Barriers and Facilitators of Self-Management in Older People with Type 1 Diabetes: A Narrative Review Focusing on Cognitive Impairment.

Over the past decades, life expectancy of people with type 1 diabetes has increased considerably, which brings potential challenges due to the process of aging. Cognitive aging and dementia, as well as reductions in visual acuity, hearing and dexterity, can influence the frequency and quality of daily self-management activities, including medication taking and insulin dosing, glucose self-monitoring, and healthy eating. This can increase the risk for hypo- and hyperglycemic events, which, in turn, may contribute to cognitive decline. Because there is a gap in understanding the barriers and facilitators of self-management in older adults with type 1 diabetes and the relationship to cognitive functioning, the authors 1) review the available literature on cognitive aging and type 1 diabetes, 2) describe what self-management in later adulthood entails and the cognitive functions required for effective self-management behaviors, 3) analyze the interaction between type 1 diabetes, cognition, aging, and self-management behaviors, and 4) describe the barriers and facilitators for self-management throughout the life span and how they may differ for older people. Potential evidence-based practices that could be developed for older adults with type 1 diabetes are discussed. There is need for further studies that clarify the impact of aging on T1D self-management, ultimately to improve diabetes care and quality of life.

Mid-life sleep is associated with cognitive performance later in life in aging American Indians: data from the Strong Heart Study.

Background: Sleep-related disorders have been associated with cognitive decline and neurodegeneration. American Indians are at increased risk for dementia. Here, we aim to characterize, for the first time, the associations between sleep characteristics and subsequent cognitive performance in a sample of aging American Indians. Methods: We performed analyses on data collected in two ancillary studies from the Strong Heart Study, which occurred approximately 10 years apart with an overlapping sample of 160 American Indians (mean age at follow-up 73.1, standard deviation 5.6; 69.3% female and 80% with high school completion). Sleep measures were derived by polysomnography and self-reported questionnaires, including sleep timing and duration, sleep latency, sleep stages, indices of sleep-disordered breathing, and self-report assessments of poor sleep and daytime sleepiness. Cognitive assessment included measures of general cognition, processing speed, episodic verbal learning, short and long-delay recall, recognition, and phonemic fluency. We performed correlation analyses between sleep and cognitive measures. For correlated variables, we conducted separate linear regressions. We analyzed the degree to which cognitive impairment, defined as more than 1.5 standard deviations below the average Modified Mini Mental State Test score, is predicted by sleep characteristics. All regression analyses were adjusted for age, sex, years of education, body mass index, study site, depressive symptoms score, difference in age from baseline to follow-up, alcohol use, and presence of APOE e4 allele. Results: We found that objective sleep characteristics measured by polysomnography, but not subjective sleep characteristics, were associated with cognitive performance approximately 10 years later. Longer sleep latency was associated with worse phonemic fluency (ß = -0.069, p = 0.019) and increased likelihood of being classified in the cognitive impairment group later in life (odds ratio 1.037, p = 0.004). Longer duration with oxygen saturation < 90% was associated with better immediate verbal memory, and higher oxygen saturation with worse total learning, short and long-delay recall, and processing speed. Conclusion: In a sample of American Indians, sleep characteristics in midlife were correlated with cognitive performance a decade later. Sleep disorders may be modifiable risk factors for cognitive impairment and dementia later in life, and suitable candidates for interventions aimed at preventing neurodegenerative disease development and progression.

Plasma Biomarkers of Brain Injury and Their Association With Brain MRI and Cognition in Type 1 Diabetes.

OBJECTIVE: To evaluate associations between plasma biomarkers of brain injury and MRI and cognitive measures in participants with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study. RESEARCH DESIGN AND METHODS: Plasma amyloid-ß-40, amyloid-ß-42, neurofilament light chain (NfL), phosphorylated Tau-181 (pTau-181), and glial fibrillary acidic protein (GFAP) were measured in 373 adults who participated in the DCCT/EDIC study. MRI assessments included total brain and white matter hyperintensity volumes, white matter mean fractional anisotropy, and indices of Alzheimer disease (AD)-like atrophy and predicted brain age. Cognitive measures included memory and psychomotor and mental efficiency tests and assessments of cognitive impairment. RESULTS: Participants were 60 (range 44-74) years old with 38 (30-51) years' T1D duration. Higher NfL was associated with an increase in predicted brain age (0.51 years per 20% increase in NfL; P < 0.001) and a 19.5% increase in the odds of impaired cognition (P < 0.01). Higher NfL and pTau-181 were associated with lower psychomotor and mental efficiency (P < 0.001) but not poorer memory. Amyloid-ß measures were not associated with study measures. A 1% increase in mean HbA1c was associated with a 14.6% higher NfL and 12.8% higher pTau-181 (P < 0.0001). CONCLUSIONS: In this aging T1D cohort, biomarkers of brain injury did not demonstrate an AD-like profile. NfL emerged as a biomarker of interest in T1D because of its association with higher HbA1c, accelerated brain aging on MRI, and cognitive dysfunction. Our study suggests that early neurodegeneration in adults with T1D is likely due to non-AD/nonamyloid mechanisms.

Risk factors associated with cognitive performance and cognitive impairment in older adults with type 1 diabetes: Data from the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) study.

BACKGROUND: Adults with type 1 diabetes (T1D) are considered at increased risk for cognitive impairment and accelerated brain aging. However, longitudinal data on cognitive impairment and dementia in this population are scarce. OBJECTIVE: To identify risk factors associated with cognitive performance and cognitive impairment in a longitudinal sample of older adults with T1D. METHODS: We analyzed data collected as part of the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) Study, in which 22 endocrinology practices participated. Randomized participants with T1D ≥60 years of age who completed at least one cognitive assessment were included in this study (n = 203). Cognitive impairment was classified using published recommendations. RESULTS: Older age, male sex, non-private health insurance, worse daily functioning, diagnosis of neuropathy, and longer duration of diabetes were associated with worse cognitive performance, but not cognitive impairment. 49 % and 39 % of the sample met criteria for cognitive impairment at baseline and 52 weeks respectively. Of the participants that had data at both time points, 10 % were normal at baseline and impaired at 52 weeks and 22 % of participants (44 % of those classified with cognitive impairment at baseline) reverted to normal over 52 weeks. CONCLUSION: This study indicated that several demographic and clinical characteristics are associated with worse cognitive performance in older adults with T1D, but there were no associations between these characteristics and cognitive impairment defined by NIH Toolbox cognitive impairment criteria. Caution is warranted when assessing cognition in older adults with T1D, as a large percentage of those identified as having cognitive impairment at baseline reverted to normal after 52 weeks. There is need for future studies on the interrelationship of cognition and aging to better understand the effects of T1D on cognitive health, to improve clinical monitoring and help mitigate the risk of dementia in this population.

Nocturnal hypoglycemia is associated with next day cognitive performance in adults with type 1 diabetes: Pilot data from the GluCog study.

OBJECTIVE:  Individuals with type 1 diabetes (T1D) have increased risk for cognitive dysfunction and high rates of sleep disturbance. Despite associations between glycemia and cognitive performance using cross-sectional and experimental methods few studies have evaluated this relationship in a naturalistic setting, or the impact of nocturnal versus daytime hypoglycemia. Ecological Momentary Assessment (EMA) may provide insight into the dynamic associations between cognition, affective, and physiological states. The current study couples EMA data with continuous glucose monitoring (CGM) to examine the within-person impact of nocturnal glycemia on next day cognitive performance in adults with T1D. Due to high rates of sleep disturbance and emotional distress in people with T1D, the potential impacts of sleep characteristics and negative affect were also evaluated. METHODS:  This pilot study utilized EMA in 18 adults with T1D to examine the impact of glycemic excursions, measured using CGM, on cognitive performance, measured via mobile cognitive assessment using the TestMyBrain platform. Multilevel modeling was used to test the within-person effects of nocturnal hypoglycemia and hyperglycemia on next day cognition. RESULTS:  Results indicated that increases in nocturnal hypoglycemia were associated with slower next day processing speed. This association was not significantly attenuated by negative affect, sleepiness, or sleep quality. CONCLUSIONS:  These results, while preliminary due to small sample size, showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive fluctuation in real world settings, an approach that may be utilized in other populations. Findings suggest reducing nocturnal hypoglycemia may improve cognition in adults with T1D.

Budget Impact Tool for Implementing Contingency Management for Co-occurring Alcohol Use Disorders and Serious Mental Illness.

OBJECTIVE: Contingency management (CM) is a behavioral intervention in which tangible incentives are provided to patients when they achieve a desired behavior (e.g., reducing or abstaining from alcohol use). The authors sought to describe the resource requirements and associated costs of various CM versions (usual, high magnitude, and shaping) tailored to a high-risk population with co-occurring serious mental illness and severe alcohol use disorder. METHODS: A microcosting analysis was conducted to identify the resource requirements of the different CM versions. This approach included semistructured interviews with site investigators, who also staffed the intervention. The resource costing method-multiplying the number of units of each resource utilized by its respective unit cost-was used to value the resources from a provider's perspective. All cost estimates were calculated in 2021 U.S. dollars. RESULTS: The cost of setting up a CM program was $6,038 per site. Assuming full capacity and 56% of urine samples meeting the requirement for receipt of the CM incentive, the average cost of 16 weeks of usual and shaping CM treatments was $1,119-$1,136 and of high-magnitude CM was $1,848-$1,865 per participant. CONCLUSIONS: A customizable tool was created to estimate the costs associated with various levels of treatment success and CM design features. After the trial, the tool will be updated and used to finalize per-participant cost for incorporation into a comprehensive economic evaluation. This costing tool will help a growing number of treatment providers who are interested in implementing CM with budgeting for and sustaining CM in their practices.

Usefulness of the Montreal Cognitive Assessment in Older Adults With Type 1 Diabetes.

Objective: Older adults with type 1 diabetes are at high risk for cognitive impairment, yet the usefulness of common cognitive screening instruments has not been evaluated in this population. Methods: A total of 201 adults ≥60 years of age with type 1 diabetes completed a battery of neuropsychological measures and the Montreal Cognitive Assessment (MoCA). Receiver operating characteristic (ROC) curves and Youden indices were used to evaluate overall screening test performance and to select an optimal MoCA cutoff score for detecting low cognitive performance, as defined as two or more neuropsychological test performances ≥1.5 SD below demographically corrected normative data. Results: The ROC area under the curve (AUC) was 0.745 (P < 0.001). The publisher-recommended cutoff score of <26 resulted in sensitivity of 60.4% and specificity of 71.4%, whereas a cutoff score of <27 resulted in sensitivity of 75.0% and specificity of 61.0%. The Youden indices for these cutoff scores were 0.318 and 0.360, respectively. Minimally acceptable sensitivity (i.e., >0.80) was obtained when using a cutoff score of <28, whereas >0.80 specificity was obtained with a cutoff score of <25. Conclusions: The MoCA has modest overall performance (AUC 0.745) as a cognitive screening instrument in older adults with type 1 diabetes. The standard cutoff score of <26/30 may not adequately detect individuals with neuropsychological testing-defined abnormal cognition. The optimal MoCA cutoff score (based on the Youden index) was <27/30. A score of <28 resulted in acceptable sensitivity but was accompanied by low specificity (42%). Future studies with a more diverse population are needed to confirm these findings.

Utility of the NIH Toolbox Cognition Battery in middle to older aged adults with longstanding type 1 diabetes: The DCCT/EDIC study.

Objective: Adults with type 1 diabetes (T1D) face an increased risk for cognitive decline and dementia. Diabetes-related and vascular risk factors have been linked to cognitive decline using detailed neuropsychological testing; however, it is unclear if cognitive screening batteries can detect cognitive changes associated with aging in T1D. Method: 1,049 participants with T1D (median age 59 years; range 43-74) from the Diabetes Control and Complications Trial (DCCT), and the follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study, completed the NIH Toolbox Cognition Battery (NIHTB-C) and Montreal Cognitive Assessment (MoCA). Neuropsychological assessments, depression, glycated hemoglobin levels (HbA1c), severe hypoglycemia, T1D complications, and vascular risk factors were assessed repeatedly over 32 years to determine associations with current NIHTB-C performance. Available cognitive data was clinically adjudicated to determine cognitive impairment status. Results: NIHTB-C scores had moderate associations (r = 0.36-0.53) with concurrently administered neuropsychological tests. In multivariate models, prior severe hypoglycemic episodes, depression symptoms, nephropathy, lower BMI, and higher HbA1c and LDL cholesterol were associated with poorer NIHTB-C Fluid Cognition Composite scores. The NIHTB-C adequately detected adjudicated cognitive impairment (Area Under the Curve = 0.86; optimal cut score ≤90). The MoCA performed similarly (Area Under the Curve = 0.83; optimal cut score ≤25). Conclusions: The NIHTB-C is sensitive to the cognitive effects of diabetes-related and vascular risk factors, correlated with neuropsychological testing, and accurately detects adjudicated cognitive impairment. These data support its use as a screening test in middle to older aged adults with T1D to determine if referral for detailed neuropsychological assessment is needed.

Ecological Momentary Assessment of Cognition in Clinical and Community Samples: Reliability and Validity Study.

BACKGROUND: The current methods of evaluating cognitive functioning typically rely on a single time point to assess and characterize an individual's performance. However, cognitive functioning fluctuates within individuals over time in relation to environmental, psychological, and physiological contexts. This limits the generalizability and diagnostic utility of single time point assessments, particularly among individuals who may exhibit large variations in cognition depending on physiological or psychological context (eg, those with type 1 diabetes [T1D], who may have fluctuating glucose concentrations throughout the day). OBJECTIVE: We aimed to report the reliability and validity of cognitive ecological momentary assessment (EMA) as a method for understanding between-person differences and capturing within-person variation in cognition over time in a community sample and sample of adults with T1D. METHODS: Cognitive performance was measured 3 times a day for 15 days in the sample of adults with T1D (n=198, recruited through endocrinology clinics) and for 10 days in the community sample (n=128, recruited from TestMyBrain, a web-based citizen science platform) using ultrabrief cognitive tests developed for cognitive EMA. Our cognitive EMA platform allowed for remote, automated assessment in participants' natural environments, enabling the measurement of within-person cognitive variation without the burden of repeated laboratory or clinic visits. This allowed us to evaluate reliability and validity in samples that differed in their expected degree of cognitive variability as well as the method of recruitment. RESULTS: The results demonstrate excellent between-person reliability (ranging from 0.95 to 0.99) and construct validity of cognitive EMA in both the sample of adults with T1D and community sample. Within-person reliability in both samples (ranging from 0.20 to 0.80) was comparable with that observed in previous studies in healthy older adults. As expected, the full-length baseline and EMA versions of TestMyBrain tests correlated highly with one another and loaded together on the expected cognitive domains when using exploratory factor analysis. Interruptions had higher negative impacts on accuracy-based outcomes (ß=-.34 to -.26; all P values <.001) than on reaction time-based outcomes (ß=-.07 to -.02; P<.001 to P=.40). CONCLUSIONS: We demonstrated that ultrabrief mobile assessments are both reliable and valid across 2 very different clinic versus community samples, despite the conditions in which cognitive EMAs are administered, which are often associated with more noise and variability. The psychometric characteristics described here should be leveraged appropriately depending on the goals of the cognitive assessment (eg, diagnostic vs everyday functioning) and the population being studied.

Assessing Clinically Significant Cognitive Impairment Using the NIH Toolbox in Individuals with Co-occurring Serious Mental Illness and Alcohol Use Disorder.

OBJECTIVE: Serious mental illnesses (SMI) and alcohol use disorder (AUD) co-occurrence (SMI-AUD) is common, yet little is known about the prevalence and risk factors of cognitive impairment for this population. We used the National Institutes of Health (NIH) Toolbox to identify clinically significant cognitive impairment (CSCI), describe the cognitive profile, and investigate whether psychiatric and AUD severity measures are associated with CSCI in individuals with SMI-AUD. METHODS: CSCI was defined as 2 or more fully corrected fluid subtest T scores below a set threshold based on an individual's crystalized composite score. Psychiatric severity measures included the Structured Clinical Interview for DSM-V (SCID-5) for SMI diagnosis and the Positive and Negative Syndrome Scale. AUD severity measures included the SCID-5 for AUD symptom severity score, years of alcohol use, and urine ethyl glucuronide levels. A multivariable logistic regression was used to investigate the adjusted effects of each variable on the probability of CSCI. RESULTS: Forty-one percent (N = 55/135) of our sample had CSCI compared with the base rate of 15% from the NIH Toolbox normative sample. Subtests measuring executive function most frequently contributed to meeting criteria for CSCI (Flanker and Dimensional Change Card Sort). A history of head injury ( P = 0.033), increased AUD symptom severity score ( P = 0.007) and increased negative symptom severity score ( P = 0.027) were associated with CSCI. CONCLUSIONS: Cognition should be considered in the treatment of people with SMI-AUD, particularly in those with history of brain injury, higher AUD symptom severity, and/or negative symptom severity.

Intraindividual Cognitive Variability and Magnetic Resonance Imaging in Aging American Indians: Data from the Strong Heart Study.

BACKGROUND: American Indians have high prevalence of risk factors for Alzheimer's disease and related dementias (ADRD) compared to the general population, yet dementia onset and frequency in this population are understudied. Intraindividual cognitive variability (IICV), a measure of variability in neuropsychological test performance within a person at a single timepoint, may be a novel, noninvasive biomarker of neurodegeneration and early dementia. OBJECTIVE: To characterize the cross-sectional associations between IICV and hippocampal, total brain volume, and white matter disease measured by magnetic resonance imaging (MRI) among older American Indians. METHODS: IICV measures for memory, executive function, and processing speed, and multidomain cognition were calculated for 746 American Indians (aged 64-95) who underwent MRI. Regression models were used to examine the associations of IICV score with hippocampal volume, total brain volume, and graded white matter disease, adjusting for age, sex, education, body mass index, intracranial volume, diabetes, stroke, hypertension, hypercholesterolemia, alcohol use, and smoking. RESULTS: Higher memory IICV measure was associated with lower hippocampal volume (Beta = -0.076; 95% CI -0.499, -0.023; p = 0.031). After adjustment for Bonferroni or IICV mean scores in the same tests, the associations were no longer significant. No IICV measures were associated with white matter disease or total brain volume. CONCLUSION: These findings suggest that the IICV measures used in this research cannot be robustly associated with cross-sectional neuroimaging features; nonetheless, the results encourage future studies investigating the associations between IICV and other brain regions, as well as its utility in the prediction of neurodegeneration and dementia in American Indians.

Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog): Observational Study Using Ecological Momentary Assessment of Cognition.

BACKGROUND: Individuals with type 1 diabetes represent a population with important vulnerabilities to dynamic physiological, behavioral, and psychological interactions, as well as cognitive processes. Ecological momentary assessment (EMA), a methodological approach used to study intraindividual variation over time, has only recently been used to deliver cognitive assessments in daily life, and many methodological questions remain. The Glycemic Variability and Fluctuations in Cognitive Status in Adults with Type 1 Diabetes (GluCog) study uses EMA to deliver cognitive and self-report measures while simultaneously collecting passive interstitial glucose in adults with type 1 diabetes. OBJECTIVE: We aimed to report the results of an EMA optimization pilot and how these data were used to refine the study design of the GluCog study. An optimization pilot was designed to determine whether low-frequency EMA (3 EMAs per day) over more days or high-frequency EMA (6 EMAs per day) for fewer days would result in a better EMA completion rate and capture more hypoglycemia episodes. The secondary aim was to reduce the number of cognitive EMA tasks from 6 to 3. METHODS: Baseline cognitive tasks and psychological questionnaires were completed by all the participants (N=20), followed by EMA delivery of brief cognitive and self-report measures for 15 days while wearing a blinded continuous glucose monitor. These data were coded for the presence of hypoglycemia (<70 mg/dL) within 60 minutes of each EMA. The participants were randomized into group A (n=10 for group A and B; starting with 3 EMAs per day for 10 days and then switching to 6 EMAs per day for an additional 5 days) or group B (N=10; starting with 6 EMAs per day for 5 days and then switching to 3 EMAs per day for an additional 10 days). RESULTS: A paired samples 2-tailed t test found no significant difference in the completion rate between the 2 schedules (t17=1.16; P=.26; Cohen dz=0.27), with both schedules producing >80% EMA completion. However, more hypoglycemia episodes were captured during the schedule with the 3 EMAs per day than during the schedule with 6 EMAs per day. CONCLUSIONS: The results from this EMA optimization pilot guided key design decisions regarding the EMA frequency and study duration for the main GluCog study. The present report responds to the urgent need for systematic and detailed information on EMA study designs, particularly those using cognitive assessments coupled with physiological measures. Given the complexity of EMA studies, choosing the right instruments and assessment schedules is an important aspect of study design and subsequent data interpretation.

Rest-Activity Rhythm Fragmentation and Weaker Circadian Strength Are Associated With Cognitive Impairment in Survivors of Acute Respiratory Failure.

Background: Survivors of acute respiratory failure (ARF) experience long-term cognitive impairment and circadian rhythm disturbance after hospital discharge. Although prior studies in aging and neurodegenerative diseases indicate actigraphy-estimated rest-activity circadian rhythm disturbances are risk factors for cognitive impairment, it is unclear if this applies to ARF survivors. This study explored the relationships of actigraphy-estimated rest-activity circadian rhythms with cognitive functioning in ARF survivors at 3 months after discharge. Methods: 13 ARF survivors (mean age 51 years and 69% males) completed actigraphy and sleep diaries for 9 days, followed by at-home neuropsychological assessment. Principal component factor analysis created global cognition and circadian rhythm variables, and these first components were used to examine the global relationships between circadian rhythm and cognitive measure scores. Results: Global circadian function was associated with global cognition function in ARF survivors (r = .70, p = .024) after adjusting for age, education, and premorbid cognition. Also, greater fragmented rest-activity circadian rhythm (estimated by intradaily variability, r = .85, p = .002), and weaker circadian strength (estimated by amplitude, r = .66, p = .039; relative strength, r = .70, p = .024; 24-h lag serial autocorrelation, r = .67, p = .035), were associated with global cognition and individual cognitive tests. Conclusions: These results suggest circadian rhythm disturbance is associated with poorer global cognition in ARF survivors. Future prospective research with larger samples is needed to confirm these results and increase understanding of the relationship between disrupted circadian rhythms and cognitive impairment among ARF survivors.

MoCA Intraindividual Cognitive Variability in Older Adults With Type 1 Diabetes.

Older adults with type 1 diabetes (T1D) may have an elevated risk of developing Alzheimer disease and related dementia. Higher intraindividual cognitive variability (IICV) has been proposed as a novel risk factor of Alzheimer disease and related dementia. Here, we examined the association between cross-domain IICV measured using the Montreal Cognitive Assessment (MoCA) and cognitive impairment measured using traditional neuropsychological tests in older individuals with T1D. Participants with T1D (N=201) completed both the MoCA and a battery of traditional neuropsychological tests. Participants with cognitive impairment, determined using traditional tests, had significantly higher IICV scores and significantly lower total MoCA scores ( P <0.001). However, the effect of the total score was greater than that of the IICV score on the likelihood of cognitive impairment (total odds ratio=3.50, IICV odds ratio=2.03, P <0.001). The MoCA total score performed better than the MoCA IICV score in identifying T1D individuals classified with cognitive impairment.

Associations between Intra-Individual Neurocognitive Variability and Prospective Memory in Veterans with Mild Traumatic Brain Injury History and Posttraumatic Stress Disorder.

OBJECTIVE: Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) frequently co-occur and are associated with neurocognitive intra-individual variability (IIV) and difficulty with prospective memory (PM). The current study aimed to examine associations between IIV and PM in this comorbid group. METHOD: Fifty veterans with a history of blast mTBI and current comorbid PTSD completed a standardized neurocognitive battery to measure IIV, and the Memory for Intentions Screening Test measuring PM. RESULTS: Adjusting for age, education, and race, higher IIV was associated with poorer time-based PM (p < .001, f2 = .34), but not event-based PM. In a subset of the sample with self-report data, higher IIV was associated with poorer self-reported retrospective memory, but not PM. CONCLUSIONS: Cognitive variability on a standardized neuropsychological battery was associated with strategically demanding PM, which is an ecologically relevant ability and highlights the possible connection between subtle cognitive difficulties in-clinic and those experienced in daily life.

Benefit of Continuous Glucose Monitoring in Reducing Hypoglycemia Is Sustained Through 12 Months of Use Among Older Adults with Type 1 Diabetes.

Objective: To evaluate glycemic outcomes in the Wireless Innovation for Seniors with Diabetes Mellitus (WISDM) randomized clinical trial (RCT) participants during an observational extension phase. Research Design and Methods: WISDM RCT was a 26-week RCT comparing continuous glucose monitoring (CGM) with blood glucose monitoring (BGM) in 203 adults aged ≥60 years with type 1 diabetes. Of the 198 participants who completed the RCT, 100 (98%) CGM group participants continued CGM (CGM-CGM cohort) and 94 (98%) BGM group participants initiated CGM (BGM-CGM cohort) for an additional 26 weeks. Results: CGM was used a median of >90% of the time at 52 weeks in both cohorts. In the CGM-CGM cohort, median time <70 mg/dL decreased from 5.0% at baseline to 2.6% at 26 weeks and remained stable with a median of 2.8% at 52 weeks (P < 0.001 baseline to 52 weeks). Participants spent more time in range 70-180 mg/dL (TIR) (mean 56% vs. 64%; P < 0.001) and had lower hemoglobin A1c (HbA1c) (mean 7.6% [59 mmol/mol] vs. 7.4% [57 mmol/mol]; P = 0.01) from baseline to 52 weeks. In BGM-CGM, from 26 to 52 weeks median time <70 mg/dL decreased from 3.9% to 1.9% (P < 0.001), TIR increased from 56% to 60% (P = 0.006) and HbA1c decreased from 7.5% (58 mmol/mol) to 7.3% (57 mmol/mol) (P = 0.025). In BGM-CGM, a severe hypoglycemic event was reported for nine participants while using BGM during the RCT and for two participants during the extension phase with CGM (P = 0.02). Conclusions: CGM use reduced hypoglycemia without increasing hyperglycemia in older adults with type 1 diabetes. These data provide further evidence for fully integrating CGM into clinical practice. Clinicaltrials.gov (NCT03240432).

Memory-Aware Active Learning in Mobile Sensing Systems.

We propose a novel active learning framework for activity recognition using wearable sensors. Our work is unique in that it takes limitations of the oracle into account when selecting sensor data for annotation by the oracle. Our approach is inspired by human-beings' limited capacity to respond to prompts on their mobile device. This capacity constraint is manifested not only in the number of queries that a person can respond to in a given time-frame but also in the time lag between the query issuance and the oracle response. We introduce the notion of mindful active learning and propose a computational framework, called EMMA, to maximize the active learning performance taking informativeness of sensor data, query budget, and human memory into account. We formulate this optimization problem, propose an approach to model memory retention, discuss the complexity of the problem, and propose a greedy heuristic to solve the optimization problem. Additionally, we design an approach to perform mindful active learning in batch where multiple sensor observations are selected simultaneously for querying the oracle. We demonstrate the effectiveness of our approach using three publicly available activity datasets and by simulating oracles with various memory strengths. We show that the activity recognition accuracy ranges from 21% to 97% depending on memory strength, query budget, and difficulty of the machine learning task. Our results also indicate that EMMA achieves an accuracy level that is, on average, 13.5% higher than the case when only informativeness of the sensor data is considered for active learning. Moreover, we show that the performance of our approach is at most 20% less than the experimental upper-bound and up to 80% higher than the experimental lower-bound. To evaluate the performance of EMMA for batch active learning, we design two instantiations of EMMA to perform active learning in batch mode. We show that these algorithms improve the algorithm training time at the cost of a reduced accuracy in performance. Another finding in our work is that integrating clustering into the process of selecting sensor observations for batch active learning improves the activity learning performance by 11.1% on average, mainly due to reducing the redundancy among the selected sensor observations. We observe that mindful active learning is most beneficial when the query budget is small and/or the oracle's memory is weak. This observation emphasizes advantages of utilizing mindful active learning strategies in mobile health settings that involve interaction with older adults and other populations with cognitive impairments.

The TestMyBrain Digital Neuropsychology Toolkit: Development and Psychometric Characteristics.

INTRODUCTION: To allow continued administration of neuropsychological evaluations remotely during the pandemic, tests from the not-for-profit platform, TestMyBrain.org (TMB), were used to develop the TMB Digital Neuropsychology Toolkit (DNT). This study details the psychometric characteristics of the DNT, as well as the infrastructure and development of the DNT. METHOD: The DNT was primarily distributed for clinical use, with (72.8%) of individuals requesting access for clinical purposes. To assess reliability and validity of the DNT, anonymous data from DNT test administrations were analyzed and compared to a large, non-clinical normative sample from TMB. RESULTS: DNT test scores showed acceptable to very good split-half reliability (.68-.99). Factor analysis revealed three latent factors, corresponding to processing speed, working memory, and a broader general cognitive ability factor that included perceptual reasoning and episodic memory. Average test scores were slightly poorer for the DNT sample than for the TMB comparison sample, as expected given the clinical use of the DNT. CONCLUSIONS: Initial estimates of reliability and validity of DNT tests support their use as digital measures of neuropsychological functioning. Tests within cognitive domains correlated highly with each other and demonstrated good reliability and validity. Future work will seek to validate DNT tests in specific clinical populations and determine best practices for using DNT outcome measures to assess engagement and psychological symptomatology.

Cognitive performance declines in older adults with type 1 diabetes: results from 32 years of follow-up in the DCCT and EDIC Study.

BACKGROUND: With improved treatment, individuals with type 1 diabetes are living longer but there is limited information on the effects of type 1 diabetes on cognitive ability as they become older adults. We followed up individuals with type 1 diabetes to identify independent risk factors for cognitive decline as people age. METHODS: 1051 participants with type 1 diabetes enrolled in the Diabetes Control and Complications Trial (DCCT) and its follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study. Participants completed cognitive assessments at baseline (median age 27 years) and 2, 5, 18, and 32 years later (median age 59). HbA1c levels, frequency of severe hypoglycaemia, non-glycemic risk factors such as elevated blood pressure, and microvascular and macrovascular complications were assessed repeatedly. We examined the effects of these on measures of memory and psychomotor and mental efficiency. These studies are registered with clinicaltrials.gov, NCT00360815 (DCCT) and NCT00360893 (EDIC). FINDINGS: Over 32 years of follow-up, we found substantive declines in memory and psychomotor and mental efficiency. Between 18 and 32 years of follow-up, the decline in psychomotor and mental efficiency was five times larger than the change from baseline to year 18. Independent of the other risk factors and comorbidities, exposure to higher HbA1c levels, more episodes of severe hypoglycaemia, and elevated systolic blood pressure were associated with greater decrements in psychomotor and mental efficiency that was most notable by year 32 (p<0·0001). The combined effect of the presence of these three risk factors is the equivalent to an additional 9·4 years of age. INTERPRETATION: Cognitive function declines with ageing in type 1 diabetes. The association of glycaemia and blood pressure levels with cognitive decline suggests that better management might preserve cognitive function. FUNDING: United States National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Disease.