BACKGROUND:Physical function is a key determinant of independence among older adults. Yet, there are barriers to assessing physical function in clinic. We developed a wearable geriatric functional assessment system (GFAS) that quickly and effortlessly evaluates physical function. METHODS:A single-arm, nonrandomized, mixed-methods, usability study evaluated the design, ergonomics, and usability of the GFAS. Participants aged >65 years with multiple chronic conditions were recruited and wore the GFAS about the clinic for 15 min. We assessed walking speed, 30-s sit-to-stand, and evaluated postural balance using a Footscan pressure plate system. In addition to transcribed exit-interviews and a Willingness to Pay questionnaire, the USE, Technology Acceptance, and System Usability Scales were evaluated. RESULTS:Of the 37 participants screened, 21 were recruited, enrolled, and consented, whose mean age was 76.6 ± 5.45 years (52.4% female), with 28.6% non-White, 19% were on Medicaid, and 52.4% were classified as having a robust Fried frailty status. Participants favored the prototype and its existing functionality (7.14 ± 2.35) and would wear it if recommended by their clinician (7.62 ± 2.50, median 8.0). All felt it was easy to use, 74% of comments outlined they would use it again, and 81% noted it was comfortable. System Usability score was 78.2 ± 14.5, USE was 5.83 ± 3.59, and Technology Acceptance demonstrated satisfaction of 7.05 ± 2.18 in using the device. CONCLUSIONS:The GFAS prototype shows considerable promise in evaluating physical function in older adults and that additional steps are needed to maximize usability.

Background: Frailty is a geriatric syndrome of significant public health concern that causes vulnerability to physiologic stressors and an increased risk of mortality and hospitalizations. Dietary intake and quality are contributing factors to the development of frailty. The Mediterranean diet is known to be one of the healthiest eating patterns with promising health impacts for prevention. We evaluated the association between Mediterranean diet patterns and frailty status. Methods: We conducted a cross-sectional study using National Health and Nutrition Examination Survey data from 2007 to 2017. We included 7300 participants aged > 60 years who completed the first day of a 24 h diet recall and had full covariate data. We constructed an alternate Mediterranean diet (aMED) score based on the quantity of specific food-group intake and categorized participants to low-, moderate-, and high-adherence groups (aMED adherence scores of 0-2, 3-4, and 5-9, respectively). Using a modified Fried Frailty phenotype (weakness, low physical activity, exhaustion, slow walking speed, and weight loss), participants were categorized as robust (met no criteria), pre-frail (met one or two criteria), and frail (met three or more criteria). Logistic regression evaluated the association of frailty (prefrail/robust as referent) and aMED adherence. Results: Included participants were mainly female (54.5%) and non-Hispanic White (80.0%). The mean (SD) aMED score was 3.6 (1.6) with 45% of participants falling into moderate aMED adherence (26% low adherence, 30% high adherence). Frailty prevalence among participants was 7.1%, with most participants classified as robust (51.0%) or pre-frail (41.9%). Fully adjusted models showed significantly reduced odds of frailty with moderate-adherence and high-adherence groups (odds ratio (95%CI) of 0.71 (0.55, 0.92) and 0.52 (0.36, 0.75), respectively). Conclusions: Mediterranean diet adherence is associated with decreased odds of frailty in older adults. These findings suggest that adherence to a Mediterranean diet may play a critical role in mitigating frailty and its associated conditions. Future research should include longitudinal and interventional studies that can definitively determine the effect of a Mediterranean diet on frailty and what food components provide the greatest benefit.

Hospitalized older adults, especially those with Alzheimer’s Disease and Related Dementias (PwD), are at high risk for delirium and distressing behaviors. Using physical restraints leads to functional decline and increased mortality. Our project aims to reduce restraint use by implementing a 4Ms approach for enhanced delirium management. Our interdisciplinary team used the Plan-Do-Study-Act methodology over a 7-month period (including 8 weeks of training) to introduce the 4Ms (Mentation, Mobility, Medication, what Matters) model in our 25-bed Acute Care for Elders (ACE) unit. Nurses were trained in the Confusion Assessment Method (CAM) for delirium assessment, CAM-S for delirium severity, and Six-item Cognitive Impairment Test (6CIT) for cognitive impairment. Staff conducted the 6CIT per admission and CAM/CAM-S per shift for patients aged ≥65, with electronic score documentation. Physical therapy assessed mobility within 24 hours of admission and during medical rounds. Medication reviews, led by the medical team and a geriatric pharmacist, occurred for new admissions. What Matters considerations were addressed through comprehensive geriatric assessments. Chart review collected data. An electronic record quantified physical restraint orders for six months pre- and post-intervention, by patient days and distinct patients per month. In the 19 weeks following data collection initiation, ACE unit patients (n = 362) averaged 81 years old, 60% female, 83% white, 14% Black, 18% were PwD, and 24% had significant cognitive impairment (6CIT score). Twenty-two percent experienced incident delirium during a mean 6-day stay. Documentation improved: CAM (68% to 86%), CAM-S (0% to 79%), 6CIT (0% to 89%). A 4Ms checklist achieved a 96% completion rate with 37.5% receiving a geriatric assessment, 25% were discharged with an ADL disability, and 12.5% had medication deprescribed. Restraint use decreased from 7.7 patient days per month to 2.2, distinct patients with restraints decreased from 2.7 to 1.7. In a high-risk older adult population, a low-resource quality improvement intervention effectively implemented the 4Ms model of care, showing a positive trend in reducing restraint use.

Among persons living with Alzheimer’s Disease and Related Dementias (PwD), ∼25% are hospitalized yearly, during which >95% of their time is spent in bed, leading to functional decline for 50% of hospitalized PwD. Hospital-based exercise interventions are a proposed strategy to mitigate this risk of hospital-associated functional decline. However, the applicability of hospital-based exercise intervention trials for PwD is poorly understood. Our narrative review explores the inclusion of PwD within such trials and the effectiveness of hospital-based exercise interventions in mitigating functional decline among PwD. This review follows PRISMA-Scr guidelines. The CENTRAL and MEDLINE databases and ClinicalTrials.gov were searched to identify randomized, controlled trials of hospital-based exercise interventions for older adults (≥65). Eligibility criteria were defined a priori. Studies were screened at the abstract and full-text level by one reviewer. A pre-defined abstraction form was used to identify study characteristics, including details of the exercise intervention, functional outcomes, and adverse events. When reported, we abstracted eligibility criteria relevant to cognitive status and proportion of PwD among the study population. Of 882 articles screened, 27 met the eligibility criteria. Seventeen trials included PwD: 9 with a hospital-based exercise intervention and 8 with a multi-component or unit-based intervention. Four studies of hospital-based exercise interventions and 3 studies of multi-component interventions reported a proportion of PwD. Of the 9 trials studying an exercise intervention, 5 found a non-significant change in functional capacity between admission and discharge within the intervention group, and 4 found a significant difference in functional capacity. No studies stratified outcomes by the presence of dementia or cognitive impairment. PwD are eligible for most trials of hospital-based exercise interventions for older adults included in this review. However, we cannot determine the applicability of hospital-based exercise interventions for PwD due to the inconsistency in reporting the prevalence of dementia within each study population. Further research must facilitate our ability to apply the results of hospital-based exercise interventions to PwD, most directly by designing and testing hospital-based exercise interventions specifically for PwD.

Weight loss may benefit older adults with obesity. However, it is unknown whether individuals with different frailty phenotypes have different outcomes following weight loss. Community-dwelling adults aged ≥65 (n = 53) with a body mass index ≥30 kg/m2 were recruited for a six-month, single-arm, technology-based weight loss study. A 45-item frailty index identified frailty status using subjective and objective measures from a baseline geriatric assessment. At baseline, n = 22 participants were classified as pre-frail (41.5%) and n = 31 were frail (58.5%), with no differences in demographic characteristics. While weight decreased significantly in both groups (pre-frail: 90.8 ± 2.7 kg to 85.5 ± 2.4 kg (p < 0.001); frail: 102.7 ± 3.4 kg to 98.5 ± 3.3 kg (p < 0.001), no differences were observed between groups for changes in weight (p = 0.30), appendicular lean mass/height2 (p = 0.47), or fat-free mass (p = 0.06). Older adults with obesity can safely lose weight irrespective of frailty status using a technology-based approach. Further investigation is needed to determine whether the impact of specific lifestyle interventions differ by frailty status.

See the related comment by Van Grootven.

BACKGROUND:Globally, the oldest old population is expected to triple by 2050. Hospitalization and malnutrition can result in progressive functional decline in older adults. Minimizing the impact of hospitalization on functional status in older adults has the potential to maintain independence, reduce health and social care costs, and maximize years in a healthy state. This study aimed to systematically review the literature to identify nutritional interventions that target physical function, body composition, and cognition in the older population (≥ 75 years). METHODS:A systematic review was conducted to evaluate the efficacy of nutritional interventions on physical function, body composition, and cognition in adults aged ≥ 75 years or mean age ≥80 years. Searches of PubMed (National Institutes of Health, National Library of Medicine), Scopus (Elsevier), EMBASE (Elsevier), Cumulative Index to Nursing and Allied Health Literature (CINAHL) with Full Text (EBSCOhost), and PsycInfo (EBSCOhost) were conducted. Screening, data extraction, and quality assessment were performed in duplicate and independently (CRD42022355984; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=355984). RESULTS:Of 8311 citations identified, 2939 duplicates were excluded. From 5372 citations, 189 articles underwent full-text review leaving a total of 12 studies for inclusion. Interventions were food-based, protein-based, carbohydrate-based, personalized, or used parenteral nutrition. Ten studies monitored anthropometric or body composition changes with three showing maintenance or improvements in lean mass, body mass index, triceps skinfold, and mid-upper arm circumference compared with the control group. Six studies monitored physical function but only the largest study found a beneficial effect on activities of daily living. Two of three studies showed the beneficial effects of nutritional intervention on cognition. CONCLUSION:There are few, high-quality, nutrition-based interventions in older adults ≥75 years. Despite heterogeneity, our findings suggest that large, longer-term (>2 weeks) nutritional interventions have the potential to maintain body composition, physical function, and cognition in adults aged 75 years and older during hospitalization.

Background Understanding how weight loss interventions in older adults with obesity impact aging biology can lay the foundation for targeted, ‘geroscience-based’ interventions. This study examines the association between changes in the senescence-associated secretory phenotypes (SASP) and changes in function in response to a weight loss intervention. Methods We conducted a post-hoc biomarker analysis on adults aged ≥ 65 years with body mass index [BMI] ≥30 kg/m2 enrolled in a six-month, non-randomized telemedicine-delivered weight loss intervention. We assessed 16 SASP cytokines using serum samples collected pre-and post-intervention. Clinical outcomes include anthropometric and physical function measurements. A weight loss responder was defined as a loss of ≥5 % of body weight. Results Mean age was 73.2 ± 3.9 years (73 % female), and BMI was 36.5 ± 5.2 kg/m2. Responders lost 7.6 ± 2.5 %, while non-responders lost 2.0 ± 2.3 % of weight (n = 16 per group, p < 0.001). We observed several significant associations between SASP cytokines and physical function and anthropometric measurement outcomes in age- and sex-adjusted linear models. These included grip strength and Interleukin-8 (IL-8) (b = 9.07) and Insulin-like Growth Factor 1 (IGF-1) (b = 2.6); gait speed and Thymus and Activation-Regulated Chemokine (TARC) (b = 0.46) and IL-7(b = 61 0.11); weight IL-6 (b = -6.77) and IL-15 (b = -2.53); BMI and IL-15 (b = -0.95); waist-to-hip ratio and osteopontin (b = -0.07) (p < 0.05 for all). Conclusions Our pilot data demonstrated an association between changes in select SASP biomarkers and increased functional ability with intentional weight loss in older adults with obesity. However, findings must be replicated in prospective randomized trials with a control group and additional SASP biomarkers.

Dietary assessments are important clinical tools used by Registered Dietitians (RDs). Current methods pose barriers to accurately assess the nutritional intake of older adults due to age-related increases in risk for cognitive decline and more complex health histories. Our qualitative study explored whether implementing Voice assistant systems (VAS) could improve current dietary recall from the perspective of 20 RDs. RDs believed the implementing VAS in dietary assessments of older adults could potentially improve patient accuracy in reporting food intake, recalling portion sizes, and increasing patient-provider efficiency during clinic visits. RDs reported that low technology literacy in older adults could be a barrier to implementation. Our study provides a better understanding of how VAS can better meet the needs of both older adults and RDs in managing and assessing dietary intake.

BACKGROUND:The population of older adults living with multiple chronic conditions (MCC) continues to grow. MCC is independently associated with functional limitation and obesity. The aim of our study was to evaluate the association between obesity and MCC, and secondarily, the combined presence of obesity and functional limitations with MCC.METHODS:We analyzed cross-sectional survey data from the National Health and Aging Trends Survey (NHATS) 2011 baseline data, a nationally representative Medicare beneficiary cohort of adults in the United States. We evaluated the coexistent prevalence of obesity and MCC overall, and by standard body mass index (BMI) categories. We then evaluated the prevalence of functional limitations (mobility, self-care, and household activities) and Fried-defined frailty status in persons with a BMI ≥ 30 kg/m2. Logistic regression was used to measure the association between MCC and BMI, and functional limitations and MCC among those with obesity.RESULTS:In the 6,600 participants, the prevalence of concurrent obesity and MCC was 30.4%. Of those with obesity, the prevalence of MCC was 84.0%, and were more likely to have MCC (adjusted OR: 2.17, 95% CI 1.86, 2.54) compared to a normal BMI. Obesity and functional limitations or frailty were more likely have MCC than individuals with obesity alone.CONCLUSIONS:We found that individuals with obesity is strongly associated with MCC and that functional limitations and frailty status have a greater association with having MCC than individuals with obesity without MCC. Future longitudinal analyses are needed to ascertain this relationship.

Acute Care for Elders (ACE) units reduce hospital-associated delirium, functional decline, and lengths of stay. However, establishing and sustaining such units have proven difficult. There are only 43 ACE units among the >3500 hospitals in the United States. This study describes an iterative quality improvement process, which allowed us to establish and sustain an ACE unit care model in a modern academic hospital. This continuous process was centered on implementing the key principles of the ACE unit model of care: patient-centered care assessments, medical care review, specialized prepared environment, early mobilization, physical therapy, and early planning for discharge to home. Quality of care and patient outcomes data for older adults admitted to our ACE unit includes mortality index (observed/expected) consistently <1 (FY22 = 0.86), 30-day readmission rate of <10% (FY22 9.31%), and length of stay index of similar to 1 (FY22 1.07). We describe how work on our ACE unit has led to hospital-wide initiatives, including dementia-friendly hospital certification. Our hope is that others can use this process to enhance the dissemination of the ACE unit model of care.

Caloric restriction and aerobic and resistance exercise are safe and effective lifestyle interventions for achieving weight loss in the obese older population (>65 years) and may improve physical function and quality of life. However, individual responses are heterogeneous. Our goal was to explore the use of untargeted metabolomics to identify metabolic phenotypes associated with achieving weight loss after a multi-component weight loss intervention. Forty-two older adults with obesity (body mass index, BMI, ≥30 kg/m2) participated in a six-month telehealth-based weight loss intervention. Each received weekly dietitian visits and twice-weekly physical therapist-led group strength training classes with a prescription for aerobic exercise. We categorized responders’ weight loss using a 5% loss of initial body weight as a cutoff. Baseline serum samples were analyzed to determine the variable importance to the projection (VIP) of signals that differentiated the responder status of metabolic profiles. Pathway enrichment analysis was conducted in Metaboanalyst. Baseline data did not differ significantly. Weight loss was 7.2 ± 2.5 kg for the 22 responders, and 2.0 ± 2.0 kg for the 20 non-responders. Mummichog pathway enrichment analysis revealed that perturbations were most significant for caffeine and caffeine-related metabolism (p = 0.00028). Caffeine and related metabolites, which were all increased in responders, included 1,3,7-trimethylxanthine (VIP = 2.0, p = 0.033, fold change (FC) = 1.9), theophylline (VIP = 2.0, p = 0.024, FC = 1.8), paraxanthine (VIP = 2.0, p = 0.028, FC = 1.8), 1-methylxanthine (VIP = 1.9, p = 0.023, FC = 2.2), 5-acetylamino-6-amino-3-methyluracil (VIP = 2.2, p = 0.025, FC = 2.2), 1,3-dimethyl uric acid (VIP = 2.1, p = 0.023, FC = 2.3), and 1,7-dimethyl uric acid (VIP = 2.0, p = 0.035, FC = 2.2). Increased levels of phytochemicals and microbiome-related metabolites were also found in responders compared to non-responders. In this pilot weight loss intervention, older adults with obesity and evidence of significant enrichment for caffeine metabolism were more likely to achieve ≥5% weight loss. Further studies are needed to examine these associations in prospective cohorts and larger randomized trials.

Sarcopenic Obesity is the co-existence of increased adipose tissue (obesity) and decreased muscle mass or strength (sarcopenia) and is associated with worse outcomes than obesity alone. The new EASO/ESPEN consensus provides a framework to standardize its definition. This study sought to evaluate whether there are preliminary differences observed in weight loss or physical function in older adults with and without sarcopenic obesity taking part in a multicomponent weight loss intervention using these new definitions. A 6-month, non-randomized, non-blinded, single-arm pilot study was conducted from 2018 to 2020 in adults ≥ 65 years with a body mass index (BMI) ≥ 30 kg/m2. Weekly dietitian visits and twice-weekly physical therapist-led exercise classes were delivered using telemedicine. We conducted a secondary retrospective analysis of the parent study (n = 53 enrolled, n = 44 completers) that investigated the feasibility of a technology-based weight management intervention in rural older adults with obesity. Herein, we applied five definitions of sarcopenic obesity (outlined in the consensus) to ascertain whether the response to the intervention differed among those with and without sarcopenic obesity. Primary outcomes evaluated included weight loss and physical function (30-s sit-to-stand). In the parent study, mean weight loss was − 4.6 kg (95

The proportion of older adults classified as having obesity now exceeds 35% and has led to a concomitant rise in the rates of obesity-related disability.1 Previous weight loss studies have shown that caloric restriction alone can lead to detrimental effects on muscle function and declines in physical function in older adults.2 Programs that also include structured resistance and aerobic exercise plans have demonstrated synergistic improvements in physical function.3 However, access to such programs is limited, particularly for patients residing in rural areas. Thus, this demographic may benefit from the technology-based delivery of health promotion interventions. We previously published feasibility findings from a 6-month technology-based intervention that offered dietary counseling and a structured exercise program for 53 older adults with obesity.4 This multicomponent diet and exercise intervention was acceptable and feasible, resulted in 4.7 ± 3.5% weight loss, and demonstrated improvements in physical function (30-s sit-to-stand: +3.1 ± 4.2 reps; 6-min walk: +42.0 ± 77.3 m). Questions remain regarding the long-term sustainability of weight loss interventions, particularly for older adults. This report shares our findings on its long-term sustainability 1 year after completion of the active intervention for both participants who responded significantly to the initial intervention and those who did not. Details on the design, setting, and recruitment of this pilot study have been previously published.4 This was a single center, pre/post, 26-week technology-based weight management intervention consisting of nutrition and exercise components. There were 53 community-dwelling participants aged ≥65 years with a body mass index (BMI) ≥30 kg/m2 residing in rural New England. The nutrition encounters included eighteen 30-min virtual one-on-one personal nutrition sessions and seven in-person group sessions. The exercise component included forty 75-min virtual group sessions, and seven in-person group sessions delivered by a physical therapist focusing on aerobic activities, resistance, flexibility, and balance. For this analysis, we evaluated participants' weight at 12 months from intervention completion relative to weight at baseline and at the time of intervention completion. These 12-month values were abstracted from the institution's electronic health record. For those with missing weight, we sent surveys to ask them their self-reported weight but had no response (n = 2). We compared outcomes for responders, defined as those who lost ≥5% of their body weight during the intervention period, and nonresponders, defined as those completing the program but did not.5 Descriptive statistics were conducted, including an ANOVA testing over time. All analyses were conducted using R version 4.1.1. Of the n = 44 that completed the intervention, 50% of the cohort (n = 22) responded to the initial intervention. There were no significant differences across several demographic variables and comorbidities between responders and non-responders (Table 1).6 Among completers, baseline and 6-month (intervention completion) weights were 97.8 ± 16.3 and 93.2 ± 15.8 kg, respectively (Δ = −4.7 ± 3.4 kg; p < 0.001). Mean weight at 18-months (12-months post-intervention completion) was 92.6 ± 16.8 kg (n = 42). This was significantly less than the baseline weight (Δ = −5.3 ± 7.1 kg; p < 0.001) but no different from the 6-month weight (Δ = −0.6 ± 6.3; p = 0.60; Figure 1). Responders' mean baseline and 6-month weights were 99.6 ± 14.8 versus 92.4 ± 13.9 kg (Δ = −7.2 ± 2.5; p < 0.001). Their mean weight at 18-months was 91.6 ± 13.6 kg (n = 21). This was significantly lower than at baseline (Δ = −8.9 ± 9.2; p < 0.001) but no different from their 6-month weight (Δ = −1.5 ± 8.6; p = 0.44; Figure 1). In non-responders, the mean weights at baseline compared to 6-months weights were 96.0 ± 17.8 versus 94.0 ± 17.7 kg (Δ = −2.0 ± 2.0; p < 0.001). Their mean weight at 18-months was 93.7 ± 19.8 kg (n = 21). This was not different than their weight at baseline (Δ = −1.8 ± 4.5; p = 0.09) or 6-month weight (Δ = +0.4 ± 4.3; p = 0.69; Figure 1). Our findings suggest that weight was maintained 12 months after completion of a technology-based, weight management program in older adults residing in rural areas. This was true for both those who significantly responded to the initial intervention and those who did not. Several studies have shown that while some weight is often regained in the period after intensive weight loss interventions for adults with obesity, participants typically remain below baseline weight, though these findings have been chiefly in younger adults.7, 8 Our overall weight maintenance findings align with those previously published for older adults.9 Our findings offer encouraging evidence that technology-based interventions may address access disparities and allow for maintained weight loss for older adults in rural areas. Due to the small size of this our pilot, we lacked statistical power to delineate what factors may have been associated with weight changes in the follow-up period. Further studies are needed to delineate these factors and the long-term net benefits on morbidity and muscle function. All authors participated in the study's conceptual design, data analysis and accuracy and creation of this research letter. No authors have any financial, personal, or potential conflicts of interest to disclose. Dr. Batsis' research reported in this publication was supported in part by the National Institute on Aging under Award Number K23AG051681. Support was also provided by the Dartmouth Health Promotion and Disease Prevention Research Center supported by Cooperative Agreement Number U48DP005018 from the Centers for Disease Control and Prevention, the Dartmouth Clinical and Translational Science Institute, under award number UL1TR001086, and the NC Translational and Clinical Sciences (NC TraCS) Institute, which is supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002489. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of NIH, CDC, or NIA. Dr. Batsis' research reported in this publication was supported in part by the National Institute on Aging under Award Number K23AG051681. Support was also provided by the Dartmouth Health Promotion and Disease Prevention Research Center supported by Cooperative Agreement Number U48DP005018 from the Centers for Disease Control and Prevention, the Dartmouth Clinical and Translational Science Institute, under award number UL1TR001086, and the NC Translational and Clinical Sciences (NC TraCS) Institute, which is supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002489. Dr. Batsis also owns equity in SynchroHealth LLC.

BACKGROUND:There is a close relationship between weight status and cognitive impairment in older adults. This study examined the association between weight status and the trajectory of cognitive decline over time in a population-based cohort of older adults in China. METHODS:We used data from adults aged ≥55 years participating in the China health and nutrition survey (1997-2018). Underweight (body mass index [BMI] ≤ 18.5 kg/m2), normal weight (18.5-23 kg/m2), overweight (23-27.5 kg/m2), and obesity (BMI ≥ 27.5 kg/m2) were defined using the World Health Organization Asian cutpoints. Global cognition was estimated every 2-4 years through a face-to-face interview using a modified telephone interview for cognitive status (scores 0-27). The association between BMI and the rate of global cognitive decline, using a restricted cubic spline for age and age category, was examined with linear mixed-effects models accounting for correlation within communities and individuals. RESULTS:We included 5 992 adults (53% female participants, mean age of 62 at baseline). We found differences in the adjusted rate of global cognitive decline by weight status (p = .01 in the cubic spline model). Models were adjusted for sex, marital status, current employment status, income, region, urbanization, education status, birth cohort, leisure activity, smoking status, and self-reported diagnosis of hypertension, diabetes, or Myocardial Infarction (MI)/stroke. In addition, significant declines by age in global cognitive function were found for all weight status categories except individuals with obesity. CONCLUSIONS:In a cohort of adults in China, cognitive decline trajectory differed by weight status. A slower rate of change was observed in participants classified as having obesity.

This presentation will discuss the most common types of induction tooling failures and the best practices to improve the performance and longevity of inductor coils, bus bars quenches and related tooling. We will discuss the harsh environment of a typical induction machine installation and what can be done to reduce contamination, which is the leading cause of tooling failure. Robust tooling designs and how water cooling is essential to longevity shall be discussed. Cooling water temperature and how the water is presented and routed through the tooling components and the impact this has on performance and longevity shall be discussed. We will discuss the use of proper materials, fittings and hoses which are often overlooked and can be detrimental to a process if not correctly selected. We will cover the induction machine and how it is essential to have a proper earth ground and the importance of proper machine fixturing and alignment. We shall discuss the importance of scheduled machine maintenance, scheduled service and calibration. The presentation will summarize the most common types of failures, how maintenance is essential for longevity and the importance of high-quality robust tooling.

BACKGROUND Dementia affects 55 million people worldwide and low muscle mass may be associated with cognitive decline. Mid-arm muscle circumference (MAMC) correlates with dual-energy Xray absorptiometry and bioelectrical impedance analyses, yet are not routinely available. Therefore, we examined the association between MAMC and cognitive performance in older adults. METHODS We included community-dwelling adults ≥55 years from the China Health and Nutrition Survey. Cognitive function was estimated based on a subset of the modified Telephone Interview for Cognitive Status (0-27, low-high) during years (1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015, 2018). A multivariable linear mixed-effects model was used to test whether MAMC was associated with rate of cognitive decline across age groups and cognitive function overall. RESULTS Of 3702 adults (53% female, 63.2 ± 7.3 years), mean MAMC was 21.4 cm ± 3.0 and baseline cognitive score was 13.6 points ±6.6. We found no evidence that the age-related rate of cognitive decline differed by MAMC (P = .77). Declines between 5-year age groups ranged from -.80 [SE (standard error) .18] to -1.09 [.22] for those at a mean MAMC, as compared to -.86 [.25] to -1.24 [.31] for those at a 1 MAMC 1 standard deviation above the mean. Higher MAMC was associated with better cognitive function with .13 [.06] higher scores for each corresponding 1 standard deviation increase in MAMC across all ages. CONCLUSION Higher MAMC at any age was associated with better cognitive performance in older adults. Understanding the relationship between muscle mass and cognition may identify at-risk subgroups needing targeted interventions to preserve cognition.

Alzheimer's disease and Alzheimer's disease related dementias (ADRD) cause disability and death in older adults.1 Specialty palliative care improves outcomes for other serious illnesses but is rarely used for ADRD.2 To address this gap, we are conducting the ADRD-PC multi-site clinical trial of specialty palliative care for hospitalized people with ADRD and their caregivers.3 To facilitate timely trial enrollment, we designed and implemented an electronic health record (EHR) algorithm to identify people with ADRD at the time of hospitalization at a single site.4 Across four sites, we now report utility of this algorithm identifying people with late-stage ADRD. We (1) examine the positive predictive value (PPV) of the EHR algorithm compared to a chart diagnosis of ADRD confirmed by research assistant (RA), and (2) the PPV of the EHR algorithm compared to physician confirmed diagnosis of late-stage ADRD. We also provide descriptive data on the efficiencies for participant enrollment. The ADRD-PC trial enrolls dyads of hospitalized patients with late-stage ADRD and their caregivers at medical centers in North Carolina (NC), Massachusetts (MA), Indiana (IN), and Colorado (CO). Dyads are randomized to either the intervention or control (usual care). Those randomized to the intervention received a dementia-specific specialty palliative care consultation with transitional care up to 2 weeks after discharge. The ADRD-PC trial began enrollment in July 2021 and is ongoing. The Institutional Review Board of record was at The University of North Carolina-Chapel Hill (UNC)—all external sites ceded oversight to UNC's IRB. Eligible hospitalized patients are over age 55, have late-stage dementia (e.g., Global Deterioration Scale5 (GDS) score of 6 or 7, or GDS 5 plus Charlson Comorbidity Index6 (CCI) score of 5 or greater), are not receiving hospice or palliative care, and have an English-speaking surrogate decision maker. Each site implemented an EHR-based algorithm.4 The EHR algorithm identifies admitted patients aged 55 or older with one of 34 ICD-10 dementia-related diagnosis codes (Appendix of original publication). Each site's RA receives a real-time notification of potentially eligible patients, and then reviews the EHR for trial eligibility, particularly the presence of late-stage dementia. Physicians who are site PIs confirm the ADRD diagnosis and stage using EHR review and consultation with the patient's attending physician (Supplementary Figure S1). We used descriptive statistics to describe participant characteristics. The PPV of the EHR algorithm for ADRD diagnosis is the probability that a participant with algorithm-identified ADRD has an established diagnosis of ADRD upon chart review by the RA. The PPV for late-stage ADRD is the total number of dyads confirmed as having late-stage dementia over all suspected cases of late-stage dementia identified by the algorithm and RA chart review. Participant characteristics are outlined in Table 1. For the n = 3786 potential cases identified by the algorithm, 2644 true positives were confirmed by RA chart review. Thus, the PPV of the algorithm to detect people with ADRD was 69.8%, and site-based PPV ranged from 54.5% to 83.0% (Table 2). The RA chart reviews identified n = 805 potential cases of late-stage ADRD, and site-PIs confirmed 688 as people with late stage (GDS 5–7) ADRD. Thus, the PPV of the EHR algorithm combined with RA chart review was 85.5%, and site-based PPV for late-stage ADRD ranged from 75.1% to 96.1%. An EHR algorithm to identify hospitalized people with ADRD facilitated rapid identification and enrollment in a clinical trial across multiple sites. Furthermore, the EHR algorithm combined with brief chart review efficiently identified people with late-stage dementia, thus facilitating enrollment in a multi-site clinical trial. A historical barrier to the success of pragmatic trials for people with ADRD has been the inability to identify participants who may benefit from the intervention.7 In 2020, investigators from the National Institute on Aging funded, IMbedded Pragmatic Alzheimer's disease and AD-Related Dementias Clinical Trials (IMPACT) collaboratory encouraged the development of accurate and complete case-finding algorithms that could be used in diverse populations and across settings.7 Currently, there is no “gold standard” algorithm for identifying people with ADRD. A major strength of this study is our successful implementation of this algorithm in multiple sites. As with similar algorithms, we likely miss potential participants who lack dementia-specific ICD10 codes, though this is more likely in early-stage ADRD. Algorithms under development using natural language processing may be more inclusive, or may permit greater specificity such as a focus on people with behavioral symptom distress.8, 9 Future adaptations of this method may facilitate ADRD clinical trials or quality improvement across various clinical settings. All listed authors had full access to all the data in the study, take responsibility for the integrity of the data and the accuracy of the data analysis, and had authority over manuscript preparation, the decision to submit the manuscript for publication, and approved its current contents. All authors meet the criteria for authorship stated in the Uniform Requirements for Manuscripts Submitted to Biomedical Journals. The authors declare no conflicts of interest. Laura Hanson's research reported in this publication was supported in part by the National Institute on Aging of the National Institutes of Health (R01 AG065394). The content is solely the authors' responsibility and does not necessarily represent the official views of the National Institutes of Health. Funding to support this work: AG065394. Supplementary Figure S1. ADRD-PC consort diagram: Screening to enrollment. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Background: The role of protein in glucose homeostasis has demonstrated conflicting results. However, little research exists on its impact following weight loss. This study examined the impact of protein supplementation on glucose homeostasis in older adults >65 years with obesity seeking to lose weight. Methods: A 12-week, nonrandomized, parallel group intervention of protein (PG) and nonprotein (NPG) arms for 28 older rural adults (body mass index (BMI) ≥ 30 kg/m2) was conducted at a community aging center. Both groups received twice weekly physical therapist-led group strength training classes. The PG consumed a whey protein supplement three times per week, post-strength training. Primary outcomes included pre/post-fasting glucose, insulin, inflammatory markers, and homeostasis model assessment of insulin resistance (HOMA-IR). Results: Mean age and baseline BMI were 72.9 ± 4.4 years and 37.6 ± 6.9 kg/m2 in the PG and 73.0 ± 6.3 and 36.6 ± 5.5 kg/m2 in the NPG, respectively. Mean weight loss was −3.45 ± 2.86 kg in the PG and −5.79 ± 3.08 kg in the NPG (p < 0.001). There was a smaller decrease in pre- vs. post-fasting glucose levels (PG: −4 mg ± 13.9 vs. NPG: −12.2 ± 25.8 mg/dL; p = 0.10), insulin (−7.92 ± 28.08 vs. −46.7 ± 60.8 pmol/L; p = 0.01), and HOMA-IR (−0.18 ± 0.64 vs. −1.08 ± 1.50; p = 0.02) in the PG compared to the NPG. Conclusions: Protein supplementation during weight loss demonstrated a smaller decrease in insulin resistance compared to the NPG, suggesting protein may potentially mitigate beneficial effects of exercise on glucose homeostasis.