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Background: There is increasing use of online surveys to improve data quality and timeliness and reduce costs. While there have been numerous cross-sectional studies comparing responses to online or paper surveys, there is little research from a longitudinal perspective. Objective: In the context of the well-established Australian Longitudinal Study on Women’s Health, we examined the patterns of responses to online or paper surveys across the first two waves of the study in which both modes were offered. We compared the following: differences between women born between 1946 and 1951 and between 1973 and 1978; types of device used for online completion; sociodemographic, behavioral, and health characteristics of women who responded online or using mailed paper surveys; and associations between mode of completion in the first survey and participation and mode of completion in the second survey. Methods: Participants in this study, who had responded to regular mailed surveys since 1996, were offered a choice of completing surveys using paper questionnaires or Web-based electronic questionnaires starting in 2012. Two groups of women were involved: an older cohort born between 1946 and 1951 aged in their 60s and a younger cohort born between 1973 and 1978 aged in their 30s when the online surveys were first introduced. We compared women who responded online on both occasions, women who responded online at the first survey and used the paper version of the second survey, women who changed from paper to online, and those who used paper for both surveys. Results: Of the 9663 women in their 60s who responded to one or both surveys, more than 50% preferred paper surveys (5290/9663, 54.74%, on the first survey and 5373/8621, 62.32%, on the second survey). If they chose the online version, most used computers. In contrast, of the 8628 women in their 30s, 56.04% (4835/8628) chose the online version at the first survey. While most favored computers to phones or tablets, many did try these alternatives on the subsequent survey. Many women who completed the survey online the first time preferred the paper version on the subsequent survey. In fact, for women in their 60s, the number who went from online to paper (1151/3851, 29.89%) exceeded the number who went from paper to online (734/5290, 13.88%). The online option was more likely to be chosen by better educated and healthier women. In both cohorts, women who completed paper surveys were more likely than online completers to become nonrespondents on the next survey. Due to the large sample size, almost all differences were statistically significant, with P

BackgroundElectronic applications are increasingly being used in hospitals for numerous purposes. ObjectiveOur aim was to assess differences in the characteristics of patients who choose paper versus electronic questionnaires and to evaluate the data quality of both approaches. MethodsBetween October 2012 and June 2013, 136 patients participated in a study on diagnosis-induced stress and anxiety. Patients were asked to fill out questionnaires at six different moments during the diagnostic phase. They were given the opportunity to fill out the questionnaires on paper or electronically (a combination of tablet and Web-based questionnaires). Demographic characteristics and completeness of returned data were compared between groups. ResultsNearly two-thirds of patients (88/136, 64.7%) chose to fill out the questionnaires on paper, and just over a third (48/136, 35.3%) preferred the electronic option. Patients choosing electronic questionnaires were significantly younger (mean 47.3 years vs mean 53.5 in the paper group, P=.01) and higher educated (P=.004). There was significantly more missing information (ie, at least one question not answered) in the paper group during the diagnostic day compared to the electronic group (using a tablet) (28/88 vs 1/48, P

Background: Survey response rates have been declining over the past decade. The more widespread use of the Internet and Web-based technologies among potential health survey participants suggests that Web-based questionnaires may be an alternative to paper questionnaires in future epidemiological studies. Objective: To compare response rates in a population of parents by using 4 different modes of data collection for a questionnaire survey of which 1 involved a nonmonetary incentive. Methods: A random sample of 3148 parents of Danish children aged 2-17 years were invited to participate in the Danish part of the NordChild 2011 survey on their children’s health and welfare. NordChild was conducted in 1984 and 1996 in collaboration with Finland, Iceland, Norway, and Sweden using mailed paper questionnaires only. In 2011, all countries used conventional paper versions only except Denmark where the parents were randomized into 4 groups: (1) 789 received a paper questionnaire only (paper), (2) 786 received the paper questionnaire and a log-in code to the Web-based questionnaire (paper/Web), (3) 787 received a log-in code to the Web-based questionnaire (Web), and (4) 786 received log-in details to the Web-based questionnaire and were given an incentive consisting of a chance to win a tablet computer (Web/tablet). In connection with the first reminder, the nonresponders in the paper, paper/Web, and Web groups were also present with the opportunity to win a tablet computer as a means of motivation. Descriptive analysis was performed using chi-square tests. Odds ratios were used to estimate differences in response rates between the 4 modes. Results: In 2011, 1704 of 3148 (54.13%) respondents answered the Danish questionnaire. The highest response rate was with the paper mode (n=443, 56.2%). The other groups had similar response rates: paper/Web (n=422, 53.7%), Web (n=420, 53.4%), and Web/tablet (n=419, 53.3%) modes. Compared to the paper mode, the odds for response rate in the paper/Web decreased by 9% (OR 0.91, 95% CI 0.74-1.10) and by 11% (OR 0.89, 95% CI 0.73-1.09) in the Web and Web/tablet modes. The total number of responders for NordChild declined from 10,291 of 15,339 (67.09%) in 1984 and 10,667 of 15,254 (69.93%) in 1996 to 7805 of 15,945 (48.95%) in 2011 with similar declines in all 5 Nordic countries. Conclusions: Web-based questionnaires could replace traditional paper questionnaires with minor effects on response rates and lower costs. The increasing effect on the response rate on participants replying for a nonmonetary incentive could only be estimated within the 2 Web-based questionnaire modes before the first reminder. Alternative platforms to reach higher participation rates in population surveys should reflect the development of electronic devices and the ways in which the population primarily accesses the Internet. [J Med Internet Res 2013;15(8):e173]

Background: Maternal, Newborn, and Child Health (MNCH) household survey data are collected mainly with pen-and-paper. Smartphone data collection may have advantages over pen-and-paper, but little evidence exists on how they compare. Objective: To compare smartphone data collection versus the use of pen-and-paper for infant feeding practices of the MNCH household survey. We compared the two data collection methods for differences in data quality (data recording, data entry, open-ended answers, and interrater reliability), time consumption, costs, interviewers’ perceptions, and problems encountered. Methods: We recruited mothers of infants aged 0 to 23 months in four village clinics in Zhaozhou Township, Zhao County, Hebei Province, China. We randomly assigned mothers to a smartphone or a pen-and-paper questionnaire group. A pair of interviewers simultaneously questioned mothers on infant feeding practices, each using the same method (either smartphone or pen-and-paper). Results: We enrolled 120 mothers, and all completed the study. Data recording errors were prevented in the smartphone questionnaire. In the 120 pen-and-paper questionnaires (60 mothers), we found 192 data recording errors in 55 questionnaires. There was no significant difference in recording variation between the groups for the questionnaire pairs (P = .32) or variables (P = .45). The smartphone questionnaires were automatically uploaded and no data entry errors occurred. We found that even after double data entry of the pen-and-paper questionnaires, 65.0% (78/120) of the questionnaires did not match and needed to be checked. The mean duration of an interview was 10.22 (SD 2.17) minutes for the smartphone method and 10.83 (SD 2.94) minutes for the pen-and-paper method, which was not significantly different between the methods (P = .19). The mean costs per questionnaire were higher for the smartphone questionnaire (¥143, equal to US $23 at the exchange rate on April 24, 2012) than for the pen-and-paper questionnaire (¥83, equal to US $13). The smartphone method was acceptable to interviewers, and after a pilot test we encountered only minor problems (eg, the system halted for a few seconds or it shut off), which did not result in data loss. Conclusions: This is the first study showing that smartphones can be successfully used for household data collection on infant feeding in rural China. Using smartphones for data collection, compared with pen-and-paper, eliminated data recording and entry errors, had similar interrater reliability, and took an equal amount of time per interview. While the costs for the smartphone method were higher than the pen-and-paper method in our small-scale survey, the costs for both methods would be similar for a large-scale survey. Smartphone data collection should be further evaluated for other surveys and on a larger scale to deliver maximum benefits in China and elsewhere. [J Med Internet Res 2012;14(5):e119]

Background The use of the Internet to administer questionnaires has many potential advantages over the use of pen-and-paper administration. Yet it is important to validate Internet administration, as most questionnaires were initially developed and validated for pen-and-paper delivery. While some have been validated for use over the Internet, these questionnaires have predominately been used amongst the healthy general population. To date, information is lacking on the validity of questionnaires administered over the Internet in patients with chronic diseases such as heart failure. Objectives To determine the validity of three heart failure questionnaires administered over the Internet compared to pen-and-paper administration in patients with heart failure. Methods We conducted a prospective randomized study using test-retest design comparing administration via the Internet to pen-and-paper administration for three heart failure questionnaires provided to patients recruited from a heart failure clinic in Toronto, Ontario, Canada: the Kansas City Cardiomyopathy Questionnaire (KCCQ), the Minnesota Living with Heart Failure Questionnaire (MLHFQ), and the Self-Care Heart Failure Index (SCHFI). Results Of the 58 subjects enrolled, 34 completed all three questionnaires. The mean difference and confidence intervals for the summary scores of the KCCQ, MLHFQ, and SCHFI were 1.2 (CI -1.5 to 4.0, scale from 0 to 100), 4.0 (CI -1.98 to 10.04, scale from 0 to 105), and 10.1 (CI 1.18 to 19.07, scale from 66.7 to 300), respectively. Conclusions Internet administration of the KCCQ appears to be equivalent to pen-and-paper administration. For the MLHFQ and SCHFI, we were unable to demonstrate equivalence. Further research is necessary to determine if the administration methods are equivalent for these instruments.

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 Virtual reality (VR) and augmented reality (AR) have recently become popular research themes. However, there are no published bibliometric reports that have analyzed the corresponding scientific literature in relation to the application of these technologies in medicine. Objective We used a bibliometric approach to identify and analyze the scientific literature on VR and AR research in medicine, revealing the popular research topics, key authors, scientific institutions, countries, and journals. We further aimed to capture and describe the themes and medical conditions most commonly investigated by VR and AR research. Methods The Web of Science electronic database was searched to identify relevant papers on VR research in medicine. Basic publication and citation data were acquired using the “Analyze” and “Create Citation Report” functions of the database. Complete bibliographic data were exported to VOSviewer and Bibliometrix, dedicated bibliometric software packages, for further analyses. Visualization maps were generated to illustrate the recurring keywords and words mentioned in the titles and abstracts. Results The analysis was based on data from 8399 papers. Major research themes were diagnostic and surgical procedures, as well as rehabilitation. Commonly studied medical conditions were pain, stroke, anxiety, depression, fear, cancer, and neurodegenerative disorders. Overall, contributions to the literature were globally distributed with heaviest contributions from the United States and United Kingdom. Studies from more clinically related research areas such as surgery, psychology, neurosciences, and rehabilitation had higher average numbers of citations than studies from computer sciences and engineering. Conclusions The conducted bibliometric analysis unequivocally reveals the versatile emerging applications of VR and AR in medicine. With the further maturation of the technology and improved accessibility in countries where VR and AR research is strong, we expect it to have a marked impact on clinical practice and in the life of patients.

Background Many weight loss programs show short-term effectiveness, but subsequent weight loss maintenance is difficult to achieve. Digital technologies offer a promising means of delivering behavior change approaches at low costs and on a wide scale. The Navigating to a Healthy Weight (NoHoW) project, which was funded by the European Union’s Horizon 2020 research and innovation program, aimed to develop, test, and evaluate a digital toolkit designed to promote successful long-term weight management. The toolkit was tested in an 18-month, large-scale, international, 2×2 factorial (motivation and self-regulation vs emotion regulation) randomized controlled trial that was conducted on adults with overweight or obesity who lost ≥5% of their body weight in the preceding 12 months before enrollment into the intervention. Objective This paper aims to describe the development of the NoHoW Toolkit, focusing on the logic models, content, and specifications, as well as the results from user testing. Methods The toolkit was developed by using a systematic approach, which included the development of the theory-based logic models, the selection of behavior change techniques, the translation of these techniques into a web-based app (NoHoW Toolkit components), technical development, and the user evaluation and refinement of the toolkit. Results The toolkit included a set of web-based tools and inputs from digital tracking devices (smart scales and activity trackers) and modules that targeted weight, physical activity, and dietary behaviors. The final toolkit comprised 34 sessions that were distributed through 15 modules and provided active content over a 4-month period. The motivation and self-regulation arm consisted of 8 modules (17 sessions), the emotion regulation arm was presented with 7 modules (17 sessions), and the combined arm received the full toolkit (15 modules; 34 sessions). The sessions included a range of implementations, such as videos, testimonies, and questionnaires. Furthermore, the toolkit contained 5 specific data tiles for monitoring weight, steps, healthy eating, mood, and sleep. Conclusions A systematic approach to the development of digital solutions based on theory, evidence, and user testing may significantly contribute to the advancement of the science of behavior change and improve current solutions for sustained weight management. Testing the toolkit by using a 2×2 design provided a unique opportunity to examine the effect of motivation and self-regulation and emotion regulation separately, as well as the effect of their interaction in weight loss maintenance.

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 Longitudinal follow-up of older persons living with HIV is essential for the ascertainment of aging-related clinical and behavioral outcomes, and self-administered questionnaires are necessary for collecting behavioral information in research involving persons living with HIV. Web-based self-reported data collection results in higher data quality than paper-and-pencil questionnaires in a wide range of populations. The option of remote web-based surveys may also increase retention in long-term research studies. However, the acceptability and feasibility of web-based data collection in clinical research involving older persons living with HIV have never been studied. Objective This study aims to assess the acceptability and feasibility of a web-based survey to collect information on sexual, substance use, and physical activity behaviors; compare the data quality of the web-based survey with that of a paper-and-pencil questionnaire; and summarize web-based survey metrics. Methods This pilot study took place within the AIDS Clinical Trials Group A5322 study, a longitudinal cohort of men and women living with HIV (aged ≥40 years), followed at 32 clinical sites in the United States and Puerto Rico. A total of 4 sites participated in this study. A web-based survey was created using self-administered questionnaires typically completed in A5322 via paper and pencil. Pilot study participants completed these questionnaires via web-based survey at one research visit in lieu of paper-and-pencil administration. Two questions were added to assess feasibility, defined as participants’ perception of the ease of web-based survey completion (very hard, hard, easy, very easy), and their preferred format (computer or tablet, paper and pencil, no preference) for completing the questions in the future (acceptability). Feasibility and acceptability were summarized overall and by demographic and clinical characteristics; the proportion of evaluable data by web-based survey versus previously administered paper-and-pencil questionnaires (data quality) was compared for each question. Results Acceptability and feasibility were high overall: 50.0% (79/158) preferred computer or tablet, 38.0% (60/158) reported no preference, and 12.0% (19/158) preferred paper and pencil; 93.0% (147/158) reported survey completion easy or very easy. Older age was associated with lower odds of preferring computer or tablet to paper and pencil (odds ratio per 1-year increase in age: 0.91, 95% CI 0.85-0.98). Individuals who found the survey hard or very hard had a lower median neurocognitive test score than those who found it easy or very easy. Data quality with web-based survey administration was similar to or higher than that with paper-and-pencil administration for most questions. Conclusions Web-based survey administration was acceptable and feasible in this cohort of older adults living with HIV, and data quality was high. Web-based surveys can be a useful tool for valid data collection and can potentially improve retention in long-term follow-up studies.

Background Incorporating cognitive testing into routine clinical practice is a challenge in multiple sclerosis (MS), given the wide spectrum of both cognitive and physical impairments people can have and the time that testing requires. Shortened paper and verbal assessments predominate but still are not used routinely. Computer-based tests are becoming more widespread; however, changes in how a paper test is implemented can impact what exactly is being assessed in an individual. The Symbol Digit Modalities Test (SDMT) is one validated test that forms part of the cognitive batteries used in MS and has some computer-based versions. We developed a tablet-based SDMT variant that has the potential to be ultimately deployed to patients’ own devices. Objective This paper aims to develop, validate, and deploy a computer-based SDMT variant, the Cognition Reaction (CoRe) test, that can reliably replicate the characteristics of the paper-based SDMT. Methods We carried out analysis using Pearson and intraclass correlations, as well as a Bland-Altman comparison, to examine consistency between the SDMT and CoRe tests and for test-retest reliability. The SDMT and CoRe tests were evaluated for sensitivity to disability levels and age. A novel metric in CoRe was found: question answering velocity could be calculated. This was evaluated in relation to disability levels and age for people with MS and compared with a group of healthy control volunteers. Results SDMT and CoRe test scores were highly correlated and consistent with 1-month retest values. Lower scores were seen in patients with higher age and some effect was seen with increasing disability. There was no learning effect evident. Question answering velocity demonstrated a small increase in speed over the 90-second duration of the test in people with MS and healthy controls. Conclusions This study validates a computer-based alternative to the SDMT that can be used in clinics and beyond. It enables accurate recording of elements of cognition relevant in MS but offers additional metrics that may offer further value to clinicians and people with MS.

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 Survey-based studies are frequently used to describe the economic impact of multiple sclerosis (MS). However, there is no validated health resource survey available, preventing comparison of study results and meaningful conclusions regarding the efficiency of long-term treatments. Objective The aim of this study was to develop and validate a tablet- and paper-based MS health resource utilization survey. Methods We developed and validated the Multiple Sclerosis Health Resource Utilization Survey (MS-HRS), consisting of 24 cost items for paper and tablet users. Data for validation came from two large German observational studies. Survey practicability was assessed according to the response rate. Reliability was described using test-retest reliability as well as Guttman lambda. Construct validity was assessed as convergent and discriminant validity via correlations with associated patient-reported outcomes and known-group analyses. Results In total, 2207 out of 2388 (response rate: 92.4%) patients completed the survey and were included to determine psychometric properties. The test-retest reliability had an intraclass correlation coefficient of 0.828 over a course of 3 months. Convergent validity analyses showed that total costs correlated positively with increased disability (r=0.411, P Conclusions The MS-HRS is a multilingual, reliable, valid, electronically available, and easy-to-administer questionnaire providing a holistic cross-sectional and longitudinal assessment of resource utilization in patients with MS.

Background In many countries, depressed individuals often first visit primary care settings for consultation, but a considerable number of clinically depressed patients remain unidentified. Introducing additional screening tools may facilitate the diagnostic process. Objective This study aimed to examine whether experience sampling method (ESM)-based measures of depressive affect and behaviors can discriminate depressed from nondepressed individuals. In addition, the added value of actigraphy-based measures was examined. Methods We used data from 2 samples to develop and validate prediction models. The development data set included 14 days of ESM and continuous actigraphy of currently depressed (n=43) and nondepressed individuals (n=82). The validation data set included 30 days of ESM and continuous actigraphy of currently depressed (n=27) and nondepressed individuals (n=27). Backward stepwise logistic regression analysis was applied to build the prediction models. Performance of the models was assessed with goodness-of-fit indices, calibration curves, and discriminative ability (area under the receiver operating characteristic curve [AUC]). Results In the development data set, the discriminative ability was good for the actigraphy model (AUC=0.790) and excellent for both the ESM (AUC=0.991) and the combined-domains model (AUC=0.993). In the validation data set, the discriminative ability was reasonable for the actigraphy model (AUC=0.648) and excellent for both the ESM (AUC=0.891) and the combined-domains model (AUC=0.892). Conclusions ESM is a good diagnostic predictor and is easy to calculate, and it therefore holds promise for implementation in clinical practice. Actigraphy shows no added value to ESM as a diagnostic predictor but might still be useful when ESM use is restricted.

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 Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. Objective This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. Methods A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. Results To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. Conclusions These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. Trial Registration Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191

Background The worldwide burden of musculoskeletal diseases is increasing. The number of newly registered rheumatologists has stagnated. Primary care, which takes up a key role in early detection of rheumatic disease, is working at full capacity. COVID-19 and its containment impede rheumatological treatment. Telemedicine in rheumatology (telerheumatology) could support rheumatologists and general practitioners. Objective The goal of this study was to investigate acceptance and preferences related to the use of telerheumatology care among German rheumatologists and general practitioners. Methods A nationwide, cross-sectional, self-completed, paper-based survey on telerheumatology care was conducted among outpatient rheumatologists and general practitioners during the pre-COVID-19 period. Results A total of 73.3% (349/476) of survey participants rated their knowledge of telemedicine as unsatisfactory, poor, or very poor. The majority of survey participants (358/480, 74.6%) answered that they do not currently use telemedicine, although 62.3% (291/467) would like to. Barriers to the implementation of telemedicine include the purchase of technology equipment (182/292, 62.3%), administration (181/292, 62.0%), and poor reimbursement (156/292, 53.4%). A total of 69.6% (117/168) of the surveyed physicians reckoned that telemedicine could be used in rheumatology. Surveyed physicians would prefer to use telemedicine to communicate directly with other physicians (370/455, 81.3%) than to communicate with patients (213/455, 46.8%). Among treatment phases, 64.4% (291/452) of participants would choose to use telemedicine during follow-up. Half of the participants would choose telecounseling as a specific approach to improve rheumatology care (91/170, 53.5%). Conclusions Before COVID-19 appeared, our results indicated generally low use but high acceptance of the implementation of telerheumatology among physicians. Participants indicated that the lack of a structural framework was a barrier to the effective implementation of telerheumatology. Training courses should be introduced to address the limited knowledge on the part of physicians in the use of telemedicine. More research into telerheumatology is required. This includes large-scale randomized controlled trials, economic analyses, and the exploration of user preferences.

Background In an effort to contain the effects of the coronavirus disease (COVID-19) pandemic, health care systems worldwide implemented telemedical solutions to overcome staffing, technical, and infrastructural limitations. In Germany, a multitude of telemedical systems are already being used, while new approaches are rapidly being developed in response to the crisis. However, the extent of the current implementation within different health care settings, the user’s acceptance and perception, as well as the hindering technical and regulatory obstacles remain unclear. Objective The aim of this paper is to assess the current status quo of the availability and routine use of telemedical solutions, user acceptance, and the subjectively perceived burdens on telemedical approaches. Furthermore, we seek to assess the perception of public information quality among professional groups and their preferred communication channels. Methods A national online survey was conducted on 14 consecutive days in March and April 2020, and distributed to doctors, nurses, and other medical professionals in the German language. Results A total of 2827 medical professionals participated in the study. Doctors accounted for 65.6% (n=1855) of the professionals, 29.5% (n=833) were nursing staff, and 4.9% (n=139) were identified as others such as therapeutic staff. A majority of participants rated the significance of telemedicine within the crisis as high (1065/2730, 39%) or neutral (n=720, 26.4%); however, there were significant differences between doctors and nurses (P=.01) as well as between the stationary sector compared to the ambulatory sector (P

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 There has been a cultural shift toward patient engagement in health, with a growing demand from patients to access their results. Objective The Lymphoma Intervention (LIVE) trial is conducted to examine the impact of return of individual patient-reported outcome (PRO) results and a web-based self-management intervention on psychological distress, self-management, satisfaction with information, and health care use in a population-based setting. Methods Return of PRO results included comparison with age- and sex-matched peers and was built into the Patient-Reported Outcomes Following Initial Treatment and Long-Term Evaluation of Survivorship registry. The self-management intervention is an adaptation of a fully automated evidence-based intervention for breast cancer survivors. Patients with lymphoma who completed the web-based questionnaire were equally randomized to care as usual, return of PRO results, and return of PRO results plus self-management intervention. Patients completed questionnaires 9 to 18 months after diagnosis (T0; n=227), 4 months (T1; n=190), 12 months (T2; n=170), and 24 months (T3; n=98). Results Of all invited patients, 51.1% (456/892) responded and web-based participants (n=227) were randomly assigned to care as usual (n=76), return of PRO results (n=74), or return of PRO results and access to Living with lymphoma (n=77). Return of PRO results was viewed by 76.7% (115/150) of those with access. No statistically significant differences were observed for psychological distress, self-management, satisfaction with information provision, and health care use between patients who received PRO results and those who did not (P>.05). Use of the self-management intervention was low (2/76, 3%), and an effect could therefore not be determined. Conclusions Return of individual PRO results seems to meet patients’ wishes but had no beneficial effects on patient outcome. No negative effects were found when individual PRO results were disclosed, and the return of individual PRO results can therefore be safely implemented in daily clinical practice. Trial Registration Netherlands Trial Register NTR5953; https://www.trialregister.nl/trial/5790 International Registered Report Identifier (IRRID) RR2-10.1186/s13063-017-1943-2

Background There is a general agreement on the importance of health-related quality of life (HRQoL). This type of information is becoming increasingly important for the value assessment of health technology assessment agencies in evaluating the benefits of new health technologies, including medicines. However, HRQoL data are often limited, and additional sources that provide this type of information may be helpful. Objective We aim to identify the HRQoL topics important to patients with melanoma based on web-based discussions on public social media forums. Methods We identified 3 public web-based forums from the United States and the United Kingdom, namely the Melanoma Patient Information Page, the Melanoma International Forum, and MacMillan. Their posts were randomly selected and coded using qualitative methods until saturation was reached. Results Of the posts assessed, 36.7% (150/409) of posts on Melanoma International Forum, 45.1% (198/439) on MacMillan, and 35.4% (128/362) on Melanoma Patient Information Page focused on HRQoL. The 2 themes most frequently mentioned were mental health and (un)certainty. The themes were constructed based on underlying and more detailed codes. Codes related to fear, worry and anxiety, uncertainty, and unfavorable effects were the most-often discussed ones. Conclusions Web-based forums are a valuable source for identifying relevant HRQoL aspects in patients with a given disease. These aspects could be cross-referenced with existing tools and they might improve the content validity of patient-reported outcome measures, including HRQoL questionnaires. In addition, web-based forums may provide health technology assessment agencies with a more holistic understanding of the external aspects affecting patient HRQoL. These aspects might support the value assessment of new health technologies and could therefore help inform topic prioritization as well as the scoping phase before any value assessment.

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 Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact. Objective The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals’ beliefs and perceptions. Methods We analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels. Results LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. “Emotion and beliefs” had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by “physical activity” (124,251/896,867, 13.85%) and “daily life” (80,730/896,867, 9%), while “food and drink,” “weather,” and “not being understood” had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within “emotion and beliefs,” 113,562/157,563 (72%) had negative sentiment. Conclusions The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals’ perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP.

Background Smartphones and their built-in sensors allow for measuring functions in disease-related domains through mobile tests. This could improve disease characterization and monitoring, and could potentially support treatment decisions for multiple sclerosis (MS), a multifaceted chronic neurological disease with highly variable clinical manifestations. Practice effects can complicate the interpretation of both improvement over time by potentially exaggerating treatment effects and stability by masking deterioration. Objective The aim of this study is to identify short-term learning and long-term practice effects in 6 active tests for cognition, dexterity, and mobility in user-scheduled, high-frequency smartphone-based testing. Methods We analyzed data from 264 people with self-declared MS with a minimum of 5 weeks of follow-up and at least 5 repetitions per test in the Floodlight Open study, a self-enrollment study accessible by smartphone owners from 16 countries. The collected data are openly available to scientists. Using regression and bounded growth mixed models, we characterized practice effects for the following tests: electronic Symbol Digit Modalities Test (e-SDMT) for cognition; Finger Pinching and Draw a Shape for dexterity; and Two Minute Walk, U-Turn, and Static Balance for mobility. Results Strong practice effects were found for e-SDMT (n=4824 trials), Finger Pinching (n=19,650), and Draw a Shape (n=19,019) with modeled boundary improvements of 40.8% (39.9%-41.6%), 86.2% (83.6%-88.7%), and 23.1% (20.9%-25.2%) over baseline, respectively. Half of the practice effect was reached after 11 repetitions for e-SDMT, 28 repetitions for Finger Pinching, and 17 repetitions for Draw a Shape; 90% was reached after 35, 94, and 56 repetitions, respectively. Although baseline performance levels were highly variable across participants, no significant differences between the short-term learning effects in low performers (5th and 25th percentile), median performers, and high performers (75th and 95th percentile) were found for e-SDMT up to the fifth trial (β=1.50-2.00). Only small differences were observed for Finger Pinching (β=1.25-2.5). For U-Turn (n=15,051) and Static Balance (n=16,797), only short-term learning effects could be observed, which ceased after a maximum of 5 trials. For Two Minute Walk (n=14,393), neither short-term learning nor long-term practice effects were observed. Conclusions Smartphone-based tests are promising for monitoring the disease trajectories of MS and other chronic neurological diseases. Our findings suggest that strong long-term practice effects in cognitive and dexterity functions have to be accounted for to identify disease-related changes in these domains, especially in the context of personalized health and in studies without a comparator arm. In contrast, changes in mobility may be more easily interpreted because of the absence of long-term practice effects, even though short-term learning effects might have to be considered.

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 The COVID-19 pandemic has highlighted the inability of health systems to leverage existing system infrastructure in order to rapidly develop and apply broad analytical tools that could inform state- and national-level policymaking, as well as patient care delivery in hospital settings. The COVID-19 pandemic has also led to highlighted systemic disparities in health outcomes and access to care based on race or ethnicity, gender, income-level, and urban-rural divide. Although the United States seems to be recovering from the COVID-19 pandemic owing to widespread vaccination efforts and increased public awareness, there is an urgent need to address the aforementioned challenges. Objective This study aims to inform the feasibility of leveraging broad, statewide datasets for population health–driven decision-making by developing robust analytical models that predict COVID-19–related health care resource utilization across patients served by Indiana’s statewide Health Information Exchange. Methods We leveraged comprehensive datasets obtained from the Indiana Network for Patient Care to train decision forest-based models that can predict patient-level need of health care resource utilization. To assess these models for potential biases, we tested model performance against subpopulations stratified by age, race or ethnicity, gender, and residence (urban vs rural). Results For model development, we identified a cohort of 96,026 patients from across 957 zip codes in Indiana, United States. We trained the decision models that predicted health care resource utilization by using approximately 100 of the most impactful features from a total of 1172 features created. Each model and stratified subpopulation under test reported precision scores >70%, accuracy and area under the receiver operating curve scores >80%, and sensitivity scores approximately >90%. We noted statistically significant variations in model performance across stratified subpopulations identified by age, race or ethnicity, gender, and residence (urban vs rural). Conclusions This study presents the possibility of developing decision models capable of predicting patient-level health care resource utilization across a broad, statewide region with considerable predictive performance. However, our models present statistically significant variations in performance across stratified subpopulations of interest. Further efforts are necessary to identify root causes of these biases and to rectify them.

Background New parents face increased risks of emotional distress and relationship dissatisfaction. Digital interventions increase support access, but few preventive programs are optimized for both parents. Objective This study aims to conduct the first randomized controlled trial on universal self-guided digital programs to support positive perinatal adjustment of both mothers and fathers. Effects of childcare information (Baby Care) and information plus an interactive program (Baby Steps Wellbeing) were compared from the third trimester baseline to 3 and 6 months subsequently. Methods The study recruited 388 co-parenting male-female adult couples expecting their first single child (26-38 weeks’ gestation), using web-based registration. Most (337/388, 86.8%) were obtained from prenatal hospital classes. Couples’ randomization was automated and stratified by Edinburgh Postnatal Depression Scale (EPDS) scores (50% couples scored high if either mother >7, father >5). All assessments were web-based self-reports: the EPDS and psychosocial quality of life were primary outcomes; relationship satisfaction, social support, and self-efficacy for parenting and support provision were secondary. Linear mixed models provided intention-to-treat analyses, with linear and quadratic effects for time and random intercepts for participants and couples. Results Selection criteria were met by 63.9% (248/388) of couples, who were all randomized. Most participants were married (400/496, 80.6%), tertiary educated (324/496, 65.3%), employed full time (407/496, 82%), and born in Australia (337/496, 67.9%). Their mean age was 32.2 years, and average gestation was 30.8 weeks. Using an EPDS cutoff score of 13, 6.9% (18/248) of men, and 16.1% (40/248) of women screened positive for depression at some time during the 6 months. Retention of both partners was 80.6% (201/248) at the 6-month assessments, and satisfaction with both programs was strong (92% ≥50). Only 37.3% (185/496) of participants accessed their program more than once, with higher rates for mothers (133/248, 53.6%) than fathers (52/248, 20.9%; P Conclusions Three secondary outcomes showed differential benefits from Baby Steps Wellbeing, but for one (parenting self-efficacy), the effect only occurred for mothers, perhaps reflecting their greater program use. Increased engagement will be needed for more definitive testing of the potential benefits of Baby StepsWellbeing for perinatal adjustment. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12614001256662; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=367277

Background Adherence to internet-delivered interventions targeting mental health such as online psychotherapeutic aftercare is important for the intervention’s impact. High dropout rates limit the impact and generalizability of findings. Baseline differences may be putting patients at risk for dropping out, making comparisons between online with face-to-face (F2F) therapy and care as usual (CAU) necessary to examine. Objective This study investigated adherence to online, F2F, and CAU interventions as well as study dropout among these groups and the subjective evaluation of the therapeutic relationship. Sociodemographic, social-cognitive, and health-related variables were considered. Methods In a randomized controlled trial, 6023 patients were recruited, and 300 completed the baseline measures (T1), 144 completed T2 (retention 44%-52%), and 95 completed T3 (retention 24%-36%). Sociodemographic variables (eg, age, gender, marital status, educational level), social-cognitive determinants (eg, self-efficacy, social support), health-related variables (eg, depressiveness), and expectation towards the treatment for patients assigned to online or F2F were measured at T1. Results There were no significant differences between the groups regarding dropout rates (χ21=0.02-1.06, P≥.30). Regarding adherence to the treatment condition, the online group outperformed the F2F and CAU conditions (P≤.01), indicating that patients randomized into the F2F and CAU control groups were much more likely to show nonadherent behavior in comparison with the online therapy groups. Within study groups, gender differences were significant only in the CAU group at T2, with women being more likely to drop out. At T3, age and marital status were also only significant in the CAU group. Patients in the online therapy group were significantly more satisfied with their treatment than patients in the F2F group (P=.02; Eta²=.09). Relationship satisfaction and success satisfaction were equally high (P>.30; Eta²=.02). Combining all study groups, patients who reported lower depressiveness scores at T1 (T2: odds ratio [OR] 0.55, 95% CI 0.35-0.87; T3: OR 0.56, 95% CI 0.37-0.92) were more likely to be retained, and patients who had higher self-efficacy (T2: OR 0.57, 95% CI 0.37-0.89; T3: OR 0.52, 95% CI 0.32-0.85) were more likely to drop out at T2 and T3. Additionally, at T3, the lower social support that patients reported was related to a higher likelihood of remaining in the study (OR 0.68, 95% CI 0.48-0.96). Comparing the 3 intervention groups, positive expectation was significantly related with questionnaire completion at T2 and T3 after controlling for other variables (T2: OR 1.64, 95% CI 1.08-2.50; T3: OR 1.59, 95% CI 1.01-2.51). Conclusions While online interventions have many advantages over F2F variants such as saving time and effort to commute to F2F therapy, they also create difficulties for therapists and hinder their ability to adequately react to patients’ challenges. Accordingly, patient characteristics that might put them at risk for dropping out or not adhering to the treatment plan should be considered in future research and practice. Online aftercare, as described in this research, should be provided more often to medical rehabilitation patients. Trial Registration ClinicalTrials.gov NCT04989842; https://clinicaltrials.gov/ct2/show/NCT04989842

Background There is a limited amount of data on the safety profile of the COVID-19 vector vaccine Gam-COVID-Vac (Sputnik V). Previous infodemiology studies showed that social media discourse could be analyzed to assess the most concerning adverse events (AE) caused by drugs. Objective We aimed to investigate mild AEs of Sputnik V based on a participatory trial conducted on Telegram in the Russian language. We compared AEs extracted from Telegram with other limited databases on Sputnik V and other COVID-19 vaccines. We explored symptom co-occurrence patterns and determined how counts of administered doses, age, gender, and sequence of shots could confound the reporting of AEs. Methods We collected a unique dataset consisting of 11,515 self-reported Sputnik V vaccine AEs posted on the Telegram group, and we utilized natural language processing methods to extract AEs. Specifically, we performed multilabel classifications using the deep neural language model Bidirectional Encoder Representations from Transformers (BERT) “DeepPavlov,” which was pretrained on a Russian language corpus and applied to the Telegram messages. The resulting area under the curve score was 0.991. We chose symptom classes that represented the following AEs: fever, pain, chills, fatigue, nausea/vomiting, headache, insomnia, lymph node enlargement, erythema, pruritus, swelling, and diarrhea. Results Telegram users complained mostly about pain (5461/11,515, 47.43%), fever (5363/11,515, 46.57%), fatigue (3862/11,515, 33.54%), and headache (2855/11,515, 24.79%). Women reported more AEs than men (1.2-fold, P Conclusions After the Sputnik V vaccination, Russian Telegram users reported mostly pain, fever, and fatigue. The Sputnik V AE profile was comparable with other vector COVID-19 vaccines. Discussion on social media could provide meaningful information about the AE profile of novel vaccines.

Background Since 2004, we have collected patient-reported outcome (PRO) data from several Danish patient populations for use at the group and patient levels. Objective The aim of this paper is to highlight trends during the last 15 years with respect to patient inclusion, the methods for collection of PRO data, the processing of the data, and the actual applications and use of the PRO measurements. Methods All PRO data have been collected using the AmbuFlex/WestChronic PRO system, which was developed by the author in 2004 and has been continuously updated since. The analysis of trends was based on a generic model applicable for any kind of clinical health data, according to which any application of clinical data may be divided into four processes: patient identification, data collection, data aggregation, and the actual data use. Data for analysis were generated by a specific application in the system and transferred for analysis to the R package. Results During the 15-year period, 78,980 patients within 28 different groups of chronic and malignant illnesses have answered 260,433 questionnaires containing a total of 13,538,760 responses. Several marked changes have taken place: (1) the creation of cohorts for clinical epidemiological research purposes has shifted towards cohorts defined by clinical use of PRO data at the patient level; (2) the development of AmbuFlex, where PRO data are used as the entire basis for outpatient follow-up instead of fixed appointments, has undergone exponential growth and the system is currently in use in 47 International Statistical Classification of Diseases and Related Health Problems groups, covering 16,000 patients and 94 departments throughout Denmark; (3) response rates (up to 92%) and low attrition rates have been reached in group level projects, and there are even higher response rates in AmbuFlex where the patients are individually referred; (4) The answering method has shifted, as while in 2005 a total of 66.5% of questionnaires were paper based, this is the case for only 4.3% in 2019; and (5) the approach methods for questionnaires and reminders have changed dramatically from letter, emails, and short message service text messaging to a national, secure electronic mail system through which 93.2% of the communication to patients took place in 2019. The combination of secure email and web-based answering has resulted in a low turnaround time in which half of responses are now received within 5 days. Conclusions The demand for clinical use of PRO measurements has increased, driven by a wish among patients as well as clinicians to use PRO to promote better symptom assessment, more patient-centered care, and more efficient use of resources. Important technological changes have occurred, creating new opportunities, and making PRO collection and use cheaper and more feasible. Several legal changes may constitute a barrier for further development as well as a barrier for better utilization of patients’ questionnaire data. The current legal restrictions on the joint use of health data imposed by the European Union’s General Data Protection Regulation makes no distinction between use and misuse, and steps should be taken to alleviate these restrictions on the joint use of PRO data.

Background The COVID-19 pandemic has resulted in profound mental health impacts among the general population worldwide. As many in-person mental health support services have been suspended or transitioned online to facilitate physical distancing, there have been numerous calls for the rapid expansion of asynchronous virtual mental health (AVMH) resources. These AVMH resources have great potential to provide support for people coping with negative mental health impacts associated with the pandemic; however, literature examining use prior to COVID-19 illustrates that the uptake of these resources is consistently low. Objective The aim of this paper is to examine the use of AVMH resources in Canada during the COVID-19 pandemic among the general population and among a participant subgroup classified as experiencing an adverse mental health impact related to the pandemic. Methods Data from this study were drawn from the first wave of a large multiwave cross-sectional monitoring survey, distributed from May 14 to 29, 2020. Participants (N=3000) were adults living in Canada. Descriptive statistics were used to characterize the sample, and bivariate cross-tabulations were used to examine the relationships between the use of AVMH resources and self-reported indicators of mental health that included a range of emotional and coping-related responses to the pandemic. Univariate and fully adjusted multivariate logistic regression models were used to examine associations between sociodemographic and health-related characteristics and use of AVMH resources in the subgroup of participants who reported experiencing one or more adverse mental health impacts identified in the set of self-reported mental health indicators. Results Among the total sample, 2.0% (n=59) of participants reported accessing AVMH resources in the prior 2 weeks to cope with stress related to the COVID-19 pandemic, with the highest rates of use among individuals who reported self-harm (n=5, 10.4%) and those who reported coping “not well” with COVID-19–related stress (n=22, 5.5%). Within the subgroup of 1954 participants (65.1% of the total sample) who reported an adverse mental health impact related to COVID-19, 54 (2.8%) reported use of AVMH resources. Individuals were more likely to have used AVMH resources if they had reported receiving in-person mental health supports, were connecting virtually with a mental health worker or counselor, or belonged to a visible minority group. Conclusions Despite substantial government investment into AVMH resources, uptake is low among both the general population and individuals who may benefit from the use of these resources as a means of coping with the adverse mental health impacts of the COVID-19 pandemic. Further research is needed to improve our understanding of the barriers to use.

Background The clinical application of voice technology provides novel opportunities in the field of telehealth. However, patients’ readiness for this solution has not been investigated among patients with cardiovascular diseases (CVD). Objective This paper aims to evaluate patients’ anticipated experiences regarding telemedicine, including voice conversational agents combined with provider-driven support delivered by phone. Methods A cross-sectional study enrolled patients with chronic CVD who were surveyed using a validated investigator-designed questionnaire combining 19 questions (eg, demographic data, medical history, preferences for using telehealth services). Prior to the survey, respondents were educated on the telemedicine services presented in the questionnaire while being assisted by a medical doctor. Responses were then collected and analyzed, and multivariate logistic regression was used to identify predictors of willingness to use voice technology. Results In total, 249 patients (mean age 65.3, SD 13.8 years; 158 [63.5%] men) completed the questionnaire, which showed good repeatability in the validation procedure. Of the 249 total participants, 209 (83.9%) reported high readiness to receive services allowing for remote contact with a cardiologist (176/249, 70.7%) and telemonitoring of vital signs (168/249, 67.5%). The voice conversational agents combined with provider-driven support delivered by phone were shown to be highly anticipated by patients with CVD. The readiness to use telehealth was statistically higher in people with previous difficulties accessing health care (OR 2.920, 95% CI 1.377-6.192) and was most frequent in city residents and individuals reporting a higher education level. The age and sex of the respondents did not impact the intention to use voice technology (P=.20 and P=.50, respectively). Conclusions Patients with cardiovascular diseases, including both younger and older individuals, declared high readiness for voice technology.

Late HIV diagnosis remains frequent among the gay, bisexual, and other men who have sex with men (GBMSM) population across Europe. HIV self-sampling could help remove barriers and facilitate access to testing for this high-risk population. We assessed the capacity of HIV self-sampling to increase the testing frequency among GBMSM living in Denmark, Germany, Greece, Portugal, Romania, and Spain, and evaluated the role of new technologies in the result communication phase. We analyzed a convenience sample of 5019 GBMSM with prior HIV testing experience who were recruited during 2016 through gay dating websites. We estimated the proportion of GBMSM who reported that the availability of self-sampling would result in an increase of their current testing frequency. We constructed a Poisson regression model for each country to calculate prevalence ratios and 95% CIs of factors associated with an increase of testing frequency as a result of self-sampling availability. Overall, 59% (between country range 54.2%-77.2%) of the participants considered that they would test more frequently for HIV if self-sampling was available in their country. In the multivariate analysis, the increase of testing frequency as a result of self-sampling availability was independently associated with reporting a higher number of unprotected anal intercourse events in all countries except for Greece. Independent associations were also observed among GBMSM who were not open about their sex life in Germany, Greece, Portugal, and Spain; those with a lower number of previous HIV tests in Denmark, Greece, Portugal, and Spain; and for those that took their last test more than 3 months previously in Germany, Portugal, Romania, and Spain. In addition, 58.4% (range 40.5%-73.6%) of the participants indicated a preference for learning their result through one-way interaction methods, mainly via email (25.6%, range 16.8%-35.2%) and through a secure website (20.3%, range 7.3%-23.7%). Almost two thirds (65%) of GBMSM indicated preferring one of these methods even if the result was reactive. Availability of HIV self-sampling kits as an additional testing methodology would lead to a much-needed increase of testing frequency, especially for the hidden, high-risk, and undertested GBMSM population. Online-based technologies without any personal interaction were preferred for the communication of the results, even for reactive results. This study was supported by the Consumers, Health and Food Executive Agency (CHAFEA: 20131101) and Acción Estratégica Intramural (PI17CIII/00037). The funding sources were not involved in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. Sí

Background Immunizing children throughout their early years prevents the spread of communicable disease and decreases the morbidity and mortality associated with many vaccine-preventable diseases. Searching online allows individuals rapid access to health information. Objective The purpose of this review was to develop an understanding of the existing literature of parents’ online health information-seeking behaviors to inform their vaccination choices for their children and to identify gaps in the literature around parents’ use of online health information and their vaccination choices. Methods A scoping review of peer-reviewed literature from Canada and the United States was performed. The following databases were utilized to perform the search: PubMed, CINAHL, Nursing & Allied Health Database, Scopus, and PsycINFO. The purpose of this review was to examine parents’ use of online information seeking related to vaccine information and to understand how parents utilize this information to inform decisions about vaccinating their children. Of the 34 papers included in the review, 4 relevant themes and subthemes were identified: information seeking, online information resources, online vaccine content, and trust in health care providers. Results Examination of the literature revealed conflicting information regarding parents’ use of social media and online resources to inform decisions around vaccinating their children. There is evidence of significant misinformation regarding vaccine risks online. Parents’ digital health literacy levels are unknown and may affect their ability to appraise online vaccination information. Conclusions Parents are seeking vaccine information from online sources. However, the influence of online vaccine information on parental vaccine practices remains uncertain.

Background As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties. Objective The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times. Methods This observational study on the emerging COVID-19 pandemic included 643 patients. An “AI trilogy” of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic. Results The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P Conclusions The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic.

Background Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities. Objective The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home). Methods A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC). Results For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11). Conclusions Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.

Background Technology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools. Objective This paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity. Methods Participants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram’s approval, participants’ Instagram photo posts were downloaded with an application program interface. Participants’ past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having “had more than a few alcoholic drinks a day,” drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level. Results Compared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts. Conclusions Researchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches.

Background The COVID-19 pandemic has necessitated a rapid increase of space in highly infectious disease intensive care units (ICUs). At Houston Methodist Hospital (HMH), a virtual intensive care unit (vICU) was used amid the COVID-19 outbreak. Objective The aim of this paper was to detail the novel adaptations and rapid expansion of the vICU that were applied to achieve patient-centric solutions while protecting staff and patients’ families during the pandemic. Methods The planned vICU implementation was redirected to meet the emerging needs of conversion of COVID-19 ICUs, including alterations to staged rollout timing, virtual and in-person staffing, and scope of application. With the majority of the hospital critical care physician workforce redirected to rapidly expanded COVID-19 ICUs, the non–COVID-19 ICUs were managed by cardiovascular surgeons, cardiologists, neurosurgeons, and acute care surgeons. HMH expanded the vICU program to fill the newly depleted critical care expertise in the non–COVID-19 units to provide urgent, emergent, and code blue support to all ICUs. Results Virtual family visitation via the Consultant Bridge application, palliative care delivery, and specialist consultation for patients with COVID-19 exemplify the successful adaptation of the vICU implementation. Patients with COVID-19, who were isolated and separated from their families to prevent the spread of infection, were able to virtually see and hear their loved ones, which bolstered the mental and emotional status of those patients. Many families expressed gratitude for the ability to see and speak with their loved ones. The vICU also protected medical staff and specialists assigned to COVID-19 units, reducing exposure and conserving personal protective equipment. Conclusions Telecritical care has been established as an advantageous mechanism for the delivery of critical care expertise during the expedited rollout of the vICU at Houston Methodist Hospital. Overall responses from patients, families, and physicians are in favor of continued vICU care; however, further research is required to examine the impact of innovative applications of telecritical care in the treatment of critically ill patients.

Background Syringe service programs (SSPs) are safe, highly effective programs for promoting health among people who inject drugs. However, resource limitations prevent the delivery of a full package of prevention services to many clients in need. Computer-tailored interventions may represent a promising approach for providing prevention information to people who inject drugs in resource-constrained settings. Objective The aim of this paper is to assess the effect of a computer-tailored behavioral intervention, called Hep-Net, on safe injection practices, substance use reduction, overdose prevention, and hepatitis C virus (HCV) testing among SSP clients. Methods Using a social network–based recruitment strategy, we recruited clients of an established SSP in Wisconsin and peers from their social networks. Participants completed a computerized baseline survey and were then randomly assigned to receive the Hep-Net intervention. Components of the intervention included an overall risk synthesis, participants’ selection of a behavioral goal, and an individualized risk reduction exercise. Individuals were followed up 3 months later to assess their behavior change. The effect of Hep-Net on receiving an HCV screening test, undergoing Narcan training, reducing the frequency of drug use, and sharing drug equipment was assessed. The individual’s readiness to change each behavior was also examined. Results From 2014 to 2015, a total of 235 people who injected drugs enrolled into the Hep-Net study. Of these, 64.3% (151/235) completed the follow-up survey 3-6 months postenrollment. Compared with the control group, individuals who received the Hep-Net intervention were more likely to undergo HCV testing (odds ratio [OR] 2.23, 95% CI 1.05-4.74; P=.04) and receive Narcan training (OR 2.25, 95% CI 0.83-6.06; P=.11), and they shared drug equipment less frequently (OR 0.06, 95% CI 0.55-0.65; P Conclusions Implementing computer-based risk reduction interventions in SSPs may reduce harms associated with the sharing of injection equipment and prevent overdose deaths; however, brief computerized interventions may not be robust enough to overcome the challenges associated with reducing and ceasing drug use when implemented in settings centered on the delivery of prevention services. Trial Registration ClinicalTrials.gov NCT02474043; https://clinicaltrials.gov/ct2/show/NCT02474043 International Registered Report Identifier (IRRID) RR1-10.2196/resprot.4830

Background: Continued smoking in cancer patients undergoing treatment results in significantly higher rates of treatment toxicities and persistent effects, increased risk of recurrence and second malignancy, and increased all-cause mortality. Despite this, routine tobacco use screening and the provision of smoking cessation treatment has yet to be implemented widely in the cancer setting. Objective: The objective of this study was to implement and evaluate the adoption and impact of an innovative Smoking Cessation e-referral System (CEASE) to promote referrals to smoking cessation programs in cancer patients. Methods: A patient-directed electronic smoking cessation platform (CEASE) was developed to promote smoking screening and referral and implemented at 1 of Canada’s largest cancer centers. The implementation and evaluation were guided by the Ottawa Model of Research Use. An interrupted time series design was used to examine the impact of CEASE on screening rates, referrals offered, and referrals accepted compared with a previous paper-based screening program. A subsample of smokers or recent quitters was also assessed and compared pre- and postimplementation to examine the effect of CEASE on subsequent contact with smoking cessation programs and quit attempts. Results: A total of 17,842 new patients attended clinics over the 20-month study period. The CEASE platform was successfully implemented across all disease sites. Screening rates increased from 44.28% (2366/5343) using the paper-based approach to 65.72% (3538/5383) using CEASE (P

Background Coronavirus disease (COVID-19) is a type of pneumonia caused by a novel coronavirus that was discovered in 2019. As of May 6, 2020, 84,407 cases and 4643 deaths have been confirmed in China. The Chinese population has expressed great concern since the COVID-19 outbreak. Meanwhile, an average of 1 billion people per day are using the Baidu search engine to find COVID-19–related health information. Objective The aim of this paper is to analyze web search data volumes related to COVID-19 in China. Methods We conducted an infodemiological study to analyze web search data volumes related to COVID-19. Using Baidu Index data, we assessed the search frequencies of specific search terms in Baidu to describe the impact of COVID-19 on public health, psychology, behaviors, lifestyles, and social policies (from February 11, 2020, to March 17, 2020). Results The search frequency related to COVID-19 has increased significantly since February 11th. Our heat maps demonstrate that citizens in Wuhan, Hubei Province, express more concern about COVID-19 than citizens from other cities since the outbreak first occurred in Wuhan. Wuhan citizens frequently searched for content related to “medical help,” “protective materials,” and “pandemic progress.” Web searches for “return to work” and “go back to school” have increased eight-fold compared to the previous month. Searches for content related to “closed community and remote office” have continued to rise, and searches for “remote office demand” have risen by 663% from the previous quarter. Employees who have returned to work have mainly engaged in the following web searches: “return to work and prevention measures,” “return to work guarantee policy,” and “time to return to work.” Provinces with large, educated populations (eg, Henan, Hebei, and Shandong) have been focusing on “online education” whereas medium-sized cities have been paying more attention to “online medical care.” Conclusions Our findings suggest that web search data may reflect changes in health literacy, social panic, and prevention and control policies in response to COVID-19.

Background The number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD). Objective This study proposes methods to diagnose PD through facial expression recognition. Methods We collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD. Results The experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved. Conclusions This study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis.

Background Coronavirus disease (COVID-19) has spread explosively worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) is a relevant screening tool due to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely occupied fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. Objective We aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and nonpneumonia diseases. Methods A simple 2D deep learning framework, named the fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning using one of four state-of-the-art pretrained deep learning models (VGG16, ResNet-50, Inception-v3, or Xception) as a backbone. For training and testing of FCONet, we collected 3993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and nonpneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training set and a testing set at a ratio of 8:2. For the testing data set, the diagnostic performance of the four pretrained FCONet models to diagnose COVID-19 pneumonia was compared. In addition, we tested the FCONet models on an external testing data set extracted from embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. Results Among the four pretrained models of FCONet, ResNet-50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100.00%, and accuracy 99.87%) and outperformed the other three pretrained models in the testing data set. In the additional external testing data set using low-quality CT images, the detection accuracy of the ResNet-50 model was the highest (96.97%), followed by Xception, Inception-v3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). Conclusions FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing data set, the FCONet model based on ResNet-50 appears to be the best model, as it outperformed other FCONet models based on VGG16, Xception, and Inception-v3.

Background Discontinuation of contact lens use is mainly caused by contact lens–associated dry eye. It is crucial to delineate contact lens–associated dry eye's multifaceted nature to tailor treatment to each patient’s individual needs for future personalized medicine. Objective This paper aims to quantify and stratify individual subjective symptoms of contact lens–associated dry eye and clarify its risk factors for future personalized medicine using the smartphone app DryEyeRhythm (Juntendo University). Methods This cross-sectional study included iPhone (Apple Inc) users in Japan who downloaded DryEyeRhythm. DryEyeRhythm was used to collect medical big data related to contact lens–associated dry eye between November 2016 and January 2018. The main outcome measure was the incidence of contact lens–associated dry eye. Univariate and multivariate adjusted odds ratios of risk factors for contact lens–associated dry eye were determined by logistic regression analyses. The t-distributed Stochastic Neighbor Embedding algorithm was used to depict the stratification of subjective symptoms of contact lens–associated dry eye. Results The records of 4454 individuals (median age 27.9 years, SD 12.6), including 2972 female participants (66.73%), who completed all surveys were included in this study. Among the included participants, 1844 (41.40%) were using contact lenses, and among those who used contact lenses, 1447 (78.47%) had contact lens–associated dry eye. Multivariate adjusted odds ratios of risk factors for contact lens–associated dry eye were as follows: younger age, 0.98 (95% CI 0.96-0.99); female sex, 1.53 (95% CI 1.05-2.24); hay fever, 1.38 (95% CI 1.10-1.74); mental illness other than depression or schizophrenia, 2.51 (95% CI 1.13-5.57); past diagnosis of dry eye, 2.21 (95% CI 1.63-2.99); extended screen exposure time >8 hours, 1.61 (95% CI 1.13-2.28); and smoking, 2.07 (95% CI 1.49-2.88). The t-distributed Stochastic Neighbor Embedding analysis visualized and stratified 14 groups based on the subjective symptoms of contact lens–associated dry eye. Conclusions This study identified and stratified individuals with contact lens–associated dry eye and its risk factors. Data on subjective symptoms of contact lens–associated dry eye could be used for prospective prevention of contact lens–associated dry eye progression.

Background Initiation of insulin therapy for the management of type 2 diabetes can be an unwelcome and distressful development for patients. Current evidence suggests that telemonitoring can help improve glycemic control in type 2 diabetes and can support empowerment to self-manage diabetes. This telemonitoring intervention was underpinned by an empowerment approach. Objective This study aimed to evaluate the clinical effectiveness and feasibility and the patients’ and health care providers’ experiences of a 12-week telemonitoring intervention with telephone support for patients commencing insulin therapy. This paper focuses on the impact on patient empowerment. Methods An observational, pre-post, multimethod, and triangulation design was employed to study a 12-week automated electronic telemonitoring intervention with telephone support from a diabetes clinical nurse specialist (CNS). Forty patients were recruited from the clinic as they were about to commence insulin therapy. In the quantitative arm, biometric data (hemoglobin A1c [HbA1c] and weight) and psychosocial data (diabetes empowerment scale [DES] scores and diabetes distress scale [DDS] scores) were gathered by the research team at baseline (T1), the end of the intervention (T2), and 3 months postintervention (T3). Data on hospital admission and general practitioner (GP) visits were collected for the duration of the study. In the qualitative arm, separate focus group interviews were conducted with the CNS team supporting the intervention (n=2) and patients (n=16). Results Of 39 patients who completed the intervention, 23 (59%) were male. The mean age of the sample was 62.4 years (range 37-80 years). The mean HbA1c (mmol/mol) decreased significantly between T1 and T2 (mean difference [MD] −17.13; P Conclusions This intervention had an empowering effect for patients in the self-management of type 2 diabetes and has the potential to meet the need for safer and more effective care in insulin initiation in the community setting. We observed a significant increase in workload for health care staff. Telemonitoring needs to be streamlined with health care delivery and accompanied by adequate support services.

Background Engaging socioeconomically disadvantaged populations in health research is vital to understanding and, ultimately, eliminating health-related disparities. Digital communication channels are increasingly used to recruit study participants, and recent trends indicate a growing need to partner with the social service sector to improve population health. However, few studies have recruited participants from social service settings using multiple digital channels. Objective This study aimed to recruit and survey 3791 adult clients of a social service organization via telephone and digital channels. This paper aimed to describe recruitment outcomes across five channels and compare participant characteristics by recruitment channel type. Methods The Cancer Communication Channels in Context Study recruited and surveyed adult clients of 2-1-1, a social service–focused information and referral system, using five channels: telephone, website, text message, web-based live chat, and email. Participants completed surveys administered either by phone (if recruited by phone) or on the web (if recruited from digital channels, ie, website, text message, Web-based live chat, or email). Measures for the current analysis included demographic and health characteristics. Results A total of 3293 participants were recruited, with 1907 recruited by phone and 1386 recruited from digital channels. Those recruited by phone had a moderate study eligibility rate (42.23%) and the highest survey completion rate (91.24%) of all channels. Individuals recruited by text message had a high study eligibility rate (94.14%) yet the lowest survey completion rate (74.0%) of all channels. Sample accrual goals were achieved for phone, text message, and website recruitment. Multivariable analyses found differences in participant characteristics by recruitment channel type. Compared with participants recruited by phone, those recruited from digital channels were younger (adjusted odds ratio [aOR] 0.96, 95% CI 0.96-0.97) and more likely to be female (aOR 1.52, 95% CI 1.23-1.88), married (aOR 1.52, 95% CI 1.22-1.89), and other than non-Hispanic black (aOR 1.48, 95% CI 1.22-1.79). Those recruited via phone also were more likely to have more than a high school education (aOR 2.17, 95% CI 1.67-2.82), have a household income ≥US $25,000 a year (aOR 2.02, 95% CI 1.56-2.61), and have children living in the home (aOR 1.26, 95% CI 1.06-1.51). Additionally, participants recruited from digital channels were less likely than those recruited by phone to have public health insurance (aOR 0.75, 95% CI 0.62-0.90) and more likely to report better overall health (aOR 1.52, 95% CI 1.27-1.83 for good-to-excellent health). Conclusions Findings indicate the feasibility and utility of recruiting socioeconomically disadvantaged adults from the social service sector using multiple communication channels, including digital channels. As social service–based health research evolves, strategic recruitment using a combination of traditional and digital channels may be warranted to avoid underrepresentation of highly medically vulnerable individuals, which could exacerbate disparities in health.

Background Many pregnant women use the internet to obtain information about pregnancy and childbirth. Over 50% of pregnant women use pregnancy apps and must search through thousands of pregnancy or women’s health–related apps available on app stores. The COVID-19 pandemic is changing how women receive prenatal care. Mobile health apps may help maintain women’s satisfaction with their prenatal care. Objective Our objective is to identify pregnancy mobile apps and to evaluate the apps using a modified APPLICATIONS (app comprehensiveness, price, privacy, literature used, in-app purchases, connectivity, advertisements, text search field, images/videos, other special features, navigation ease, subjective presentation) scoring system. Methods A list of pregnancy apps was identified in the first 20 Google search results using the search term “pregnancy app.” After excluding irrelevant, inaccurate, malfunctioning, or no longer available apps, all unique apps were downloaded and evaluated with the modified APPLICATIONS scoring system, which includes both objective and subjective criteria and evaluation of special features. Results A list of 57 unique pregnancy apps was generated. After 28 apps were excluded, the remaining 29 apps were evaluated, with a mean score of 9.4 points out of a maximum of 16. The highest scoring app scored 15 points. Over 60% (18/29) of apps did not have comprehensive information for every stage of pregnancy or did not contain all four desired components of pregnancy apps: health promotion/patient education, communication, health tracking, and notifications and reminders. Only 24% (7/29) of apps included a text search field, and only 28% (8/29) of apps cited literature. Conclusions Our search yielded many high-scoring apps, but few contained all desired components and features. This list of identified and rated apps can lessen the burden on pregnant women and providers to find available apps on their own. Although health care providers should continue to vet apps before recommending them to patients, these findings also highlight that a Google search is a successful way for patients and providers to find useful and comprehensive pregnancy apps.

Background Pain is a complex experience that involves sensory-discriminative and cognitive-emotional neuronal processes. It has long been known across cultures that pain can be relieved by mindful breathing (MB). There is a common assumption that MB exerts its analgesic effect through interoception. Interoception refers to consciously refocusing the mind’s attention to the physical sensation of internal organ function. Objective In this study, we dissect the cortical analgesic processes by imaging the brains of healthy subjects exposed to traditional MB (TMB) and compare them with another group for which we augmented MB to an outside sensory experience via virtual reality breathing (VRB). Methods The VRB protocol involved in-house–developed virtual reality 3D lungs that synchronized with the participants’ breathing cycles in real time, providing them with an immersive visual-auditory exteroception of their breathing. Results We found that both breathing interventions led to a significant increase in pain thresholds after week-long practices, as measured by a thermal quantitative sensory test. However, the underlying analgesic brain mechanisms were opposite, as revealed by functional near-infrared spectroscopy data. In the TMB practice, the anterior prefrontal cortex uniquely modulated the premotor cortex. This increased its functional connection with the primary somatosensory cortex (S1), thereby facilitating the S1-based sensory-interoceptive processing of breathing but inhibiting its other role in sensory-discriminative pain processing. In contrast, virtual reality induced an immersive 3D exteroception with augmented visual-auditory cortical activations, which diminished the functional connection with the S1 and consequently weakened the pain processing function of the S1. Conclusions In summary, our study suggested two analgesic neuromechanisms of VRB and TMB practices—exteroception and interoception—that distinctively modulated the S1 processing of the ascending noxious inputs. This is in line with the concept of dualism (Yin and Yang).

Background ClinicalTrials.gov (CT.gov) is the most comprehensive internet-based register of different types of clinical studies. Expanded access is the use of unapproved drugs, biologics, or medical devices outside of clinical trials. One of the key problems in expanded access is the availability to both health care providers and patients of information about unapproved treatments. Objective We aimed to evaluate CT.gov as a potential source of information about expanded access programs. Methods We assessed the completeness of information in the records of 228 expanded access programs registered with CT.gov from February 2017 through May 2020. Moreover, we examined what percentage of published expanded access studies has been registered with CT.gov. Logistic regression (univariate and multivariate) and mediation analyses were used to identify the predictors of the absence of some information and a study’s nonregistration. Results We found that some important data were missing from the records of many programs. Information that was missing most often included a detailed study description, facility information, central contact person, and eligibility criteria (55.3%, 54.0%, 41.7%, and 17.5% of the programs, respectively). Multivariate analysis showed that information about central contact person was more likely to be missing from records of studies registered in 2017 (adjusted OR 21.93; 95% CI 4.42-172.29; P.01). Conclusions Currently, CT.gov is a quite fragmentary source of data on expanded access programs. This problem is important because CT.gov is the only publicly available primary source of information about specific programs. We suggest the actions that should be taken by different stakeholders to fully exploit this register as a source of information about expanded access.