 3-5 hours, and >5 hours. In all subjects, SJL was calculated as the difference between mid-points of sleep on free days and work days. The correlations between SJL and age/body mass index/MSFsc were assessed by Pearson correlation. Random forest was used to characterize which factors (ie, age, body mass index, sex, nocturnal and daytime sleep durations, and exercise) mostly contribute to SJL and MSFsc. Results: The mean total sleep duration of this Chinese sample is about 7 hours, with females sleeping on average 17 minutes longer than males. People taking longer naps sleep less during the night, but they have longer total 24-hour sleep durations. MSFsc follows a normal distribution, and the percentages of early, intermediate, and late chronotypes are approximately 26.76% (13,266/49,573), 58.59% (29,045/49,573), and 14.64% (7257/49,573). Adolescents are later types compared to adults. Age is the most important predictor of MSFsc suggested by our random forest model (relative feature importance: 0.772). No gender differences are found in chronotypes. We found that SJL follows a normal distribution and 17.07% (12,151/71,176) of Chinese have SJL longer than 1 hour. Nearly a third (22,442/71,176, 31.53%) of Chinese have SJL<0. The results showed that 53.72% (7127/13,266), 25.46% (7396/29,045), and 12.71% (922/7257) of the early, intermediate, and late chronotypes have SJL<0, respectively. SJL correlates with MSFsc (r=0.54, P<.001) but not with body mass index (r=0.004, P=.30). Random forest model suggests that age, nocturnal sleep, and daytime nap durations are the features contributing to SJL (their relative feature importance is 0.441, 0.349, and 0.204, respectively). Conclusions: Our data suggest a higher proportion of early compared to late chronotypes in Chinese. Chinese have less SJL than the results reported in European populations, and more than half of the early chronotypes have negative SJL. In the Chinese population, SJL is not associated with body mass index. People of later chronotypes and long sleepers suffer more from SJL.
Background: Social media platforms constitute a rich data source for natural language processing tasks such as named entity recognition, relation extraction, and sentiment analysis. In particular, social media platforms about health provide a different insight into patient’s experiences with diseases and treatment than those found in the scientific literature. Objective: This paper aimed to report a study of entities related to chronic diseases and their relation in user-generated text posts. The major focus of our research is the study of biomedical entities found in health social media platforms and their relations and the way people suffering from chronic diseases express themselves. Methods: We collected a corpus of 17,624 text posts from disease-specific subreddits of the social news and discussion website Reddit. For entity and relation extraction from this corpus, we employed the PKDE4J tool developed by Song et al (2015). PKDE4J is a text mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Results: Using PKDE4J, we extracted 2 types of entities and relations: biomedical entities and relations and subject-predicate-object entity relations. In total, 82,138 entities and 30,341 relation pairs were extracted from the Reddit dataset. The most highly mentioned entities were those related to oncological disease (2884 occurrences of cancer) and asthma (2180 occurrences). The relation pair anatomy-disease was the most frequent (5550 occurrences), the highest frequent entities in this pair being cancer and lymph. The manual validation of the extracted entities showed a very good performance of the system at the entity extraction task (3682/5151, 71.48% extracted entities were correctly labeled). Conclusions: This study showed that people are eager to share their personal experience with chronic diseases on social media platforms despite possible privacy and security issues. The results reported in this paper are promising and demonstrate the need for more in-depth studies on the way patients with chronic diseases express themselves on social media platforms.
Background: Individuals with chronic obstructive pulmonary disease (COPD) live with the burden of a progressive life-threatening condition that is often accompanied by anxiety and depression. The severity of the condition is usually considered from a clinical perspective and characterized according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification of severity (1-4) and a risk assessment (A through D) that focuses on the patient’s symptoms and number of exacerbations, but information about perceived health or ability to manage the condition are rarely included. Objective: We evaluated 3 patient-reported outcome measurements (PROMs) to examine how these can be used to report on individuals with COPD who were supported by a digitally assisted intervention that aims to increase the patient’s management of their condition to improve their well-being. Methods: A total of 93 individuals with COPD were enrolled. At baseline and after 6 and 12 months, we measured self-reported self-management (Health Education Impact Questionnaire, heiQ) and health literacy (Health Literacy Questionnaire, HLQ), and physical and mental health (Short Form-36, SF-36) PROMs were collected. The scores of the 19 PROM dimensions were related to COPD severity, that is, GOLD risk assessment, pulmonary function at entry, and number of exacerbations of a period up to 12 months. The initial PROM scores were also compared with pulmonary function, exacerbations, and GOLD risk assessment to predict the number of contacts within the first 90 days. Results: At baseline, 2 dimensions from heiQ and SF-36 Physical health differed significantly between GOLD risk factor groups, indicating more distress and poorer attitudes and health status with increasing severity (GOLD risk assessment). Pulmonary function (FEV1) was negatively associated with the severity of the condition. After 6 months, we observed an increase in heiQ6 (skill and technique acquisition) and a reduction in emotional distress. The latter effect persisted after 12 months, where heiQ4 (self-monitoring and insight) also increased. HLQ3 (actively managing my health) decreased after 6 and 12 months. The number of exacerbations and the GOLD risk factor assessment predicted the number of contacts during the first 90 days. Furthermore, 2 of the PROMS heiQ6 (skill and technique acquisition) and HLQ8 (ability to find good health information) evaluated at baseline were associated with the number of contacts within the first 90 after enrollment. The pulmonary function was not associated with the number of contacts. Conclusions: Our data suggest that selected dimensions from HLQ, heiQ, and SF-36 can be used as PROMs in relation to COPD to provide researchers and clinicians with greater insight into how this condition affects individuals’ ability to understand and manage their condition and perception of their physical and mental health. The PROMs add to the information obtained with the clinical characteristics including the GOLD risk factor assessment. International Registered Report Identifier (IRRID): RR2-10.2196/resprot.6506
Background: Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. Objective: The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. Methods: The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. Results: Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent’s health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents’ health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. Conclusions: The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.
Background: A critical issue in understanding the benefits of Web-based interventions is the lack of information on the sustainability of those benefits. Sustainability in studies is often determined using group-level analyses that might obscure our understanding of who actually sustains change. Person-centric methods might provide a deeper knowledge of whether benefits are sustained and who tends to sustain those benefits. Objective: The aim of this study was to conduct a person-centric analysis of longitudinal outcomes, examining well-being in participants over the first 3 months following a Web-based happiness intervention. We predicted we would find distinct trajectories in people’s pattern of response over time. We also sought to identify what aspects of the intervention and the individual predicted an individual’s well-being trajectory. Methods: Data were gathered from 2 large studies of Web-based happiness interventions: one in which participants were randomly assigned to 1 of 14 possible 1-week activities (N=912) and another wherein participants were randomly assigned to complete 0, 2, 4, or 6 weeks of activities (N=1318). We performed a variation of K-means cluster analysis on trajectories of life satisfaction (LS) and affect balance (AB). After clusters were identified, we used exploratory analyses of variance and logistic regression models to analyze groups and compare predictors of group membership. Results: Cluster analysis produced similar cluster solutions for each sample. In both cases, participant trajectories in LS and AB fell into 1 of 4 distinct groups. These groups were as follows: those with high and static levels of happiness (n=118, or 42.8%, in Sample 1; n=306, or 52.8%, in Sample 2), those who experienced a lasting improvement (n=74, or 26.8% in Sample 1; n=104, or 18.0%, in Sample 2), those who experienced a temporary improvement but returned to baseline (n=37, or 13.4%, in Sample 1; n=82, or 14.2%, in Sample 2), and those with other trajectories (n=47, or 17.0%, in Sample 1; n=87, or 15.0% in Sample 2). The prevalence of depression symptoms predicted membership in 1 of the latter 3 groups. Higher usage and greater adherence predicted sustained rather than temporary benefits. Conclusions: We revealed a few common patterns of change among those completing Web-based happiness interventions. A noteworthy finding was that many individuals began quite happy and maintained those levels. We failed to identify evidence that the benefit of any particular activity or group of activities was more sustainable than any others. We did find, however, that the distressed portion of participants was more likely to achieve a lasting benefit if they continued to practice, and adhere to, their assigned Web-based happiness intervention.
Background: Despite substantial improvements in technology and the increased demand for technology-enabled behavioral health tools among consumers, little progress has been made in easing the burden of mental illness. This may be because of the inherent challenges of conducting traditional clinical trials in a rapidly evolving technology landscape. Objective: This study sought to validate the effectiveness of Pacifica, a popular commercially available app for the self-management of mild-to-moderate stress, anxiety, and depression. Methods: A total of 500 adults with mild-to-moderate anxiety or depression were recruited from in-app onboarding to participate in a randomized waitlist controlled trial of Pacifica. We conducted an all-virtual study, recruiting, screening, and randomizing participants through a Web-based participant portal. Study participants used the app for 1 month, with no level of use required, closely mimicking real-world app usage. Participants in the waitlist group were given access to the app after 1 month. Measurements included self-reported symptoms of stress, anxiety, depression, and self-efficacy. We performed an intent-to-treat analysis to examine the interactive effects of time and condition. Results: We found significant interactions between time and group. Participants in the active condition demonstrated significantly greater decreases in depression, anxiety, and stress and increases in self-efficacy. Although we did not find a relationship between overall engagement with the app and symptom improvement, participants who completed relatively more thought record exercises sustained improvements in their symptoms through the 2-month follow-up to a greater degree than those who completed fewer. In addition, we found that participants who reported concomitantly taking psychiatric medications during the trial benefitted less from the app, as measured by the symptoms of anxiety and stress. Conclusions: This study provides evidence that Pacifica, a popular commercially available self-help app, is effective in reducing self-reported symptoms of depression, anxiety, and stress, particularly among individuals who utilize thought records and are not taking psychiatric medication. Trial Registration: ClinicalTrials.gov NCT03333707; https://clinicaltrials.gov/ct2/show/NCT03333707 (Archived by WebCite at http://www.webcitation.org/78YE07ADB)
Background: Despite the rapid adoption of genomic sequencing in clinical practice, clinical sequencing reports in electronic health record (EHR) systems are currently being written in unstructured for...
Background: Despite the rapid adoption of genomic sequencing in clinical practice, clinical sequencing reports in electronic health record (EHR) systems are currently being written in unstructured formats such as PDF or free text. These formats hinder the implementation of a clinical decision support system and secondary research applications. Therefore, there is an urgent need to standardize genomic sequencing reports in EHR systems. Objective: To implement standardized machine-processable clinical sequencing reports in an EHR system, the ISO/TS 20428 international standard was developed for a structured template. This study aims to verify the actual use of the ISO/TS 20428 standard in clinical practice settings. Methods: Here, we describe the practical implementation of ISO/TS 20428 using Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) genomics implementation guidance to efficiently deliver required genomic sequencing results to clinicians through an EHR system. Results: We successfully administered a structured genomic sequencing report in a tertiary hospital in Korea based on international standards. In total, 90 FHIR resources were used. Among 41 resources for the required fields, 26 were reused and 15 were extended. For the optional fields, 28 were reused and 21 were extended. Conclusions: To share and apply genomic sequencing data in both clinical practice and translational research, it is essential to identify the applicability of the standard based information system in a practical setting. This prototyping work proves that clinical genomics sequencing reporting data can be effectively implemented in an EHR system using the existing ISO/TS 20428 standard and FHIR resources.
Background: Bipolar disorder is a prevalent mental disease imposing a high societal burden. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention...
Background: Bipolar disorder is a prevalent mental disease imposing a high societal burden. Accurate forecasting of symptom scores can be used to improve disease monitoring, enable early intervention and eventually help prevent costly hospitalizations. While several studies have examined the use of smartphone data to detect mood, only few studies deal with forecasting mood one or more days ahead of time. Objective: The objective of this work is to examine the feasibility of forecasting daily subjective mood based on daily self-assessments collected from bipolar disorder patients via a smartphone-based system in a randomized clinical trial. Methods: We apply hierarchical Bayesian regression models, a multi-task learning method, to account for individual differences and forecast mood up to seven days ahead based on 15,975 smartphone self-assessments from 84 bipolar disorder patients participating in a randomized clinical trial. We report the results of two time-series cross-validation one day ahead prediction experiments corresponding to two different real-world scenarios and compare the outcomes to commonly used baselines methods. We then apply the best model to evaluate a seven-day forecast. Results: The best performing model used a history of 4 days of self-assessments to predict future mood scores with historical mood being the most important predictor variable. The proposed hierarchical Bayesian regression model outperformed pooled and separate models in a one-day forecast time series cross-validation experiment and achieved predicted R^2=0.51 and RMSE=0.32 for mood scores on a scale of -3 to 3. When increasing the forecast horizon, forecast errors also increase and the forecast regresses towards the mean of the data distribution. Conclusions: We found that our proposed method can forecast mood several days ahead with low error compared to common baseline methods. The applicability of a mood forecast in clinical treatment of bipolar disorder is also discussed.
Background: Diabetes poses heavy social and economic burdens on the world. Diabetes management mobile apps show great potential for diabetes self-management. However, the uptake of diabetes apps among...
Background: Diabetes poses heavy social and economic burdens on the world. Diabetes management mobile apps show great potential for diabetes self-management. However, the uptake of diabetes apps among diabetes patients is poor. The factors influencing patients’ intention to use these apps are unclear. Understanding patients’ behavioral intention is necessary to support the development and promotion of diabetes app use. Objective: To identify the determinants of patients’ intention to use diabetes apps based on an integrated theoretical model. Methods: The hypotheses of our research model were developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) integrated with context-related hypotheses. From 20 April to 20 May 2019, adult diabetes patients across China who were familiar with diabetes management mobile apps were surveyed using the web-based survey tool Sojump (Changsha ran Xing InfoTech Ltd). Structural equation modeling was used to analyze the data. Results: A total of 746 qualified questionnaires were collected. The fitness indices suggested that the collected data fit well with the research model. The model explained 62.6% of the variance in performance expectancy and 57.1% of the variance in behavioral intention. Performance expectancy and social influence had the strongest total effects on behavioral intention (β=.482 p=0.001). Performance expectancy (β=.482 P=.001), social influence (β=.223 P=.003), facilitating conditions (β=.17 P=.006), perceived disease threat (β=.073 P=.005) and perceived privacy risk (β=-.073 P=.012) had direct effects on behavioral intention. Additionally, social influence, effort expectancy and facilitating conditions had indirect effects on behavioral intention that were mediated by performance expectancy. Social influence had the highest indirect effects among the three constructs (β=.259 P=.001). Conclusions: Performance expectancy and social influence are the most important determinants of the intention to use diabetes apps. Healthcare technology companies must improve the usefulness of apps and carry out research to provide clinical evidence for the apps’ effectiveness, which will benefit the promotion of these apps. Facilitating conditions and perceived privacy risk also have an impact on behavioral intention. Therefore, it is necessary to improve facilitating conditions and provide solid privacy protection. Our study supports the use of UTAUT in explaining patients’ intention to use diabetes management mobile apps. Context-related determinants should also be taken into consideration.
Background: Supporting patients to engage in (online) self-management tools is increasingly gaining in importance, but the engagement of healthcare professionals lags behind. This can partly be explai...
Background: Supporting patients to engage in (online) self-management tools is increasingly gaining in importance, but the engagement of healthcare professionals lags behind. This can partly be explained by resistance among healthcare professionals. Objective: The objective of this study was to investigate drivers of resistance among oncology nurses towards online self-management tools in cancer care. Methods: Drawing from earlier research, combining clinical and marketing perspectives, we developed the Resistance to Innovation model (RTI-model). The RTI-model distinguishes between passive and active resistance, which can be enhanced or reduced by functional drivers (incompatibility, complexity, lack of value, risk) and psychological drivers (role ambiguity, social pressure from the institute, peers, and patients). Both types of drivers can be moderated by staff-, organization-, patient- and environment-related factors. We executed a survey covering all components of the RTI-model on a cross-sectional sample of nurses working in oncology in the Netherlands. Structural equation modelling was used to test the full model, using a hierarchical approach. Results: The goodness of fit statistic of the uncorrected base model of the RTI-model (n=239) was acceptable (χ2(df) = 9.243 (1); CFI=0.95; TLI=0.21; RMSEA=0.19; SRMR=0.016). In line with the RTI-model we indeed found that passive and active resistance among oncology nurses towards (online) self-management tools were driven by both functional and psychological drivers. Passive resistance was enhanced by complexity, lack of value, and risk, and reduced by institutional social pressure. Active resistance was enhanced by complexity, lack of value, and social pressure from peers, and reduced by social pressure from the institute and patients. Nurses’ expertise regarding (online) self-management moderated the effects of complexity, lack of value, risk, role ambiguity, and social pressure from thePassive and active resistance are driven by functional and psychological drivers, and these drivers are moderated by expertise, managerial support and governmental influence. institute, peers, and patients (P=.030). Managerial support moderated complexity, lack of value, role ambiguity, and social pressure from peers and the institute (P=.004). Governmental influence moderated the effects of complexity, lack of value, risk, role ambiguity, and social pressure from peers and the institute (P=.037). Conclusions: Passive and active resistance are driven by functional and psychological drivers, and these drivers are moderated by expertise, managerial support and governmental influence.
Background: To develop websites that enhance Internet users’ health knowledge, it is important to identify relevant factors associated with obtaining health knowledge via the Internet. Although an a...
Background: To develop websites that enhance Internet users’ health knowledge, it is important to identify relevant factors associated with obtaining health knowledge via the Internet. Although an association between eHealth literacy (eHL) and knowledge of colorectal cancer (CRC) has been reported, little is known whether eHL is associated with obtaining knowledge of CRC via the Internet. Objective: This study aimed to compare the results obtained from Internet users with high or low eHL in searching and using a reputable cancer website to gain CRC knowledge. Methods: This study used respondents to Internet based pre-and post-surveys conducted in 2012. Potential respondents (n = 3,307) were identified from registered individuals aged 40–59 years (n = 461,160) in a Japanese Internet survey company. A total of 1,069 participants responded (response rate: 32.3%), and these pre-survey responders were then divided into high or low eHL groups using the Japanese eHealth Literacy Scale median score (23.5 points). From each group, 130 randomly selected individuals were invited to review the contents of a reputable CRC website, the Cancer Information Service managed by the National Cancer Center, and to respond to a post-survey via e-mail; responses were obtained from 107 individuals from each group. Twenty responses to knowledge statements regarding the definition, risk factors, screening prevention and symptoms of CRC were obtained at pre- and post-surveys, and differences in the correct responses between high and low eHL groups compared using the McNemar test. Results: The mean age of the participants was 49.1 (5.5) years. Four statements showed a significant increase in correct responses in both eHL groups pre- and post-survey: “S4. The risk of CRC is greater as a person gets older” (high eHL: P = 0.039, low eHL: P = 0.012), “S8. Cigarette smoking is a risk factor for CRC” (high eHL: P < 0.001, low eHL: P = 0.020), “S11. Obesity is a risk factor for CRC” (high eHL: P = 0.030, low eHL: P = 0.047), and “S12. Excess alcohol consumption is a risk factor for CRC” (high eHL: P = 0.002, low eHL: P = 0.003). Three statements showed a statistically significant increase in correct responses in the high eHL group only: “S1. CRC is cancer of the colon or rectum” (P = 0.003), “S5. The risk of CRC is the same between men and women” (P = 0.041), and “S9. Red meat intake is a risk factor for CRC” (P = 0.002), whereas only one response did in the low eHL group: “S17. Bloody stools are a symptom of CRC” (P = 0.004). Conclusions: Low eHL Internet users appeared less capable of obtaining knowledge of CRC through searching and understanding information from a reputable cancer website than high eHL Internet users.
Background: SiSU Wellness health check stations collect data on a range of self-reported and machine measured health indicators, including diabetes status, physical activity levels, waist circumferenc...
Background: SiSU Wellness health check stations collect data on a range of self-reported and machine measured health indicators, including diabetes status, physical activity levels, waist circumference, dietary practices, heart rate, blood pressure, weight, Body Mass Index (BMI) and body fat percentage. Users of the health stations are able to monitor their progress and changes to health over time by connecting their health check station profile to a free application downloadable from Google Play or the iTunes store. The stations and associated application are intended to assist consumers by allowing them to monitor their health status over time and providing alerts to consumers when follow up with a General Practitioner (GP) is recommended. Objective: To assess the demographic representativeness of SiSU Health Station users, identify the factors associated with repeat utilisation stations, and determine whether the health status of repeat users changed between their baseline and final health checks. Methods: SiSU Health Station users were compared with 2014-2015 Australian National Health Survey participants on key demographic and health characteristics to determine representativeness. Binary logistic regression analyses were used to compare demographic and health characteristics of repeat and one-time users. Baseline and final health checks of repeat users were compared using McNemar’s Tests and Wilcoxon Signed Rank Tests. The relationship between number of checks and final health scores was investigated using generalised linear models. Results: Data from 180,442 SiSU Health Station health checks conducted at 192 locations across Australia between October 2017 and June 2018, including 8,441 repeat users. SiSU Health Stations located in Priceline Pharmacies accounted for 98.4% of checks.
The demographic profile of SiSU Health Station users differs from that of the general population. A larger proportion of SiSU users were female (55.87% vs 50.72%), younger (47.87% vs 34.49% under 35 years) and socio-economically advantaged (35.68% vs 20.325. When considering the gender profile of Priceline Pharmacy customers, males were found to be substantially over-indexed on health station usage, accounting for 44.10% of health checks but only 3.00% of customers.
Compared with NHS participants, a smaller proportion of SiSU Health Station users were overweight or obese, were smokers, had high blood pressure or had diabetes. When data were weighted for demographic differences, only rates of high blood pressure were found to be lower for SiSU users compared to National Health Survey participants (OR=1.26, p<0.001). Repeat users were more likely to be female (OR=1.37, p<0.001), younger (OR=0.99, p<0.001), and from high socio-economic status areas - those residing in SEIFA quintiles 4 and 5 were significantly more likely to be repeat users compared to those residing in quintile 1 (OR=1.243, p<0.001 and OR=1.151, p<0.001 respectively). Repeat users were more likely to have higher body mass index (OR=1.02 p<0.001), high blood pressure (OR=1.15, p<0.001), and less likely to be smokers (OR=0.77, p<0.001).
Significant improvements in health status were observed for repeat users. Mean BMI decreased by 0.97kg/m2 from baseline to final check (z=-14.24, p<0.001), while the proportion of people with high blood pressure decreased from 15.8% to 12.9% (2=38.21, p<0.001). The proportion of smokers decreased from 11.9% to 10.1% (2=48.39, p<0.001). The number of repeat health checks was found to be significantly associated with smoking status (OR=0.96, p<0.048), but not with higher blood pressure (p=0.142) or BMI (p=0.225). Conclusions: These findings provide valuable insight into the health benefits of health stations for self-monitoring and partially support previous research regarding the effect of demographics and health status on uptake of self-management of health.
