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Background High-frequency patient-reported outcome (PRO) assessments are used to measure patients' symptoms after surgery for surgical research; however, the quality of those longitudinal PRO data has seldom been discussed. Objective The aim of this study was to determine data quality-influencing factors and to profile error trajectories of data longitudinally collected via paper-and-pencil (P&P) or web-based assessment (electronic PRO [ePRO]) after thoracic surgery. Methods We extracted longitudinal PRO data with 678 patients scheduled for lung surgery from an observational study (n=512) and a randomized clinical trial (n=166) on the evaluation of different perioperative care strategies. PROs were assessed by the MD Anderson Symptom Inventory Lung Cancer Module and single-item Quality of Life Scale before surgery and then daily after surgery until discharge or up to 14 days of hospitalization. Patient compliance and data error were identified and compared between P&P and ePRO. Generalized estimating equations model and 2-piecewise model were used to describe trajectories of error incidence over time and to identify the risk factors. Results Among 678 patients, 629 with at least 2 PRO assessments, 440 completed 3347 P&P assessments and 189 completed 1291 ePRO assessments. In total, 49.4% of patients had at least one error, including (1) missing items (64.69%, 1070/1654), (2) modifications without signatures (27.99%, 463/1654), (3) selection of multiple options (3.02%, 50/1654), (4) missing patient signatures (2.54%, 42/1654), (5) missing researcher signatures (1.45%, 24/1654), and (6) missing completion dates (0.30%, 5/1654). Patients who completed ePRO had fewer errors than those who completed P&P assessments (ePRO: 30.2% [57/189] vs. P&P: 57.7% [254/440]; P Conclusions It is possible to improve data quality of longitudinally collected PRO through ePRO, compared with P&P. However, ePRO-related sampling bias needs to be considered when designing clinical research using longitudinal PROs as major outcomes.

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

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

Background 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 Accurate medication reconciliation reduces the risk of drug incompatibilities and adverse events that can occur during transitions in care. Community pharmacies (CPs) are a crucial part of the health care system and could be involved in collecting essential information on conventional and supplementary drugs used at home. Objective The aim of this paper was to establish an alliance between our cancer institute, Istituto Romagnolo per lo Studio dei Tumori (IRST), and CPs, the latter entrusted with the completion of a pharmacological recognition survey. We also aimed to integrate the national information technology (IT) platform of CPs with the electronic medical records of IRST. Methods Cancer patients undergoing antiblastic treatments were invited to select a CP taking part in the study and to complete the pharmacological recognition step. The information collected by the pharmacist was sent to the electronic medical records of IRST through the new IT platform, after which the oncologist performed the reconciliation process. Results A total of 66 CPs completed surveys for 134 patients. An average of 5.9 drugs per patient was used at home, with 12 or more used in the most advanced age groups. Moreover, 60% (80/134) of the patients used nonconventional products or critical foods. Some potential interactions between nonconventional medications and cancer treatments were reported. Conclusions In the PROF-1 (Progetto di Rete in Oncologia con le Farmacie di comunità della Romagna) study, an alliance was created between our cancer center and CPs to improve medication reconciliation, and a new integrated IT platform was validated. Trial Registration ClinicalTrials.gov NCT04796142; https://clinicaltrials.gov/ct2/show/NCT04796142

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 Although past research has focused on COVID-19–related frames in the news media, such research may not accurately capture and represent the perspectives of people from diverse backgrounds. Additionally, research on the public attention to COVID-19 as reflected through frames on social media is scarce. Objective This study identified the frames about the COVID-19 pandemic in the public discourse on Twitter, which voices diverse opinions. This study also investigated the amount of public attention to those frames on Twitter. Methods We collected 22 trending hashtags related to COVID-19 in the United States and 694,582 tweets written in English containing these hashtags in March 2020 and analyzed them via thematic analysis. Public attention to these frames was measured by evaluating the amount of public engagement with frames and public adoption of those frames. Results We identified 9 frames including “public health guidelines,” “quarantine life,” “solidarity,” “evidence and facts,” “call for action,” “politics,” “post-pandemic life,” “shortage panic,” and “conflict.” Results showed that some frames such as “call for action” are more appealing than others during a global pandemic, receiving greater public adoption and engagement. The “call for action” frame had the highest engagement score, followed by “conflict” and “evidence and facts.” Additionally, “post-pandemic life” had the highest adoption score, followed by “call for action” and “shortage panic.” The findings indicated that the frequency of a frame on social media does not necessarily mean greater public adoption of or engagement with the frame. Conclusions This study contributes to framing theory and research by demonstrating how trending hashtags can be used as new user-generated data to identify frames on social media. This study concludes that the identified frames such as “quarantine life” and “conflict” and themes such as “isolation” and “toilet paper panic” represent the consequences of the COVID-19 pandemic. The consequences could be (1) exclusively related to COVID-19, such as hand hygiene or isolation; (2) related to any health crisis such as social support of vulnerable groups; and (3) generic that are irrespective of COVID-19, such as homeschooling or remote working.

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 Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. Objective The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. Methods We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.

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 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 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 There are limited evidence-based strategies that have been shown to increase the rate at which peer-reviewed articles are cited. In a previously reported randomized controlled trial, we demonstrated that promotion of article links in an online cross-publisher distribution platform (TrendMD) persistently augments citation rates after 12 months, leading to a statistically significant 50% increase in citations relative to the control. Objective This study aims to investigate if the citation advantage of promoted articles upholds after 36 months. Methods A total of 3200 published articles in 64 peer-reviewed journals across 8 subject areas were block randomized at the subject level to either the TrendMD group (n=1600) or the control group (n=1600) of the study. Articles were promoted in the TrendMD Network for 6 months. We compared the citation rates in both groups after 36 months. Results At 36 months, we found the citation advantage endured; articles randomized to TrendMD showed a 28% increase in mean citations relative to the control. The difference in mean citations at 36 months for articles randomized to TrendMD versus the control was 10.52 (95% CI 3.79-17.25) and was statistically significant (P=.001). Conclusions To our knowledge, this is the first randomized controlled trial to demonstrate how a postpublication article promotion intervention can be used to persistently augment citations of peer-reviewed articles. TrendMD is an efficient digital tool for knowledge translation and dissemination to targeted audiences to facilitate the uptake of research.

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.

Background Mobile apps targeting women with breast cancer can facilitate access to information, improve well-being, and record reports of treatment-related symptoms. However, it is important to confirm the benefits of these apps before they are used as a tool in clinical care. Objective The aim of this study was to evaluate the functionality and the usability of a mobile app created to guide and monitor patients with breast cancer undergoing radiation therapy. Methods The evaluation process of the mobile app was performed in 2 steps with 8 professionals, including nurses, physician, medical physicists, and communication networks engineer. The first step was the focus group, which allowed obtaining suggestions proposed by the participants regarding the improvement of the mobile app. The second step was the individual filling in of an evaluation tool to obtain objective measures about the mobile app. A minimum concordance index of 80% was considered to ensure the adequacy of the material. Results After the mobile app was evaluated by 8 professionals, only 1 item of the evaluation tool, that is, concerning the potentiality of the app to be used by users of different educational levels, obtained a concordance index Conclusions The mobile app titled “AMOR Mama” was considered suitable, which suggests its contribution to an educational health technology to guide and monitor patients with breast cancer undergoing radiation therapy. More studies with this target population should be carried out to assess the performance and quality of the mobile app during its use.

Background Patients may use two information sources about a health care provider’s quality: online physician reviews, which are written by patients to reflect their subjective experience, and report cards, which are based on objective health outcomes. Objective The aim of this study was to examine the impact of online ratings on patient choice of cardiac surgeon compared to that of report cards. Methods We obtained ratings from a leading physician review platform, Vitals; report card scores from Pennsylvania Cardiac Surgery Reports; and information about patients’ choices of surgeons from inpatient records on coronary artery bypass graft (CABG) surgeries done in Pennsylvania from 2008 to 2017. We scraped all reviews posted on Vitals for surgeons who performed CABG surgeries in Pennsylvania during our study period. We linked the average overall rating and the most recent report card score at the time of a patient’s surgery to the patient’s record based on the surgeon’s name, focusing on fee-for-service patients to avoid impacts of insurance networks on patient choices. We used random coefficient logit models with surgeon fixed effects to examine the impact of receiving a high online rating and a high report card score on patient choice of surgeon for CABG surgeries. Results We found that a high online rating had positive and significant effects on patient utility, with limited variation in preferences across individuals, while the impact of a high report card score on patient choice was trivial and insignificant. About 70.13% of patients considered no information on Vitals better than a low rating; the corresponding figure was 26.66% for report card scores. The findings were robust to alternative choice set definitions and were not explained by surgeon attrition, referral effect, or admission status. Our results also show that the interaction effect of rating information and a time trend was positive and significant for online ratings, but small and insignificant for report cards. Conclusions A patient’s choice of surgeon is affected by both types of rating information; however, over the past decade, online ratings have become more influential, while the effect of report cards has remained trivial. Our findings call for information provision strategies that incorporate the advantages of both online ratings and report cards.

Background Although disclosing the predictors of different behavioral and psychological symptoms of dementia (BPSD) is the first step in developing person-centered interventions, current understanding is limited, as it considers BPSD as a homogenous construct. This fails to account for their heterogeneity and hinders development of interventions that address the underlying causes of the target BPSD subsyndromes. Moreover, understanding the influence of proximal factors—circadian rhythm–related factors (ie, sleep and activity levels) and physical and psychosocial unmet needs states—on BPSD subsyndromes is limited, due to the challenges of obtaining objective and/or continuous time-varying measures. Objective The aim of this study was to explore factors associated with BPSD subsyndromes among community-dwelling older adults with dementia, considering sets of background and proximal factors (ie, actigraphy-measured sleep and physical activity levels and diary-based caregiver-perceived symptom triggers), guided by the need-driven dementia-compromised behavior model. Methods A prospective observational study design was employed. Study participants included 145 older adults with dementia living at home. The mean age at baseline was 81.2 (SD 6.01) years and the sample consisted of 86 (59.3%) women. BPSD were measured with a BPSD diary kept by caregivers and were categorized into seven subsyndromes. Independent variables consisted of background characteristics and proximal factors (ie, sleep and physical activity levels measured using actigraphy and caregiver-reported contributing factors assessed using a BPSD diary). Generalized linear mixed models (GLMMs) were used to examine the factors that predicted the occurrence of BPSD subsyndromes. We compared the models based on the Akaike information criterion, the Bayesian information criterion, and likelihood ratio testing. Results Compared to the GLMMs with only background factors, the addition of actigraphy and diary-based data improved model fit for every BPSD subsyndrome. The number of hours of nighttime sleep was a predictor of the next day’s sleep and nighttime behaviors (odds ratio [OR] 0.9, 95% CI 0.8-1.0; P=.005), and the amount of energy expenditure was a predictor for euphoria or elation (OR 0.02, 95% CI 0.0-0.5; P=.02). All subsyndromes, except for euphoria or elation, were significantly associated with hunger or thirst and urination or bowel movements, and all BPSD subsyndromes showed an association with environmental change. Age, marital status, premorbid personality, and taking sedatives were predictors of specific BPSD subsyndromes. Conclusions BPSD are clinically heterogeneous, and their occurrence can be predicted by different contributing factors. Our results for various BPSD suggest a critical window for timely intervention and care planning. Findings from this study will help devise symptom-targeted and individualized interventions to prevent and manage BPSD and facilitate personalized dementia care.

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 A 24-week self-directed digitally delivered intervention was found to improve pain and function in people with knee osteoarthritis (OA). However, it is possible that this intervention may be better suited to certain subgroups of people with knee OA compared to others. Objective The aim of this study was to explore whether certain individual baseline characteristics moderate the effects of a self-directed digitally delivered intervention on changes in pain and function over 24 weeks in people with knee OA. Methods An exploratory analysis was conducted on data from a randomized controlled trial involving 206 people with a clinical diagnosis of knee OA. This trial compared a self-directed digitally delivered intervention comprising of web-based education, exercise, and physical activity program supported by automated exercise behavior change mobile phone text messages to web-based education alone (control). The primary outcomes were changes in overall knee pain (assessed on an 11-point numerical rating scale) and physical function (assessed using the Western Ontario and McMaster Universities Osteoarthritis Index function subscale [WOMAC]) at 24 weeks. Five baseline patient characteristics were selected as the potential moderators: (1) number of comorbidities, (2) number of other painful joints, (3) pain self-efficacy, (4) exercise self-efficacy, and (5) self-perceived importance of exercise. Separate linear regression models for each primary outcome and each potential moderator were fit, including treatment group, moderator, and interaction between treatment group and moderator, adjusting for the outcome at baseline. Results There was evidence that pain self-efficacy moderated the effect of the intervention on physical function compared to the control at 24 weeks (interaction P=.02). Posthoc assessment of the mean change in WOMAC function by treatment arm showed that each 1-unit increase in baseline pain self-efficacy was associated with a 1.52 (95% CI 0.27 to 2.78) unit improvement in the control group. In contrast, a reduction of 0.62 (95% CI –1.93 to 0.68) units was observed in the intervention group with each unit increase in pain self-efficacy. There was only weak evidence that pain self-efficacy moderated the effect of the intervention on pain and that number of comorbidities, number of other painful joints, exercise self-efficacy, or exercise importance moderated the effect of the intervention on pain or function. Conclusions With the exception of pain self-efficacy, which moderated changes in function but not pain, we found limited evidence that our selected baseline patient characteristics moderated intervention outcomes. This indicates that people with a range of baseline characteristics respond similarly to the unsupervised digitally delivered exercise intervention. As these findings are exploratory in nature, they require confirmation in future studies.

Background Fatigue is the most commonly reported symptom in patients with persistent complaints following COVID-19 (ie, long COVID). Longitudinal studies examining the intensity of fatigue and differentiating between physical and mental fatigue are lacking. Objective The objectives of this study were to (1) assess the severity of fatigue over time in members of online long COVID peer support groups, and (2) assess whether members of these groups experienced mental fatigue, physical fatigue, or both. Methods A 2-wave web-based follow-up study was conducted in members of online long COVID peer support groups with a confirmed diagnosis approximately 3 and 6 months after the onset of infectious symptoms. Demographics, COVID-19 diagnosis, received health care (from medical professionals or allied health care professionals), fatigue (Checklist Individual Strength–subscale subjective fatigue [CIS-Fatigue]; 8-56 points), and physical and mental fatigue (self-constructed questions; 3-21 points) were assessed. Higher scores indicated more severe fatigue. A CIS-Fatigue score ≥36 points was used to qualify patients as having severe fatigue. Results A total of 239 patients with polymerase chain reaction/computed tomography–confirmed COVID-19 completed the survey 10 weeks (SD 2) and 23 weeks (SD 2) after onset of infectious symptoms, respectively (T1 and T2). Of these 239 patients, 198 (82.8%) were women; 142 (59.4%) had no self-reported pre-existing comorbidities; 208 (87%) self-reported being in good health before contracting COVID-19; and 62 (25.9%) were hospitalized during acute infection. The median age was 50 years (IQR 39-56). The vast majority of patients had severe fatigue at T1 and T2 (n=204, 85.4%, and n=188, 78.7%, respectively). No significant differences were found in the prevalence of normal, mild, and severe fatigue between T1 and T2 (P=.12). The median CIS-Fatigue score was 48 points (IQR 42-53) at T1, and it decreased from T1 to T2 (median change: –2 points, IQR –7 to 3; P Conclusions Fatigue in members of online long COVID support groups with a confirmed COVID-19 diagnosis decreases from 10 to 23 weeks after onset of symptoms. Despite this, severe fatigue remains highly prevalent. Both physical and mental fatigue are present. It remains unclear whether and to what extent fatigue will resolve spontaneously in the longer term. Trial Registration Netherlands Trial Register NTR8705; https://www.trialregister.nl/trial/8705.

Journal of medical internet research : JMIR 23(9), e28018 (2021). doi:10.2196/28018, Published by Healthcare World, Richmond, Va.

Background: The number of smartphone apps that focus on the prevention, diagnosis, and treatment of depression is increasing. A promising approach to increase the effectiveness of the apps while reducing the individual’s burden is the use of just-in-time adaptive intervention (JITAI) mechanisms. JITAIs are designed to improve the effectiveness of the intervention and reduce the burden on the person using the intervention by providing the right type of support at the right time. The right type of support and the right time are determined by measuring the state of vulnerability and the state of receptivity, respectively. Objective: The aim of this study is to systematically assess the use of JITAI mechanisms in popular apps for individuals with depression. Methods: We systematically searched for apps addressing depression in the Apple App Store and Google Play Store, as well as in curated lists from the Anxiety and Depression Association of America, the United Kingdom National Health Service, and the American Psychological Association in August 2020. The relevant apps were ranked according to the number of reviews (Apple App Store) or downloads (Google Play Store). For each app, 2 authors separately reviewed all publications concerning the app found within scientific databases (PubMed, Cochrane Register of Controlled Trials, PsycINFO, Google Scholar, IEEE Xplore, Web of Science, ACM Portal, and Science Direct), publications cited on the app’s website, information on the app’s website, and the app itself. All types of measurements (eg, open questions, closed questions, and device analytics) found in the apps were recorded and reviewed. Results: None of the 28 reviewed apps used JITAI mechanisms to tailor content to situations, states, or individuals. Of the 28 apps, 3 (11%) did not use any measurements, 20 (71%) exclusively used self-reports that were insufficient to leverage the full potential of the JITAIs, and the 5 (18%) apps using self-reports and passive measurements used them as progress or task indicators only. Although 34% (23/68) of the reviewed publications investigated the effectiveness of the apps and 21% (14/68) investigated their efficacy, no publication mentioned or evaluated JITAI mechanisms. Conclusions: Promising JITAI mechanisms have not yet been translated into mainstream depression apps. Although the wide range of passive measurements available from smartphones were rarely used, self-reported outcomes were used by 71% (20/28) of the apps. However, in both cases, the measured outcomes were not used to tailor content and timing along a state of vulnerability or receptivity. Owing to this lack of tailoring to individual, state, or situation, we argue that the apps cannot be considered JITAIs. The lack of publications investigating whether JITAI mechanisms lead to an increase in the effectiveness or efficacy of the apps highlights the need for further research, especially in real-world apps., Journal of Medical Internet Research, 23 (9), ISSN:1438-8871

Background The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. Objective This study aims to evaluate whether changes in phone sensor–derived behavioral features were associated with subsequent changes in mental health symptoms. Methods This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. Results A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=−0.23, P=.02; Locations: r=−0.36, P Conclusions Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms.

Background The COVID-19 pandemic has led to a rapid increase in virtual care use across the globe. Many health care systems have responded by creating virtual care billing codes that allow physicians to see their patients over telephone or video. This rapid liberalization of billing requirements, both in Canada and other countries, has led to concerns about potential abuse, but empirical data are limited. Objective The objectives of this study were to examine whether there were substantial changes in physicians’ ambulatory visit volumes coinciding with the liberalization of virtual care billing rules and to describe the characteristics of physicians who significantly increased their ambulatory visit volumes during this period. We also sought to describe the relationship between visit volume changes in 2020 and the volumes of virtual care use among individual physicians and across specialties. Methods We conducted a population-based, retrospective cohort study using health administrative data from the Ontario Health Insurance Plan, which was linked to the ICES Physician Database. We identified a unique cohort of providers based on physicians’ billings and calculated the ratio of total in-person and virtual ambulatory visits over the period from January to June 2020 (virtual predominating) relative to that over the period from January to June 2019 (in-person predominating) for each physician. Based on these ratios, we then stratified physicians into four groups: low-, same-, high-, and very high–use physicians. We then calculated various demographic and practice characteristics of physicians in each group. Results Among 28,383 eligible physicians in 2020, the mean ratio of ambulatory visits in January to June 2020:2019 was 0.99 (SD 2.53; median 0.81, IQR 0.59-1.0). Out of 28,383 physicians, only 2672 (9.4%) fell into the high-use group and only 291 (1.0%) fell into the very high–use group. High-use physicians were younger, more recent graduates, more likely female, and less likely to be international graduates. They also had, on average, lower-volume practices. There was a significant positive correlation between percent virtual care and the 2020:2019 ratio only in the group of physicians who maintained their practice (R=0.35, P Conclusions During the early stages of the pandemic, the introduction of virtual care did not lead to significant increases in visit volume. Our results provide reassuring evidence that relaxation of billing requirements early in the COVID-19 pandemic in Ontario were not associated with widespread and aberrant billing behaviors. Furthermore, the strong relationship between the ability to maintain practice volumes and the use of virtual care suggests that the introduction of virtual care allowed for continued access to care for patients.

Background The human papillomavirus (HPV) vaccination rate in Japan has fallen to nearly zero since the suspension of governmental proactive recommendations in 2013, owing to the development of purported adverse events. Objective This study aimed to evaluate the effects of a brief web-based educational intervention using the theory of behavioral insights on the willingness of adults to consider the HPV vaccine for their daughters and sons. Methods We recruited 1660 participants aged 20 years or older in March 2018 via a webpage and provided them with a 10-item questionnaire related to the following aspects: awareness regarding HPV infection and vaccination, willingness for immunization, and actions for prevention. We randomly stratified participants based on sex and age with or without a brief educational intervention involving scientific information presented in an easy-to-read format. Results Only 484 (29.2%) of the respondents were aware of the benefits of HPV vaccination. Although only 352 (21.2%) of the respondents displayed a willingness for immunization of their daughters, there were 40 (4.8%) more respondents in the intervention group with this willingness (adjusted odds ratio [aOR] 1.32, 95% CI 1.04-1.69). In a subanalysis, the willingness toward vaccination for daughters in men was significantly higher in the intervention group (aOR 1.46, 95% CI 1.05-2.02). However, such a difference was not observed among women (aOR 1.20, 95% CI 0.83-1.73). Conclusions This study suggests that a brief web-based educational intervention increases the willingness of adults to consider the HPV vaccine for their children, especially among men. Thus, providing adequate information to men may be a useful strategy to improve the currently low rates of HPV vaccination. Trial Registration UMIN Clinical Trials Registry UMIN000049745 (UMIN-CTR); https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000049745

Background Inpatient health care facilities restricted inpatient visitation due to the COVID-19 pandemic. There is no existing evidence of how they communicated these policies to the public nor the impact of their communication choices on public perception. Objective This study aims to describe patterns of inpatient visitation policies during the initial peak of the COVID-19 pandemic in the United States and the communication of these policies to the general public, as well as to identify communication strategies that maximize positive impressions of the facility despite visitation restrictions. Methods We conducted a sequential, exploratory, mixed methods study including a qualitative analysis of COVID-19 era visitation policies published on Pennsylvania-based facility websites, as captured between April 30 and May 20, 2020 (ie, during the first peak of the COVID-19 pandemic in the United States). We also conducted a factorial survey-based experiment to test how key elements of hospitals’ visitation policy communication are associated with individuals’ willingness to seek care in October 2020. For analysis of the policies, we included all inpatient facilities in Pennsylvania. For the factorial experiment, US adults were drawn from internet research panels. The factorial survey-based experiment presented composite policies that varied in their justification for restricted visitation, the degree to which the facility expressed ownership of the policy, and the inclusion of family-centered care support plans. Our primary outcome was participants’ willingness to recommend the hypothetical facility using a 5-point Likert scale. Results We identified 104 unique policies on inpatient visitation from 363 facilities’ websites. The mean Flesch-Kincaid Grade Level for the policies was 14.2. Most policies prohibited family presence (99/104, 95.2%). Facilities justified the restricted visitation policies on the basis of community protection (59/104, 56.7%), authorities’ guidance or regulations (34/104, 32.7%), or scientific rationale (23/104, 22.1%). A minority (38/104, 36.5%) addressed how restrictive visitation may impair family-centered care. Most of the policies analyzed used passive voice to communicate restrictions. A total of 1321 participants completed the web-based survey. Visitation policy elements significantly associated with willingness to recommend the facility included justifications based on community protection (OR 1.44, 95% CI 1.24-1.68) or scientific rationale (OR 1.30, 95% CI 1.12-1.51), rather than those based on a governing authority. The facility expressed a high degree of ownership over the decision (OR 1.16, 95% CI 1.04-1.29), rather than a low degree of ownership; and inclusion of family-centered care support plans (OR 2.80, 95% CI 2.51-3.12), rather than no such support. Conclusions Health systems can immediately improve public receptiveness of restrictive visitation policies by emphasizing community protection, ownership over the facility’s policy, and promoting family-centered care.

Background Skin and subcutaneous disease is the fourth-leading cause of the nonfatal disease burden worldwide and constitutes one of the most common burdens in primary care. However, there is a severe lack of dermatologists, particularly in rural Chinese areas. Furthermore, although artificial intelligence (AI) tools can assist in diagnosing skin disorders from images, the database for the Chinese population is limited. Objective This study aims to establish a database for AI based on the Chinese population and presents an initial study on six common skin diseases. Methods Each image was captured with either a digital camera or a smartphone, verified by at least three experienced dermatologists and corresponding pathology information, and finally added to the Xiangya-Derm database. Based on this database, we conducted AI-assisted classification research on six common skin diseases and then proposed a network called Xy-SkinNet. Xy-SkinNet applies a two-step strategy to identify skin diseases. First, given an input image, we segmented the regions of the skin lesion. Second, we introduced an information fusion block to combine the output of all segmented regions. We compared the performance with 31 dermatologists of varied experiences. Results Xiangya-Derm, as a new database that consists of over 150,000 clinical images of 571 different skin diseases in the Chinese population, is the largest and most diverse dermatological data set of the Chinese population. The AI-based six-category classification achieved a top 3 accuracy of 84.77%, which exceeded the average accuracy of dermatologists (78.15%). Conclusions Xiangya-Derm, the largest database for the Chinese population, was created. The classification of six common skin conditions was conducted based on Xiangya-Derm to lay a foundation for product research.

Background The COVID-19 pandemic has led to unprecedented use of telehealth, including by primary care professionals (PCPs) who serve adolescents. Objective To inform future practice and policies, we sought to characterize PCPs’ recent experience using adolescent telehealth as well as their support for it after the COVID-19 pandemic is over. Methods From February to March 2021, we conducted a web-based survey of 1047 PCPs in the United States. Our national sample included physicians (747/1047, 71%), advanced practice providers (177/1047, 17%), and nurses (123/1047, 12%) who provided primary care to adolescents aged 11-17 years. Results Most PCPs reported using telehealth for a low, moderate, or high proportion of their adolescent patients in the three months prior to the survey (424/1047, 40%, 286/1047, 27%, and 219/1047, 21%, respectively); only 11% (118/1047) reported no use. A majority of respondents agreed that adolescent telehealth increases access to care (720/1047, 69%) and enables them to provide high-quality care (560/1047, 53%). Few believed that adolescent telehealth takes too much time (142/1047, 14%) or encourages health care overuse (157/1047, 15%). Most supported giving families the option of adolescent telehealth for primary care after the pandemic is over (683/1047, 65%) and believed that health insurance plans should continue to reimburse for telehealth visits (863/1047, 82%). Approximately two-thirds (702/1047, 67%) wanted to offer adolescent telehealth visits after the pandemic, with intentions being higher among those with recent telehealth experience (P Conclusions PCPs in our national sample reported widespread use of and predominantly positive attitudes toward adolescent telehealth. Our findings also suggest broad support among PCPs for continuing to offer adolescent telehealth after the COVID-19 pandemic ends.

Background The control of vaccine hesitancy and the promotion of vaccination are key protective measures against COVID-19. Objective This study assesses the prevalence of vaccine hesitancy and the vaccination rate and examines the association between factors of the health belief model (HBM) and vaccination. Methods A convenience sample of 2531 valid participants from 31 provinces and autonomous regions of mainland China were enrolled in this online survey study from January 1 to 24, 2021. Multivariable logistic regression was used to identify the associations of the vaccination rate and HBM factors with the prevalence of vaccine hesitancy after other covariates were controlled. Results The prevalence of vaccine hesitancy was 44.3% (95% CI 42.3%-46.2%), and the vaccination rate was 10.4% (9.2%-11.6%). The factors that directly promoted vaccination behavior were a lack of vaccine hesitancy (odds ratio [OR] 7.75, 95% CI 5.03-11.93), agreement with recommendations from friends or family for vaccination (OR 3.11, 95% CI 1.75-5.52), and absence of perceived barriers to COVID-19 vaccination (OR 0.51, 95% CI 0.35-0.75). The factors that were directly associated with a higher vaccine hesitancy rate were a high level of perceived barriers (OR 1.63, 95% CI 1.36-1.95) and perceived benefits (OR 0.51, 95% CI 0.32-0.79). A mediating effect of self-efficacy, influenced by perceived barriers (standardized structure coefficient [SSC]=−0.71, P Conclusions It may be possible to increase the vaccination rate by reducing vaccine hesitancy and perceived barriers to vaccination and by encouraging volunteers to advocate for vaccination to their friends and family members. It is also important to reduce vaccine hesitancy by enhancing self-efficacy for vaccination, due to its crucial mediating function.

Background Patient portals play an important role in connecting patients with their medical care team, which improves patient engagement in treatment plans, decreases unnecessary visits, and reduces costs. During natural disasters, patients’ needs increase, whereas available resources, specifically access to care, become limited. Objective This study aims to examine patients’ health needs during a natural crisis by analyzing the electronic messages sent during Hurricane Harvey to guide future disaster planning efforts. Methods We explored patient portal use data from a large Greater Houston area health care system focusing on the initial week of the Hurricane Harvey disaster, beginning with the date of landfall, August 25, 2017, to August 31, 2017. A mixed methods approach was used to assess patients’ immediate health needs and concerns during the disruption of access to routine and emergent medical care. Quantitative analysis used logistic regression models to assess the predictive characteristics of patients using the portal during Hurricane Harvey. This study also included encounters by type (emergency, inpatient, observation, outpatient, and outpatient surgery) and time (before, during, and after Hurricane Harvey). For qualitative analysis, the content of these messages was examined using the constant comparative method to identify emerging themes found within the message texts. Results Out of a total of 557,024 patients, 4079 (0.73%) sent a message during Hurricane Harvey, whereas 31,737 (5.69%) used the portal. Age, sex, race, and ethnicity were predictive factors for using the portal and sending a message during the natural disaster. We found that prior use of the patient portal increased the likelihood of portal use during Hurricane Harvey (odds ratio 13.688, 95% CI 12.929-14.491) and of sending a portal message during the disaster (odds ratio 14.172, 95% CI 11.879-16.907). Having an encounter 4 weeks before or after Hurricane Harvey was positively associated with increased use of the portal and sending a portal message. Patients with encounters during the main Hurricane Harvey week had a higher increased likelihood of portal use across all five encounter types. Qualitative themes included: access, prescription requests, medical advice (chronic conditions, acute care, urgent needs, and Hurricane Harvey–related injuries), mental health, technical difficulties, and provider constraints. Conclusions The patient portal can be a useful tool for communication between patients and providers to address the urgent needs and concerns of patients as a natural disaster unfolds. This was the first known study to include encounter data to understand portal use compared with care provisioning. Prior use was predictive of both portal use and message sending during Hurricane Harvey. These findings could inform the types of demands that may arise in future disaster situations and can serve as the first step in intentionally optimizing patient portal usability for emergency health care management during natural disasters.

Background The COVID-19 outbreak has now become a pandemic and has had a serious adverse impact on global public health. The effect of COVID-19 on the lungs can be determined through 2D computed tomography (CT) imaging, which requires a high level of spatial imagination on the part of the medical provider. Objective The purpose of this study is to determine whether viewing a 3D hologram with mixed reality techniques can improve medical professionals’ understanding of the pulmonary lesions caused by COVID-19. Methods The study involved 60 participants, including 20 radiologists, 20 surgeons, and 20 medical students. Each of the three groups was randomly divided into two groups, either the 2D CT group (n=30; mean age 29 years [range 19-38 years]; males=20) or the 3D holographic group (n=30; mean age 30 years [range 20=38 years]; males=20). The two groups completed the same task, which involved identifying lung lesions caused by COVID-19 for 6 cases using a 2D CT or 3D hologram. Finally, an independent radiology professor rated the participants' performance (out of 100). All participants in two groups completed a Likert scale questionnaire regarding the educational utility and efficiency of 3D holograms. The National Aeronautics and Space Administration Task Load Index (NASA-TLX) was completed by all participants. Results The mean task score of the 3D hologram group (mean 91.98, SD 2.45) was significantly higher than that of the 2D CT group (mean 74.09, SD 7.59; P Conclusions A 3D hologram with mixed reality techniques can be used to help medical professionals, especially medical students and newly hired doctors, better identify pulmonary lesions caused by COVID-19. It can be used in medical education to improve spatial awareness, increase interest, improve understandability, and lower the learning curve. Trial Registration Chinese Clinical Trial Registry ChiCTR2100045845; http://www.chictr.org.cn/showprojen.aspx?proj=125761

Background Almost 50% of the adults in the United States have hypertension. Although clinical trials indicate that home blood pressure monitoring can be effective in managing hypertension, the reported results might not materialize in practice because of patient adherence problems. Objective The aims of this study are to characterize the adherence of Medicaid patients with hypertension to daily telemonitoring, identify the impacts of adherence reminder calls, and investigate associations with blood pressure control. Methods This study targeted Medicaid patients with hypertension from the state of Texas. A total of 180 days of blood pressure and pulse data in 2016-2018 from a telemonitoring company were analyzed for mean transmission rate and mean blood pressure change. The first 30 days of data were excluded because of startup effects. The protocols required the patients to transmit readings by a specified time daily. Patients not transmitting their readings received an adherence reminder call to troubleshoot problems and encourage transmission. The patients were classified into adherent and nonadherent cohorts; adherent patients were those who transmitted data on at least 80% of the days. Results The mean patient age was 73.2 (SD 11.7) years. Of the 823 patients, 536 (65.1%) were women, and 660 (80.2%) were urban residents. The adherent cohort (475/823, 57.7%) had mean transmission rates of 74.9% before the adherence reminder call and 91.3% after the call, whereas the nonadherent cohort (348/823, 42.3%) had mean transmission rates of 39% and 58% before and after the call, respectively. From month 1 to month 5, the transmission rates dropped by 1.9% and 10.2% for the adherent and nonadherent cohorts, respectively. The systolic and diastolic blood pressure values improved by an average of 2.2 and 0.7 mm Hg (P Conclusions Although we found that patients can achieve high levels of adherence, many experience adherence problems. Although adherence reminder calls help, they may not be sufficient. Telemonitoring lowered blood pressure, as has been observed in clinical trials. Furthermore, blood pressure control was positively associated with adherence.

Background Patient-clinician secure messaging is an important function in patient portals and enables patients and clinicians to communicate on a wide spectrum of issues in a timely manner. With its growing adoption and patient engagement, it is time to comprehensively study the secure messages and user behaviors in order to improve patient-centered care. Objective The aim of this paper was to analyze the secure messages sent by patients and clinicians in a large multispecialty health system at Mayo Clinic, Rochester. Methods We performed message-based, sender-based, and thread-based analyses of more than 5 million secure messages between 2010 and 2017. We summarized the message volumes, patient and clinician population sizes, message counts per patient or clinician, as well as the trends of message volumes and user counts over the years. In addition, we calculated the time distribution of clinician-sent messages to understand their workloads at different times of a day. We also analyzed the time delay in clinician responses to patient messages to assess their communication efficiency and the back-and-forth rounds to estimate the communication complexity. Results During 2010-2017, the patient portal at Mayo Clinic, Rochester experienced a significant growth in terms of the count of patient users and the total number of secure messages sent by patients and clinicians. Three clinician categories, namely “physician—primary care,” “registered nurse—specialty,” and “physician—specialty,” bore the majority of message volume increase. The patient portal also demonstrated growing trends in message counts per patient and clinician. The “nurse practitioner or physician assistant—primary care” and “physician—primary care” categories had the heaviest per-clinician workload each year. Most messages by the clinicians were sent from 7 AM to 5 PM during a day. Yet, between 5 PM and 7 PM, the physicians sent 7.0% (95,785/1,377,006) of their daily messages, and the nurse practitioner or physician assistant sent 5.4% (22,121/408,526) of their daily messages. The clinicians replied to 72.2% (1,272,069/1,761,739) patient messages within 1 day and 90.6% (1,595,702/1,761,739) within 3 days. In 95.1% (1,499,316/1,576,205) of the message threads, the patients communicated with their clinicians back and forth for no more than 4 rounds. Conclusions Our study found steady increases in patient adoption of the secure messaging system and the average workload per clinician over 8 years. However, most clinicians responded timely to meet the patients’ needs. Our study also revealed differential patient-clinician communication patterns across different practice roles and care settings. These findings suggest opportunities for care teams to optimize messaging tasks and to balance the workload for optimal efficiency.

Background The COVID-19 pandemic required rapid implementation of virtual antenatal care to keep pregnant women safe. This transition from face-to-face usual care had to be embraced by patients and professionals alike. Objective We evaluated patients’ and professionals’ experiences with virtual antenatal clinic appointments during the COVID-19 pandemic to determine satisfaction and inquire into the safety and quality of care received. Methods A total of 148 women who attended a virtual antenatal clinic appointment at our UK tertiary obstetric care center over a 2-week period provided feedback (n=92, 62% response rate). A further 37 health care professionals (HCPs) delivering care in the virtual antenatal clinics participated in another questionnaire study (37/45, 82% response rate). Results We showed that women were highly satisfied with the virtual clinics, with 86% (127/148) rating their experience as good or very good, and this was not associated with any statistically significant differences in age (P=.23), ethnicity (P=.95), number of previous births (P=.65), or pregnancy losses (P=.94). Even though 56% (83/148) preferred face-to-face appointments, 44% (65/148) either expressed no preference or preferred virtual, and these preferences were not associated with significant differences in patient demographics. For HCPs, 67% (18/27) rated their experience of virtual clinics as good or very good, 78% (21/27) described their experience as the same or better than face-to-face clinics, 15% (4/27) preferred virtual clinics, and 44% (12/27) had no preference. Importantly, 67% (18/27) found it easy or very easy to adapt to virtual clinics. Over 90% of HCPs agreed virtual clinics should be implemented long-term. Conclusions Our study demonstrates high satisfaction with telephone antenatal clinics during the pandemic, which supports the transition toward widespread digitalization of antenatal care suited to 21st-century patients and professionals.

Background Internet-based cognitive behavior therapy (I-CBT) for adolescents with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) has been shown to be effective in a randomized controlled trial (RCT; Fatigue in Teenagers on the Internet [FITNET]). FITNET can cause a significant reduction in fatigue and disability. Objective We aimed to investigate whether FITNET treatment implemented in routine clinical care (IMP-FITNET) was as effective, using the outcomes of the FITNET RCT as the benchmark. Methods Outcomes of CFS/ME adolescents who started IMP-FITNET between October 2012 and March 2018 as part of routine clinical care were compared to the outcomes in the FITNET RCT. The primary outcome was fatigue severity assessed posttreatment. The secondary outcomes were self-reported physical functioning, school attendance, and recovery rates. Clinically relevant deterioration was assessed posttreatment, and for this outcome, a face-to-face CBT trial was used as the benchmark. The attitude of therapists toward the usability of IMP-FITNET was assessed through semistructured interviews. The number of face-to-face consultations during IMP-FITNET was registered. Results Of the 384 referred adolescents with CFS/ME, 244 (63.5%) started IMP-FITNET, 84 (21.9%) started face-to-face CBT, and 56 (14.6%) were not eligible for CBT. Posttreatment scores for fatigue severity (mean 26.0, SD 13.8), physical functioning (mean 88.2, SD 15.0), and full school attendance (mean 84.3, SD 26.5) fell within the 95% CIs of the FITNET RCT. Deterioration of fatigue and physical functioning after IMP-FITNET was observed at rates of 1.2% (n=3) and 4.1% (n=10), respectively, which is comparable to a waiting list condition (fatigue: 1.2% vs 5.7%, χ21=3.5, P=.06; physical functioning: 4.1% vs 11.4%, χ21=3.3, P=.07). Moreover, 41 (16.8%) IMP-FITNET patients made use of face-to-face consultations. Conclusions IMP-FITNET is an effective and safe treatment for adolescents with CFS/ME in routine clinical care.

Background Premature ejaculation (PE) is one of the most described psychosocial stress and sexual complaints worldwide. Previous investigations have focused predominantly on the prospective identification of cases that meet researchers’ specific criteria. The genuine demand from patients with regard to information on PE and related issues may thus be neglected. Objective This study aims to examine the online search trend and user demand related to PE on a national and regional scale using the dominant major search engine in mainland China. Methods The Baidu Index was queried using the PE-related terms for the period of January 2011 to December 2020. The search volume for each term was recorded to analyze the search trend and demographic distributions. For user interest, the demand and trend data were collected and analyzed. Results Of the 36 available PE search keywords, 4 PE searching topics were identified. The Baidu Search Index for each PE topic varied from 46.30% (86,840,487/187,558,154) to 6.40% (12,009,307/187,558,154). The annual percent change (APC) for the complaint topic was 48.80% (P Conclusions The fluctuating online popularity of PE searches reflects the real-time population demands. It may help medical professionals better understand population interest, population concerns, regional variations, and gender differences on a nationwide scale and make disease-specific health care policies. The internet search data could be more reliable when the insufficient and lagging registry data are completed.

Background COVID-19 preventive perceptions and behaviors, especially among US millennials, are an important means by which the pandemic can be slowed and negative health outcomes can be averted. Objective This manuscript aims to advance knowledge on COVID-19 preventive perceptions and behaviors and their main predictors, including digital health information–seeking behavior (HISB), political party identification, and COVID-19 testing status. Methods Two cross-sectional online surveys of US millennials were conducted from April 10 to 14, 2020 (N=274) (ie, Study 1), and from April 27 to May 7, 2020 (N=1037) (ie, Study 2). In the regression models, dependent variables included preventive behaviors (eg, wearing a face mask and social distancing) as well as four preventive perceptions: severity (ie, a person’s conception of the seriousness of COVID-19), susceptibility (ie, a person’s conception of the likelihood of being infected with COVID-19), self-efficacy (ie, a person’s perception that he or she can wear a face mask and perform social distancing to prevent COVID-19 infection), and response efficacy (ie, a person’s perception of whether wearing a face mask and social distancing can prevent COVID-19 infection). Key independent variables included digital HISB for self, digital HISB for another person, political party identification, and COVID-19 testing status. Results Millennials reported lower levels of perceived susceptibility than the other three preventive perceptions (ie, severity, self-efficacy, and response efficacy), as well as fairly high levels of preventive behaviors. Unlike HISB for another person, digital HISB for self was positively associated with preventive perceptions and behaviors. In Study 1, respondents with higher levels of digital HISB for self had significantly higher perceptions of severity (β=.22, P Conclusions As the largest and most digitally savvy generation, US millennials saw COVID-19 as a severe threat, but one that they were less susceptible to. For millennials, digital HISB for self, but not for another person, was critical to the development of preventive perceptions and behaviors.

Background Corona contact tracing apps are a novel and promising measure to reduce the spread of COVID-19. They can help to balance the need to maintain normal life and economic activities as much as possible while still avoiding exponentially growing case numbers. However, a majority of citizens need to be willing to install such an app for it to be effective. Hence, knowledge about drivers for app uptake is crucial. Objective This study aimed to add to our understanding of underlying psychological factors motivating app uptake. More specifically, we investigated the role of concern for one’s own health and concern to unknowingly infect others. Methods A two-wave survey with 346 German-speaking participants from Switzerland and Germany was conducted. We measured the uptake of two decentralized contact tracing apps officially launched by governments (Corona-Warn-App, Germany; SwissCovid, Switzerland), as well as concerns regarding COVID-19 and control variables. Results Controlling for demographic variables and general attitudes toward the government and the pandemic, logistic regression analysis showed a significant effect of self-focused concerns (odds ratio [OR] 1.64, P=.002). Meanwhile, concern of unknowingly infecting others did not contribute significantly to the prediction of app uptake over and above concern for one’s own health (OR 1.01, P=.92). Longitudinal analyses replicated this pattern and showed no support for the possibility that app uptake provokes changes in levels of concern. Testing for a curvilinear relationship, there was no evidence that “too much” concern leads to defensive reactions and reduces app uptake. Conclusions As one of the first studies to assess the installation of already launched corona tracing apps, this study extends our knowledge of the motivational landscape of app uptake. Based on this, practical implications for communication strategies and app design are discussed.

BackgroundNews media coverage of antimask protests, COVID-19 conspiracies, and pandemic politicization has overemphasized extreme views but has done little to represent views of the general public. Investigating the public’s response to various pandemic restrictions can provide a more balanced assessment of current views, allowing policy makers to craft better public health messages in anticipation of poor reactions to controversial restrictions.ObjectiveUsing data from social media, this infoveillance study aims to understand the changes in public opinion associated with the implementation of COVID-19 restrictions (eg, business and school closures, regional lockdown differences, and additional public health restrictions, such as social distancing and masking).MethodsCOVID-19–related tweets in Ontario (n=1,150,362) were collected based on keywords between March 12 and October 31, 2020. Sentiment scores were calculated using the VADER (Valence Aware Dictionary and Sentiment Reasoner) algorithm for each tweet to represent its negative to positive emotion. Public health restrictions were identified using government and news media websites. Dynamic regression models with autoregressive integrated moving average errors were used to examine the association between public health restrictions and changes in public opinion over time (ie, collective attention, aggregate positive sentiment, and level of disagreement), controlling for the effects of confounders (ie, daily COVID-19 case counts, holidays, and COVID-19–related official updates).ResultsIn addition to expected direct effects (eg, business closures led to decreased positive sentiment and increased disagreements), the impact of restrictions on public opinion was contextually driven. For example, the negative sentiment associated with business closures was reduced with higher COVID-19 case counts. While school closures and other restrictions (eg, masking, social distancing, and travel restrictions) generated increased collective attention, they did not have an effect on aggregate sentiment or the level of disagreement (ie, sentiment polarization). Partial (ie, region-targeted) lockdowns were associated with better public response (ie, higher number of tweets with net positive sentiment and lower levels of disagreement) compared to province-wide lockdowns.ConclusionsOur study demonstrates the feasibility of a rapid and flexible method of evaluating the public response to pandemic restrictions using near real-time social media data. This information can help public health practitioners and policy makers anticipate public response to future pandemic restrictions and ensure adequate resources are dedicated to addressing increases in negative sentiment and levels of disagreement in the face of scientifically informed, but controversial, restrictions.

Background Food insecurity is a global public health challenge, affecting predominately the most vulnerable people in society, including older adults. For this population, eHealth interventions represent an opportunity for promoting healthy lifestyle habits, thus mitigating the consequences of food insecurity. However, before their widespread dissemination, it is essential to evaluate the feasibility and acceptability of these interventions among end users. Objective This study aims to explore the feasibility and acceptability of a home-based eHealth intervention focused on improving dietary and physical activity through an interactive television (TV) app among older adults with food insecurity. Methods A pilot noncontrolled quasi-experimental study was designed with baseline and 3-month follow-up assessments. Older adult participants with food insecurity were recruited from 17 primary health care centers in Portugal. A home-based intervention program using an interactive TV app aimed at promoting healthy lifestyle behaviors was implemented over 12 weeks. Primary outcomes were feasibility (self-reported use and interest in eHealth) and acceptability (affective attitude, burden, ethicality, perceived effectiveness, and self-efficacy), which were evaluated using a structured questionnaire with a 7-point Likert scale. Secondary outcomes were changes in food insecurity (Household Food Insecurity Scale), quality of life (European Quality of Life Questionnaire with five dimensions and three levels and Functional Assessment of Chronic Illness Therapy-Fatigue), physical function (Health Assessment Questionnaire, Elderly Mobility Scale, grip strength, and regularity of exercise), and nutritional status (adherence to the Mediterranean diet). Results A sample of 31 older adult individuals with food insecurity was enrolled in the 12-week intervention program with no dropouts. A total of 10 participants self-reported low use of the TV app. After the intervention, participants were significantly more interested in using eHealth to improve food insecurity (baseline median 1.0, IQR 3.0; 3-month median 5.0, IQR 5.0; P=.01) and for other purposes (baseline median 1.0, IQR 2.0; 3-month median 6.0, IQR 2.0; P=.03). High levels of acceptability were found both before and after (median range 7.0-7.0, IQR 2.0-0.0 and 5.0-7.0, IQR 2.0-2.0, respectively) the intervention, with no significant changes for most constructs. Clinically, there was a reduction of 40% in food insecurity (P=.001), decreased fatigue (mean −3.82, SD 8.27; P=.02), and improved physical function (Health Assessment Questionnaire: mean −0.22, SD 0.38; P=.01; Elderly Mobility Scale: mean −1.50, SD 1.08; P=.01; regularity of exercise: baseline 10/31, 32%; 3 months 18/31, 58%; P=.02). No differences were found for the European Quality of Life Questionnaire with five dimensions and three levels, grip strength, or adherence to the Mediterranean diet. Conclusions The home-based eHealth intervention was feasible and highly acceptable by participants, thus supporting a future full-scale trial. The intervention program not only reduced the proportion of older adults with food insecurity but also improved participants’ fatigue and physical function. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.6626

Background Harm reduction services reduce the negative consequences of drug injection and are often embedded within syringe service programs (SSPs). However, people who inject drugs (PWID) suboptimally engage with such services because of stigma, fear, transportation restrictions, and limited hours of operation. Mobile health (mHealth) apps may provide an opportunity to overcome these barriers and extend the reach of SSPs beyond that of the traditional brick-and-mortar models. Objective This study aims to assess the prevalence of smartphone ownership, the level of comfort in providing the personal information required to use mHealth apps, and interest in using an mHealth app to access harm reduction services among PWID to guide the development of an app. Methods We administered a survey to 115 PWID who were enrolled via respondent-driven sampling from July 2018 to July 2019. We examined the extent to which PWID had access to smartphones; were comfortable in providing personal information such as name, email, and address; and expressed interest in various app-based services. We measured participant characteristics (demographics, health status, and behaviors) and used binary logistic and Poisson regressions to identify independent correlates of mHealth-related variables. The primary regression outcomes included summary scores for access, comfort, and interest. The secondary outcomes included binary survey responses for individual comfort or interest components. Results Most participants were White (74/105, 70.5%), male (78/115, 67.8%), and middle-aged (mean=41.7 years), and 67.9% (74/109) owned a smartphone. Participants reported high levels of comfort in providing personal information to use an mHealth app, including name (96/109, 88.1%), phone number (92/109, 84.4%), email (85/109, 77.9%), physical address (85/109, 77.9%), and linkage to medical records (72/109, 66.1%). Participants also reported strong interest in app-based services, including medication or sterile syringe delivery (100/110, 90.9%), lab or appointment scheduling (90/110, 81.8%), medication reminders (77/110, 70%), educational material (65/110, 59.1%), and group communication forums (64/110, 58.2%). Most participants were comfortable with the idea of home delivery of syringes (93/109, 85.3%). Homeless participants had lower access to smartphones (adjusted odds ratio [AOR] 0.15, 95% CI 0.05-0.46; P=.001), but no other participant characteristics were associated with primary outcomes. Among secondary outcomes, recent SSP use was positively associated with comfort with the home delivery of syringes (AOR 3.29, 95% CI 1.04-10.3 P=.04), and being older than 50 years was associated with an increased interest in educational materials (AOR 4.64, 95% CI 1.31-16.5; P=.02) and group communication forums (AOR 3.69, 95% CI 1.10-12.4; P=.04). Conclusions Our findings suggest that aside from those experiencing homelessness or unstable housing, PWID broadly have access to smartphones, are comfortable with sharing personal information, and express interest in a wide array of services within an app. Given the suboptimal access to and use of SSPs among PWID, an mHealth app has a high potential to address the harm reduction needs of this vulnerable population.

Background: Mental health disorders affect 1 in 10 people globally, of whom approximately 300 million are affected by depression. At least half of the people affected by depression remain untreated. Although cognitive behavioral therapy (CBT) is an effective treatment, access to mental health specialists, habitually challenging, has worsened because of the COVID-19 pandemic. Internet-based CBT is an effective and feasible strategy to increase access to treatment for people with depression. Mental health apps may further assist in facilitating self-management for people affected by depression; however, accessing the correct app may be cumbersome given the large number and wide variety of apps offered by public app marketplaces. Objective: This study aims to systematically assess the features, functionality, data security, and congruence with evidence of self-guided CBT-based apps targeting users affected by depression that are available in major app stores. Methods: We conducted a systematic assessment of self-guided CBT-based apps available in Google Play and the Apple App Store. Apps launched or updated since August 2018 were identified through a systematic search in the 42matters database using CBT-related terms. Apps meeting the inclusion criteria were downloaded and assessed using a Samsung Galaxy J7 Pro (Android 9) and iPhone 7 (iOS 13.3.1). Apps were appraised using a 182-question checklist developed by the research team, assessing their general characteristics, technical aspects and quality assurance, and CBT-related features, including 6 evidence-based CBT techniques (ie, psychoeducation, behavioral activation, cognitive restructuring, problem solving, relaxation, and exposure for comorbid anxiety) as informed by a CBT manual, CBT competence framework, and a literature review of internet-based CBT clinical trial protocols. The results were reported as a narrative review using descriptive statistics. Results: The initial search yielded 3006 apps, of which 98 met the inclusion criteria and were systematically assessed. There were 20 well-being apps; 65 mental health apps, targeting two or more common mental health disorders, including depression; and 13 depression apps. A total of 28 apps offered at least four evidence-based CBT techniques, particularly depression apps. Cognitive restructuring was the most common technique, offered by 79% (77/98) of the apps. Only one-third of the apps offered suicide risk management resources, whereas 17% (17/98) of the apps offered COVID-19-related information. Although most apps included a privacy policy, only a third of the apps presented it before account creation. In total, 82% (74/90) of privacy policies stated sharing data with third-party service providers. Half of the app development teams included academic institutions or health care providers. Conclusions: Only a few self-guided CBT-based apps offer comprehensive CBT programs or suicide risk management resources. Sharing of users' data is widespread, highlighting shortcomings in health app market governance. To fulfill their potential, self-guided CBT-based apps should follow evidence-based clinical guidelines, be patient centered, and enhance users' data security., Journal of Medical Internet Research, 23 (7), ISSN:1438-8871