Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Patient-generated health data or lifelog is becoming an important factor in customized health care as digital health technologies such as personal health record and wearables are advanced. Although utilizing the technologies for a variety of chronic health conditions is reported to be acceptable and useful, there is a lack of evidence of associations between the use of the technologies and the change of health outcome and the sustainable use of digital health application. Objective: We aim at examining the impact of the use of personal health record and wearables on health outcome improvement and sustained use of the health application that can be associated with patient engagement. Methods: We developed an Android-based smartphone application and utilized a wristband-type activity tracker (Samsung Charm) to collect data on health-related daily activities from individual patients. Dietary record, daily step counts, sleep log, subjective stress amount, blood pressure, and weight values were recorded. We conducted a prospective randomized clinical trial across four weeks on those diagnosed with obstructive sleep apnea (OSA) who had visited the outpatient clinic of Seoul National University Bundang Hospital. The trial randomly assigned 60 patients to three subgroups including two intervention groups: 1) mobile app and wearable device users (n = 20), 2) mobile app only users (n = 20), and 3) controls (n = 20). The primary outcome measure was weight change. Body weights before and after the trial were recorded and analyzed during clinic visits. Changes in OSA-related respiratory parameters such as Respiratory Disturbance, Apnea-Hypopnea, and Oxygenation Desaturation indexes and snoring comprised the secondary outcome and were analyzed for each participant. Results: Among 60 participants (40 in two intervention groups and 20 in a control group) who were enrolled, 43 completed the study. We collected the individual data for each group during the trial, specifically, anthropometric measurement and lab test results for health outcomes, and the application usage logs for sustainable use were collected and analyzed. The body weight showed a significant reduction (1.66%) in the two intervention groups after intervention, and the mobile app only group showed more weight loss (2.13%) compared to the controls (0.47%) (P =.01). There were no significant changes in sleep-related health outcomes. For a sustainable use point of view, the average daily step counts (8,165 steps) from the app plus wearable group were significantly higher than those (6,034 steps) from the app only group because they collected step count data from different devices (P =.02). The average rate of data collection was not different in physical activity (49.82% vs 49.96%, P =.99), food intake (32.67% vs 32.82%, P =.98), sleep (32.01% vs 32.45%, P =.95), stress (30.11% vs 27.33%, P =.70) and weight (32.82% vs 31.87%, P =.90) in the app plus wearable and app only groups, respectively. Conclusions: We tried to integrate personal health record data that allows clinicians and patients to share lifelog data with the clinical workflow to support lifestyle interventions. Our results suggest that personal health record-based intervention is successful in losing body weight and improvement in lifestyle behavior. We designed a clinical trial to collect data about physical activity and diet using wearable devices in patients with lifestyle-related diseases and deliver care interventions across four weeks. Our results reveal how patients and clinicians can apply personal health data and mobile applications. Clinical Trial: ClinicalTrials.gov NCT03200223

Background: Neonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data. Objective: The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. Methods: We used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. Results: The performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance. Conclusions: The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

Background: Despite the clinical importance of sepsis-associated acute kidney injury (SA-AKI), little is known about its epidemiology. We aimed to investigate the incidence and outcomes of SA-AKI, as well as the risk factors for mortality among patients with severe SA-AKI in critically ill patients. Methods: This secondary multicenter, observational, prospective cohort analysis of sepsis in South Korea evaluated patients aged ≥ 19 years admitted to intensive care units with a diagnosis of sepsis. The primary outcome was the incidence of SA-AKI, defined using the new consensus definition of the Acute Disease Quality Initiative 28 Workgroup. Secondary outcomes were in-hospital mortality and risk factors for in-hospital mortality. Results: Between September 2019 and December 2022, 5100 patients were admitted to intensive care units with a diagnosis of sepsis, and 3177 (62.3%) developed SA-AKI. A total of 613 (19.3%), 721 (22.7%), and 1843 (58.0%) patients had stage 1, 2, and 3 SA-AKI, respectively. Severe SA-AKI (stages 2 and 3 combined) was associated with an increased risk of in-hospital mortality. Adherence to the fluid resuscitation component of the one-hour sepsis bundle was associated with a decreased risk of in-hospital mortality in severe SA-AKI (adjusted odds ratio, 0.62; 95% confidence interval, 0.48–0.79; P < 0.001). Conclusions: Of the patients admitted to the intensive care unit for sepsis, 62.3% developed SA-AKI. Severe SA-AKI was associated with an increased risk of mortality. Adherence to the fluid resuscitation component of the one-hour sepsis bundle can potentially improve outcomes in these patients.

Background: Despite the rapid adoption of electronic health records (EHRs) resulting from the reimbursement program of the US government, EHR adoption in behavioral hospitals is still slow, and there remains a lack of evidence regarding barriers and facilitators to the implementation of mental health care EHRs. Objective: The aim of this study is to analyze the experience of mental health professionals to explore the perceived barriers, facilitators, and critical ideas influencing the implementation and usability of a mental health care EHR. Methods: In this phenomenological qualitative study, we interviewed physicians, nurses, pharmacists, mental health clinicians, and administrative professionals separately at 4 behavioral hospitals in the United States. We conducted semistructured interviews (N=43) from behavioral hospitals involved in the adoption of the mental health care EHR. Purposeful sampling was used to maximize the diversity. Transcripts were coded and analyzed for emergent domains. An exploratory data analysis was conducted. Results: Content analyses revealed 7 barriers and 4 facilitators. The most important barriers to implementing the mental health care EHR were the low levels of computer proficiency among nurses, complexity of the system, alert fatigue, and resistance because of legacy systems. This led to poor usability, low acceptability, and distrust toward the system. The major facilitators to implementing the mental health care EHR were well-executed training programs, improved productivity, better quality of care, and the good usability of the mental health care EHR. Conclusions: Health care professionals expected to enhance their work productivity and interprofessional collaboration by introducing the mental health care EHR. Routine education for end users is an essential starting point for the successful implementation of mental health care EHR electronic decision support. When adopting the mental health care EHR, managers need to focus on common practices in behavioral hospitals, such as documenting structured data in their organizations and adopting a seamless workflow of mental health care into the system.