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1. Cognitive Symptoms of Post–COVID-19 Condition and Daily Functioning

   https://doi.org/10.1001/jamanetworkopen.2023.56098  

   Jaywant, Abhishek ; Gunning, Faith M. ; Oberlin, Lauren E. ; Santillana, Mauricio ; Ognyanova, Katherine ; Druckman, James N. ; Baum, Matthew A. ; Lazer, David ; Perlis, Roy H. ( February 2024 , JAMA Network Open)

   The frequent occurrence of cognitive symptoms in post–COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations.

   To investigate the prevalence of self-reported cognitive symptoms in post–COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post–COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status.

   Two waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023. Participants included survey respondents aged 18 years and older.

   Post–COVID-19 condition, defined as self-report of symptoms attributed to COVID-19 beyond 2 months after the initial month of illness.

   Seven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9.

   The 14 767 individuals reporting test-confirmed COVID-19 illness at least 2 months before the survey had a mean (SD) age of 44.6 (16.3) years; 568 (3.8%) were Asian, 1484 (10.0%) were Black, 1408 (9.5%) were Hispanic, and 10 811 (73.2%) were White. A total of 10 037 respondents (68.0%) were women and 4730 (32.0%) were men. Of the 1683 individuals reporting post–COVID-19 condition, 955 (56.7%) reported at least 1 cognitive symptom experienced daily, compared with 3552 of 13 084 (27.1%) of those who did not report post–COVID-19 condition. More daily cognitive symptoms were associated with a greater likelihood of reporting at least moderate interference with functioning (unadjusted odds ratio [OR], 1.31 [95% CI, 1.25-1.36]; adjusted [AOR], 1.30 [95% CI, 1.25-1.36]), lesser likelihood of full-time employment (unadjusted OR, 0.95 [95% CI, 0.91-0.99]; AOR, 0.92 [95% CI, 0.88-0.96]) and greater severity of depressive symptoms (unadjusted coefficient, 1.40 [95% CI, 1.29-1.51]; adjusted coefficient 1.27 [95% CI, 1.17-1.38). After including depressive symptoms in regression models, associations were also found between cognitive symptoms and at least moderate interference with everyday functioning (AOR, 1.27 [95% CI, 1.21-1.33]) and between cognitive symptoms and lower odds of full-time employment (AOR, 0.92 [95% CI, 0.88-0.97]).

   The findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post–COVID-19 condition and associated with greater self-reported functional impairment, lesser likelihood of full-time employment, and greater depressive symptom severity. Screening for and addressing cognitive symptoms is an important component of the public health response to post–COVID-19 condition.

    

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2. Investigating Receptivity and Affect Using Machine Learning: Ecological Momentary Assessment and Wearable Sensing Study

   https://doi.org/10.2196/46347  

   King, Zachary D ; Yu, Han ; Vaessen, Thomas ; Myin-Germeys, Inez ; Sano, Akane ( January 2024 , JMIR mHealth and uHealth)

   As mobile health (mHealth) studies become increasingly productive owing to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. Many health constructs are dependent on subjective responses, and without such responses, researchers are left with little to no ground truth to accompany our ever-growing biobehavioral data. This issue can significantly impact the quality of a study, particularly for populations known to exhibit lower compliance rates. To address this challenge, researchers have proposed innovative approaches that use machine learning (ML) and sensor data to modify the timing and delivery of surveys. However, an overarching concern is the potential introduction of biases or unintended influences on participants’ responses when implementing new survey delivery methods.

   This study aims to demonstrate the potential impact of an ML-based ecological momentary assessment (EMA) delivery system (using receptivity as the predictor variable) on the participants’ reported emotional state. We examine the factors that affect participants’ receptivity to EMAs in a 10-day wearable and EMA–based emotional state–sensing mHealth study. We study the physiological relationships indicative of receptivity and affect while also analyzing the interaction between the 2 constructs.

   We collected data from 45 healthy participants wearing 2 devices measuring electrodermal activity, accelerometer, electrocardiography, and skin temperature while answering 10 EMAs daily, containing questions about perceived mood. Owing to the nature of our constructs, we can only obtain ground truth measures for both affect and receptivity during responses. Therefore, we used unsupervised and supervised ML methods to infer affect when a participant did not respond. Our unsupervised method used k-means clustering to determine the relationship between physiology and receptivity and then inferred the emotional state during nonresponses. For the supervised learning method, we primarily used random forest and neural networks to predict the affect of unlabeled data points as well as receptivity.

   Our findings showed that using a receptivity model to trigger EMAs decreased the reported negative affect by >3 points or 0.29 SDs in our self-reported affect measure, scored between 13 and 91. The findings also showed a bimodal distribution of our predicted affect during nonresponses. This indicates that this system initiates EMAs more commonly during states of higher positive emotions.

   Our results showed a clear relationship between affect and receptivity. This relationship can affect the efficacy of an mHealth study, particularly those that use an ML algorithm to trigger EMAs. Therefore, we propose that future work should focus on a smart trigger that promotes EMA receptivity without influencing affect during sampled time points.

    

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3. Sleep disparities in the first month of college: implications for academic achievement

   https://doi.org/10.1093/sleepadvances/zpac041  

   Bermudez, Vanessa N. ; Fearon-Drake, Danielle ; Wheelis, Meaghann ; Cohenour, Michelle ; Suntai, Zainab ; Scullin, Michael K. ( November 2022 , SLEEP Advances)

   We investigated sleep disparities and academic achievement in college.

   Participants were 6,002 first-year college students attending a midsize private university in the southern United States [62.0% female, 18.8% first-generation, 37.4% Black, Indigenous, or People of Color (BIPOC) students]. During the first 3–5 weeks of college, students reported their typical weekday sleep duration, which we classified as short sleep (<7 hours), normal sleep (7–9 hours), or long sleep (>9 hours).

   The odds for short sleep were significantly greater in BIPOC students (95% CI: 1.34–1.66) and female students (95% CI: 1.09–1.35), and the odds for long sleep were greater in BIPOC students (95% CI: 1.38–3.08) and first-generation students (95% CI: 1.04–2.53). In adjusted models, financial burden, employment, stress, STEM academic major, student athlete status, and younger age explained unique variance in sleep duration, fully mediating disparities for females and first-generation students (but only partially mediating disparities for BIPOC students). Short and long sleep predicted worse GPA across students’ first year in college, even after controlling for high school academic index, demographics, and psychosocial variables.

   Higher education should address sleep health early in college to help remove barriers to success and reduce disparities.

    

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4. Hydration in relation to water insecurity, heat index, and lactation status in two small‐scale populations in hot‐humid and hot‐arid environments

   https://doi.org/10.1002/ajhb.23447  

   Bethancourt, Hilary J. ; Swanson, Zane S. ; Nzunza, Rosemary ; Huanca, Tomas ; Conde, Esther ; Kenney, W. Larry ; Young, Sera L. ; Ndiema, Emmanuel ; Braun, David ; Pontzer, Herman ; et al ( June 2020 , American Journal of Human Biology)

   This study compared the prevalence of concentrated urine (urine specific gravity ≥1.021), an indicator of hypohydration, across Tsimane' hunter‐forager‐horticulturalists living in hot‐humid lowland Bolivia and Daasanach agropastoralists living in hot‐arid Northern Kenya. It tested the hypotheses that household water and food insecurity would be associated with higher odds of hypohydration.

   This study collected spot urine samples and corresponding weather data along with data on household water and food insecurity, demographics, and health characteristics among 266 Tsimane' households (N = 224 men, 235 women, 219 children) and 136 Daasanach households (N = 107 men, 120 women, 102 children).

   The prevalence of hypohydration among Tsimane' men (50.0%) and women (54.0%) was substantially higher (P < .001) than for Daasanach men (15.9%) and women (17.5%); the prevalence of hypohydration among Tsimane' (37.0%) and Daasanach (31.4%) children was not significantly different (P= .33). Multiple logistic regression models suggested positive but not statistically significant trends between household water insecurity and odds of hypohydration within populations, yet some significant joint effects of water and food insecurity were observed. Heat index (2°C) was associated with a 23% (95% confidence interval [CI]: 1.09‐1.40,P= .001), 34% (95% CI: 1.18‐1.53,P < .0005), and 23% (95% CI: 1.04‐1.44,P= .01) higher odds of hypohydration among Tsimane' men, women, and children, respectively, and a 48% (95% CI: 1.02‐2.15,P= .04) increase in the odds among Daasanach women. Lactation status was also associated with hypohydration among Tsimane' women (odds ratio = 3.35, 95% CI: 1.62‐6.95,P= .001).

   These results suggest that heat stress and reproductive status may have a greater impact on hydration status than water insecurity across diverse ecological contexts.

    

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5. Wildfire Smoke Is Associated With an Increased Risk of Cardiorespiratory Emergency Department Visits in Alaska

   https://doi.org/10.1029/2020GH000349  

   Hahn, M. B. ; Kuiper, G. ; O'Dell, K. ; Fischer, E. V. ; Magzamen, S. ( May 2021 , GeoHealth)

   Alaskan wildfires have major ecological, social, and economic consequences, but associated health impacts remain unexplored. We estimated cardiorespiratory morbidity associated with wildfire smoke (WFS) fine particulate matter with a diameter less than 2.5 μm (PM_2.5) in three major population centers (Anchorage, Fairbanks, and the Matanuska‐Susitna Valley) during the 2015–2019 wildfire seasons. To estimate WFS PM_2.5, we utilized data from ground‐based monitors and satellite‐based smoke plume estimates. We implemented time‐stratified case‐crossover analyses with single and distributed lag models to estimate the effect of WFS PM_2.5on cardiorespiratory emergency department (ED) visits. On the day of exposure to WFS PM_2.5, there was an increased odds of asthma‐related ED visits among 15–65 year olds (OR = 1.12, 95% CI = 1.08, 1.16), people >65 years (OR = 1.15, 95% CI = 1.01, 1.31), among Alaska Native people (OR = 1.16, 95% CI = 1.09, 1.23), and in Anchorage (OR = 1.10, 95% CI = 1.05, 1.15) and Fairbanks (OR = 1.12, 95% CI = 1.07, 1.17). There was an increased risk of heart failure related ED visits for Alaska Native people (Lag Day 5 OR = 1.13, 95% CI = 1.02, 1.25). We found evidence that rural populations may delay seeking care. As the frequency and magnitude of Alaskan wildfires continue to increase due to climate change, understanding the health impacts will be imperative. A nuanced understanding of the effects of WFS on specific demographic and geographic groups facilitates data‐driven public health interventions and fire management protocols that address these adverse health effects.

    

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