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Background Urinary tract infections (UTIs) are one of the most common conditions in women. Current information on the presentation, management, and natural course of the infection is based on paper diaries filled out and subsequently posted by patients. Objective The aim of this study is to explore the feasibility of a smartphone app to assess the natural course and management of UTIs. Methods A smartphone app was developed to collect data from study participants presenting with symptoms of UTI in general practice. After initial demographic and treatment information, symptom severity was recorded by the patient after a reminder on their smartphone, which occurred twice daily for a period of 7 days or until symptom resolution. Results A total of 181 women aged 18-76 years downloaded the smartphone app. The duration of symptoms was determined from the results of 178 participants. All patients submitted a urine sample, most patients were prescribed an antibiotic (163/181, 90.1%), and 38.7% (70/181) of the patients had a positive culture. Moderately bad or worse symptoms lasted a mean of 3.8 (SD 3.2; median 4) days, and 70.2% (125/178) of the patients indicated that they were cured on day 4 after consultation. This compares with other research assessing symptom duration and management of UTIs using paper diaries. Patients were very positive about the usability of the smartphone app and often found the reminders supportive. On the basis of the feedback and the analysis of the data, some suggestions for improvement were made. Conclusions Smartphone diaries for symptom scores over the course of infections are an efficient and acceptable means of collecting data in research.

Background Elective colorectal cancer (CRC) surgeries offer enhanced surgical outcomes but demand high self-efficacy in prehabilitation and competency in self-care and disease management postsurgery. Conventional strategies to meet perioperative needs have not been pragmatic, and there remains a pressing need for novel technologies that could improve health outcomes. Objective The aim of this paper was to describe the development of a smartphone-based interactive CRC self-management enhancement psychosocial program (iCanManage) in order to improve health outcomes among patients who undergo elective CRC surgeries and their family caregivers. Methods A multidisciplinary international team comprising physicians, specialist nurses, a psychologist, software engineers, academic researchers, cancer survivors, patient ambassadors, and ostomy care medical equipment suppliers was formed to facilitate the development of this patient-centric digital solution. The process occurred in several stages: (1) review of current practice through clinic visits and on-site observations; (2) review of literature and findings from preliminary studies; (3) content development grounded in an underpinning theory; (4) integration of support services; and (5) optimizing user experience through improving interface aesthetics and customization. In our study, 5 participants with CRC performed preliminary assessments on the quality of the developed solution using the 20-item user version of the Mobile App Rating Scale (uMARS), which had good psychometric properties. Results Based on the collected uMARS data, the smartphone app was rated highly for functionality, aesthetics, information quality, and perceived impact, and moderately for engagement and subjective quality. Several limiting factors such as poor agility in the adoption of digital technology and low eHealth literacy were identified despite efforts to promote engagement and ensure ease of use of the mobile app. To overcome such barriers, additional app-training sessions, an instruction manual, and regular telephone calls will be incorporated into the iCanManage program during the trial period. Conclusions This form of multidisciplinary collaboration is advantageous as it can potentially streamline existing care paths and allow the delivery of more holistic care to the CRC population during the perioperative period. Should the program be found to be effective and sustainable, hospitals adopting this digital solution may achieve better resource allocation and reduce overall health care costs in the long run. Trial Registration ClinicalTrials.gov NCT04159363; https://clinicaltrials.gov/ct2/show/NCT04159363

Background Revisits within 30 days to an emergency department (ED), observation care unit, or inpatient setting following patient discharge continue to be a challenge, especially in urban settings. In addition to the consequences for the patient, these revisits have a negative impact on a health system’s finances in a value-based care or global budget environment. LifeBridge Health, a community health system in Maryland, United States, implemented an automated mobile patient engagement application as part of our enterprise-wide digital health strategy to improve patient engagement and reduce revisits to the ED. Objective The aim of this paper was to evaluate the effectiveness of a customized automated digital patient engagement application (GetWell Loop) to reduce 30-day revisits after home discharge from an ED. Methods The LifeBridge Health Innovation Department and ED staff from 2 participating health system hospitals collaborated with GetWellNetwork to customize their patient engagement application with automated check-in questions and other on-demand resources (eg, streaming content explaining aspects of self-care during COVID-19). An application link was emailed to adult patients discharged home from the ED. A study of ED visits for patients treated for general medicine and cardiology conditions between August 1, 2018, and July 31, 2019, was conducted using CRISP (Chesapeake Regional Information System for our Patients), Maryland’s state-designated health information exchange. We also used data within GetWell Loop (GetWellNetwork) to track patient activation and engagement. The primary outcome was the number of ED patients who experienced a 30-day revisit and who did or did not activate their GetWell Loop account. Secondary outcomes included the overall activation rate and the rate of engagement as measured by the number of logins, alerts, and comments generated by patients through the application. Bivariate analysis comparing outcomes among patients who activated the GetWell Loop application to patients who did not was conducted using the Fisher exact test. Multivariate logistic regression modeling with elastic net regularization was also performed to account for potential confounders and potential collinearity of covariates. Results During this 1-year study, 1062 (27.4%) of 3866 of all emergency patients treated for general medicine or cardiology conditions, who received an invite to use the digital application, activated their account. The patients discharged from the ED, who were treated for general medicine conditions (n=2087) and who activated their GetWell Loop account, experienced a 30-day revisit rate of 17.3% (n=101) compared with 24.6% (n=369) for those who did not activate their account (P Conclusions Our results suggest that a significant percentage of patients are willing to utilize a digital application following ED discharge to better engage in their own care, and that usage of such digital applications may significantly reduce 30-day revisit rates. LifeBridge Health’s experience demonstrates that health care systems can leverage automated mobile apps to improve patient engagement and successfully impact clinical outcomes at scale.

Background Pain-related mobile apps targeting pain assessment commonly limit pain assessment to pain behaviors and physiological aspects. However, current guidelines state that pain assessment should follow the biopsychosocial model, clearly addressing biological, psychological, and social aspects of the pain experience. Existing reviews also highlight that pain specialists and end users are not commonly involved in the development process of mobile apps for pain assessment, negatively affecting the quality of the available apps. Objective This study aimed to develop a mobile app for pain assessment (AvaliaDor) and assess its usability, validity, reliability, and measurement error in a sample of real patients with chronic pain recruited from a physiotherapy clinic. Methods This study was divided into 2 phases: phase 1—development of the AvaliaDor app; and phase 2—assessment of the apps’ usability, reliability, measurement error, and validity. AvaliaDor was developed (phase 1) based on the literature and the recommendations of physiotherapists and patients with pain in cycles of evaluation, inclusion of recommendations, and reevaluation until no further changes were required. The final version of the app was then tested in patients with musculoskeletal pain attending a private physiotherapy practice (phase 2) who were asked to use the app twice on 2 consecutive days for reliability purposes. In addition, participants had to complete a set of paper-based scales (Brief Pain Inventory, painDETECT, Pain Catastrophizing Scale, and Tampa Scale for Kinesiophobia), which were used to assess the validity (criterion validity and hypothesis testing) of the app, and the Post-Study System Usability Questionnaire was used to assess its usability. Results The development process (phase 1) included 5 physiotherapists external to the research team and 5 patients with musculoskeletal pain, and it resulted in the creation of an app named AvaliaDor, which includes an assessment of pain intensity, location, and phenotype; associated disability; and the issues of pain catastrophizing and fear of movement. A total of 52 patients with pain (mean age 50.12 years, SD 11.71 years; 39 females) participated in phase 2 and used the app twice. The Pearson correlation coefficient between the scores on the paper-based scales and the app ranged between 0.81 and 0.93 for criterion validity and between 0.41 and 0.59 for hypothesis testing. Test-retest reliability was moderate to good (intraclass correlation coefficient between 0.67 and 0.90) and the score for usability was 1.16 (SD 0.27), indicating good usability. Conclusions A mobile app named AvaliaDor was developed to assess the intensity, location, and phenotype of pain; associated disability; and the issues of pain catastrophizing and fear of movement in a user-centered design process. The app was shown to be usable, valid, and reliable for assessing pain from a biopsychosocial perspective in a heterogeneous group of patients with pain. Future work can explore the long-term use of AvaliaDor in clinical contexts and its advantages for the assessment and management of patients with pain.

Background Studies that use ecological momentary assessments (EMAs) or wearable sensors to track numerous attributes, such as physical activity, sleep, and heart rate, can benefit from reductions in missing data. Maximizing compliance is one method of reducing missing data to increase the return on the heavy investment of time and money into large-scale studies. Objective This paper aims to identify the extent to which compliance can be prospectively predicted from individual attributes and initial compliance. Methods We instrumented 757 information workers with fitness trackers for 1 year and conducted EMAs in the first 56 days of study participation as part of an observational study. Their compliance with the EMA and fitness tracker wearing protocols was analyzed. Overall, 31 individual characteristics (eg, demographics and personalities) and behavioral variables (eg, early compliance and study portal use) were considered, and 14 variables were selected to create beta regression models for predicting compliance with EMAs 56 days out and wearable compliance 1 year out. We surveyed study participation and correlated the results with compliance. Results Our modeling indicates that 16% and 25% of the variance in EMA compliance and wearable compliance, respectively, could be explained through a survey of demographics and personality in a held-out sample. The likelihood of higher EMA and wearable compliance was associated with being older (EMA: odds ratio [OR] 1.02, 95% CI 1.00-1.03; wearable: OR 1.02, 95% CI 1.01-1.04), speaking English as a first language (EMA: OR 1.38, 95% CI 1.05-1.80; wearable: OR 1.39, 95% CI 1.05-1.85), having had a wearable before joining the study (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.50, 95% CI 1.23-1.83), and exhibiting conscientiousness (EMA: OR 1.25, 95% CI 1.04-1.51; wearable: OR 1.34, 95% CI 1.14-1.58). Compliance was negatively associated with exhibiting extraversion (EMA: OR 0.74, 95% CI 0.64-0.85; wearable: OR 0.67, 95% CI 0.57-0.78) and having a supervisory role (EMA: OR 0.65, 95% CI 0.54-0.79; wearable: OR 0.66, 95% CI 0.54-0.81). Furthermore, higher wearable compliance was negatively associated with agreeableness (OR 0.68, 95% CI 0.56-0.83) and neuroticism (OR 0.85, 95% CI 0.73-0.98). Compliance in the second week of the study could help explain more variance; 62% and 66% of the variance in EMA compliance and wearable compliance, respectively, was explained. Finally, compliance correlated with participants’ self-reflection on the ease of participation, usefulness of our compliance portal, timely resolution of issues, and compensation adequacy, suggesting that these are avenues for improving compliance. Conclusions We recommend conducting an initial 2-week pilot to measure trait-like compliance and identify participants at risk of long-term noncompliance, performing oversampling based on participants’ individual characteristics to avoid introducing bias in the sample when excluding data based on noncompliance, using an issue tracking portal, and providing special care in troubleshooting to help participants maintain compliance.

Background The Scale-Up Project Evaluating Responsiveness to Home Exercise And Lifestyle Tele-Health (SUPER-HEALTH) initiative is a large randomized controlled study that aims to overcome logistical barriers to exercise via telehealth for people with physical disabilities. However, at the start of the COVID-19 pandemic, enrollment was halted due to limited operations at the testing site, which included no onsite visits that involved participant data collection. In response to the limited operations, a modified data collection protocol was developed for virtual enrollment of study participants. Objective This paper presents feasibility data on using teleassessments to enroll people with mobility impairment into a home-based exercise trial. Methods The modified protocol replaced onsite enrollment and data collection visits with teleassessments using a computer tablet and testing equipment that was shipped to the participants’ home address prior to the synchronous teleassessments conducted by an exercise physiologist through Zoom. The participants were mailed a teleassessment toolkit that included a digital blood pressure cuff, spirometer, hand dynamometer, mini disc cone, and measuring tape (to complete standardized testing). The teleassessment measures included resting blood pressure and heart rate, forced vital capacity, grip strength, Five Times Sit to Stand, and Timed Up and Go. Feasibility metrics included technological effectiveness, efficiency, and safety. The technological effectiveness of the telehealth assessment was determined by the percentage of sessions completed without technical issues with ≥90% criteria set a priori. Efficiency was measured by a session duration of ≤2 hours. Safety was measured by the number of adverse events related to the teleassessments reported. Results Data from 36 participants were included in this feasibility study, and 34 (94%) participants completed all teleassessments without technical issues. For efficiency, the teleassessment sessions were completed in a mean time of 65 minutes and a maximum session length of 110 minutes. There were no adverse events reported to indicate concerns with the safety of teleassessments. Conclusions The modified teleassessment protocol, in response to COVID-19 restrictions, may be a feasible process for enrolling adults with mobility impairment into a home exercise trial who otherwise would have not been able to participate. Trial Registration ClinicalTrials.gov NCT03024320; https://clinicaltrials.gov/ct2/show/NCT03024320

Background Access to neurological care for Parkinson disease (PD) is a rare privilege for millions of people worldwide, especially in resource-limited countries. In 2013, there were just 1200 neurologists in India for a population of 1.3 billion people; in Africa, the average population per neurologist exceeds 3.3 million people. In contrast, 60,000 people receive a diagnosis of PD every year in the United States alone, and similar patterns of rising PD cases—fueled mostly by environmental pollution and an aging population—can be seen worldwide. The current projection of more than 12 million patients with PD worldwide by 2040 is only part of the picture given that more than 20% of patients with PD remain undiagnosed. Timely diagnosis and frequent assessment are key to ensure timely and appropriate medical intervention, thus improving the quality of life of patients with PD. Objective In this paper, we propose a web-based framework that can help anyone anywhere around the world record a short speech task and analyze the recorded data to screen for PD. Methods We collected data from 726 unique participants (PD: 262/726, 36.1% were women; non-PD: 464/726, 63.9% were women; average age 61 years) from all over the United States and beyond. A small portion of the data (approximately 54/726, 7.4%) was collected in a laboratory setting to compare the performance of the models trained with noisy home environment data against high-quality laboratory-environment data. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet, “the quick brown fox jumps over the lazy dog.” We extracted both standard acoustic features (mel-frequency cepstral coefficients and jitter and shimmer variants) and deep learning–based embedding features from the speech data. Using these features, we trained several machine learning algorithms. We also applied model interpretation techniques such as Shapley additive explanations to ascertain the importance of each feature in determining the model’s output. Results We achieved an area under the curve of 0.753 for determining the presence of self-reported PD by modeling the standard acoustic features through the XGBoost—a gradient-boosted decision tree model. Further analysis revealed that the widely used mel-frequency cepstral coefficient features and a subset of previously validated dysphonia features designed for detecting PD from a verbal phonation task (pronouncing “ahh”) influence the model’s decision the most. Conclusions Our model performed equally well on data collected in a controlled laboratory environment and in the wild across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with an audio-enabled device and help the participants screen for PD remotely, contributing to equity and access in neurological care.

Background A high-salt diet is a risk factor for hypertension and cardiovascular disease; therefore, reducing dietary salt intake is a key part of prevention strategies. There are few effective salt reduction interventions suitable for delivery in the primary care setting, where the majority of the management and diagnosis of hypertension occurs. Objective The aim of this study is to assess the feasibility of a complex behavioral intervention to lower salt intake in people with elevated blood pressure and test the trial procedures for a randomized controlled trial to investigate the intervention’s effectiveness. Methods This feasibility study was an unblinded, randomized controlled trial of a mobile health intervention for salt reduction versus an advice leaflet (control). The intervention was developed using the Behavior Change Wheel and comprised individualized, brief advice from a health care professional with the use of the SaltSwap app. Participants with an elevated blood pressure recorded in the clinic were recruited through primary care practices in the United Kingdom. Primary outcomes assessed the feasibility of progression to a larger trial, including follow-up attendance, fidelity of intervention delivery, and app use. Secondary outcomes were objectively assessed using changes in salt intake (measured via 24-hour urine collection), salt content of purchased foods, and blood pressure. Qualitative outcomes were assessed using the think-aloud method, and the process outcomes were evaluated. Results A total of 47 participants were randomized. All progression criteria were met: follow-up attendance (45/47, 96%), intervention fidelity (25/31, 81%), and app use (27/31, 87%). There was no evidence that the intervention significantly reduced the salt content of purchased foods, salt intake, or blood pressure; however, this feasibility study was not powered to detect changes in secondary outcomes. Process and qualitative outcomes demonstrated that the trial design was feasible and the intervention was acceptable to both individuals and practitioners and positively influenced salt intake behaviors. Conclusions The intervention was acceptable and feasible to deliver within primary care; the trial procedures were practicable, and there was sufficient signal of potential efficacy to change salt intake. With some improvements to the intervention app, a larger trial to assess intervention effectiveness for reducing salt intake and blood pressure is warranted. Trial Registration International Standard Randomized Controlled Trial Number (ISRCTN): 20910962; https://www.isrctn.com/ISRCTN20910962

Background The standard of care in general wards includes periodic manual measurements, with the data entered into track-and-trigger charts, either on paper or electronically. Wearable devices may support health care staff, improve patient safety, and promote early deterioration detection in the interval between periodic measurements. However, regulatory standards for ambulatory cardiac monitors estimating heart rate (HR) and respiratory rate (RR) do not specify performance criteria during patient movement or clinical conditions in which the patient’s oxygen saturation varies. Therefore, further validation is required before clinical implementation and deployment of any wearable system that provides continuous vital sign measurements. Objective The objective of this study is to determine the agreement between a chest-worn patch (VitalPatch) and a gold standard reference device for HR and RR measurements during movement and gradual desaturation (modeling a hypoxic episode) in a controlled environment. Methods After the VitalPatch and gold standard devices (Philips MX450) were applied, participants performed different movements in seven consecutive stages: at rest, sit-to-stand, tapping, rubbing, drinking, turning pages, and using a tablet. Hypoxia was then induced, and the participants’ oxygen saturation gradually reduced to 80% in a controlled environment. The primary outcome measure was accuracy, defined as the mean absolute error (MAE) of the VitalPatch estimates when compared with HR and RR gold standards (3-lead electrocardiography and capnography, respectively). We defined these as clinically acceptable if the rates were within 5 beats per minute for HR and 3 respirations per minute (rpm) for RR. Results Complete data sets were acquired for 29 participants. In the movement phase, the HR estimates were within prespecified limits for all movements. For RR, estimates were also within the acceptable range, with the exception of the sit-to-stand and turning page movements, showing an MAE of 3.05 (95% CI 2.48-3.58) rpm and 3.45 (95% CI 2.71-4.11) rpm, respectively. For the hypoxia phase, both HR and RR estimates were within limits, with an overall MAE of 0.72 (95% CI 0.66-0.78) beats per minute and 1.89 (95% CI 1.75-2.03) rpm, respectively. There were no significant differences in the accuracy of HR and RR estimations between normoxia (≥90%), mild (89.9%-85%), and severe hypoxia ( Conclusions The VitalPatch was highly accurate throughout both the movement and hypoxia phases of the study, except for RR estimation during the two types of movements. This study demonstrated that VitalPatch can be safely tested in clinical environments to support earlier detection of cardiorespiratory deterioration. Trial Registration ISRCTN Registry ISRCTN61535692; https://www.isrctn.com/ISRCTN61535692

Background Prevention through Intervention is a community paramedicine program developed by Birmingham Fire and Rescue Services in Alabama. This program aims to reduce dependency on emergency medical services (EMS) for nonemergency-related events through education and to lower the frequency of emergency calls in underserved populations. A telehealth intervention with an emphasis on hypoglycemia was implemented to (1) tailor the intervention to meet the educational needs of participants and (2) facilitate follow-ups. A pre-post pilot feasibility evaluation of the telehealth intervention was conducted. Objective This paper describes the results of the feasibility evaluation, implementation challenges, and the lessons learned about the deployment of a hypoglycemia prevention program in an underserved area and its evaluation. Methods This single-arm pretest-posttest intervention included (1) an initial in-person visit (week 1), (2) 3 weekly telecoaching calls (weeks 2-4), (3) 1 biweekly call (week 6), and (4) a final in-person visit (week 8) for collecting posttest data from individuals who called EMS due to hypoglycemic events. In-person visits included educational sessions conducted by EMS personnel. Participants’ education included tailored content related to hypoglycemia. Weekly telecoaching calls focused on hypoglycemia symptom monitoring and education reinforcement via a telehealth dashboard. The primary measures focused on feasibility measures, and exploratory measures focused on the fear of hypoglycemia, self-efficacy, and a knowledge of diabetes. Results A total of 40 participants participated in the intervention. However, the study was marred with high attrition. The various factors behind the low retention rate were discussed. There was a decreasing trend in all three subdomains of the fear of hypoglycemia from pretest to posttest. There was also a significant increase in participants’ self-efficacy in hypoglycemia self-management (P=.03). Conclusions This study shows preliminary and promising results for a community-based intervention specifically for hypoglycemia. However, the socioeconomic setting in which the intervention was delivered may have resulted in high dropout rates and low attendance during the intervention, which are considerations for future telehealth studies. Trial Registration ClinicalTrials.gov NCT03665870; https://clinicaltrials.gov/ct2/show/NCT03665870

Background Rabies remains a neglected and poorly controlled disease throughout the developing world, particularly in Africa and Asia, where most human rabies deaths occur. Objective This study aimed to describe the epidemiology of rabies exposures, its trend, and its geographical distribution in Yemen. Methods Cumulative data from a rabies surveillance system for the period 2011-2017 were obtained from the National Rabies Control Program as paper-based annual reports. Data included the number of persons bitten by a suspected rabid animal, their gender and age, and the result of the animal’s laboratory test. Human cases were defined as those exposed to rabies virus bitten by a suspected rabid animal, exposed to a confirmed rabid animal and then received postexposure prophylaxis (PEP), and deaths occurred after exposure to a confirmed rabid animal after having rabies symptoms during 2011-2017. Results From 2011 to 2017, a total of 76,049 persons were bitten by a suspected rabid animal. Of these, 21,927 (28.83%) were exposed to positively confirmed rabid animals and then received PEP, and 295 (0.38%) rabies-related deaths occurred. Of all cases with rabies exposure, 50,882 (66.91%) were males. The most affected age group by animal bites (31,816/76,041, 41.84%), positive exposure (8945/21,927, 40.79%), and rabies deaths (143/295, 48.47%) was 5-14 years. Rabies vaccines and immunoglobulins quantities were least available in 2016 and 2017. The annual incidence rate of exposure to animal bites and rabies exposure was 50 and 14 per 100,000, respectively. The annual mortality rate was 2 per 1,000,000. The highest incidence rate of animal bites was in Dhamar (112 per 100,000) and Ibb (94 per 100,000), whereas the highest incidence of exposed cases was in Amanat Al Asimah (40 per 100,000) and Ibb (37 per 100,000). Mortality rate was the highest in Amanat Al Asimah (6 deaths per 1,000,000) followed by Ibb and Dhamar (4 deaths per 1,000,000 in both). Conclusions Rabies remains a worrying health problem in Yemen with higher percentage reported among children and males. Targeting school-age populations by education, communication, and information campaigns about preventive measures is strongly recommended. An electronic system should be introduced to improve reporting. It is important to have a sufficient supply of vaccines and immunoglobulins in control units, especially in the at-risk or impacted governorates. Future studies are suggested to determine incidences and risk factors of disease progression.

Background The long-term impact and cost-effectiveness of weight management programs depend on posttreatment weight maintenance. There is growing evidence that interventions based on third-wave cognitive behavioral therapy, particularly acceptance and commitment therapy (ACT), could improve long-term weight management; however, these interventions are typically delivered face-to-face by psychologists, which limits the scalability of these types of intervention. Objective The aim of this study is to use an evidence-, theory-, and person-based approach to develop an ACT-based intervention for weight loss maintenance that uses digital technology and nonspecialist guidance to minimize the resources needed for delivery at scale. Methods Intervention development was guided by the Medical Research Council framework for the development of complex interventions in health care, Intervention Mapping Protocol, and a person-based approach for enhancing the acceptability and feasibility of interventions. Work was conducted in two phases: phase 1 consisted of collating and analyzing existing and new primary evidence and phase 2 consisted of theoretical modeling and intervention development. Phase 1 included a synthesis of existing evidence on weight loss maintenance from previous research, a systematic review and network meta-analysis of third-wave cognitive behavioral therapy interventions for weight management, a qualitative interview study of experiences of weight loss maintenance, and the modeling of a justifiable cost for a weight loss maintenance program. Phase 2 included the iterative development of guiding principles, a logic model, and the intervention design and content. Target user and stakeholder panels were established to inform each phase of development, and user testing of successive iterations of the prototype intervention was conducted. Results This process resulted in a guided self-help ACT-based intervention called SWiM (Supporting Weight Management). SWiM is a 4-month program consisting of weekly web-based sessions for 13 consecutive weeks followed by a 4-week break for participants to reflect and practice their new skills and a final session at week 18. Each session consists of psychoeducational content, reflective exercises, and behavioral experiments. SWiM includes specific sessions on key determinants of weight loss maintenance, including developing skills to manage high-risk situations for lapses, creating new helpful habits, breaking old unhelpful habits, and learning to manage interpersonal relationships and their impact on weight management. A trained, nonspecialist coach provides guidance for the participants through the program with 4 scheduled 30-minute telephone calls and 3 further optional calls. Conclusions This comprehensive approach facilitated the development of an intervention that is based on scientific theory and evidence for supporting people with weight loss maintenance and is grounded in the experiences of the target users and the context in which it is intended to be delivered. The intervention will be refined based on the findings of a planned pilot randomized controlled trial.

©Elina Mattila, Susanne Hansen, Lise Bundgaard, Lauren Ramsey, Alice Dunning, Marlene N Silva, Marja Harjumaa, Miikka Ermes, Marta M Marques, Marcela Matos, Sofus C Larsen, Jorge Encantado, Inês Santos, Graham Horgan, Ruairi O'Driscoll, Jake Turicchi, Cristiana Duarte, António L Palmeira, R James Stubbs, Berit Lilienthal Heitmann, Liisa Lähteenmäki. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.01.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included., Background: Digital behavior change interventions (DBCIs) offer a promising channel for providing health promotion services. However, user experience largely determines whether they are used, which is a precondition for effectiveness. Objective: The primary aim of this study is to evaluate user experiences with the NoHoW Toolkit (TK)-a DBCI that targets weight loss maintenance-over a 12-month period by using a mixed methods approach and to identify the main strengths and weaknesses of the TK and the external factors affecting its adoption. The secondary aim is to objectively describe the measured use of the TK and its association with user experience. Methods: An 18-month, 2×2 factorial randomized controlled trial was conducted. The trial included 3 intervention arms receiving an 18-week active intervention and a control arm. The user experience of the TK was assessed quantitatively through electronic questionnaires after 1, 3, 6, and 12 months of use. The questionnaires also included open-ended items that were thematically analyzed. Focus group interviews were conducted after 6 months of use and thematically analyzed to gain deeper insight into the user experience. Log files of the TK were used to evaluate the number of visits to the TK, the total duration of time spent in the TK, and information on intervention completion. Results: The usability level of the TK was rated as satisfactory. User acceptance was rated as modest; this declined during the trial in all the arms, as did the objectively measured use of the TK. The most appreciated features were weekly emails, graphs, goal setting, and interactive exercises. The following 4 themes were identified in the qualitative data: engagement with features, decline in use, external factors affecting user experience, and suggestions for improvements. Conclusions: The long-term user experience of the TK highlighted the need to optimize the technical functioning, appearance, and content of the DBCI before and during the trial, similar to how a commercial app would be optimized. In a trial setting, the users should be made aware of how to use the intervention and what its requirements are, especially when there is more intensive intervention content., This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement number 643309.

Background Poor patient uptake of cardiac rehabilitation (CR) remains a challenge for multiple reasons including geographic, time, cultural, cost, and psychological constraints. Objective We evaluated the impact on CR participation rates associated with the addition of the option of mobile app–based CR (Cardihab) for patients declining conventional CR. Methods A total of 204 consecutive patients were offered CR following angioplasty; of these, 99 were in cohort 1 (offered conventional CR only) and 105 were in cohort 2 (app-based CR offered to those declining conventional CR). Patients in each cohort were followed throughout a 6-week CR program and participation rates were compared for both groups. Patients in cohort 2 declining both forms of CR were interviewed to assess reasons for nonparticipation. Results CR participation improved from 21% (95% CI 14%-30%) to 63% (95% CI 53%-71%) with the addition of the app (P Conclusions Providing patients with the additional option of an app-based CR program substantially improved CR participation. Technology and psychological barriers can limit CR participation. Further innovation in CR delivery systems is required to improve uptake.

Background During August 2017, increased numbers of suspected dengue fever cases were reported in the hospitals of Rawalpindi district. A case control study was conducted to determine the risk factors among urban areas, dengue serotype, and recommend preventive measures. Objective The objective of the investigation was to determine the risk factors among urban areas, dengue serotype, and recommend preventive measures. Methods A case was defined as having acute febrile illness with one or more of the following symptoms: retro-orbital pain, headache, rash, myalgia, arthralgia, and hemorrhage. The cases were residents of Rawalpindi and were confirmed for dengue fever from August 30, 2017, to October 30, 2017. All NS1 confirmed cases from urban areas of Rawalpindi were recruited from tertiary care hospitals. Age- and sex-matched controls were selected from the same community with a 1:1 ratio. Frequency, univariate, and multivariate analyses were performed at 95% CI with P Results Totally 373 cases were recruited. The mean age was 36 (SD 2.9) years (range 10-69 years), and 280 cases (75%) were male. The most affected age group was 21-30 years (n=151, attack rate [AR] 40%), followed by 31-40 years (n=66, AR 23%). Further, 2 deaths were reported (case fatality rate of 0.53%). The most frequent signs or symptoms were fever (n=373, 100%), myalgia and headache (n=320, 86%), and retro-orbital pain (n=272, 73%). Serotype identification was carried out in 322 cases, and DEN-2 was the dominant serotype (n=126, 34%). Contact with a confirmed dengue case (odds ratio [OR] 4.27; 95% CI 3.14-5.81; P Conclusions Stored water in containers inside houses and subsequent mosquito breeding were the most probable causes of this outbreak. Based on the study findings, undertaking activities to improve the use of mosquito repellents and removing sources of breeding (uncovered water stored indoors) are some recommendations for preventing dengue outbreaks.

Background High blood pressure or hypertension is a vastly prevalent chronic condition among adults that can, if not appropriately treated, contribute to several life-threatening secondary diseases and events, such as stroke. In addition to first-line medication, self-management in daily life is crucial for tertiary prevention and can be supported by mobile health apps, including medication reminders. However, the prescription of medical apps is a relatively novel approach. There is limited information regarding the determinants of acceptance of such mobile health (mHealth) apps among patients as potential users and physicians as impending prescribers in direct comparison. Objective The present study aims to investigate the determinants of the acceptance of health apps (in terms of intention to use) among patients for personal use and physicians for clinical use in German-speaking countries. Moreover, we assessed patients’ preferences regarding different delivery modes for self-care service (face-to-face services, apps, etc). Methods Based on an extended model of the unified theory of acceptance and use of technology (UTAUT2), we performed a web-based cross-sectional survey to explore the acceptance of mHealth apps for self-management of hypertension among patients and physicians in Germany. In addition to UTAUT2 variables, we measured self-reported self-efficacy, eHealth literacy, previous experiences with health apps, perceived threat to privacy, and protection motivation as additional determinants of mHealth acceptance. Data from 163 patients and 46 physicians were analyzed using hierarchical regression and mediation analyses. Results As expected, a significant influence of the unified theory of acceptance and use of technology (UTAUT) predictors on intentions to use hypertension apps was confirmed, especially for performance expectancy. Intention to use was moderate in patients (mean 3.5; SD 1.1; range 1-5) and physicians (mean 3.4, SD 0.9), and did not differ between both groups. Among patients, a higher degree of self-reported self-efficacy and protection motivation contributed to an increased explained variance in acceptance with R2=0.09, whereas eHealth literacy was identified as exerting a positive influence on physicians (increased R2=0.10). Furthermore, our findings indicated mediating effects of performance expectancy on the acceptance among patients but not among physicians. Conclusions In summary, this study has identified performance expectancy as the most important determinant of the acceptance of mHealth apps for self-management of hypertension among patients and physicians. Concerning patients, we also identified mediating effects of performance expectancy on the relationships between effort expectancy and social influence and the acceptance of apps. Self-efficacy and protection motivation also contributed to an increase in the explained variance in app acceptance among patients, whereas eHealth literacy was a predictor in physicians. Our findings on additional determinants of the acceptance of health apps may help tailor educational material and self-management interventions to the needs and preferences of prospective users of hypertension apps in future research.

Background Guidelines for physical activity and exercise during pregnancy recommend that all women without contraindications engage in regular physical activity to improve both their own health and the health of their baby. Many women are uncertain how to safely engage in physical activity and exercise during this life stage and are increasingly using mobile apps to access health-related information. However, the extent to which apps that provide physical activity and exercise advice align with current evidence-based pregnancy recommendations is unclear. Objective This study aims to conduct a systematic search and content analysis of apps that promote physical activity and exercise in pregnancy to examine the alignment of the content with current evidence-based recommendations; delivery, format, and features of physical activity and exercise instruction; and credentials of the app developers. Methods Systematic searches were conducted in the Australian App Store and Google Play Store in October 2020. Apps were identified using combinations of search terms relevant to pregnancy and exercise or physical activity and screened for inclusion (with a primary focus on physical activity and exercise during pregnancy, free to download or did not require immediate paid subscription, and an average user rating of ≥4 out of 5). Apps were then independently reviewed using an author-designed extraction tool. Results Overall, 27 apps were included in this review (Google Play Store: 16/27, 59%, and App Store: 11/27, 41%). Two-thirds of the apps provided some information relating to the frequency, intensity, time, and type principles of exercise; only 11% (3/27) provided this information in line with current evidence-based guidelines. Approximately one-third of the apps provided information about contraindications to exercise during pregnancy and referenced the supporting evidence. None of the apps actively engaged in screening for potential contraindications. Only 15% (4/27) of the apps collected information about the user’s current exercise behaviors, 11% (3/27) allowed users to personalize features relating to their exercise preferences, and a little more than one-third provided information about developer credentials. Conclusions Few exercise apps designed for pregnancy aligned with current evidence-based physical activity guidelines. None of the apps screened users for contraindications to physical activity and exercise during pregnancy, and most lacked appropriate personalization features to account for an individual’s characteristics. Few involved qualified experts during the development of the app. There is a need to improve the quality of apps that promote exercise in pregnancy to ensure that women are appropriately supported to engage in exercise and the potential risk of injury, complications, and adverse pregnancy outcomes for both mother and child is minimized. This could be done by providing expert guidance that aligns with current recommendations, introducing screening measures and features that enable personalization and tailoring to individual users, or by developing a recognized system for regulating apps.

Background: Continuous positive airway pressure (CPAP) is an effective treatment for obstructive sleep apnea (OSA), but treatment compliance is often unsatisfactory. Objective: The aim of this study was to assess the effectiveness and cost-effectiveness of an intelligent monitoring system for improving CPAP compliance. Methods: This is a prospective, open label, parallel, randomized controlled trial including 60 newly diagnosed patients with OSA requiring CPAP (Apnea–Hypopnea Index [AHI] >15) from Lleida, Spain. Participants were randomized (1:1) to standard management or the MiSAOS intelligent monitoring system, involving (1) early compliance detection, thus providing measures of patient’s CPAP compliance from the very first days of usage; (2) machine learning–based prediction of midterm future CPAP compliance; and (3) rule-based recommendations for the patient (app) and care team. Clinical and anthropometric variables, daytime sleepiness, and quality of life were recorded at baseline and after 6 months, together with patient’s compliance, satisfaction, and health care costs. Results: Randomized patients had a mean age of 57 (SD 11) years, mean AHI of 50 (SD 27), and 13% (8/60) were women. Patients in the intervention arm had a mean (95% CI) of 1.14 (0.04-2.23) hours/day higher adjusted CPAP compliance than controls (P=.047). Patients’ satisfaction was excellent in both arms, and up to 88% (15/17) of intervention patients reported willingness to keep using the MiSAOS app in the future. No significant differences were found in costs (control: mean €90.2 (SD 53.14) (US $105.76 [SD 62.31]); intervention: mean €96.2 (SD 62.13) (US $112.70 [SD 72.85]); P=.70; €1=US $1.17 was considered throughout). Overall costs combined with results on compliance demonstrated cost-effectiveness in a bootstrap-based simulation analysis. Conclusions: A machine learning–based intelligent monitoring system increased daily compliance, reported excellent patient satisfaction similar to that reported in usual care, and did not incur in a substantial increase in costs, thus proving cost-effectiveness. This study supports the implementation of intelligent eHealth frameworks for the management of patients with CPAP-treated OSA and confirms the value of patients’ empowerment in the management of chronic diseases. This work is part of the myOSA project (RTC-2014-3138-1), funded by the Spanish Ministry of Economy, Industry and Competitiveness (Ministerio de Economía, Industria y Competitividad) and Agencia Estatal de Investigación, under the framework “Retos-Colaboración”, State Scientific and Technical Research and Innovation Plan 2013-2016. The work was cofunded by the European Regional Development Fund (ERDF), “A way to make Europe”. JdB acknowledges receiving financial support from the Catalan Health Department (Pla Estratègic de Recerca i Innovació en Salut [PERIS] 2016: SLT002/16/00364) and Instituto de Salud Carlos III (ISCIII; Miguel Servet 2019: CP19/00108), co-funded by the European Social Fund (ESF), “Investing in your future”. Funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Background Social isolation has a negative impact on the quality of life of older people; therefore, studies have focused on identifying its sociodemographic, economic, and health determinants. In view of the growing importance of the internet as a means of communication, it is essential to assess whether internet use interferes with social isolation. Objective This study specifically aims to clarify the relationship between internet use and social isolation of individuals aged ≥50 years, for which other surveys present contradictory results. Methods We performed logistic regression analysis with social isolation as the dependent variable, internet use as the interest variable, and several other sociodemographic, economic, and health characteristics of the individuals as control variables. The sample size was 67,173 individuals aged 50 years and older from 17 European countries (Portugal, Greece, Italy, Spain, Denmark, Sweden, Austria, Belgium, France, Germany, Switzerland, Luxemburg, Poland, Czech Republic, Slovenia, Estonia, and Croatia) plus Israel, who were interviewed in the Survey of Health, Ageing and Retirement in Europe (SHARE), wave 6. Results The results show that countries differ in the level of social isolation and rate of internet use by individuals aged 50 years and older. They also evidence that in most of the countries analyzed, social isolation of internet users was lower compared to that of nonusers after controlling for a set of sociodemographic, economic, and health characteristics of the individuals that have been previously described in the literature as determinants of social isolation. Indeed, on average, although 31.4% of individuals in the nonuser group experienced high social isolation, only 12.9% of individuals who used the internet experienced this condition. Conclusions Internet users show lower social isolation. This result underlines the importance of promoting e-inclusion in Europe as a way to counter social isolation of individuals aged 50 years and older.

Background Detection and quantification of intra-abdominal free fluid (ie, ascites) on computed tomography (CT) images are essential processes for finding emergent or urgent conditions in patients. In an emergency department, automatic detection and quantification of ascites will be beneficial. Objective We aimed to develop an artificial intelligence (AI) algorithm for the automatic detection and quantification of ascites simultaneously using a single deep learning model (DLM). Methods We developed 2D DLMs based on deep residual U-Net, U-Net, bidirectional U-Net, and recurrent residual U-Net (R2U-Net) algorithms to segment areas of ascites on abdominopelvic CT images. Based on segmentation results, the DLMs detected ascites by classifying CT images into ascites images and nonascites images. The AI algorithms were trained using 6337 CT images from 160 subjects (80 with ascites and 80 without ascites) and tested using 1635 CT images from 40 subjects (20 with ascites and 20 without ascites). The performance of the AI algorithms was evaluated for diagnostic accuracy of ascites detection and for segmentation accuracy of ascites areas. Of these DLMs, we proposed an AI algorithm with the best performance. Results The segmentation accuracy was the highest for the deep residual U-Net model with a mean intersection over union (mIoU) value of 0.87, followed by U-Net, bidirectional U-Net, and R2U-Net models (mIoU values of 0.80, 0.77, and 0.67, respectively). The detection accuracy was the highest for the deep residual U-Net model (0.96), followed by U-Net, bidirectional U-Net, and R2U-Net models (0.90, 0.88, and 0.82, respectively). The deep residual U-Net model also achieved high sensitivity (0.96) and high specificity (0.96). Conclusions We propose a deep residual U-Net–based AI algorithm for automatic detection and quantification of ascites on abdominopelvic CT scans, which provides excellent performance.

Background A description of individuals with SARS-CoV-2 infection comparing the first and second waves could help adapt health services to manage this highly transmissible infection. Objective We aimed to describe the epidemiology of individuals with suspected SARS-CoV-2 infection, and the characteristics of patients with a positive test comparing the first and second waves in Catalonia, Spain. Methods This study had 2 stages. First, we analyzed daily updated data on SARS-CoV-2 infection in individuals from Girona (Catalonia). Second, we compared 2 retrospective cohorts of patients with a positive reverse-transcription polymerase chain reaction or rapid antigen test for SARS-CoV-2. The severity of patients with a positive test was defined by their admission to hospital, admission to intermediate respiratory care, admission to the intensive care unit, or death. The first wave was from March 1, 2020, to June 24, 2020, and the second wave was from June 25, 2020, to December 8, 2020. Results The numbers of tests and cases were lower in the first wave than in the second wave (26,096 tests and 3140 cases in the first wave versus 140,332 tests and 11,800 cases in the second wave), but the percentage of positive results was higher in the first wave than in the second wave (12.0% versus 8.4%). Among individuals with a positive diagnostic test, 818 needed hospitalization in the first wave and 680 in the second; however, the percentage of hospitalized individuals was higher in the first wave than in the second wave (26.1% versus 5.8%). The group that was not admitted to hospital included older people and those with a higher percentage of comorbidities in the first wave, whereas the characteristics of the groups admitted to hospital were more alike. Conclusions Screening systems for SARS-CoV-2 infection were scarce during the first wave, but were more adequate during the second wave, reflecting the usefulness of surveillance systems to detect a high number of asymptomatic infected individuals and their contacts, to help control this pandemic. The characteristics of individuals with SARS-CoV-2 infection in the first and second waves differed substantially; individuals in the first wave were older and had a worse health condition.

BackgroundIn the absence of official clinical trial information, data from social networks can be used by public health and medical researchers to assess public claims about loosely regulated substances such as cannabidiol (CBD). For example, this can be achieved by comparing the medical conditions targeted by those selling CBD against the medical conditions patients commonly treat with CBD.ObjectiveThe objective of this study was to provide a framework for public health and medical researchers to use for identifying and analyzing the consumption and marketing of unregulated substances. Specifically, we examined CBD, which is a substance that is often presented to the public as medication despite complete evidence of efficacy and safety.MethodsWe collected 567,850 tweets by searching Twitter with the Tweepy Python package using the terms “CBD” and “cannabidiol.” We trained two binary text classifiers to create two corpora of 167,755 personal use and 143,322 commercial/sales tweets. Using medical, standard, and slang dictionaries, we identified and compared the most frequently occurring medical conditions, symptoms, side effects, body parts, and other substances referenced in both corpora. In addition, to assess popular claims about the efficacy of CBD as a medical treatment circulating on Twitter, we performed sentiment analysis via the VADER (Valence Aware Dictionary for Sentiment Reasoning) model on the personal CBD tweets.ResultsWe found references to medically relevant terms that were unique to either personal or commercial CBD tweet classes, as well as medically relevant terms that were common to both classes. When we calculated the average sentiment scores for both personal and commercial CBD tweets referencing at least one of 17 medical conditions/symptoms terms, an overall positive sentiment was observed in both personal and commercial CBD tweets. We observed instances of negative sentiment conveyed in personal CBD tweets referencing autism, whereas CBD was also marketed multiple times as a treatment for autism within commercial tweets.ConclusionsOur proposed framework provides a tool for public health and medical researchers to analyze the consumption and marketing of unregulated substances on social networks. Our analysis showed that most users of CBD are satisfied with it in regard to the condition that it is being advertised for, with the exception of autism.

Background Cervical spine dysfunction is a condition with high personal, social, and economic impact worldwide. Although its etiology is described as multifactorial, there is a need for further clarification. The literature has demonstrated the anatomical, physiological, and pathophysiological relationship among the cervical spine, temporomandibular joint, and visceral system. To guide and contribute to the accuracy of the physical assessment performed by nurses, we will study the influence of the stomatognathic system and viscerosomatic reflexes on pain and joint stiffness of the cervical spine. Objective The aim of this study is to describe a pilot study protocol to investigate the influence of the stomatognathic system and viscerosomatic reflexes on cervical structures. Methods A pilot study with a quasi-experimental design was conducted with 50 volunteers from the university population of the Universidade Católica Portuguesa-Porto. We studied the influence of changes in the usual intercuspation, the occlusal deprogramming, and the pressure stimulus of the reflex skin region of the ilium/colon in the cervical spine. This study was divided into 2 phases. In the first phase, we performed the kinematic and pain analysis during the passive mobilization of the upper cervical spine using the Motion Capture System at the Motion Capture Laboratory at UCP-Porto and the Visual Analog Scale. In the second phase, we evaluated the pain threshold on palpation of the erector neck muscles and the structures of the stomatognathic system using algometry. The influence of viscerosomatic reflexes on the structures of the stomatognathic system was also analyzed. Results Selection and preparation of the data collection site, acquisition of materials, constitution of the sample group and data collection were completed. The analysis of the results is being carried out. Conclusions The data from this study will allow for the detection of the possible influence of the stomatognathic system and viscerosomatic reflexes on pain and range of motion of the upper cervical spine, providing data for future randomized studies. We have also identified potential limitations of this study. International Registered Report Identifier (IRRID) RR1-10.2196/31878

Background Symptoms related to endometriosis have a significant impact on the quality of life, and symptoms often recur. The experience sampling method (ESM), a digital questioning method characterized by randomly repeated momentary assessments, has several advantages over traditionally used measurements, including the ability to assess the temporal relationship between variables such as physical, mental, and social factors. Objective The aim of this study is to develop an ESM tool for patients with endometriosis to accurately measure symptoms and their course over time, allowing for personalized treatment and adequate monitoring of treatment efficacy in individual patients. Methods On the basis of international guidelines, items from validated questionnaires were selected through a literature review and during focus groups and multidisciplinary expert meetings. Data analysis was conducted using ATLAS.ti (ATLAS.ti Scientific Software Development GmbH). The feasibility and usability of the newly developed momentary assessment tool were tested for 28 consecutive days in 5 patients with endometriosis-related pain symptoms. Results Momentary assessment items contained questions concerning endometriosis symptoms, general somatic symptoms, psychological symptoms, contextual information, and the use of food and medication. A morning questionnaire on sleep and sexuality was included. In a pilot study, the patients considered the tool easy to use but time consuming. The average compliance rate of momentary assessments was 37.8% (106/280), with the highest completion rate during the first week (39/70, 56%). Therefore, it is advisable to use the ESM for a maximum of 7 days. Conclusions A new digital tool for endometriosis symptom assessment was developed using the ESM, which may help overcome the limitations of current retrospective questionnaires. After validation and testing, future studies will be planned to evaluate the use of this tool in a clinical setting in order to propose a personalized treatment plan for women with endometriosis.

Background Clinical and virologic characteristics of COVID-19 infections in veterans in New England have not been described. The average US veteran is a male older than the general US population. SARS-CoV-2 infection is known to cause poorer outcomes among men and older adults, making the veteran population an especially vulnerable group for COVID-19. Objective This study aims to evaluate clinical and virologic factors impacting COVID-19 outcomes. Methods This retrospective chart review included 476 veterans in six New England states with confirmed SARS-CoV-2 infection between April and September 2020. Whole genome sequencing was performed on SARS-CoV-2 RNA isolated from these veterans, and the correlation of genomic data to clinical outcomes was evaluated. Clinical and demographic variables were collected by manual chart review and were correlated to the end points of peak disease severity (based on oxygenation requirements), hospitalization, and mortality using multivariate regression analyses. Results Of 476 veterans, 274 had complete and accessible charts. Of the 274 veterans, 92.7% (n=254) were men and 83.2% (n=228) were White, and the mean age was 63 years. In the multivariate regression, significant predictors of hospitalization (C statistic 0.75) were age (odds ratio [OR] 1.05, 95% CI 1.03-1.08) and non-White race (OR 2.39, 95% CI 1.13-5.01). Peak severity (C statistic 0.70) also varied by age (OR 1.07, 95% CI 1.03-1.11) and O2 requirement on admission (OR 45.7, 95% CI 18.79-111). Mortality (C statistic 0.87) was predicted by age (OR 1.06, 95% CI 1.01-1.11), dementia (OR 3.44, 95% CI 1.07-11.1), and O2 requirement on admission (OR 6.74, 95% CI 1.74-26.1). Most (291/299, 97.3%) of our samples were dominated by the spike protein D614G substitution and were from SARS-CoV-2 B.1 lineage or one of 37 different B.1 sublineages, with none representing more than 8.7% (26/299) of the cases. Conclusions In a cohort of veterans from the six New England states with a mean age of 63 years and a high comorbidity burden, age was the largest predictor of hospitalization, peak disease severity, and mortality. Non-White veterans were more likely to be hospitalized, and patients who required oxygen on admission were more likely to have severe disease and higher rates of mortality. Multiple SARS-CoV-2 lineages were distributed in patients in New England early in the COVID-19 era, mostly related to viruses from New York State with D614G mutation.

Background Mobile health (mHealth) apps may provide an efficient way for patients with lower urinary tract symptoms (LUTS) to log and communicate symptoms and medication side effects with their clinicians. Objective The aim of this study was to explore the perceptions of older men with LUTS after using an mHealth app to track their symptoms and tamsulosin side effects. Methods Structured phone interviews were conducted after a 2-week study piloting the daily use of a mobile app to track the severity of patient-selected LUTS and tamsulosin side effects. Quantitative and qualitative data were considered. Results All 19 (100%) pilot study participants completed the poststudy interviews. Most of the men (n=13, 68%) reported that the daily questionnaires were the right length, with 32% (n=6) reporting that the questionnaires were too short. Men with more severe symptoms were less likely to report changes in perception of health or changes in self-management; 47% (n=9) of the men reported improved awareness of symptoms and 5% (n=1) adjusted fluid intake based on the questionnaire. All of the men were willing to share app data with their clinicians. Thematic analysis of qualitative data yielded eight themes: (1) orientation (setting up app, format, symptom selection, and side-effect selection), (2) triggers (routine or habit and symptom timing), (3) daily questionnaire (reporting symptoms, reporting side effects, and tailoring), (4) technology literacy, (5) perceptions (awareness, causation or relevance, data quality, convenience, usefulness, and other apps), (6) self-management, (7) clinician engagement (communication and efficiency), and (8) improvement (reference materials, flexibility, language, management recommendations, and optimize clinician engagement). Conclusions We assessed the perceptions of men using an mHealth app to monitor and improve management of LUTS and medication side effects. LUTS management may be further optimized by tailoring the mobile app experience to meet patients’ individual needs, such as tracking a greater number of symptoms and integrating the app with clinicians’ visits. mHealth apps are likely a scalable modality to monitor symptoms and improve care of older men with LUTS. Further study is required to determine the best ways to tailor the mobile app and to communicate data to clinicians or incorporate data into the electronical medical record meaningfully.

Background Mobile apps for mental health are available on the market. Although they seem to be promising for improving the accessibility of mental health care, little is known about their acceptance, design methodology, evaluation, and integration into psychotherapy protocols. This makes it difficult for health care professionals to judge whether these apps may help them and their patients. Objective Our aim is to describe and evaluate a protocol for the participatory design of mobile apps for mental health. In this study, participants and psychotherapists are engaged in the early phases of the design and development of the app empowered by conversational artificial intelligence (AI). The app supports interventions for stress management training based on cognitive behavioral theory. Methods A total of 21 participants aged 33-61 years with mild to moderate levels of stress, anxiety, and depression (assessed by administering the Italian versions of the Symptom Checklist-90-Revised, Occupational Stress Indicator, and Perceived Stress Scale) were assigned randomly to 2 groups, A and B. Both groups received stress management training sessions along with cognitive behavioral treatment, but only participants assigned to group A received support through a mobile personal health care agent, designed for mental care and empowered by AI techniques. Psychopathological outcomes were assessed at baseline (T1), after 8 weeks of treatment (T2), and 3 months after treatment (T3). Focus groups with psychotherapists who administered the therapy were held after treatment to collect their impressions and suggestions. Results Although the intergroup statistical analysis showed that group B participants could rely on better coping strategies, group A participants reported significant improvements in obsessivity and compulsivity and positive distress symptom assessment. The psychotherapists’ acceptance of the protocol was good. In particular, they were in favor of integrating an AI-based mental health app into their practice because they could appreciate the increased engagement of patients in pursuing their therapy goals. Conclusions The integration into practice of an AI-based mobile app for mental health was shown to be acceptable to both mental health professionals and users. Although it was not possible in this experiment to show that the integration of AI-based conversational technologies into traditional remote psychotherapy significantly decreased the participants’ levels of stress and anxiety, the experimental results showed significant trends of reduction of symptoms in group A and their persistence over time. The mental health professionals involved in the experiment reported interest in, and acceptance of, the proposed technology as a promising tool to be included in a blended model of psychotherapy.

Background Nurses are at the forefront of the COVID-19 pandemic. During the pandemic, nurses have faced an elevated risk of exposure and have experienced the hazards related to a novel virus. While being heralded as lifesaving heroes on the front lines of the pandemic, nurses have experienced more physical, mental, and psychosocial problems as a consequence of the COVID-19 outbreak. Social media discussions by nursing professionals participating in publicly formed Facebook groups constitute a valuable resource that offers longitudinal insights. Objective This study aimed to explore how COVID-19 impacted nurses through capturing public sentiments expressed by nurses on a social media discussion platform and how these sentiments changed over time. Methods We collected over 110,993 Facebook discussion posts and comments in an open COVID-19 group for nurses from March 2020 until the end of November 2020. Scraping of deidentified offline HTML tags on social media posts and comments was performed. Using subject-matter expert opinions and social media analytics (ie, topic modeling, information retrieval, and sentiment analysis), we performed a human-in-a-loop analysis of nursing professionals’ key perspectives to identify trends of the COVID-19 impact among at-risk nursing communities. We further investigated the key insights of the trends of the nursing professionals’ perspectives by detecting temporal changes of comments related to emotional effects, feelings of frustration, impacts of isolation, shortage of safety equipment, and frequency of safety equipment uses. Anonymous quotes were highlighted to add context to the data. Results We determined that COVID-19 impacted nurses’ physical, mental, and psychosocial health as expressed in the form of emotional distress, anger, anxiety, frustration, loneliness, and isolation. Major topics discussed by nurses were related to work during a pandemic, misinformation spread by the media, improper personal protective equipment (PPE), PPE side effects, the effects of testing positive for COVID-19, and lost days of work related to illness. Conclusions Public Facebook nursing groups are venues for nurses to express their experiences, opinions, and concerns and can offer researchers an important insight into understanding the COVID-19 impact on health care workers.

Background Chemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. Objective This study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. Methods Data were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. Results The decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. Conclusions This study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs.

Background Life Goals is an evidence-based self-management intervention that assists individuals with bipolar disorder (BD) by aligning BD symptom coping strategies with their personal goals. The intervention can be availed via in-person and telephonic sessions, and it has been recently developed as an individualized, customizable mobile app. Objective We examined the feasibility, usability, and acceptability of the Life Goals self-management app among individuals diagnosed with BD who used the app for up to 6 months. Methods A total of 28 individuals with BD used the Life Goals app on their personal smartphone for 6 months. They completed key clinical outcome measurements of functioning, disability, and psychiatric symptoms at baseline, 3 months, and 6 months, in addition to a poststudy survey about usability and satisfaction. Results Participants used the app for a median of 25 times (IQR 13-65.75), and for a longer time during the first 3 months of the study. The modules on depression and anxiety were the most frequently used, accounting for 35% and 22% of total usage, respectively. Overall, the study participants found the app useful (15/25, 60%) and easy to use (18/25, 72%), and they reported that the screen displayed the material adequately (22/25, 88%). However, less than half of the participants found the app helpful in managing their health (10/25, 40%) or in making progress on their wellness goals (9/25, 36%). Clinical outcomes showed a trend for improvements in mental and physical health and mania-related well-being. Conclusions The Life Goals app showed feasibility of use among individuals with BD. Higher user engagement was observed in the initial 3 months with users interested more frequently in the mood modules than other wellness modules. Participants reported acceptability with the ease of app use and satisfaction with the app user interface, but the app showed low success in encouraging self-management within this small sample. The Life Goals app is a mobile health technology that can provide individuals with serious mental illness with more flexible access to evidence-based treatments.

Background Poor physical fitness has a negative impact on overall health status. An increasing number of health-related mobile apps have emerged to reduce the burden of medical care and the inconvenience of long-distance travel. However, few studies have been conducted on home-based fitness tests using apps. Insufficient monitoring of physiological signals during fitness assessments have been noted. Therefore, we developed R Plus Health, a digital health app that incorporates all the components of a fitness assessment with concomitant physiological signal monitoring. Objective The aim of this study is to investigate the test-retest reliability of home-based fitness assessments using the R Plus Health app in healthy adults. Methods A total of 31 healthy young adults self-executed 2 fitness assessments using the R Plus Health app, with a 2- to 3-day interval between assessments. The fitness assessments included cardiorespiratory endurance, strength, flexibility, mobility, and balance tests. The intraclass correlation coefficient was computed as a measure of the relative reliability of the fitness assessments and determined their consistency. The SE of measurement, smallest real difference at a 90% CI, and Bland–Altman analyses were used to assess agreement, sensitivity to real change, and systematic bias detection, respectively. Results The relative reliability of the fitness assessments using R Plus Health was moderate to good (intraclass correlation coefficient 0.8-0.99 for raw scores, 0.69-0.99 for converted scores). The SE of measurement and smallest real difference at a 90% CI were 1.44-6.91 and 3.36-16.11, respectively, in all fitness assessments. The 95% CI of the mean difference indicated no significant systematic error between the assessments for the strength and balance tests. The Bland–Altman analyses revealed no significant systematic bias between the assessments for all tests, with a few outliers. The Bland–Altman plots illustrated narrow limits of agreement for upper extremity strength, abdominal strength, and right leg stance tests, indicating good agreement between the 2 assessments. Conclusions Home-based fitness assessments using the R Plus Health app were reliable and feasible in young, healthy adults. The results of the fitness assessments can offer a comprehensive understanding of general health status and help prescribe safe and suitable exercise training regimens. In future work, the app will be tested in different populations (eg, patients with chronic diseases or users with poor fitness), and the results will be compared with clinical test results. Trial Registration Chinese Clinical Trial Registry ChiCTR2000030905; http://www.chictr.org.cn/showproj.aspx?proj=50229

Background Continuous α1a-blockade is the first-line treatment for lower urinary tract symptoms (LUTS) among older men with suspected benign prostatic hyperplasia. Variable efficacy and safety for individual men necessitate a more personalized, data-driven approach to prescribing and deprescribing tamsulosin for LUTS in older men. Objective We aim to evaluate the feasibility and usability of the PERSONAL (Placebo–Controlled, Randomized, Patient-Selected Outcomes, N-of-1 Trials) mobile app for tracking daily LUTS severity and medication side effects among older men receiving chronic tamsulosin therapy. Methods We recruited patients from the University of California, San Francisco health care system to participate in a 2-week pilot study. The primary objectives were to assess recruitment feasibility, study completion rates, frequency of symptom tracking, duration of tracking sessions, and app usability rankings measured using a follow-up survey. As secondary outcomes, we evaluated whether daily symptom tracking led to changes in LUTS severity, perceptions of tamsulosin, overall quality of life, medication adherence between baseline and follow-up surveys, and perceived app utility. Results We enrolled 19 men within 23 days, and 100% (19/19) of the participants completed the study. Each participant selected a unique combination of symptoms to track and recorded data in the PERSONAL app, with a median daily completion rate of 79% (11/14 days). The median duration of the app session was 44 (IQR 33) seconds. On a scale of 1 (strongly disagree) to 5 (strongly agree), the participants reported that the PERSONAL app was easy to use (mean 4.3, SD 1.0), that others could learn to use it quickly (mean 4.2, SD 0.9), and that they felt confident using the app (mean 4.4, SD 0.8). LUTS severity, quality of life, and medication adherence remained unchanged after the 2-week study period. Fewer men were satisfied with tamsulosin after using the app (14/19, 74% vs 17/19, 89% at baseline), although the perceived benefit from tamsulosin remained unchanged (18/19, 95% at baseline and at follow-up). In total, 58% (11/19) of the participants agreed that the PERSONAL app could help people like them manage their urinary symptoms. Conclusions This pilot study demonstrated the high feasibility and usability of the PERSONAL mobile app to track patient-selected urinary symptoms and medication side effects among older men taking tamsulosin to manage LUTS. We observed that daily symptom monitoring had no adverse effects on the secondary outcomes. This proof-of-concept study establishes a framework for future mobile app studies, such as digital n-of-1 trials, to collect comprehensive individual-level data for personalized LUTS management in older men.

Background After determining the key childbirth monitoring items from experts, we designed an algorithm (LaD) to represent the experts’ suggestions and validated it. In this paper we describe an abridged algorithm for labor and delivery management and use theoretical case to compare its performance with human childbirth experts. Objective The objective of this study was to describe the LaD algorithm, its development, and its validation. In addition, in the validation phase we wanted to assess if the algorithm was inferior, equivalent, or superior to human experts in recommending the necessary clinical actions during childbirth decision making. Methods The LaD algorithm encompasses the tracking of 6 of the 12 childbirth parameters monitored using the World Health Organization (WHO) partograph. It has recommendations on how to manage a patient when parameters are outside the normal ranges. We validated the algorithm with purposively selected experts selecting actions for a stratified sample of patient case scenarios. The experts’ selections were compared to obtain pairwise sensitivity and false-positive rates (FPRs) between them and the algorithm. Results The mean weighted pairwise sensitivity among experts was 68.2% (SD 6.95; 95% CI 59.6-76.8), whereas that between experts and the LaD algorithm was 69.4% (SD 17.95; 95% CI 47.1-91.7). The pairwise FPR among the experts ranged from 12% to 33% with a mean of 23.9% (SD 9.14; 95% CI 12.6-35.2), whereas that between experts and the algorithm ranged from 18% to 43% (mean 26.3%; SD 10.4; 95% CI 13.3-39.3). The was a correlation (mean 0.67 [SD 0.06]) in the actions selected by the expert pairs for the different patient cases with a reliability coefficient (α) of .91. Conclusions The LaD algorithm was more sensitive, but had a higher FPR than the childbirth experts, although the differences were not statistically significant. An electronic tool for childbirth monitoring with fewer WHO-recommended parameters may not be inferior to human experts in labor and delivery clinical decision support.

Background The COVID-19 pandemic has impacted people’s lives at unprecedented speed and scale, including how they eat and work, what they are concerned about, how much they move, and how much they can earn. Traditional surveys in the area of public health can be expensive and time-consuming, and they can rapidly become outdated. The analysis of big data sets (such as electronic patient records and surveillance systems) is very complex. Google Trends is an alternative approach that has been used in the past to analyze health behaviors; however, most existing studies on COVID-19 using these data examine a single issue or a limited geographic area. This paper explores Google Trends as a proxy for what people are thinking, needing, and planning in real time across the United States. Objective We aimed to use Google Trends to provide both insights into and potential indicators of important changes in information-seeking patterns during pandemics such as COVID-19. We asked four questions: (1) How has information seeking changed over time? (2) How does information seeking vary between regions and states? (3) Do states have particular and distinct patterns in information seeking? (4) Do search data correlate with—or precede—real-life events? Methods We analyzed searches on 38 terms related to COVID-19, falling into six themes: social and travel; care seeking; government programs; health programs; news and influence; and outlook and concerns. We generated data sets at the national level (covering January 1, 2016, to April 15, 2020) and state level (covering January 1 to April 15, 2020). Methods used include trend analysis of US search data; geographic analyses of the differences in search popularity across US states from March 1 to April 15, 2020; and principal component analysis to extract search patterns across states. Results The data showed high demand for information, corresponding with increasing searches for coronavirus linked to news sources regardless of the ideological leaning of the news source. Changes in information seeking often occurred well in advance of action by the federal government. The popularity of searches for unemployment claims predicted the actual spike in weekly claims. The increase in searches for information on COVID-19 care was paralleled by a decrease in searches related to other health behaviors, such as urgent care, doctor’s appointments, health insurance, Medicare, and Medicaid. Finally, concerns varied across the country; some search terms were more popular in some regions than in others. Conclusions COVID-19 is unlikely to be the last pandemic faced by the United States. Our research holds important lessons for both state and federal governments in a fast-evolving situation that requires a finger on the pulse of public sentiment. We suggest strategic shifts for policy makers to improve the precision and effectiveness of non-pharmaceutical interventions and recommend the development of a real-time dashboard as a decision-making tool.

Background There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra–short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. Objective The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. Methods The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study. Results Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2). Conclusions We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients. International Registered Report Identifier (IRRID) RR2-10.2196/17783

Background Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects. Objective The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients. Methods The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models. Results Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier. Conclusions We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities. International Registered Report Identifier (IRRID) RR2-10.2196/17783

Background A high proportion of health care services are persistently utilized by a small subpopulation of patients. To improve clinical outcomes while reducing costs and utilization, population health management programs often provide targeted interventions to patients who may become persistent high users/utilizers (PHUs). Enhanced prediction and management of PHUs can improve health care system efficiencies and improve the overall quality of patient care. Objective The aim of this study was to detect key classes of diseases and medications among the study population and to assess the predictive value of these classes in identifying PHUs. Methods This study was a retrospective analysis of insurance claims data of patients from the Johns Hopkins Health Care system. We defined a PHU as a patient incurring health care costs in the top 20% of all patients’ costs for 4 consecutive 6-month periods. We used 2013 claims data to predict PHU status in 2014-2015. We applied latent class analysis (LCA), an unsupervised clustering approach, to identify patient subgroups with similar diagnostic and medication patterns to differentiate variations in health care utilization across PHUs. Logistic regression models were then built to predict PHUs in the full population and in select subpopulations. Predictors included LCA membership probabilities, demographic covariates, and health utilization covariates. Predictive powers of the regression models were assessed and compared using standard metrics. Results We identified 164,221 patients with continuous enrollment between 2013 and 2015. The mean study population age was 19.7 years, 55.9% were women, 3.3% had ≥1 hospitalization, and 19.1% had 10+ outpatient visits in 2013. A total of 8359 (5.09%) patients were identified as PHUs in both 2014 and 2015. The LCA performed optimally when assigning patients to four probability disease/medication classes. Given the feedback provided by clinical experts, we further divided the population into four diagnostic groups for sensitivity analysis: acute upper respiratory infection (URI) (n=53,232; 4.6% PHUs), mental health (n=34,456; 12.8% PHUs), otitis media (n=24,992; 4.5% PHUs), and musculoskeletal (n=24,799; 15.5% PHUs). For the regression models predicting PHUs in the full population, the F1-score classification metric was lower using a parsimonious model that included LCA categories (F1=38.62%) compared to that of a complex risk stratification model with a full set of predictors (F1=48.20%). However, the LCA-enabled simple models were comparable to the complex model when predicting PHUs in the mental health and musculoskeletal subpopulations (F1-scores of 48.69% and 48.15%, respectively). F1-scores were lower than that of the complex model when the LCA-enabled models were limited to the otitis media and acute URI subpopulations (45.77% and 43.05%, respectively). Conclusions Our study illustrates the value of LCA in identifying subgroups of patients with similar patterns of diagnoses and medications. Our results show that LCA-derived classes can simplify predictive models of PHUs without compromising predictive accuracy. Future studies should investigate the value of LCA-derived classes for predicting PHUs in other health care settings.

Background Cognitive stimulation therapy appears to show promising results in the rehabilitation of impaired cognitive processes in attention deficit hyperactivity disorder. Objective Encouraged by this evidence and the ever-increasing use of technology and artificial intelligence for therapeutic purposes, we examined whether cognitive stimulation therapy implemented on a mobile device and controlled by an artificial intelligence engine can be effective in the neurocognitive rehabilitation of these patients. Methods In this randomized study, 29 child participants (25 males) underwent training with a smart, digital, cognitive stimulation program (KAD_SCL_01) or with 3 commercial video games for 12 weeks, 3 days a week, 15 minutes a day. Participants completed a neuropsychological assessment and a preintervention and postintervention magnetoencephalography study in a resting state with their eyes closed. In addition, information on clinical symptoms was collected from the child´s legal guardians. Results In line with our main hypothesis, we found evidence that smart, digital, cognitive treatment results in improvements in inhibitory control performance. Improvements were also found in visuospatial working memory performance and in the cognitive flexibility, working memory, and behavior and general executive functioning behavioral clinical indexes in this group of participants. Finally, the improvements found in inhibitory control were related to increases in alpha-band power in all participants in the posterior regions, including 2 default mode network regions of the interest: the bilateral precuneus and the bilateral posterior cingulate cortex. However, only the participants who underwent cognitive stimulation intervention (KAD_SCL_01) showed a significant increase in this relationship. Conclusions The results seem to indicate that smart, digital treatment can be effective in the inhibitory control and visuospatial working memory rehabilitation in patients with attention deficit hyperactivity disorder. Furthermore, the relation of the inhibitory control with alpha-band power changes could mean that these changes are a product of plasticity mechanisms or changes in the neuromodulatory dynamics. Trial Registration ISRCTN Registry ISRCTN71041318; https://www.isrctn.com/ISRCTN71041318

Background Medication nonadherence is a costly problem that is common in clinical use and clinical trials alike, with significant adverse consequences. Digital pill systems have proved to be effective and safe solutions to the challenges of nonadherence, with documented success in improving adherence and health outcomes. Objective The aim of this human factors validation study is to evaluate a novel digital pill system, the ID-Cap System from etectRx, for usability among patient users in a simulated real-world use environment. Methods A total of 17 patients with diverse backgrounds who regularly take oral prescription medications were recruited. After training and a period of training decay, the participants were asked to complete 12 patient-use scenarios during which errors or difficulties were logged. The participants were also interviewed about their experiences with the ID-Cap System. Results The participants ranged in age from 27 to 74 years (mean 51 years, SD 13.8 years), and they were heterogeneous in other demographic factors as well, such as education level, handedness, and sex. In this human factors validation study, the patient users completed 97.5% (196/201) of the total use scenarios successfully; 75.1% (151/201) were completed without any failures or errors. The participants found the ID-Cap System easy to use, and they were able to accurately and proficiently record ingestion events using the device. Conclusions The participants demonstrated the ability to safely and effectively use the ID-Cap System for its intended use. The ID-Cap System has great potential as a useful tool for encouraging medication adherence and can be easily implemented by patient users.

Background Virtual reality (VR) is as effective a technique as traditional cognitive behavioral therapy (CBT) and a promising tool for treating panic disorder symptoms because VR exposure can be safer and has better acceptability than in vivo exposure and is more immersive than exposure through imagination. CBT techniques can be delivered more effectively using VR as well. So far, VR has required high-quality devices, but the development of mobile VR technology has improved user availability. At the same time, a well-structured form of VR can be reproduced and used anywhere. This means that VR can be used to provide a self-guided form of treatment and address the high treatment costs of evidence-based therapy and the lack of professional therapists. This study aimed to investigate the potential of self-guided VR as an alternative to high-cost treatment. Objective The main goal of this study was to offer data about the efficacy of a mobile app-based self-led VR CBT in the treatment of panic disorder. Methods A total of 54 subjects with panic disorder were enrolled in this study and randomly assigned to either the VR treatment group or waitlist group. The VR treatment was designed to be total 12 sessions for 4 weeks. The VR treatment consists of 4 steps in which patients are gradually exposed to phobic stimuli while learning to cope with panic symptoms in each stage. The effectiveness of treatment was assessed through the Panic Disorder Severity Scale, Hamilton Rating Scale for Depression, Body Sensations Questionnaire, Albany Panic and Phobia Questionnaire, Anxiety Sensitivity Index, State-Trait Anxiety Inventory, Hospital Anxiety and Depression Scale, Korean Inventory of Social Avoidance and Distress Scale, Korean Inventory of Depressive Symptomatology, and Perceived Stress Scale. In addition, physiological changes using heart rate variability were evaluated. Results In within-group analyses, the VR treatment group exhibited improvements in panic disorder symptoms, anxiety, and depression after 4 weeks, while the waitlist group did not show any significant improvement. Compared to the waitlist group, the VR treatment group showed significantly greater improvements in the Panic Disorder Severity Scale in both completer analysis and intention-to-treat analysis. Heart rate variability in the VR treatment group showed improvement in normalized high frequency from baseline to postassessment with no significant differences in any outcome measure between groups. Conclusions The self-guided, mobile app-based VR intervention was effective in the treatment of panic symptoms and restoring the autonomic nervous system demonstrating the validity of the use of VR for self-guided treatment. VR treatment can be a cost-effective therapeutic approach. Trial Registration ClinicalTrials.gov NCT04985019; https://clinicaltrials.gov/ct2/show/NCT04985019

Background The role of fluorodeoxyglucose–positron emission tomography/computed tomography (FDG-PET/CT) in indolent lymphoma has been minimally studied. Objective This study aims to assess the value of FDG-PET/CT in predicting the prognosis of indolent lymphoma. Methods We prospectively recruited 42 patients with indolent lymphoma. A total of 2 patients were excluded, and 40 underwent baseline PET/CT and follow-up at various time points. A total of 9 patients were observed only, 7 received 4 doses of rituximab alone, and 24 received chemoimmunotherapy. Metabolic response on follow-up PET/CT was assessed using the maximum standardized uptake value (SUVmax) and Deauville criteria (DC). We aimed to obtain the best SUVmax and DC to predict optimal survival rates, risk stratification, and optimize therapeutic strategies. The mean follow-up from the initial diagnosis was 33.83 months. Results SUVmax Conclusions We demonstrated the utility of PET/CT in indolent lymphomas. SUVmax (

Background Opioid overdose (OD) and related deaths have significantly increased in the United States over the last 2 decades. Existing studies have mostly focused on demographic and clinical risk factors in noncritical care settings. Social and behavioral determinants of health (SBDH) are infrequently coded in the electronic health record (EHR) and usually buried in unstructured EHR notes, reflecting possible gaps in clinical care and observational research. Therefore, SBDH often receive less attention despite being important risk factors for OD. Natural language processing (NLP) can alleviate this problem. Objective The objectives of this study were two-fold: First, we examined the usefulness of NLP for SBDH extraction from unstructured EHR text, and second, for intensive care unit (ICU) admissions, we investigated risk factors including SBDH for nonfatal OD. Methods We performed a cross-sectional analysis of admission data from the EHR of patients in the ICU of Beth Israel Deaconess Medical Center between 2001 and 2012. We used patient admission data and International Classification of Diseases, Ninth Revision (ICD-9) diagnoses to extract demographics, nonfatal OD, SBDH, and other clinical variables. In addition to obtaining SBDH information from the ICD codes, an NLP model was developed to extract 6 SBDH variables from EHR notes, namely, housing insecurity, unemployment, social isolation, alcohol use, smoking, and illicit drug use. We adopted a sequential forward selection process to select relevant clinical variables. Multivariable logistic regression analysis was used to evaluate the associations with nonfatal OD, and relative risks were quantified as covariate-adjusted odds ratios (aOR). Results The strongest association with nonfatal OD was found to be drug use disorder (aOR 8.17, 95% CI 5.44-12.27), followed by bipolar disorder (aOR 2.69, 95% CI 1.68-4.29). Among others, major depressive disorder (aOR 2.57, 95% CI 1.12-5.88), being on a Medicaid health insurance program (aOR 2.26, 95% CI 1.43-3.58), history of illicit drug use (aOR 2.09, 95% CI 1.15-3.79), and current use of illicit drugs (aOR 2.06, 95% CI 1.20-3.55) were strongly associated with increased risk of nonfatal OD. Conversely, Blacks (aOR 0.51, 95% CI 0.28-0.94), older age groups (40-64 years: aOR 0.65, 95% CI 0.44-0.96; >64 years: aOR 0.16, 95% CI 0.08-0.34) and those with tobacco use disorder (aOR 0.53, 95% CI 0.32-0.89) or alcohol use disorder (aOR 0.64, 95% CI 0.42-1.00) had decreased risk of nonfatal OD. Moreover, 99.82% of all SBDH information was identified by the NLP model, in contrast to only 0.18% identified by the ICD codes. Conclusions This is the first study to analyze the risk factors for nonfatal OD in an ICU setting using NLP-extracted SBDH from EHR notes. We found several risk factors associated with nonfatal OD including SBDH. SBDH are richly described in EHR notes, supporting the importance of integrating NLP-derived SBDH into OD risk assessment. More studies in ICU settings can help health care systems better understand and respond to the opioid epidemic.

Background Multimorbidity is the co-occurrence of two or more chronic diseases. Objective This study, based on self-reported medical diagnosis, aims to investigate the dynamic distribution of multimorbidity across sociodemographic levels and its impacts on health-related issues over 15 years in Brazil using national data. Methods Data were analyzed using descriptive statistics, hypothesis tests, and logistic regression. The study sample comprised 679,572 adults (18-59 years of age) and 115,699 elderly people (≥60 years of age) from the two latest cross-sectional, multiple-cohort, national-based studies: the National Sample Household Survey (PNAD) of 1998, 2003, and 2008, and the Brazilian National Health Survey (PNS) of 2013. Results Overall, the risk of multimorbidity in adults was 1.7 times higher in women (odds ratio [OR] 1.73, 95% CI 1.67-1.79) and 1.3 times higher among people without education (OR 1.34, 95% CI 1.28-1.41). Multiple chronic diseases considerably increased with age in Brazil, and people between 50 and 59 years old were about 12 times more likely to have multimorbidity than adults between 18 and 29 years of age (OR 11.89, 95% CI 11.27-12.55). Seniors with multimorbidity had more than twice the likelihood of receiving health assistance in community services or clinics (OR 2.16, 95% CI 2.02-2.31) and of being hospitalized (OR 2.37, 95% CI 2.21-2.56). The subjective well-being of adults with multimorbidity was often worse than people without multiple chronic diseases (OR=12.85, 95% CI: 12.07-13.68). These patterns were similar across all 4 cohorts analyzed and were relatively stable over 15 years. Conclusions Our study shows little variation in the prevalence of the multimorbidity of chronic diseases in Brazil over time, but there are differences in the prevalence of multimorbidity across different social groups. It is hoped that the analysis of multimorbidity from the two latest Brazil national surveys will support policy making on epidemic prevention and management.

Background Cirrhosis, or scarring of the liver, is a debilitating condition that affects millions of US adults. Early identification, linkage to care, and retention of care are critical for preventing severe complications and death from cirrhosis. Objective The purpose of this study is to conduct a preimplementation formative evaluation to identify factors that could impact implementation of the Population-Based Cirrhosis Identification and Management System (P-CIMS) in clinics serving patients with cirrhosis. P-CIMS is a web-based informatics tool designed to facilitate patient outreach and cirrhosis care management. Methods Semistructured interviews were conducted between January and May 2016 with frontline providers in liver disease and primary care clinics at 3 Veterans Health Administration medical centers. A total of 10 providers were interviewed, including 8 physicians and midlevel providers from liver-related specialty clinics and 2 primary care providers who managed patients with cirrhosis. The Consolidated Framework for Implementation Research guided the development of the interview guides. Inductive consensus coding and content analysis were used to analyze transcribed interviews and abstracted coded passages, elucidated themes, and insights. Results The following themes and subthemes emerged from the analyses: outer setting: needs and resources for patients with cirrhosis; inner setting: readiness for implementation (subthemes: lack of resources, lack of leadership support), and implementation climate (subtheme: competing priorities); characteristics of individuals: role within clinic; knowledge and beliefs about P-CIMS (subtheme: perceived and realized benefits; useful features; suggestions for improvement); and perceptions of current practices in managing cirrhosis cases (subthemes: preimplementation process for identifying and linking patients to cirrhosis care; structural and social barriers to follow-up). Overall, P-CIMS was viewed as a powerful tool for improving linkage and retention, but its integration in the clinical workflow required leadership support, time, and staffing. Providers also cited the need for more intuitive interface elements to enhance usability. Conclusions P-CIMS shows promise as a powerful tool for identifying, linking, and retaining care in patients living with cirrhosis. The current evaluation identified several improvements and advantages of P-CIMS over current care processes and provides lessons for others implementing similar population-based identification and management tools in populations with chronic disease.

Background Iatrogenic hypoglycemia is a common occurrence among hospitalized patients and is associated with poor clinical outcomes and increased mortality. Clinical decision support systems can be used to reduce the incidence of this potentially avoidable adverse event. Objective This study aims to determine the desired features and functionality of a real-time informatics alert to prevent iatrogenic hypoglycemia in a hospital setting. Methods Using the Agency for Healthcare Research and Quality Five Rights of Effective Clinical Decision Support Framework, we conducted a mixed methods study using an electronic survey and focus group sessions of hospital-based providers. The goal was to elicit stakeholder input to inform the future development of a real-time informatics alert to target iatrogenic hypoglycemia. In addition to perceptions about the importance of the problem and existing barriers, we sought input regarding the content, format, channel, timing, and recipient for the alert (ie, the Five Rights). Thematic analysis of focus group sessions was conducted using deductive and inductive approaches. Results A 21-item electronic survey was completed by 102 inpatient-based providers, followed by 2 focus group sessions (6 providers per session). Respondents universally agreed or strongly agreed that inpatient iatrogenic hypoglycemia is an important problem that can be addressed with an informatics alert. Stakeholders expressed a preference for an alert that is nonintrusive, accurate, communicated in near real time to the ordering provider, and provides actionable treatment recommendations. Several electronic medical record tools, including alert indicators in the patient header, glucose management report, and laboratory results section, were deemed acceptable formats for consideration. Concerns regarding alert fatigue were prevalent among both survey respondents and focus group participants. Conclusions The design preferences identified in this study will provide the framework needed for an informatics team to develop a prototype alert for pilot testing and evaluation. This alert will help meet the needs of hospital-based clinicians caring for patients with diabetes who are at a high risk of treatment-related hypoglycemia.

Background Fat fraction values obtained from magnetic resonance imaging (MRI) can be used to obtain an accurate diagnosis of fatty liver diseases. However, MRI is expensive and cannot be performed for everyone. Objective In this study, we aim to develop multi-view ultrasound image–based convolutional deep learning models to detect fatty liver disease and yield fat fraction values. Methods We extracted 90 ultrasound images of the right intercostal view and 90 ultrasound images of the right intercostal view containing the right renal cortex from 39 cases of fatty liver (MRI–proton density fat fraction [MRI–PDFF] ≥ 5%) and 51 normal subjects (MRI–PDFF < 5%), with MRI–PDFF values obtained from Good Gang-An Hospital. We obtained combined liver and kidney-liver (CLKL) images to train the deep learning models and developed classification and regression models based on the VGG19 model to classify fatty liver disease and yield fat fraction values. We employed the data augmentation techniques such as flip and rotation to prevent the deep learning model from overfitting. We determined the deep learning model with performance metrics such as accuracy, sensitivity, specificity, and coefficient of determination (R2). Results In demographic information, all metrics such as age and sex were similar between the two groups—fatty liver disease and normal subjects. In classification, the model trained on CLKL images achieved 80.1% accuracy, 86.2% precision, and 80.5% specificity to detect fatty liver disease. In regression, the predicted fat fraction values of the regression model trained on CLKL images correlated with MRI–PDFF values (R2=0.633), indicating that the predicted fat fraction values were moderately estimated. Conclusions With deep learning techniques and multi-view ultrasound images, it is potentially possible to replace MRI–PDFF values with deep learning predictions for detecting fatty liver disease and estimating fat fraction values.

Background The COVID-19 pandemic has radically shifted living practices, thereby influencing changes in the health status and behaviors of every person. Objective The aim of this study was to investigate the impact of COVID-19 on the self-reported health status and health behaviors along with any associated factors in adults in the Republic of Korea wherein no stringent lockdown measures were implemented during the pandemic. Methods We conducted a web-based self-reported survey from November 2020 to December 2020. The study participants (N=2097) were identified through quota sampling by age, sex, and geographical regions among residents aged 19 years or older in Korea. The survey collected information on basic demographics, changes in self-reported health status, and health behaviors during the COVID-19 pandemic. Self-reported health status and health behaviors were categorized into 3 groups: unchanged, improved, or worsened. A chi-square test and logistic regression analyses were conducted. Results With regard to changes in the self-reported health status, the majority (1478/2097, 70.5%) of the participants reported that their health was unchanged, while 20% (420/2097) of the participants reported having worser health after the COVID-19 outbreak. With regard to changes in health behaviors, the proportion of participants who increased tobacco consumption was similar to that of those who decreased tobacco consumption (110/545, 20.2% vs 106/545, 19.5%, respectively), while the proportion of those who decreased their drinking frequency was more than twice as many as those who increased their drinking frequency (578/1603, 36.1% vs 270/1603, 16.8%, respectively). Further, those who decreased their exercising frequency were more than those who increased their exercising frequency (333/823, 15.9% vs 211/823, 10%, respectively). The factor that had the greatest influence on lifestyle was age. In the subgroup analysis, the group aged 20-29 years had the highest number of individuals with both a worsened (100/377, 26.5%) and an improved (218/377, 15.7%) health status. Further, individuals aged 20-29 years had greater odds of increased smoking (6.44, 95% CI 2.15-19.32), increased alcohol use (4.64, 95% CI 2.60-8.28), and decreased moderate or higher intensity aerobic exercise (3.39, 95% CI 1.82-6.33) compared to individuals aged 60 years and older. Younger adults showed deteriorated health behaviors, while older adults showed improved health behaviors. Conclusions The health status and the behavior of the majority of the Koreans were not found to be heavily affected by the COVID-19 outbreak. However, in some cases, changes in health status or health behavior were identified. This study highlighted that some groups were overwhelmingly affected by COVID-19 compared to others. Certain groups reported experiencing both worsening and improving health, while other groups reported unchanged health status. Age was the most influential factor for behavior change; in particular, the younger generation’s negative health behaviors need more attention in terms of public health. As COVID-19 prolongs, public health interventions for vulnerable groups may be needed.

Background Engagement with digital interventions is a well-known predictor of treatment outcomes, but this knowledge has had limited actionable value. Instead, learning why engagement with digital interventions impact treatment outcomes can lead to targeted improvements in their efficacy. Objective This study aimed to test a serial mediation model of an Acceptance and Commitment Therapy (ACT) smartphone intervention for smoking cessation. Methods In this randomized controlled trial, participants (N=2415) from 50 US states were assigned to the ACT-based smartphone intervention (iCanQuit) or comparison smartphone intervention (QuitGuide). Their engagement with the apps (primary measure: number of logins) was measured during the first 3 months, ACT processes were measured at baseline and 3 months (acceptance of internal cues to smoke, valued living), and smoking cessation was measured at 12 months with 87% follow-up retention. Results There was a significant serial mediation effect of iCanQuit on smoking cessation through multiple indicators of intervention engagement (ie, total number of logins, total number of minutes used, and total number of unique days of use) and in turn through increases in mean acceptance of internal cues to smoke from baseline to 3 months. Analyses of the acceptance subscales showed that the mediation was through acceptance of physical sensations and emotions, but not acceptance of thoughts. There was no evidence that the effect of the iCanQuit intervention was mediated through changes in valued living. Conclusions In this first study of serial mediators underlying the efficacy of smartphone apps for smoking cessation, our results suggest the effect of the iCanQuit ACT-based smartphone app on smoking cessation was mediated through multiple indicators of engagement and in turn through increases in the acceptance of physical sensations and emotions that cue smoking. Trial Registration Clinical Trials.gov NCT02724462; https://clinicaltrials.gov/ct2/show/NCT02724462

Background Czech older adults have lower rates of physical activity than the average population and lag behind in the use of digital technologies, compared with their peers from other European countries. Objective This study aims to assess the feasibility of intensive behavior monitoring through technology in Czech adults aged ≥50 years. Methods Participants (N=30; mean age 61.2 years, SD 6.8 years, range 50-74 years; 16/30, 53% male; 7/30, 23% retired) were monitored for 12 weeks while wearing a Fitbit Charge 2 monitor and completed three 8-day bursts of intensive data collection through surveys presented on a custom-made mobile app. Web-based surveys were also completed before and at the end of the 12-week period (along with poststudy focus groups) to evaluate participants’ perceptions of their experience in the study. Results All 30 participants completed the study. Across the three 8-day bursts, participants completed 1454 out of 1744 (83% compliance rate) surveys administered 3 times per day on a pseudorandom schedule, 451 out of 559 (81% compliance rate) end-of-day surveys, and 736 episodes of self-reported planned physical activity (with 29/736, 3.9% of the reports initiated but returned without data). The overall rating of using the mobile app and Fitbit was above average (74.5 out of 100 on the System Usability Scale). The majority reported that the Fitbit (27/30, 90%) and mobile app (25/30, 83%) were easy to use and rated their experience positively (25/30, 83%). Focus groups revealed that some surveys were missed owing to notifications not being noticed or that participants needed a longer time window for survey completion. Some found wearing the monitor in hot weather or at night uncomfortable, but overall, participants were highly motivated to complete the surveys and be compliant with the study procedures. Conclusions The use of a mobile survey app coupled with a wearable device appears feasible for use among Czech older adults. Participants in this study tolerated the intensive assessment schedule well, but lower compliance may be expected in studies of more diverse groups of older adults. Some difficulties were noted with the pairing and synchronization of devices on some types of smartphones, posing challenges for large-scale studies.

Background Poor self-management of heart failure (HF) contributes to devastating health consequences. Our innovative sensor-controlled digital game (SCDG) integrates data from sensors to trigger game rewards, progress, and feedback based on the real-time behaviors of individuals with HF. Objective The aim of this study is to compare daily weight monitoring and physical activity behavior adherence by older adults using an SCDG intervention versus a sensors-only intervention in a feasibility randomized controlled trial. Methods English-speaking adults with HF aged 55 years or older who owned a smartphone and could walk unassisted were recruited from Texas and Oklahoma from November 2019 to August 2020. Both groups were given activity trackers and smart weighing scales to track behaviors for 12 weeks. The feasibility outcomes of recruitment, retention, intervention engagement, and satisfaction were assessed. In addition to daily weight monitoring and physical activity adherence, the participants’ knowledge, functional status, quality of life, self-reported HF behaviors, motivation to engage in behaviors, and HF-related hospitalization were also compared between the groups at baseline and at 6, 12, and 24 weeks. Results A total of 38 participants with HF—intervention group (IG; 19/38, 50%) and control group (CG; 19/38, 50%)—were enrolled in the study. Of the 38 participants, 18 (47%) were women, 18 (47%) were aged 65 years or older, 21 (55%) had been hospitalized with HF in the past 6 months, and 29 (76%) were White. Furthermore, of these 38 participants, 31 (82%)—IG (15/19, 79%) and CG (16/19, 84%)—had both weight monitoring and physical activity data at the end of 12 weeks, and 27 (71%)—IG (14/19, 74%) and CG (13/19, 68%)—participated in follow-up assessments at 24 weeks. For the IG participants who installed the SCDG app (15/19, 79%), the number of days each player opened the game app was strongly associated with the number of days the player engaged in weight monitoring (r=0.72; P=.04) and the number of days with physical activity step data (r=0.9; P Conclusions Playing an SCDG on smartphones was feasible and acceptable for older adults with HF for motivating daily weight monitoring and physical activity. A larger efficacy trial of the SCDG intervention will be needed to validate trends of improvement in daily weight monitoring and physical activity behaviors. Trial Registration ClinicalTrials.gov NCT03947983; https://clinicaltrials.gov/ct2/show/NCT03947983