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Background: There is currently a lack of comprehensive, yet intuitive and usable formative evaluation frameworks of health information technology (HIT) implementations. Objective: We therefore sought to develop and apply such a framework: the Technology, People, Organizations and Macro-environmental factors (TPOM) Framework. Methods: We drew on qualitative data from three national formative evaluations of different HIT (i.e. electronic health record, electronic prescribing and clinical decision support functionality) interventions. The combined dataset comprised 703 semi-structured interviews, 663 hours of observations and 864 documents gathered from a range of care settings across NHS England and NHS Scotland. Data analysis took place over a period of 10 years guided by a framework we iteratively developed that was informed by the existing evidence base. Results: TPOM dimensions are intimately related and each include a number of sub-themes that evaluators need to consider. Whilst technological functionalities are crucial in getting an initiative off the ground, system design needs to be cognizant of the accompanying social and organizational transformations required to ensure that technologies deliver the desired value for a variety of stakeholders. Wider structural changes, characterized by shifting policy landscapes and markets, influence technologies and the ways these are used by organizations and staff. Conclusions: The TPOM Framework supports formative evaluations of health IT implementation and digitally enabled transformation efforts. There is now a need for prospective application of the TOPM Framework to determine its value.
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: Adolescents’ use of social media, which has increased considerably in the past decade, has both positive and negative influences on adolescents’ health and health behaviors. As social media is the most prominent communication tool of choice for adolescents, it is important to understand the relationship between the frequency of social media use and health behaviors among this population. Objective: The objective of our study was to examine the associations between the frequency of social media use and physical activity and sleep adequacy among middle and high school students. Methods: We used data from the Monitoring the Future survey (2014 and 2015), a nationally representative, annual, cross-sectional survey of American 8th-, 10th-, and 12th-grade students (N=43,994). Health behaviors examined were frequency of vigorous physical activity and frequency of getting 7 hours of sleep (never/seldom, sometimes, and every day/nearly every day). We measured frequency of social media use using a Likert-like scale (never, a few times a year, 1-2 times a month, once a week, or every day). Multivariable generalized ordered logistic regressions examined the association of social media use with different levels of physical activity and sleep. We estimated marginal effects (MEs) for the main independent variable (social media use frequency) by holding all other variables at their observed values. Results: The study population comprised 51.13% (21,276/42,067) female students, 37.48% (17,160/43,994) from the South, and 80.07% (34,953/43,994) from a metropolitan area, with 76.90% (33,831/43,994) reporting using social media every day. Among physically active students, frequent social media use was associated with a higher likelihood of vigorous daily exercise (ME 50.1%, 95% CI 49.2%-51.0%). Among sedentary students, frequent social media use was associated with a lower likelihood of vigorous daily exercise (ME 15.8%, 95% CI 15.1%-16.4%). Moderately active students who used social media once or twice a month had the highest likelihood of reporting vigorous daily exercise (ME 42.0%, 95% CI 37.6%-46.3%). Among those who normally got adequate sleep, daily social media users were least likely to report adequate sleep (ME 41.3%, 95% CI 40.4%-42.1%). Among those who were usually sleep deprived, daily social media users were more likely to report adequate sleep (ME 18.3%, 95% CI 17.6%-19.0%). Conclusions: Regular social media use every day was associated with a reinforcement of health behaviors at both extremes of health behaviors, whereas a medium intensity of social media use was associated with the highest levels of physical activity and lowest sleep adequacy among those with moderate health behaviors. Hence, finding an optimal level of social media use that is beneficial to a variety of health behaviors would be most beneficial to adolescents who are in the middle of the health behavior spectrum.
Background: The ability to perform vape tricks (ie, blowing large vapor clouds or shapes like rings) using e-cigarettes appeals to youth. Vape tricks are promoted on social media, but the promotion of vape tricks on social media is not well understood. Objective: The aim of this study was to examine how vape tricks were promoted on YouTube to youth. Methods: Videos on vape tricks that could be accessed by underage youth were identified. The videos were coded for number of views, likes, dislikes, and content (ie, description of vape tricks, e-cigarette devices used for this purpose, video sponsors [private or industry], brand marketing, and contextual characteristics [eg, model characteristics, music, and profanity]). Results: An analysis of 59 sample videos on vape tricks identified 25 distinct vape tricks. These videos had more likes than dislikes (11 to 1 ratio) and a 32,017 median view count. 48% (28/59) of the videos were posted by industry accounts (27% [16/59] provaping organizations, 15% [9/59] online shops, and 3% [2/59] vape shops) and 53% by private accounts (55% [17/31] private users, 26% [8/31] vape enthusiasts, and 19% [6/31] YouTube influencers); 53% (31/59) of the videos promoted a brand of e-cigarette devices, e-liquids, or online/vape shops, and 99% of the devices used for vape tricks were advanced generation devices. The models in the videos were 80.2% (160/198) male, 51.5% white (102/198), and 61.6% (122/198) aged 18 to 24 years; 85% (50/59) of the videos had electronic dance music and hip hop, and 32% (19/59) had profanity. Conclusions: Vape trick videos on YouTube, about half of which were industry sponsored, were accessible to youth. Restrictions of e-cigarette marketing on social media, such as YouTube, are needed.
Background: Personalized risk assessments can help medical providers determine targeted populations for counseling and risk reduction interventions. Objective: The objective of this study was to develop a social media platform–based HIV risk prediction tool for men who have sex with men (MSM) in China based on an independent MSM cohort to help medical providers determine target populations for counseling and risk reduction treatments. Methods: A prospective cohort of MSM from Shenyang, China, followed from 2009 to 2016, was used to develop and validate the prediction model. The eligible MSM were randomly assigned to the training and validation dataset, and Cox proportional hazards regression modeling was conducted using predictors for HIV seroconversion selected by the training dataset. Discrimination and calibration were performed, and the related nomogram and social media platform–based HIV risk assessment tool were constructed. Results: The characteristics of the sample between the training dataset and the validation dataset were similar. The risk prediction model identified the following predictors for HIV seroconversion: the main venue used to find male sexual partners, had condomless receptive or insertive anal intercourse, and used rush poppers. The model was well calibrated. The bootstrap C-index was 0.75 (95% CI 0.65-0.85) in the training dataset, and 0.60 (95% CI 0.45-0.74) in the validation dataset. The calibration plots showed good agreement between predicted risk and the actual proportion of no HIV infection in both the training and validation datasets. Nomogram and WeChat-based HIV incidence risk assessment tools for MSM were developed. Conclusions: This social media platform–based HIV infection risk prediction tool can be distributed easily, improve awareness of personal HIV infection risk, and stratify the MSM population based on HIV risk, thus informing targeted interventions for MSM at greatest risk for HIV infection.
Background: It is estimated that 20% to 50% of patients do not take their medication correctly, and this leads to increased morbidity and inefficacy of therapeutic approaches. Fostering treatment adherence is a priority objective for all health systems. The growth of mobile apps to facilitate therapeutic adherence has significantly increased in recent years. However, the effectiveness of the apps for this purpose has not been evaluated. Objective: This study aimed to analyze whether mobile apps are perceived as useful for managing medication at home and if they actually contribute to increasing treatment adherence in patients. Methods: We carried out a systematic review of research published using Scopus, Cochrane Library, ProQuest, and MEDLINE databases and analyzed the information about their contribution to increasing therapeutic adherence and the perceived usefulness of mobile apps. This review examined studies published between 2000 and 2017. Results: Overall, 11 studies fulfilled the inclusion criteria. The sample sizes of these studies varied between 16 and 99 participants. In addition, 7 studies confirmed that the mobile app increased treatment adherence. In 5 of them, the before and after adherence measures suggested significant statistical improvements, when comparing self-reported adherence and missed dose with a percentage increase ranging between 7% and 40%. The users found mobile apps easy to use and useful for managing their medication. The patients were mostly satisfied with their use, with an average score of 8.1 out of 10. Conclusions: The use of mobile apps helps increase treatment adherence, and they are an appropriate method for managing medication at home.
Background: As inpatient medical rehabilitation serves to promote work ability, vocational reintegration is a crucial outcome. However, previous Web-based trials on coping with work-related stress have been limited to Web-based recruitment of study participants. Objective: The aim of our study was to evaluate the implementation of an empirically supported transdiagnostic psychodynamic Web-based aftercare program GSA (Gesund und Stressfrei am Arbeitsplatz [Healthy and stress-less at the workplace])-Online plus into the clinical routine of inpatient medical rehabilitation, to identify characteristics of patients who have received the recommendation for GSA-Online plus, and to determine helpfulness of the intervention and satisfaction of the participants as well as improvement in quality of life and mental health status of the regular users of GSA-Online plus. Methods: GSA-Online plus was prescribed by physicians at termination of orthopedic psychosomatic inpatient rehabilitation. Participants’ use of the program, work-related attitudes, distress, and quality of life were assessed on the Web. Results: In 2 rehabilitation centers, 4.4% (112/2562) of rehabilitants got a recommendation for GSA-Online plus during inpatient rehabilitation. Compared with usual person aftercare, the Web-based aftercare program was rarely recommended by physicians. Recommendations were made more frequently in psychosomatic (69/1172, 5.9%) than orthopedic (43/1389, 3.1%) rehabilitation (χ2 1=11.845, P=.001, Cramér V=−0.068) and to younger patients (P=.004, d=0.28) with longer inpatient treatment duration (P<.001, r=−0.12) and extended sick leaves before inpatient medical rehabilitation (P=.004; Cramér V=0.072). Following recommendation, 77% (86/112) of rehabilitants participated in Web-based aftercare. Completers (50/86, 58%) reported statistically significant improvements between discharge of inpatient treatment and the end of the aftercare program for subjective work ability (P=.02, d=0.41), perceived stress (P=.01, d=−0.38), functioning (P=.002, d=−0.60), and life satisfaction (P=.008, d=0.42). Conclusions: Physicians’ recommendations of Web-based aftercare are well accepted by patients who derive considerable benefits from participation. However, a low rate of prescription compared with other usual aftercare options points to barriers among physicians to prescribing Web-based aftercare.
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: There is currently a lack of comprehensive, yet intuitive and usable formative evaluation frameworks of health information technology (HIT) implementations. Objective: We therefore sought...
Background: There is currently a lack of comprehensive, yet intuitive and usable formative evaluation frameworks of health information technology (HIT) implementations. Objective: We therefore sought to develop and apply such a framework: the Technology, People, Organizations and Macro-environmental factors (TPOM) Framework. Methods: We drew on qualitative data from three national formative evaluations of different HIT (i.e. electronic health record, electronic prescribing and clinical decision support functionality) interventions. The combined dataset comprised 703 semi-structured interviews, 663 hours of observations and 864 documents gathered from a range of care settings across NHS England and NHS Scotland. Data analysis took place over a period of 10 years guided by a framework we iteratively developed that was informed by the existing evidence base. Results: TPOM dimensions are intimately related and each include a number of sub-themes that evaluators need to consider. Whilst technological functionalities are crucial in getting an initiative off the ground, system design needs to be cognizant of the accompanying social and organizational transformations required to ensure that technologies deliver the desired value for a variety of stakeholders. Wider structural changes, characterized by shifting policy landscapes and markets, influence technologies and the ways these are used by organizations and staff. Conclusions: The TPOM Framework supports formative evaluations of health IT implementation and digitally enabled transformation efforts. There is now a need for prospective application of the TOPM Framework to determine its value.
Background: Patient monitoring is central to the safety of state-of-the-art perioperative and intensive care medicine. While current state-of-the-art patient monitors display vital signs in the form o...
Background: Patient monitoring is central to the safety of state-of-the-art perioperative and intensive care medicine. While current state-of-the-art patient monitors display vital signs in the form of numbers and curve forms, Visual Patient technology creates an easy to interpret virtual patient avatar model, which, in a previous study, enabled anesthesia providers to perceive more vital sign information during short glances than conventional monitoring. Objective: In this study, we used eye-tracking technology to study the deeper mechanisms underlying information perception in both, conventional and avatar-based patient monitoring. Methods: In this prospective, multi-center study with a within subject design, we showed 32 anesthesia experts (physicians and nurse anesthetists) a total of four 3- and 10-second monitoring scenarios alternatingly as either routine conventional or avatar-based monitoring in random sequence. All participants observed the same scenarios with both monitoring technologies. After each scenario, we asked participants to report the status of the vital signs. Using an eye-tracker, we recorded the participants’ gaze paths as they observed the scenarios. From the eye-tracking recordings, we evaluated which vital signs the participants had visually fixated, how often and for how long during a scenario, and therefore, could have potentially read and perceived this vital sign. We compared the frequencies and durations with which the participants had visually fixated the vital signs between the two monitoring technologies. Results: Participants visually fixated more vital signs per scenario, median (IQR): 10 (9-11) vs. 6 (4-8), p<0.001 in avatar-based monitoring (median of differences: 3 vital signs (95% confidence interval [95%CI 3-4]). In all four scenarios, the participants visually fixated nine of the 11 total vital signs shown statistically significantly longer using the avatar. Four critical vital signs, i.e., pulse rate, blood pressure, oxygen saturation, and respiratory rate were visible almost the entire time of a scenario with avatar-based monitoring, while with conventional monitoring, these were only visible for fractions of the observations. Visual fixation of a certain vital sign was associated with the correct perception of that certain vital sign in both technologies. Phi coefficient for avatar: 0.358, for conventional monitoring: 0.515, both p<0.001. Conclusions: This study uncovered, by use of eye-tracking, one of the mechanisms responsible for the improved information transfer in avatar-based monitoring. The design of the avatar technology, which presents the information about multiple vital signs integrated into forms and colors of the corresponding anatomical parts of a patient avatar model results in more information being visible with every visual fixation. With this finding confirmed by eye-tracking, this study adds a new and higher level of empirical evidence as to why avatar-based monitoring improves the perception of vital sign information compared to conventional monitoring.
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.
