The Coronavirus Disease 2019 Rebound Study: A Prospective Cohort Study to Evaluate Viral and Symptom Rebound Differences in Participants Treated With Nirmatrelvir Plus Ritonavir Versus Untreated Controls.

BACKGROUND: The uptake of nirmatrelvir plus ritonavir (NPR) in patients with coronavirus disease 2019 (COVID-19) has been limited by concerns around the rebound phenomenon despite the scarcity of evidence around its epidemiology. The purpose of this study was to prospectively compare the epidemiology of rebound in NPR-treated and untreated participants with acute COVID-19 infection. METHODS: We designed a prospective, observational study in which participants who tested positive for COVID-19 and were clinically eligible for NPR were recruited to be evaluated for either viral or symptom clearance and rebound. Participants were assigned to the treatment or control group based on their decision to take NPR. Following initial diagnosis, both groups were provided 12 rapid antigen tests and asked to test on a regular schedule for 16 days and answer symptom surveys. Viral rebound based on test results and COVID-19 symptom rebound based on patient-reported symptoms were evaluated. RESULTS: Viral rebound incidence was 14.2% in the NPR treatment group (n = 127) and 9.3% in the control group (n = 43). Symptom rebound incidence was higher in the treatment group (18.9%) compared to controls (7.0%). There were no notable differences in viral rebound by age, gender, preexisting conditions, or major symptom groups during the acute phase or at the 1-month interval. CONCLUSIONS: This preliminary report suggests that rebound after clearance of test positivity or symptom resolution is higher than previously reported. However, notably we observed a similar rate of rebound in both the NPR treatment and control groups. Large studies with diverse participants and extended follow-up are needed to better understand the rebound phenomena.

The hospital at home in the USA: current status and future prospects.

The annual cost of hospital care services in the US has risen to over $1 trillion despite relatively worse health outcomes compared to similar nations. These trends accentuate a growing need for innovative care delivery models that reduce costs and improve outcomes. HaH-a program that provides patients acute-level hospital care at home-has made significant progress over the past two decades. Technological advancements in remote patient monitoring, wearable sensors, health information technology infrastructure, and multimodal health data processing have contributed to its rise across hospitals. More recently, the COVID-19 pandemic brought HaH into the mainstream, especially in the US, with reimbursement waivers that made the model financially acceptable for hospitals and payors. However, HaH continues to face serious challenges to gain widespread adoption. In this review, we evaluate the peer-reviewed evidence and discuss the promises, challenges, and what it would take to tap into the future potential of HaH.

Heart Rate Variability in Psychiatric Disorders: A Systematic Review.

Introduction: Heart rate variability (HRV) is a measure of the fluctuation in time interval between consecutive heart beats. Decreased heart rate variability has been shown to have associations with autonomic dysfunction in psychiatric conditions such as depression, substance abuse, anxiety, and schizophrenia, although its use as a prognostic tool remains highly debated. This study aims to review the current literature on heart rate variability as a diagnostic and prognostic tool in psychiatric populations. Methods: A literature search was conducted using the MEDLINE, EMBASE, Cochrane, and PsycINFO libraries to identify full-text studies involving adult psychiatric populations that reported HRV measurements. From 1647 originally identified, 31 studies were narrowed down through an abstract and full-text screen. Studies were excluded if they enrolled adolescents or children, used animal models, enrolled patients with another primary diagnosis other than psychiatric as outlined by the diagnostic and statistical manual of mental disorders (DSM) V, or if they assessed HRV in the context of treatment rather than diagnosis. Study quality assessment was conducted using a modified Downs and Blacks quality assessment tool for observational rather than interventional studies. Data were reported in four tables: 1) summarizing study characteristics, 2) methods of HRV detection, 3) key findings and statistics, and 4) quality assessment. Results: There is significant variability between studies in their methodology of recording as well as reporting HRV, which makes it difficult to meaningfully interpret data that is clinically applicable due to the presence of significant bias in existing studies. The presence of an association between HRV and the severity of various psychiatric disorders, however, remains promising. Conclusion: Future studies should be done to further explore how HRV parameters may be used to enhance the diagnosis and prognosis of several psychiatric disorders.

Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography.

Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.

Smartphone apps in the COVID-19 pandemic.

At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics.

Improving participant representation in the era of digital clinical studies.

Traditional clinical research relies on conventional strategies to invite and enroll research participants. However, these strategies often fail to reach potential participants from marginalized communities or that reflect the diversity of the nation, such as race, ethnicity, or geography. As we discuss here, the digital clinical study model sets the stage for improved and equitable participation in biomedical research.

The Power of Patient Engagement With Electronic Health Records as Research Participants.

Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants.

Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.

BACKGROUND: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. METHODS: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. FINDINGS: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. INTERPRETATION: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. FUNDING: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.

Left Atrial Strain and the Risk of Atrial Arrhythmias From Extended Ambulatory Cardiac Monitoring: MESA.

Background Abnormalities in left atrial (LA) function often occur before LA structural changes and clinically identified atrial fibrillation (AF). Little is known about the relationship between LA strain and the risk of subclinical atrial arrhythmias detected from extended ambulatory cardiac monitoring. Methods and Results A total of 1441 participants of MESA (Multi-Ethnic Study of Atherosclerosis) completed speckle-tracking echocardiography and cardiac monitoring during 2016 to 2018 (mean age, 73 years); participants in AF during echocardiography or during the entire cardiac monitoring period were excluded. Absolute values of LA reservoir, booster pump, and conduit strains were measured. We evaluated associations of LA strain with monitor-detected AF, premature atrial contractions, and supraventricular tachycardia. Primary analyses adjusted for demographic variables, blood pressure, diabetes, smoking, and clinical cardiovascular disease. Cardiac monitoring (median, 14 days) detected AF in 3%. Each SD (4.0%) lower (worse) LA booster pump strain was associated with 84% higher risk of monitor-detected AF (95% CI, 30%-162%), 39% higher premature atrial contraction frequency (95% CI, 27%-53%), and 19% higher supraventricular tachycardia frequency (95% CI, 10%-29%). Additional adjustment for NT-proBNP (N-terminal pro-B-type natriuretic peptide), LA volume index, tissue Doppler a' peak velocity, left ventricular ejection fraction, and global longitudinal strain had little impact on associations. Findings were similar for LA reservoir strain and null for LA conduit strain. Conclusions In a multiethnic community-based cohort, impaired LA strain was an important correlate of subclinical atrial arrhythmias, even after adjustment for conventional measures of LA structure and function.

Shamba Maisha: a pilot study assessing impacts of a micro-irrigation intervention on the health and economic wellbeing of HIV patients.

BACKGROUND: HIV/AIDS negatively impacts poverty alleviation and food security, which reciprocally hinder the rapid scale up and effectiveness of HIV care programs. Nyanza province has the highest HIV prevalence (15.3%), and is the third highest contributor (2.4 million people) to rural poverty in Kenya. Thus, we tested the feasibility of providing a micro-irrigation pump to HIV-positive farmers in order to evaluate its impact on health and economic advancement among HIV-positive patients and their families. METHODS: Thirty HIV-positive patients enrolled in the Family AIDS Care and Education Services (FACES) program in Kisumu, Kenya were provided a micro-financed loan to receive an irrigation pump and farming guidance from KickStart, the developer of the pump. Economic data, CD4 counts, household health and loan repayment history were collected 12 months after the pumps were distributed. RESULTS: Mean annual family income increased by $1,332 over baseline. CD4 counts did not change significantly. Though income increased, only three (10%) participants had paid off more than a quarter of the loan. CONCLUSIONS: We demonstrated the feasibility of an income-generating micro-irrigation intervention among HIV-positive patients and the collection of health and economic data. While family income improved significantly, loan repayment rates were low- likely complicated by the drought that occurred in Kenya during the intervention period.

Cuffless Blood Pressure Monitoring: Promises and Challenges.

Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring, at 15- to 30-minute intervals usually over 24 hours, provides sufficiently continuous readings that are superior to the office-based snapshot, but this system is not suitable for frequent repeated use. A true continuous BP measurement that could collect BP passively and frequently would require a cuffless method that could be worn by the patient, with the data stored electronically much the same way that heart rate and heart rhythm are already done routinely. Ideally, BP should be measured continuously and frequently during diverse activities during both daytime and nighttime in the same subject by means of novel devices. There is increasing excitement for newer methods to measure BP on the basis of sensors and algorithm development. As new devices are refined and their accuracy is improved, it will be possible to better assess masked hypertension, nocturnal hypertension, and the severity and variability of BP. In this review, we discuss the progression in the field, particularly in the last 5 years, ending with sensor-based approaches that incorporate machine learning algorithms to personalized medicine.

Prevalence of Positive COVID-19 Test Results Collected by Digital Self-report in the US and Germany.

This cohort study examines traditional surveillance and self-reported COVID-19 test result data collected from independent smartphone app­based studies in the US and Germany.