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BackgroundIncreasing physical activity (PA) levels in adolescents aged 12 to 18 years is associated with prevention of unhealthy weight gain and improvement in cardiovascular fitness. The widespread availability of mobile health (mHealth) and wearable devices offers self-monitoring and motivational features for increasing PA levels and improving adherence to exercise programs. ObjectiveThe aim of this scoping review was to identify the efficacy or effectiveness of mHealth intervention strategies for facilitating PA among adolescents aged 12 to 18 years. MethodsWe conducted a systematic search for peer-reviewed studies published between 2008 and 2018 in the following electronic databases: PubMed, Google Scholar, PsychINFO, or SportDiscus. The search terms used included mHealth or “mobile health” or apps, “physical activity” or exercise, children or adolescents or teens or “young adults” or kids, and efficacy or effectiveness. Articles published outside of the date range (July 2008 to October 2018) and non-English articles were removed before abstract review. Three reviewers assessed all abstracts against the inclusion and exclusion criteria. Any uncertainties or differences in opinion were discussed as a group. The inclusion criteria were that the studies should (1) have an mHealth component, (2) target participants aged between 12 and 18 years, (3) have results on efficacy or effectiveness, and (4) assess PA-related outcomes. Reviews, abstracts only, protocols without results, and short message service text messaging–only interventions were excluded. We also extracted potentially relevant papers from reviews. At least 2 reviewers examined all full articles for fit with the criteria and extracted data for analysis. Data extracted from selected studies included study population, study type, components of PA intervention, and PA outcome results. ResultsOverall, 126 articles were initially identified. Reviewers pulled 18 additional articles from excluded review papers. Only 18 articles were passed onto full review, and 16 were kept for analysis. The included studies differed in the sizes of the study populations (11-607 participants), locations of the study sites (7 countries), study setting, and study design. Overall, 5 mHealth intervention categories were identified: website, website+wearable, app, wearable+app, and website+wearable+app. The most common measures reported were subjective weekly PA (4/13) and objective daily moderate-to-vigorous PA (5/13) of the 19 different PA outcomes assessed. Furthermore, 5 of 13 studies with a control or comparison group showed a significant improvement in PA outcomes between the intervention group and the control or comparison group. Of those 5 studies, 3 permitted isolation of mHealth intervention components in the analysis. ConclusionsPA outcomes for adolescents improved over time through mHealth intervention use; however, the lack of consistency in chosen PA outcome measures, paucity of significant outcomes via between-group analyses, and the various study designs that prevent separating the effects of intervention components calls into question their true effect.

BackgroundOnly one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm. ObjectiveWe aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups. MethodsWe recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant. ResultsAmong the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years. ConclusionsOur analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program.

Background: Racial and ethnic minoritized people with dementia (PWD) are at high risk of disenrollment from hospice, yet little is known about the relationship between hospice quality and racial disparities in disenrollment among PWD. Objective: To assess the association between race and disenrollment between and within hospice quality categories in PWD. Design/Setting/Subjects: Retrospective cohort study of 100% Medicare beneficiaries 65+ enrolled in hospice with a principal diagnosis of dementia, July 2012-December 2017. Race and ethnicity (White/Black/Hispanic/Asian and Pacific Islander [AAPI]) was assessed with the Research Triangle Institute (RTI) algorithm. Hospice quality was assessed with the publicly-available Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey item on overall hospice rating, including a category for hospices exempt from public reporting (unrated). Results: The sample included 673,102 PWD (mean age 86, 66% female, 85% White, 7.3% Black, 6.3% Hispanic, 1.6% AAPI) enrolled in 4371 hospices nationwide. Likelihood of disenrollment was higher in hospices in the lowest quartile of quality ratings (vs. highest quartile) for both White (adjusted odds ratio [AOR] 1.12 [95% confidence interval 1.06-1.19]) and minoritized PWD (AOR range 1.2-1.3) and was substantially higher in unrated hospices (AOR range 1.8-2.0). Within both low- and high-quality hospices, minoritized PWD were more likely to be disenrolled compared with White PWD (AOR range 1.18-1.45). Conclusions: Hospice quality predicts disenrollment, but does not fully explain disparities in disenrollment for minoritized PWD. Efforts to improve racial equity in hospice should focus both on increasing equity in access to high-quality hospices and improving care for racial minoritized PWD in all hospices.

The integration of technologies such as continuous glucose monitors, insulin pumps, and smart pens into diabetes management has the potential to support the transformation of health care services that provide a higher quality of diabetes care, lower costs and administrative burdens, and greater empowerment for people with diabetes and their caregivers. Among people with diabetes, older adults are a distinct subpopulation in terms of their clinical heterogeneity, care priorities, and technology integration. The scientific evidence and clinical experience with these technologies among older adults are growing but are still modest. In this review, we describe the current knowledge regarding the impact of technology in older adults with diabetes, identify major barriers to the use of existing and emerging technologies, describe areas of care that could be optimized by technology, and identify areas for future research to fulfill the potential promise of evidence-based technology integrated into care for this important population.

BACKGROUND: Older smokers account for the greatest tobacco-related morbidity and mortality in the USA, while quitting smoking remains the single most effective preventive health intervention for reducing the risk of smoking-related illness. Yet, knowledge about patterns of smoking and smoking cessation in older adults is lacking. OBJECTIVE: Assess trends in prevalence of cigarette smoking between 1998 and 2018 and identify patterns and predictors of smoking cessation in US older adults. DESIGN: Retrospective cohort study PARTICIPANTS: Individuals aged 55+ enrolled in the nationally representative Health and Retirement Study, 1998-2018 MAIN MEASURES: Current smoking was assessed with the question: Do you smoke cigarettes now? Quitting smoking was defined as having at least two consecutive waves (between 2 and 4 years) in which participants who were current smokers in 1998 reported they were not currently smoking in subsequent waves. KEY RESULTS: Age-adjusted smoking prevalence decreased from 15.9% in 1998 (95% confidence interval (CI) 15.2, 16.7) to 11.2% in 2018 (95% CI 10.4, 12.1). Among 2187 current smokers in 1998 (mean age 64, 56% female), 56% of those living to age 90 had a sustained period of smoking cessation. Smoking less than 10 cigarettes/day was strongly associated with an increased likelihood of quitting smoking (subdistribution hazard ratio 2.3; 95% CI 1.9, 2.8), compared to those who smoked more than 20 cigarettes/day. CONCLUSIONS: Smoking prevalence among older persons has declined and substantial numbers of older smokers succeed in quitting smoking for a sustained period. These findings highlight the need for continued aggressive efforts at tobacco cessation among older persons.

BackgroundHospitalizations among people with dementia (PWD) may precipitate behavioral changes, leading to the psychotropic medication use despite adverse outcomes and limited efficacy. We sought to determine the incidence of new psychotropic medication use among community-dwelling PWD after hospital discharge and, among new users, the proportion with prolonged use.MethodsThis was a retrospective cohort study using a 20% random sample of Medicare claims in 2017, including hospitalized PWD with traditional and Part D Medicare who were 68 years or older. The primary outcome was incident prescribing at discharge of psychotropics including antipsychotics, sedative-hypnotics, antiepileptics, and antidepressants. This was defined as new prescription fills (i.e., from classes not used in 180 days preadmission) within 7 days of hospital or skilled nursing facility discharge. Prolonged use was defined as the proportion of new users who continued to fill newly prescribed medications beyond 90 days of discharge.ResultsThe cohort included 117,022 hospitalized PWD with a mean age of 81 years; 63% were female. Preadmission, 63% were using at least 1 psychotropic medication; 10% were using medications from ≥3 psychotropic classes. These included antidepressants (44% preadmission), antiepileptics (29%), sedative-hypnotics (21%), and antipsychotics (11%). The proportion of PWD discharged from the hospital with new psychotropics ranged from 1.9% (antipsychotics) to 2.9% (antiepileptics); 6.6% had at least one new class started. Among new users, prolonged use ranged from 36% (sedative-hypnotics) to 63% (antidepressants); across drug classes, prolonged use occurred in 51%. Predictors of newly initiated psychotropics included length of stay (≥median vs. ConclusionsHospitalized PWD have a high prevalence of preadmission psychotropic medication use; against this baseline, discharge from the hospital with new psychotropics is relatively uncommon. Nevertheless, prolonged use of newly initiated psychotropics occurs in a substantial proportion of this population.

OBJECTIVE: To estimate rates of severe hypoglycemia and falls among older adults with diabetes and evaluate their association. RESEARCH DESIGN AND METHODS: Survey in an age-stratified, random sample adults with diabetes age 65-100 years; respondents were asked about severe hypoglycemia (requiring assistance) and falls in the past 12 months. Prevalence ratios (adjusted for age, sex, race/ethnicity) estimated the increased risk of falls associated with severe hypoglycemia. RESULTS: Among 2,158 survey respondents, 79 (3.7%) reported severe hypoglycemia, of whom 68 (86.1%) had no ED visit or hospitalization for hypoglycemia. Falls were reported by 847 (39.2%), of whom 745 (88.0%) had no fall documented in outpatient or inpatient records. Severe hypoglycemia was associated with a 70% greater prevalence of falls (adjusted prevalence ratio = 1.7 (95% CI, 1.3-2.2)). CONCLUSION: While clinical documentation of events likely reflects severity or care-seeking behavior, severe hypoglycemia and falls are common, under-reported life-threatening events.

There is a need to investigate motivational constructs for students traditionally underrepresented in educational research. In this study, we measured academic self-handicapping behavior, achievement goal orientations, and achievement in math and literacy in 327 urban, middle school students. Moderated mediation regression analyses were conducted to examine whether the relation of prior achievement with self-handicapping behavior was explained by achievement goals and whether these relations depended on underrepresented minority student status (URM). It was found that URM status moderated the relation of achievement with self-handicapping and that performance goals explained these relations in literacy, but not in math. Educational systems might consider these findings as a starting point for interventions to decrease self-handicapping behavior to support URM students' academic motivation.

BackgroundElectronic health record (EHR) prediction models may be easier to use in busy clinical settings since EHR data can be auto-populated into models. This study assessed whether adding functional status and/or Medicare claims data (which are often not available in EHRs) improves the accuracy of a previously developed Veterans Affairs (VA) EHR-based mortality index.MethodsThis was a retrospective cohort study of veterans aged 75 years and older enrolled in VA primary care clinics followed from January 2014 to April 2020 (n = 62,014). We randomly split participants into development (n = 49,612) and validation (n = 12,402) cohorts. The primary outcome was all-cause mortality. We performed logistic regression with backward stepwise selection to develop a 100-predictor base model using 854 EHR candidate variables, including demographics, laboratory values, medications, healthcare utilization, diagnosis codes, and vitals. We incorporated functional measures in a base + function model by adding activities of daily living (range 0-5) and instrumental activities of daily living (range 0-7) scores. Medicare data, including healthcare utilization (e.g., emergency department visits, hospitalizations) and diagnosis codes, were incorporated in a base + Medicare model. A base + function + Medicare model included all data elements. We assessed model performance with the c-statistic, reclassification metrics, fraction of new information provided, and calibration plots.ResultsIn the overall cohort, mean age was 82.6 years and 98.6% were male. At the end of follow-up, 30,263 participants (48.8%) had died. The base model c-statistic was 0.809 (95% CI 0.805-0.812) in the development cohort and 0.804 (95% CI 0.796-0.812) in the validation cohort. Validation cohort c-statistics for the base + function, base + Medicare, and base + function + Medicare models were 0.809 (95% CI 0.801-0.816), 0.811 (95% CI 0.803-0.818), and 0.814 (95% CI 0.807-0.822), respectively. Adding functional status and Medicare data resulted in similarly small improvements among other model performance measures. All models showed excellent calibration.ConclusionsIncorporation of functional status and Medicare data into a VA EHR-based mortality index led to small but likely clinically insignificant improvements in model performance.

BACKGROUND: Guidelines recommend nursing home (NH) residents with cognitive impairment receive less intensive glycemic treatment and less frequent fingerstick monitoring. Our objective was to determine whether current practice aligns with guideline recommendations by examining fingerstick frequency in Veterans Affairs (VA) NH residents with diabetes across cognitive impairment levels. METHODS: We identified VA NH residents with diabetes aged ≥65 residing in VA NHs for >30 days between 2016 and 2019. Residents were grouped by cognitive impairment status based on the Cognitive Function Scale: cognitively intact, mild impairment, moderate impairment, and severe impairment. We also categorized residents into mutually exclusive glucose-lowering medication (GLM) categories: (1) no GLMs, (2) metformin only, (3) sulfonylureas/other GLMs (+/- metformin but no insulin), (4) long-acting insulin (+/- oral/other GLMs but no short-acting insulin), and (5) any short-acting insulin. Our outcome was mean daily fingersticks on day 31 of NH admission. RESULTS: Among 13,637 NH residents, mean age was 75 years and mean hemoglobin A1c was 7.0%. The percentage of NH residents on short-acting insulin varied by cognitive status from 22.7% in residents with severe cognitive impairment to 33.9% in residents who were cognitively intact. Mean daily fingersticks overall on day 31 was 1.50 (standard deviation = 1.73). There was a greater range in mean fingersticks across GLM categories compared to cognitive status. Fingersticks ranged widely across GLM categories from 0.39 per day (no GLMs) to 3.08 (short-acting insulin), while fingersticks ranged slightly across levels of cognitive impairment from 1.11 (severe cognitive impairment) to 1.59 (cognitively intact). CONCLUSION: NH residents receive frequent fingersticks regardless of level of cognitive impairment, suggesting that cognitive status is a minor consideration in monitoring decisions. Future studies should determine whether decreasing fingersticks in NH residents with moderate/severe cognitive impairment can reduce burdens without compromising safety.

BACKGROUND: Many clinical and financial decisions for older adults depend on the future risk of disability and mortality. Prognostic tools for long-term disability risk in a general population are lacking. We aimed to create a comprehensive prognostic tool that predicts the risk of mortality, of activities of daily living (ADL) disability, and walking disability simultaneously using the same set of variables. METHODS: We conducted a longitudinal analysis of the nationally-representative Health and Retirement Study (HRS). We included community-dwelling adults aged ≥70 years who completed a core interview in the 2000 wave of HRS, with follow-up through 2018. We evaluated 40 predictors encompassing demographics, diseases, physical functioning, and instrumental ADLs. We applied novel methods to optimize three models simultaneously while prioritizing variables that take less time to ascertain during backward stepwise elimination. The death prediction model used Cox regression and both the models for walking disability and for ADL disability used Fine and Gray competing-risk regression. We examined calibration plots and generated optimism-corrected statistics of discrimination using bootstrapping. To simulate unavailable patient data, we also evaluated models excluding one or two variables from the final model. RESULTS: In 6646 HRS participants, 2662 developed walking disability, 3570 developed ADL disability, and 5689 died during a median follow-up of 9.5 years. The final prognostic tool had 16 variables. The optimism-corrected integrated area under the curve (iAUC) was 0.799 for mortality, 0.685 for walking disability, and 0.703 for ADL disability. At each percentile of predicted mortality risk, there was a substantial spread in the predicted risks of walking disability and ADL disability. Discrimination and calibration remained good even when missing one or two predictors from the model. This model is now available on ePrognosis (https://eprognosis.ucsf.edu/alexlee.php) CONCLUSIONS: Given the variability in disability risk for people with similar mortality risks, using individualized risks of disabilities may inform clinical and financial decisions for older adults.

BACKGROUNDProlonged symptoms after SARS-CoV-2 infection are well documented. However, which factors influence development of long-term symptoms, how symptoms vary across ethnic groups, and whether long-term symptoms correlate with biomarkers are points that remain elusive.METHODSAdult SARS-CoV-2 reverse transcription PCR-positive (RT-PCR-positive) patients were recruited at Stanford from March 2020 to February 2021. Study participants were seen for in-person visits at diagnosis and every 1-3 months for up to 1 year after diagnosis; they completed symptom surveys and underwent blood draws and nasal swab collections at each visit.RESULTSOur cohort (n = 617) ranged from asymptomatic to critical COVID-19 infections. In total, 40% of participants reported at least 1 symptom associated with COVID-19 six months after diagnosis. Median time from diagnosis to first resolution of all symptoms was 44 days; median time from diagnosis to sustained symptom resolution with no recurring symptoms for 1 month or longer was 214 days. Anti-nucleocapsid IgG level in the first week after positive RT-PCR test and history of lung disease were associated with time to sustained symptom resolution. COVID-19 disease severity, ethnicity, age, sex, and remdesivir use did not affect time to sustained symptom resolution.CONCLUSIONWe found that all disease severities had a similar risk of developing post-COVID-19 syndrome in an ethnically diverse population. Comorbid lung disease and lower levels of initial IgG response to SARS-CoV-2 nucleocapsid antigen were associated with longer symptom duration.TRIAL REGISTRATIONClinicalTrials.gov, NCT04373148.FUNDINGNIH UL1TR003142 CTSA grant, NIH U54CA260517 grant, NIEHS R21 ES03304901, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative, Sunshine Foundation, Crown Foundation, and Parker Foundation.

Mastery goal structures, which communicate value for developing deeper understanding, are an important classroom support for student motivation and engagement, especially in the context of science learning aligned with the Next Generation Science Standards. Prior research has identified key dimensions of goal structures, but a more nuanced examination of the variability of teacher-enacted and student-perceived goal structures within and across classrooms is needed. Using a concurrent mixed-methods approach, we developed case studies of how three 7th-grade science teachers enacted different goal structures while teaching the same chemistry unit and how their students perceived these goal structures. Student perceptions were largely consistent with our observational analysis and suggested that a positive social climate and autonomy support are important elements of mastery goal structure. However, balancing socio-emotional support with sufficient academic rigor may be especially important for students with high levels of mastery goal orientation and self-efficacy in science. Implications for research include the need for further research linking classroom stimuli to variability in perceived goal structure, especially across students with different motivational characteristics. Implications for practice include strategies for science teachers to promote perceptions of a mastery goal focus in students, particularly through feedback and recognition practices.

In this article, we provide guidance for educational psychologists who want to advance equity-focused school policies and practices through engaged scholarship. We conceptualize a philosophy of engagement and describe how it can facilitate psychologists' efforts to address the social conditions of people from historically marginalized communities. Using our own philosophy of engagement as a case in point (which we refer to as communally engaged educational psychology), we share stories that showcase opportunities for roles that scholars can play in supporting education decision-makers and community members. Specifically, we illustrate that communal engagement involves building a tribe; identifying pockets of affirmation; establishing communal incubators; and leveraging existing networks to disseminate stories emerging from partnership activities. We then offer a roadmap for putting communal engagement into action by remixing Anderman's (2011) ten challenges for educational psychologists. We conclude with a discussion of the tensions and difficulties scholars may need to navigate when enacting a philosophy of engagement.

BackgroundGuidelines recommend breast and colorectal cancer screening for older adults with a life expectancy >10 years. Most mortality indexes require clinician data entry, presenting a barrier for routine use in care. Electronic health records (EHR) are a rich clinical data source that could be used to create individualized life expectancy predictions to identify patients for cancer screening without data entry.ObjectiveTo develop and internally validate a life expectancy calculator from structured EHR data.DesignRetrospective cohort study using national Veteran's Affairs (VA) EHR databases.PatientsVeterans aged 50+ with a primary care visit during 2005.Main measuresWe assessed demographics, diseases, medications, laboratory results, healthcare utilization, and vital signs 1 year prior to the index visit. Mortality follow-up was complete through 2017. Using the development cohort (80% sample), we used LASSO Cox regression to select ~100 predictors from 913 EHR data elements. In the validation cohort (remaining 20% sample), we calculated the integrated area under the curve (iAUC) and evaluated calibration.Key resultsIn 3,705,122 patients, the mean age was 68 years and the majority were male (97%) and white (85%); nearly half (49%) died. The life expectancy calculator included 93 predictors; age and gender most strongly contributed to discrimination; diseases also contributed significantly while vital signs were negligible. The iAUC was 0.816 (95% confidence interval, 0.815, 0.817) with good calibration.ConclusionsWe developed a life expectancy calculator using VA EHR data with excellent discrimination and calibration. Automated life expectancy prediction using EHR data may improve guideline-concordant breast and colorectal cancer screening by identifying patients with a life expectancy >10 years.

BackgroundThe determinants of coronavirus disease 2019 (COVID-19) disease severity and extrapulmonary complications (EPCs) are poorly understood. We characterized relationships between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and disease severity, clinical deterioration, and specific EPCs.MethodsWe used quantitative and digital polymerase chain reaction (qPCR and dPCR) to quantify SARS-CoV-2 RNA from plasma in 191 patients presenting to the emergency department with COVID-19. We recorded patient symptoms, laboratory markers, and clinical outcomes, with a focus on oxygen requirements over time. We collected longitudinal plasma samples from a subset of patients. We characterized the role of RNAemia in predicting clinical severity and EPCs using elastic net regression.ResultsOf SARS-CoV-2-positive patients, 23.0% (44 of 191) had viral RNA detected in plasma by dPCR, compared with 1.4% (2 of 147) by qPCR. Most patients with serial measurements had undetectable RNAemia within 10 days of symptom onset, reached maximum clinical severity within 16 days, and symptom resolution within 33 days. Initially RNAemic patients were more likely to manifest severe disease (odds ratio, 6.72 [95% confidence interval, 2.45-19.79]), worsening of disease severity (2.43 [1.07-5.38]), and EPCs (2.81 [1.26-6.36]). RNA loads were correlated with maximum severity (r = 0.47 [95% confidence interval, .20-.67]).ConclusionsdPCR is more sensitive than qPCR for the detection of SARS-CoV-2 RNAemia, which is a robust predictor of eventual COVID-19 severity and oxygen requirements, as well as EPCs. Because many COVID-19 therapies are initiated on the basis of oxygen requirements, RNAemia on presentation might serve to direct early initiation of appropriate therapies for the patients most likely to deteriorate.

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

Rationale & objectiveThe associations of the glomerular markers of kidney disease, estimated glomerular filtration rate (eGFR) and albuminuria, with frailty and cognition are well established. However, the relationship of kidney tubule injury and dysfunction with frailty and cognition is unknown.Study designObservational cross-sectional study.Setting & participants2,253 participants with eGFR

Background/objectivesChronic kidney disease (CKD) is associated with frailty. Fibroblast growth factor 23 (FGF23) is elevated in CKD and associated with frailty among non-CKD older adults and individuals with human immunodeficiency virus. Whether FGF23 is associated with frailty and falls in CKD is unknown.DesignCross-sectional and longitudinal observational study.SettingSystolic Blood Pressure Intervention Trial (SPRINT), a randomized trial evaluating standard (systolic blood pressure [SBP]

Background/objectiveGuidelines recommend less intensive glycemic treatment and less frequent glucose monitoring for nursing home (NH) residents. However, little is known about the frequency of fingerstick (FS) glucose monitoring in this population. Our objective was to examine the frequency of FS glucose monitoring in Veterans Affairs (VA) NH residents with diabetes mellitus, type II (T2DM).Design and settingNational retrospective cohort study in 140 VA NHs.ParticipantsNH residents with T2DM and older than 65 years admitted to VA NHs between 2013 and 2015 following discharge from a VA hospital.MeasurementsNH residents were classified into five groups based on their highest hypoglycemia risk glucose-lowering medication (GLM) each day: no GLMs; metformin only; sulfonylureas; long-acting insulin; and any short-acting insulin. Our outcome was a daily count of FS measurements.ResultsAmong 17,474 VA NH residents, mean age was 76 (standard deviation (SD) = 8) years and mean hemoglobin A1c was 7.6% (SD = 1.5%). On day 1 after NH admission, 49% of NH residents were on short-acting insulin, decreasing slightly to 43% at day 90. Overall, NH residents had an average of 1.9 (95% confidence interval (CI) = 1.8-1.9) FS measurements on NH day 1, decreasing to 1.4 (95% CI = 1.3-1.4) by day 90. NH residents on short-acting insulin had the most frequent FS measurements, with 3.0 measurements (95% CI = 2.9-3.0) on day 1, decreasing to 2.6 measurements (95% CI = 2.5-2.7) by day 90. Less frequent FS measurements were seen for NH residents receiving long-acting insulin (2.1 (95% CI = 2.0-2.2) on day 1) and sulfonylureas (1.7 (95% CI = 1.5-1.8) on day 1). Even NH residents on metformin monotherapy had 1.1 (95% CI = 1.1-1.2) measurements on day 1, decreasing to 0.5 (95% CI = 0.4-0.6) measurements on day 90.ConclusionAlthough guidelines recommend less frequent glucose monitoring for NH residents, we found that many VA NH residents receive frequent FS monitoring. Given the uncertain benefits and potential for substantial patient burdens and harms, our results suggest decreasing FS monitoring may be warranted for many low hypoglycemia risk NH residents.

OBJECTIVE: To assess the association of severe hypoglycemia measured at baseline with cardiovascular disease (CVD) among community-dwelling older individuals with diabetes, a group particularly susceptible to hypoglycemia. RESEARCH DESIGN AND METHODS: We included older adults with diabetes from the Atherosclerosis Risk in Communities (ARIC) study who attended visit 5 (2011-2013, baseline). Severe hypoglycemia at baseline was defined with use of first position ICD-9 codes from hospitalizations, emergency department visits, and ambulance calls. We examined cross-sectional associations of severe hypoglycemia with echocardiographic indices of cardiac structure-function. We prospectively evaluated the risks of incident or recurrent CVD (coronary heart disease, stroke, or heart failure) and all-cause mortality, from baseline to 31 December 2018, using negative binomial and Cox regression models. RESULTS: Among 2,193 participants (mean [SD] age 76 [5] years, 57% female, 32% Blacks), 79 had a history of severe hypoglycemia at baseline. Severe hypoglycemia was associated with a lower left ventricular (LV) ejection fraction (adjusted β-coefficient -3.66% [95% CI -5.54, -1.78]), higher LV end diastolic volume (14.80 mL [95% CI 8.77, 20.84]), higher E-to-A ratio (0.11 [95% CI 0.03, 0.18]), and higher septal E/e (2.48 [95% CI 1.13, 3.82]). In adjusted models, severe hypoglycemia was associated with incident or recurrent CVD (incidence rate ratio 2.19 (95% CI 1.24, 3.88]) and all-cause mortality (hazard ratio 1.71 [95% CI 1.10, 2.67]) among those without prevalent CVD. CONCLUSIONS: Our findings suggest that a history of severe hypoglycemia is associated with alterations in cardiac function and is an important marker of future cardiovascular risk in older adults.

ObjectiveThis randomized trial experimentally manipulated social status to assess effects on acute eating behavior and 24-hour energy balance.MethodsParticipants (n = 133 Hispanics; age 15-21 years; 60.2% females) were randomized to low social status ("LOW") or high social status ("HIGH") conditions in a rigged game of Monopoly (Hasbro, Inc.). Acute energy intake in a lunchtime meal was measured by food scales. Twenty-four-hour energy balance was assessed via summation of resting metabolic rate (metabolic cart), physical activity energy expenditure (accelerometer), thermic effect of food, and subtraction of twenty-four-hour energy intake (food diary).ResultsIn the total sample, no significant differences were observed by study condition at lunchtime. LOW females consumed a greater percent of lunchtime daily energy needs (37.5%) relative to HIGH females (34.3%); however, this difference was not statistically significant (P = 0.291). In males, however, LOW consumed significantly less (36.5%) of their daily energy needs relative to HIGH males (45.8%; P = 0.001). For 24-hour energy balance, sex differences were nearly significant (P = 0.057; LOW females: surplus +200 kcal; HIGH males: surplus +445 kcal). Food-insecure individuals consumed a nearly significant greater lunchtime percent daily energy than those with food security (40.7% vs. 36.3%; P = 0.0797).ConclusionsThe data demonstrate differential acute and 24-hour eating behavior responses between Hispanic male and female adolescents in experimentally manipulated conditions of low social status.

IntroductionFor older adults with type 2 diabetes, the American Diabetes Association (ADA) Framework uses comorbidities and functional status to categorize patients by estimated life expectancy to guide individualization of glycemic treatment. We evaluated whether modifying the ADA Framework by removing three comorbidities and incorporating age could improve life expectancy stratification and better identify patients likely to benefit from intensive treatment.Research design and methodsWe examined 3166 Health and Retirement Study participants aged ≥65 with diabetes from 1998 to 2004, using a prospective cohort design with mortality follow-up through 2016. We classified participants into one of three ADA Framework categories: Healthy, Intermediate Health, and Poor Health. We created modified categories by excluding comorbidities weakly associated with mortality (hypertension, arthritis, and incontinence). Using Gompertz regression, we estimated life expectancy across age strata for both original and modified ADA Framework categories.ResultsThe original ADA Framework classified 34% as Healthy (likely to benefit from intensive treatment), 50% as Intermediate Health, and 16% as Poor Health (unlikely to benefit from intensive treatment). Our comorbidity modification reclassified 20% of participants from Intermediate Health to Healthy. Using the modified ADA Framework, median life expectancy of the Healthy varied greatly by age (aged 65-69: 16.3 years; aged ≥80: 7.6 years), indicating differing likelihood of benefit. Additionally, age ≥80 made extended life expectancy unlikely (median life expectancy for Healthy 7.6 years, Intermediate Health 5.9 years, Poor Health 2.5 years), suggesting adults ≥80 are unlikely to benefit from intensive treatment.ConclusionsModifying the ADA Framework by incorporating age and focusing on comorbidities associated with mortality improved life expectancy stratification, resulting in different treatment recommendations for many older adults.

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

AimsBiomarkers of kidney tubule injury, inflammation and fibrosis have been studied extensively and established as risk markers of adverse kidney and cardiovascular disease (CVD) outcomes. However, associations of markers of kidney tubular function with adverse clinical events have not been well studied, especially in persons with chronic kidney disease (CKD).Methods and resultsUsing a sample of 2377 persons with CKD at the baseline Systolic Blood Pressure Intervention Trial (SPRINT) visit, we evaluated the association of three urine tubular function markers, alpha-1 microglobulin (α1m), beta-2 microglobulin (β2m), and uromodulin, with a composite CVD endpoint (myocardial infarction, acute coronary syndrome, stroke, acute decompensated heart failure, or death from cardiovascular causes) and mortality using Cox proportional hazards regression, adjusted for baseline estimated glomerular filtration rate (eGFR), albuminuria, and CVD risk factors. In unadjusted analysis, over a median follow-up of 3.8 years, α1m and β2m had positive associations with composite CVD events and mortality, whereas uromodulin had an inverse association with risk for both outcomes. In multivariable analysis including eGFR and albuminuria, a two-fold higher baseline concentration of α1m was associated with higher risk of CVD [hazard ratio (HR) 1.25; 95% confidence interval (CI): 1.10-1.45] and mortality (HR 1.25; 95% CI: 1.10-1.46), whereas β2m had no association with either outcome. A two-fold higher uromodulin concentration was associated with lower CVD risk (HR 0.79; 95% CI: 0.68-0.90) but not mortality (HR 0.86; 95% CI: 0.73-1.01) after adjusting for similar confounders.ConclusionAmong non-diabetic persons with CKD, biomarkers of tubular function are associated with CVD events and mortality independent of glomerular function and albuminuria.

Chronic kidney disease is a strong risk factor for cardiovascular disease (CVD), but clinical kidney measures (estimated glomerular filtration rate and albuminuria) do not fully reflect the multiple aspects of kidney tubules influencing cardiovascular health. Applied methods are needed to integrate numerous tubule biomarkers into useful prognostic scores. In SPRINT (Systolic Blood Pressure Intervention Trial) participants with chronic kidney disease at baseline (estimated glomerular filtration ratecr&cys