Many factors can impact survey completion rates, including survey length, sensitivity of the topics addressed, and clarity of wording. This study used cognitive interviews (CIs), a methodological tool that can aid in developing and refining elements for multi-faceted assessments, and previous survey response patterns to refine, streamline, and increase response rates of RADx-UP Common Data Elements (CDEs) for survey/questionnaire use.

Ten previously enrolled CO-CREATE study participants were interviewed between May–June 2023. Interviewees identified CDEs that were “confusing, uncomfortable, and/or not applicable,” along with their reasoning. Interview data were analyzed using a rapid qualitative analytic approach, resulting in a summary matrix categorized by language. For further contextualization, CDE response rates were calculated for the 9147 surveys administered during the CO-CREATE study (May 2021–March 2023) and compared against their survey position.

Of the 94 CDEs evaluated in the CIs, 20 (21.3%) were flagged by one or more interviewees. Nine (9.6%) English while fourteen (14.9%) Spanish CDEs were flagged by interviewees, with some overlap. Also, CDE response rates differed according to position in the survey, with lower response rates for questions positioned later in the survey. Following review by the research team and the RADx-UP program, 10 English and 15 Spanish were revised, and seven were removed in both languages in the final survey.

Our findings underscore the importance of integrating community member perspectives to enhance the relevance and clarity of assessment instruments, optimizing the impact of public health research among underrepresented populations.

To characterise the association between risk of poor glycaemic control and self-reported and area-level food insecurity among adult patients with type 2 diabetes.

We performed a retrospective, observational analysis of cross-sectional data routinely collected within a health system. Logistic regressions estimated the association between glycaemic control and the dual effect of self-reported and area-level measures of food insecurity.

The health system included a network of ambulatory primary and speciality care sites and hospitals in Bronx County, NY.

Patients diagnosed with type 2 diabetes who completed a health-related social need (HRSN) assessment between April 2018 and December 2019.

5500 patients with type 2 diabetes were assessed for HRSN with 7·1 % reporting an unmet food need. Patients with self-reported food needs demonstrated higher odds of having poor glycaemic control compared with those without food needs (adjusted OR (aOR): 1·59, 95 % CI: 1·26, 2·00). However, there was no conclusive evidence that area-level food insecurity alone was a significant predictor of glycaemic control (aOR: 1·15, 95 % CI: 0·96, 1·39). Patients with self-reported food needs residing in food-secure (aOR: 1·83, 95 % CI: 1·22, 2·74) and food-insecure (aOR: 1·72, 95 % CI: 1·25, 2·37) areas showed higher odds of poor glycaemic control than those without self-reported food needs residing in food-secure areas.

These findings highlight the importance of utilising patient- and area-level social needs data to identify individuals for targeted interventions with increased risk of adverse health outcomes.

The aim of this project is to study to which extent salience alterations influence the severity of psychotic symptoms. However, rather than studying them individually, we decided to focus on their interplay with two additional variables, that is: observing their effect in a vulnerability phase (adolescence) and with another added, well-recognized risk factor (cannabis use).

The reason for this study design lies in the fact that, in our opinion, it is fundamental to observe the trajectory of psychotic symptoms over a continuum; however, rather than adopting a longitudinal approach, we decided to structure it as a cross-sectional study confronting patients from two age brackets - adolescence and adulthood.

The primary purpose of this study was to assess a difference between THC-abusing and non-abusing patients in adolescent and adult cohorts, using the Italian version of the psychometric scale “Aberrant Salience Inventory” (ASI), and the possible correlation with more severe psychotic symptoms. The employment of several different psychometric scales and the inclusion of a variegated cohort allowed to pursue multiple secondary objectives.

We recruited 192 patients, subsequently divided into six subgroups based on age and department of recruitment (whether adolescent or adult psychiatric or neurologic units - the latter serving as controls). Each individual was administered a set of questionnaires and a socio-demographic survey; the set included: Aberrant Salience Inventory (ASI), Community Assessment of Psychic Experiences (CAPE), Positive and Negative Syndrome Scale (PANSS), Montgomery-Asberg Depression Rating Scale (MADRS), Mania Rating Scale (MRS), Hamilton Anxiety Scale (HAM-A), Association for Methodology and Documentation in Psychiatry (AMDP) and Cannabis Experience Questionnaire (CEQ).

The data analysis showed statistically significant (p<0.05) differences between adolescents and adults with psychotic symptoms in all of the three scales of PANSS and in MADRS. These two groups were homogenous for both cannabis use and ASI score. The intra-group comparison (either adolescent or adult) showed a hierarchical pattern in the scores of psychometric scales according to the diagnostic subgroup of allocation: patients with psychotic symptoms showed an higher level of psychopathology in all measures when compared to patients from the psychiatric unit without psychotic symptoms, which in turn scored higher than the patients from the neurologic unit.

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The results of the present study may suggest that when salience alterations occur in adolescents with cannabis exposure, we might observe worsened positive and negative psychotic symptoms; their influence might be relevant also in other domains, especially regarding the depressive and anxiety spectrums.

None Declared

The timing of tracheostomy for intensive care unit patients is controversial, with conflicting findings on early versus late tracheostomy.

Patients undergoing tracheostomy from 2001through 2012 were identified from the Medical Information Mart for Intensive Care-III database. Early tracheostomy was defined as less than the 25th percentile of time from intensive care unit admission to tracheostomy (time to tracheostomy). Statistical analysis for tracheostomy timing on intensive care unit length of stay and mortality were conducted.

Of the 1,566 patients that were included, patients with early tracheostomy had shorter intensive care unit length of stay (27.32 vs 12.55 days, p < 0.001) and lower mortality (12.9 per cent vs 9.0 per cent, p = 0.039). Multivariate logistic regression analysis found an association between increasing time to tracheostomy and mortality (odds ratio: 1.029, 95 per cent confidence interval 1.007–1.051, p = 0.009).

Our analysis revealed that patients with early tracheostomy were more likely to have shorter intensive care unit lengths of stay and lower mortality. Our data suggest that early tracheostomy should be given strong consideration in appropriately selected patients.

Symptom clustering research provides a unique opportunity for understanding complex medical conditions. The objective of this study was to apply a variable-centered analytic approach to understand how symptoms may cluster together, within and across domains of functioning in mild cognitive impairment (MCI) and dementia, to better understand these conditions and potential etiological, prevention, and intervention considerations.

Cognitive, motor, sensory, emotional, and social measures from the NIH Toolbox were analyzed using exploratory factor analysis (EFA) from a dataset of 165 individuals with a research diagnosis of either amnestic MCI or dementia of the Alzheimer’s type.

The six-factor EFA solution described here primarily replicated the intended structure of the NIH Toolbox with a few deviations, notably sensory and motor scores loading onto factors with measures of cognition, emotional, and social health. These findings suggest the presence of cross-domain symptom clusters in these populations. In particular, negative affect, stress, loneliness, and pain formed one unique symptom cluster that bridged the NIH Toolbox domains of physical, social, and emotional health. Olfaction and dexterity formed a second unique cluster with measures of executive functioning, working memory, episodic memory, and processing speed. A third novel cluster was detected for mobility, strength, and vision, which was considered to reflect a physical functioning factor. Somewhat unexpectedly, the hearing test included did not load strongly onto any factor.

This research presents a preliminary effort to detect symptom clusters in amnestic MCI and dementia using an existing dataset of outcome measures from the NIH Toolbox.

Transcutaneous vagus nerve stimulation (tVNS) is a promising potential intervention for Alzheimer's disease (AD) due to its influence on brain functions and mechanisms important in disease progression. Regions of interest include projection to the nucleus of the solitary tract, locus coeruleus, and hippocampus. Deterioration of the hippocampus is one of the most prominent early characteristics of AD, particularly during the mild cognitive impairment (MCI) stage. tVNS could modify function of the hippocampus. We examined resting state functional connectivity from the bilateral hippocampus in response to tVNS in patients with MCI.

Fifty older adults (28 women, 60-89 years of age) diagnosed with MCI were assessed. MCI was confirmed via diagnostic consensus conference with a neurologist and neuropsychologist (sources of information: Montreal Cognitive Assessment Test [MoCA], Clinical Dementia Rating scale [CDR], Functional Activities Questionnaire (FAQ), Hopkins Verbal Learning Test - Revised [HVLT-R] and medical record review). Resting state functional magnetic resonance imaging (fMRI) was collected on a 3T Siemens Prisma scanner while participants received either unilateral tVNS (left tragus, n = 25) or sham stimulation (left ear lobe, n = 25). fMRI data were processed using CONN toolbox v18b and hippocampal seed to voxel (whole brain) analyses were conducted with voxel and cluster level multiple comparison correction.

Contrasting tVNS and sham stimulation, whole-brain seed-to-voxel analysis demonstrated significant changes in connectivity from the left hippocampus to several cortical and subcortical regions bilaterally. Specifically, there was increased connectivity to prefrontal regions and cingulate gyri, and decreased connectivity to anterior and medial temporal lobes. A seed-to-voxel analysis from the right hippocampus indicated significant decrease in connectivity to a single cluster of regions in the left anterior temporal lobe in response to tVNS.

In conclusion, tVNS modified connectivity from the hippocampus to multiple brain regions implicated in semantic and salience functions, in which disruption correlates with deterioration in AD. These findings indicate afferent target engagement of tVNS. Future work is needed to investigate the long-term effects of tVNS in patients with MCI and whether it could contribute to meaningful cognitive change and subsequent improvements in quality of life.

Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.

Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.

Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.

We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).

This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.

Cognitive training has shown promise for improving cognition in older adults. Aging involves a variety of neuroanatomical changes that may affect response to cognitive training. White matter hyperintensities (WMH) are one common age-related brain change, as evidenced by T2-weighted and Fluid Attenuated Inversion Recovery (FLAIR) MRI. WMH are associated with older age, suggestive of cerebral small vessel disease, and reflect decreased white matter integrity. Higher WMH load associates with reduced threshold for clinical expression of cognitive impairment and dementia. The effects of WMH on response to cognitive training interventions are relatively unknown. The current study assessed (a) proximal cognitive training performance following a 3-month randomized control trial and (b) the contribution of baseline whole-brain WMH load, defined as total lesion volume (TLV), on pre-post proximal training change.

Sixty-two healthy older adults ages 65-84 completed either adaptive cognitive training (CT; n=31) or educational training control (ET; n=31) interventions. Participants assigned to CT completed 20 hours of attention/processing speed training and 20 hours of working memory training delivered through commercially-available Posit Science BrainHQ. ET participants completed 40 hours of educational videos. All participants also underwent sham or active transcranial direct current stimulation (tDCS) as an adjunctive intervention, although not a variable of interest in the current study. Multimodal MRI scans were acquired during the baseline visit. T1- and T2-weighted FLAIR images were processed using the Lesion Segmentation Tool (LST) for SPM12. The Lesion Prediction Algorithm of LST automatically segmented brain tissue and calculated lesion maps. A lesion threshold of 0.30 was applied to calculate TLV. A log transformation was applied to TLV to normalize the distribution of WMH. Repeated-measures analysis of covariance (RM-ANCOVA) assessed pre/post change in proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures in the CT group compared to their ET counterparts, controlling for age, sex, years of education and tDCS group. Linear regression assessed the effect of TLV on post-intervention proximal composite and sub-composite, controlling for baseline performance, intervention assignment, age, sex, years of education, multisite scanner differences, estimated total intracranial volume, and binarized cardiovascular disease risk.

RM-ANCOVA revealed two-way group*time interactions such that those assigned cognitive training demonstrated greater improvement on proximal composite (Total Training Composite) and sub-composite (Processing Speed Training Composite, Working Memory Training Composite) measures compared to their ET counterparts. Multiple linear regression showed higher baseline TLV associated with lower pre-post change on Processing Speed Training sub-composite (ß = -0.19, p = 0.04) but not other composite measures.

These findings demonstrate the utility of cognitive training for improving postintervention proximal performance in older adults. Additionally, pre-post proximal processing speed training change appear to be particularly sensitive to white matter hyperintensity load versus working memory training change. These data suggest that TLV may serve as an important factor for consideration when planning processing speed-based cognitive training interventions for remediation of cognitive decline in older adults.

PLWH report using cannabis for both recreational reasons and HIV symptom management (e.g., nausea, pain, depression/anxiety). Recent literature suggests that cannabis may attenuate HIV symptoms and neuroinflammation, which are strongly related to neurocognition. Additionally, older adults who are particularly vulnerable to cognitive impairment experience a decline in the endogenous cannabinoid system with age. Therefore, the aims of the present study were 1) to determine if cannabis use is associated with cognitive performance in PLWH, 2) to determine if age moderates the relationship between cannabis use and cognition in PLWH, and 3) to determine if there are differences in cognition in cannabis non-users, occasional users, and daily users among PLWH.

The sample included 225 PLWH (78% undetectable; 51% female, Mean age=49.10) who were classified as non-users (n=52), occasional users (n=53), or daily users (n=120). Cannabis use was measured via the Timeline Follow-back (TLFB). Cognition was examined using the NIH Toolbox Cognition Battery, which included measures of attention, working memory, executive function, processing speed, and episodic memory, as well as a fluid cognition composite score.

Increased frequency of cannabis use was weakly positively associated with episodic memory performance, r(224) = 0.15, p<0.05. Results of the multiple regression indicate that frequency of cannabis use was not significantly associated with any of the six cognitive domains. However, there was a significant interaction between age and cannabis use in the domains of attention (ß= 0.13, p < 0.05), working memory (ß= 0.12, p < 0.05), and episodic memory (ß= 0.15, p < 0.05), suggesting worse cognitive performance in older adults who use cannabis as compared to younger adults in this sample. When participants were grouped based on use status, there were no significant main effects of group.

After controlling for the effects of demographic factors and HIV disease severity, no significant negative associations between cannabis use and cognition were observed, suggesting that cannabis use is not related to cognitive impairment in PLWH. However, results were clarified by a significant interaction, indicating that older adults who use cannabis perform worse in the domains of attention, working memory, and episodic memory compared to younger adults, suggesting synergistic cognitive effects of age and cannabis use. We additionally found preliminary evidence for a potential positive effect of cannabis use on episodic memory in the overall sample. Future studies examining biological and behavioral mechanisms of improvement will be necessary to better examine this relationship.