Vik Kheterpal

Type 2 diabetes (T2D) is a multifaceted disease associated with several factors, including diet, genetics, exercise, sleep and gut microbiome. Current diagnostic and monitoring methods based on episodic assays like glycated hemoglobin (HbA1c) fail to capture its full complexity. Here, in a prospective cohort of 1,137 participants in the United States, we analyzed multimodal data from 347 deeply phenotyped individuals (174 normoglycemic, 79 prediabetic and 94 T2D). We found significant differences in the distribution of glucose spike metrics among different diabetes states, with longer expected time for spike resolution and higher values of nocturnal hypoglycemia in T2D. We identified significant correlations between mean glucose level and gut microbiome diversity, and between expected time for spike resolution and resting heart rate. Our multimodal glycemic risk profiles, validated in 1,955 normoglycemic and 114 prediabetic individuals from an independent cohort, improved risk stratification by highlighting substantial variability among individuals with the same value of HbA1c. Such a multimodal approach provides a detailed phenotype that can potentially improve T2D prevention, diagnosis and treatment, and is more informative than HbA1c.

Background: Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings. Objective: The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting. Methods: Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices. Results: We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity. Conclusions: Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.

BACKGROUND Step counting is comparable among many research-grade and consumer-grade accelerometers in laboratory settings. OBJECTIVE The purpose of this study was to compare the agreement between Actical and Apple Watch step-counting in a community setting. METHODS Among Third Generation Framingham Heart Study participants (N=3486), we examined the agreement of step-counting between those who wore a consumer-grade accelerometer (Apple Watch Series 0) and a research-grade accelerometer (Actical) on the same days. Secondarily, we examined the agreement during each hour when both devices were worn to account for differences in wear time between devices. RESULTS We studied 523 participants (n=3223 person-days, mean age 51.7, SD 8.9 years; women: n=298, 57.0%). Between devices, we observed modest correlation (intraclass correlation [ICC] 0.56, 95% CI 0.54-0.59), poor continuous agreement (29.7%, n=957 of days having steps counts with ≤15% difference), a mean difference of 499 steps per day higher count by Actical, and wide limits of agreement, roughly ±9000 steps per day. However, devices showed stronger agreement in identifying who meets various steps per day thresholds (eg, at 8000 steps per day, kappa coefficient=0.49), for which devices were concordant for 74.8% (n=391) of participants. In secondary analyses, in the hours during which both devices were worn (n=456 participants, n=18,760 person-hours), the correlation was much stronger (ICC 0.86, 95% CI 0.85-0.86), but continuous agreement remained poor (27.3%, n=5115 of hours having step counts with ≤15% difference) between devices and was slightly worse for those with mobility limitations or obesity. CONCLUSIONS Our investigation suggests poor overall agreement between steps counted by the Actical device and those counted by the Apple Watch device, with stronger agreement in discriminating who meets certain step thresholds. The impact of these challenges may be minimized if accelerometers are used by individuals to determine whether they are meeting physical activity guidelines or tracking step counts. It is also possible that some of the limitations of these older accelerometers may be improved in newer devices.

Mobile health (mHealth) interventions may enhance positive health behaviors, but randomized trials evaluating their efficacy are uncommon. Our goal was to determine if a mHealth intervention augmented and extended benefits of center-based cardiac rehabilitation (CR) for physical activity levels at 6-months. We delivered a randomized clinical trial to low and moderate risk patients with a compatible smartphone enrolled in CR at two health systems. All participants received a compatible smartwatch and usual CR care. Intervention participants received a mHealth intervention that included a just-in-time-adaptive intervention (JITAI) as text messages. The primary outcome was change in remote 6-minute walk distance at 6-months stratified by device type. Here we report the results for 220 participants enrolled in the study (mean [SD]: age 59.6 [10.6] years; 67 [30.5%] women). For our primary outcome at 6 months, there is no significant difference in the change in 6 min walk distance across smartwatch types (Intervention versus control: +31.1 meters Apple Watch, −7.4 meters Fitbit; p = 0.28). Secondary outcomes show no difference in mean step counts between the first and final weeks of the study, but a change in 6 min walk distance at 3 months for Fitbit users. Amongst patients enrolled in center-based CR, a mHealth intervention did not improve 6-month outcomes but suggested differences at 3 months in some users.

Background: Few studies have examined the association between depressive symptom trajectories and physical activity collected by mobile health (mHealth) devices. Objective: We aimed to investigate if antecedent depressive symptom trajectories predict subsequent physical activity among participants in the electronic Framingham Heart Study (eFHS). Methods: We performed group-based multi-trajectory modeling to construct depressive symptom trajectory groups using both depressive symptoms (Center for Epidemiological Studies-Depression [CES-D] scores) and antidepressant medication use in eFHS participants who attended 3 Framingham Heart Study research exams over 14 years. At the third exam, eFHS participants were instructed to use a smartphone app for submitting physical activity index (PAI) surveys. In addition, they were provided with a study smartwatch to track their daily step counts. We performed linear mixed models to examine the association between depressive symptom trajectories and physical activity including app-based PAI and smartwatch-collected step counts over a 1-year follow-up adjusting for age, sex, wear hour, BMI, smoking status, and other health variables. Results: We identified 3 depressive symptom trajectory groups from 722 eFHS participants (mean age 53, SD 8.5 years; n=432, 60% women). The low symptom group (n=570; mean follow-up 287, SD 109 days) consisted of participants with consistently low CES-D scores, and a small proportion reported antidepressant use. The moderate symptom group (n=71; mean follow-up 280, SD 118 days) included participants with intermediate CES-D scores, who showed the highest and increasing likelihood of reporting antidepressant use across 3 exams. The high symptom group (n=81; mean follow-up 252, SD 116 days) comprised participants with the highest CES-D scores, and the proportion of antidepressant use fell between the other 2 groups. Compared to the low symptom group, the high symptom group had decreased PAI (mean difference –1.09, 95% CI –2.16 to –0.01) and the moderate symptom group walked fewer daily steps (823 fewer, 95% CI –1421 to –226) during the 1-year follow-up. Conclusions: Antecedent depressive symptoms or antidepressant medication use was associated with lower subsequent physical activity collected by mHealth devices in eFHS. Future investigation of interventions to improve mood including via mHealth technologies to help promote people’s daily physical activity is needed.

The performance of rapid antigen tests (Ag-RDTs) for screening asymptomatic and symptomatic persons for SARS-CoV-2 is not well established. To evaluate the performance of Ag-RDTs for detection of SARS-CoV-2 among symptomatic and asymptomatic participants. This prospective cohort study enrolled participants between October 2021 and January 2022. Participants completed Ag-RDTs and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 every 48 hours for 15 days. Participants were enrolled digitally throughout the mainland United States. They self-collected anterior nasal swabs for Ag-RDTs and RT-PCR testing. Nasal swabs for RT-PCR were shipped to a central laboratory, whereas Ag-RDTs were done at home. Of 7361 participants in the study, 5353 who were asymptomatic and negative for SARS-CoV-2 on study day 1 were eligible. In total, 154 participants had at least 1 positive RT-PCR result. The sensitivity of Ag-RDTs was measured on the basis of testing once (same-day), twice (after 48 hours), and thrice (after a total of 96 hours). The analysis was repeated for different days past index PCR positivity (DPIPPs) to approximate real-world scenarios where testing initiation may not always coincide with DPIPP 0. Results were stratified by symptom status. Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDTs twice 48 hours apart resulted in an aggregated sensitivity of 93.4% (95% CI, 90.4% to 95.9%) among symptomatic participants on DPIPPs 0 to 6. When singleton positive results were excluded, the aggregated sensitivity on DPIPPs 0 to 6 for 2-time serial testing among asymptomatic participants was lower at 62.7% (CI, 57.0% to 70.5%), but it improved to 79.0% (CI, 70.1% to 87.4%) with testing 3 times at 48-hour intervals. Participants tested every 48 hours; therefore, these data cannot support conclusions about serial testing intervals shorter than 48 hours. The performance of Ag-RDTs was optimized when asymptomatic participants tested 3 times at 48-hour intervals and when symptomatic participants tested 2 times separated by 48 hours. National Institutes of Health RADx Tech program.

Background The performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) in temporal relation to symptom onset or exposure is unknown, as is the impact of vaccination on this relationship. Objective To evaluate the performance of Ag-RDT compared with RT-PCR based on day after symptom onset or exposure in order to decide on ‘when to test’. Design, Setting, and Participants The Test Us at Home study was a longitudinal cohort study that enrolled participants over 2 years old across the United States between October 18, 2021 and February 4, 2022. All participants were asked to conduct Ag-RDT and RT-PCR testing every 48 hours over a 15-day period. Participants with one or more symptoms during the study period were included in the Day Post Symptom Onset (DPSO) analyses, while those who reported a COVID-19 exposure were included in the Day Post Exposure (DPE) analysis. Exposure Participants were asked to self-report any symptoms or known exposures to SARS-CoV-2 every 48-hours, immediately prior to conducting Ag-RDT and RT-PCR testing. The first day a participant reported one or more symptoms was termed DPSO 0, and the day of exposure was DPE 0. Vaccination status was self-reported. Main Outcome and Measures Results of Ag-RDT were self-reported (positive, negative, or invalid) and RT-PCR results were analyzed by a central laboratory. Percent positivity of SARS-CoV-2 and sensitivity of Ag-RDT and RT-PCR by DPSO and DPE were stratified by vaccination status and calculated with 95% confidence intervals. Results A total of 7,361 participants enrolled in the study. Among them, 2,086 (28.3%) and 546 (7.4%) participants were eligible for the DPSO and DPE analyses, respectively. Unvaccinated participants were nearly twice as likely to test positive for SARS-CoV-2 than vaccinated participants in event of symptoms (PCR+: 27.6% vs 10.1%) or exposure (PCR+: 43.8% vs. 22.2%). The highest proportion of vaccinated and unvaccinated individuals tested positive on DPSO 2 and DPE 5-8. Performance of RT-PCR and Ag-RDT did not differ by vaccination status. Ag-RDT detected 78.0% (95% Confidence Interval: 72.56-82.61) of PCR-confirmed infections by DPSO 4. For exposed participants, Ag-RDT detected 84.9% (95% CI: 75.0-91.4) of PCR-confirmed infections by day five post-exposure (DPE 5). Conclusions and Relevance Performance of Ag-RDT and RT-PCR was highest on DPSO 0-2 and DPE 5 and did not differ by vaccination status. These data suggests that serial testing remains integral to enhancing the performance of Ag-RDT.

Background Over-the-counter rapid antigen tests for SARS-CoV-2 with an Emergency Use Authorization (EUA) in the United States generally include a condition of authorization to evaluate the test’s performance in asymptomatic individuals when used serially. A goal of this study was to investigate the performance of SARS-CoV-2 antigen serial testing and generate data to support regulatory decisions. Objective To describe a novel study design to evaluate serial use of rapid antigen tests in detecting SARS-CoV-2 virus among asymptomatic individuals. Design Prospective cohort study using a decentralized approach. Eligible participants from across the U.S. could enroll and complete this study from their home environment through a study app. Participant enrollment was prioritized based on regional 7-day case rates, participants’ vaccination status, and sociodemographic characteristics prior to enrollment. Prioritization criteria were adjusted on a daily or weekly basis. Enrolled participants were mailed rapid antigen tests and molecular comparator collection kits and asked to test every 48 hours for 15 days. Three companies’ rapid antigen tests were used in the study; assignment of participant to a test was criteria-based and non-random, precluding head-to-head comparison between the tests. Participants Mainland United States residents over 2 years old with no reported COVID-19 symptoms in the 14 days prior to study enrollment. Main Measures Participant demographics, COVID-19 vaccination status, and geographic distribution were used to understand the impact of the site-less recruitment and enrollment strategy. Key Results A total of 7,361 participants enrolled in the study between October 18, 2021 and February 15, 2022. Throughout the study, 369 participants tested positive for SARS-CoV-2, including 167 who were asymptomatic and tested negative on SARS-CoV-2 molecular assays to start the study. This exceeded the initial enrollment goals of 60 positive participants. We enrolled participants from 44 of the 48 mainland U.S. states, and geographic distribution of participants shifted in accordance with the changing COVID-19 prevalence nationwide. Conclusions The novel, digital site-less approach employed in the ‘Test Us At Home’ study enabled rapid, efficient, and rigorous evaluation of rapid diagnostics for COVID-19, and can be adapted across research disciplines to optimize study enrollment and accessibility.

Background Performance of Rapid Antigen Tests for SARS-CoV-2 (Ag-RDT) varies over the course of an infection, and their performance is not well established among asymptomatic individuals. Objective Evaluate performance of Ag-RDT for detection of SARS-CoV-2 in relation to onset of infection for symptomatic and asymptomatic participants. Design, Setting, and Participants Prospective cohort study conducted from October 2021 to February 2022 among participants > 2 years-old from across the US who enrolled using a smartphone app. During each testing encounter, participants self-collected one nasal swab and performed Ag-RDT at home; at-least fifteen minutes later, a second nasal swab was self-collected and shipped for SARS-CoV-2 RT-PCR at a central lab. Both nasal swabs were collected 7 times at 48-hour intervals (over approximately 14 days) followed by an extra nasal swab collection with home Ag-RDT test 48-hours after their last PCR sample. Each participant was assigned to one of the three emergency use authorized (EUA) Ag-RDT tests used in this study. This analysis was limited to participants who were asymptomatic and tested negative by antigen and molecular test on their first day of study participation. Exposure SARS-CoV-2 positivity was determined by testing a single home-collected anterior nasal sample with three FDA EUA molecular tests, where 2 out 3 positive test results were needed to determine a SARS-CoV-2 positive result. Onset of infection was defined as day on which the molecular PCR comparator result was positive for the first time. Main Outcomes and Measures Sensitivity of Ag-RDT was measured based on testing once (same-day), twice (at 48-hours) and thrice (at 96 hours). Analysis was repeated for different Days Post Index PCR Positivity (DPIPP) and stratified based on symptom-status on a given DPIPP. Results A total of 7,361 participants enrolled in the study and 5,609 were eligible for this analysis. Among 154 eligible participants who tested positive for SARS-CoV-2 infection based on RT-PCR, 97 were asymptomatic and 57 had symptoms at onset of infection (DPIPP 0). Serial testing with Ag-RDT twice over 48-hours resulted in an aggregated sensitivity of 93.4% (95% CI: 89.1-96.1%) among symptomatic participants on DPIPP 0-6. Among the 97 people who were asymptomatic at the onset of infection, 19 were singleton RT-PCR positive, i.e., their positive test was preceded and followed by a negative RT-PCR test within 48-hours. Excluding these singleton positives, aggregated sensitivity on DPIPP 0-6 for two-time serial-testing among asymptomatic participants was lower 62.7% (54.7-70.0%) but improved to 79.0% (71.0-85.3%) with serial testing three times at 48-hour interval. Discussion Performance of Ag-RDT within first week of infection was optimized when asymptomatic participants tested three-times at 48-hour intervals and when symptomatic participants tested two-times separated by 48-hours. Question What is the performance of serial rapid antigen testing (Ag-RDT) in the first week of infection among symptomatic and asymptomatic SARS-CoV-2 infections? Findings Serial testing with Ag-RDT two-times separated by 48-hours resulted in detection of more than 90% of SARS-CoV-2 infections when symptomatic participants began testing within first week from onset of molecular positivity; participants who were asymptomatic when they began testing within the first-week of molecular positivity observed a sensitivity of 79.0% when they performed three rapid antigen-tests, 48 hours apart. Meaning To optimize detection of SARS-CoV-2 infection with home antigen tests, people suspected to be infected with SARS-CoV-2 virus should test twice at least 48-hours apart if they are symptomatic and three times at 48-hour intervals if they do not have symptoms (asymptomatic). Comparator positive composite definition of molecular positivity if majority of molecular assays were positive Days Past Index Comparator Positive (DPIPP) Number of calendar-days past the day when first Comparator positive was observed Onset of Infection DPIPP 0, when first Comparator positive was observed Symptomatic and Asymptomatic Cases Based on presence or absence of self-reported symptoms on the day of testing. Sensitivity was measured for Symptomatic and Asymptomatic cases on DPIPP 0-10 First week of Infection DPIPP 0 - 6

Long-term use of digital devices is critical for successful clinical or research use, but digital health studies are challenged by a rapid drop-off in participation. A nested e-cohort (eFHS) is embedded in the Framingham Heart Study and uses three system components: a new smartphone app, a digital blood pressure (BP) cuff, and a smartwatch. This study aims to identify factors associated with the use of individual eFHS system components over 1-year. Among 1948 eFHS enrollees, we examine participants who returned surveys within 90 days (n = 1918), and those who chose to use the smartwatch (n = 1243) and BP cuff (n = 1115). For each component, we investigate the same set of candidate predictors for usage and use generalized linear mixed models to select predictors (P < 0.1, P value from Z test statistic), adjusting for age, sex, and time (app use: 3-month period, device use: weekly). A multivariable model with the predictors selected from initial testing is used to identify factors associated with use of components (P < 0.05, P value from Z test statistic) adjusting for age, sex, and time. In multivariable models, older age is associated with higher use of all system components. Female sex and higher education levels are associated with higher completion of app-based surveys whereas higher scores for depressive symptoms, and lower than excellent self-rated health are associated with lower use of the smartwatch over the 12-month follow-up. Our findings show that sociodemographic and health related factors are significantly associated with long-term use of digital devices. Future research is needed to test interventional strategies focusing on these factors to evaluate improvement in long-term engagement.