Automated Analysis of Drawing Process to Estimate Global Cognition in Older Adults: Preliminary International Validation on the US and Japan Data Sets.

BACKGROUND: With the aging of populations worldwide, early detection of cognitive impairments has become a research and clinical priority, particularly to enable preventive intervention for dementia. Automated analysis of the drawing process has been studied as a promising means for lightweight, self-administered cognitive assessment. However, this approach has not been sufficiently tested for its applicability across populations. OBJECTIVE: The aim of this study was to evaluate the applicability of automated analysis of the drawing process for estimating global cognition in community-dwelling older adults across populations in different nations. METHODS: We collected drawing data with a digital tablet, along with Montreal Cognitive Assessment (MoCA) scores for assessment of global cognition, from 92 community-dwelling older adults in the United States and Japan. We automatically extracted 6 drawing features that characterize the drawing process in terms of the drawing speed, pauses between drawings, pen pressure, and pen inclinations. We then investigated the association between the drawing features and MoCA scores through correlation and machine learning-based regression analyses. RESULTS: We found that, with low MoCA scores, there tended to be higher variability in the drawing speed, a higher pause:drawing duration ratio, and lower variability in the pen's horizontal inclination in both the US and Japan data sets. A machine learning model that used drawing features to estimate MoCA scores demonstrated its capability to generalize from the US dataset to the Japan dataset (R2=0.35; permutation test, P<.001). CONCLUSIONS: This study presents initial empirical evidence of the capability of automated analysis of the drawing process as an estimator of global cognition that is applicable across populations. Our results suggest that such automated analysis may enable the development of a practical tool for international use in self-administered, automated cognitive assessment.

Dynamics of Loneliness Among Older Adults During the COVID-19 Pandemic: Pilot Study of Ecological Momentary Assessment With Network Analysis.

OBJECTIVE: The COVID-19 pandemic has had potentially severe psychological implications for older adults, including those in retirement communities, due to restricted social interactions, but the day-to-day experience of loneliness has received limited study. We sought to investigate sequential association, if any, between loneliness, activity, and affect. METHODS: We used ecological momentary assessment (EMA) with dynamic network analysis to investigate the affective and behavioral concomitants of loneliness in 22 residents of an independent living sector of a continuing care retirement community (mean age 80.2; range 68-93 years). RESULTS: Participants completed mean 83.9% of EMA surveys (SD = 16.1%). EMA ratings of loneliness were moderately correlated with UCLA loneliness scale scores. Network models showed that loneliness was contemporaneously associated with negative affect (worried, anxious, restless, irritable). Negative (but not happy or positive) mood tended to be followed by loneliness and then by exercise or outdoor physical activity. Negative affect had significant and high inertia (stability). CONCLUSIONS: The data suggest that EMA is feasible and acceptable to older adults. EMA-assessed loneliness was moderately associated with scale-assessed loneliness. Network models in these independent living older adults indicated strong links between negative affect and loneliness, but feelings of loneliness were followed by outdoor activity, suggesting adaptive behavior among relatively healthy adults.

Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing.

Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications.

SARS-CoV-2 detection status associates with bacterial community composition in patients and the hospital environment.

BACKGROUND: SARS-CoV-2 is an RNA virus responsible for the coronavirus disease 2019 (COVID-19) pandemic. Viruses exist in complex microbial environments, and recent studies have revealed both synergistic and antagonistic effects of specific bacterial taxa on viral prevalence and infectivity. We set out to test whether specific bacterial communities predict SARS-CoV-2 occurrence in a hospital setting. METHODS: We collected 972 samples from hospitalized patients with COVID-19, their health care providers, and hospital surfaces before, during, and after admission. We screened for SARS-CoV-2 using RT-qPCR, characterized microbial communities using 16S rRNA gene amplicon sequencing, and used these bacterial profiles to classify SARS-CoV-2 RNA detection with a random forest model. RESULTS: Sixteen percent of surfaces from COVID-19 patient rooms had detectable SARS-CoV-2 RNA, although infectivity was not assessed. The highest prevalence was in floor samples next to patient beds (39%) and directly outside their rooms (29%). Although bed rail samples more closely resembled the patient microbiome compared to floor samples, SARS-CoV-2 RNA was detected less often in bed rail samples (11%). SARS-CoV-2 positive samples had higher bacterial phylogenetic diversity in both human and surface samples and higher biomass in floor samples. 16S microbial community profiles enabled high classifier accuracy for SARS-CoV-2 status in not only nares, but also forehead, stool, and floor samples. Across these distinct microbial profiles, a single amplicon sequence variant from the genus Rothia strongly predicted SARS-CoV-2 presence across sample types, with greater prevalence in positive surface and human samples, even when compared to samples from patients in other intensive care units prior to the COVID-19 pandemic. CONCLUSIONS: These results contextualize the vast diversity of microbial niches where SARS-CoV-2 RNA is detected and identify specific bacterial taxa that associate with the viral RNA prevalence both in the host and hospital environment. Video Abstract.

Experience of International Air Transportation and Subsequent Lung Transplant in a Patient with COVID-19-associated Acute Respiratory Distress Syndrome: a Case Report.

We report an inspiring case of a 55-year-old Korean female diagnosed with coronavirus disease 2019 (COVID-19)-associated acute respiratory distress syndrome (ARDS) in Mexico. The patient was assessed for lung transplant as a salvage therapy for treatment-refractory ARDS following no signs of clinical improvement for > 7 weeks, despite best treatment. The patient was transported from Mexico to Korea by air ambulance under venovenous extracorporeal membrane oxygenation (ECMO) support. She was successfully bridged to lung transplant on day 88, 49 days after the initiation of ECMO support. ECMO was successfully weaned at the end of operation, and no bleeding or primary graft dysfunction was observed within the first 72 hours. The patient was liberated from mechanical ventilation on postoperative day 9 and transferred to the general ward 5 days later. Despite the high doses of immunosuppressants, there was no evidence of viral reactivation after transplant. At 3 months post-transplantation, she was discharged to home without complication. Our experience suggests that successful lung transplant for COVID-19-associated ARDS is feasible even in a patient with prolonged pre-transplant ECMO support. Lung transplant may be considered a salvage therapy for COVID-19-associated ARDS that does not respond to conventional treatments.

Health Impacts of the Stay-at-Home Order on Community-Dwelling Older Adults and How Technologies May Help: Focus Group Study.

BACKGROUND: As of March 2021, in the USA, the COVID-19 pandemic has resulted in over 500,000 deaths, with a majority being people over 65 years of age. Since the start of the pandemic in March 2020, preventive measures, including lockdowns, social isolation, quarantine, and social distancing, have been implemented to reduce viral spread. These measures, while effective for risk prevention, may contribute to increased social isolation and loneliness among older adults and negatively impact their mental and physical health. OBJECTIVE: This study aimed to assess the impact of the COVID-19 pandemic and the resulting "Stay-at-Home" order on the mental and physical health of older adults and to explore ways to safely increase social connectedness among them. METHODS: This qualitative study involved older adults living in a Continued Care Senior Housing Community (CCSHC) in southern California, USA. Four 90-minute focus groups were convened using the Zoom Video Communications platform during May 2020, involving 21 CCSHC residents. Participants were asked to describe how they were managing during the "stay-at-home" mandate that was implemented in March 2020, including its impact on their physical and mental health. Transcripts of each focus group were analyzed using qualitative methods. RESULTS: Four themes emerged from the qualitative data: (1) impact of the quarantine on health and well-being, (2) communication innovation and technology use, (3) effective ways of coping with the quarantine, and (4) improving access to technology and training. Participants reported a threat to their mental and physical health directly tied to the quarantine and exacerbated by social isolation and decreased physical activity. Technology was identified as a lifeline for many who are socially isolated from their friends and family. CONCLUSIONS: Our study findings suggest that technology access, connectivity, and literacy are potential game-changers to supporting the mental and physical health of older adults and must be prioritized for future research.