Nutritional Risk In Older Adults Research Articles

Nutritional Risk, Depression, and Physical Function in Older People Living Alone.

This study investigated depression and physical function as factors associated with nutritional risk in older adults living alone. The study included 2896 people 65 years or older who lived alone. Data were collected in South Korea between September and November 2020. Descriptive statistics, a chi-squared test, independent samples t-test, weighted multiple regression analysis, and binary logistic regression analysis were performed using IBM SPSS for Windows ver. 23.0. In this study, 44.8% of participants were in the nutritional risk group. Furthermore, 60.9% of those at risk for depression, 75.1% of those with instrumental activities of daily living (IADLs) dependency, and 59.1% of those with chewing limitations were at nutritional risk. The factors that increased nutritional risk in the weighted multiple regression analysis were depression (β = 0.27, p < 0.001), chewing limitations (β = 0.12, p < 0.001), IADL dependency (β = 0.09, p < 0.001), and basic physical movement (β = 0.04, p = 0.020). Binary logistic regression analysis showed that those with IADL dependency had a 2.59 times higher nutritional risk than those with IADL non-dependency (p < 0.001). The nutritional risk group had a higher risk of depression (2.01 times higher [p < 0.001]), chewing limitations (1.76 times higher [p < 0.001]), and basic physical movement limitations (1.35 times higher [p = 0.009]) than the good nutritional group. Therefore, nutritional screening is required of older individuals living alone. To mitigate nutritional risks, it is necessary to assess depression and physical function, including IADL dependency.

An analysis of nutritional risk factors in older adults with gastrointestinal tumours

IntroductionThe aim of this study was to investigate risk factors for nutritional risk in older adults with gastrointestinal tumours. Materials and MethodsA total of 170 eligible hospitalised older adults with gastrointestinal tumours were included. Their clinical characteristics were collected, their nutritional risk was screened by NRS 2002, and then patients were divided into a nutritional risk group and a non-nutritional risk group. The observation indicators included body mass index (BMI), muscle mass, muscle strength, and calf circumference. The third lumbar skeletal muscle index (L3 SMI) was calculated using abdominal computed tomography (CT) scan results, and grip strength/muscle strength, 6-m walking speed and calf circumference were measured. Sarcopenia was diagnosed according to the criteria of the Asian Sarcopenia Working Group (AWGS). Finally, we analysed the relationship between nutritional risk and sarcopenia and other related factors (BMI, calf circumference, L3 SMI, grip strength/muscle strength, 6-m walking speed) in older adults with gastrointestinal tumours by multivariate logistic regression analysis. ResultsOlder adults with gastrointestinal tumours who were at nutritional risk accounted for 51.8% of patients in this study. The differences between sex, tumour stage, age, BMI, calf circumference, L3 SMI, grip strength/muscle strength, 6-m walking speed, and prevalence of sarcopenia were statistically significant in two groups (all P < 0.05). Multivariate logistic regression analysis showed that age, BMI, grip strength/muscle strength, and sarcopenia were risk factors of nutritional risk in older adults with gastrointestinal tumours (all P < 0.05). DiscussionOlder adults with gastrointestinal cancer had a higher proportion of nutritional risk, and L3 SMI, grip strength/muscle strength were independent risk factors for nutritional risk. In clinical practice, attention to nutritional risk screening and sarcopenia development in older adults with gastrointestinal cancer is warranted.

The relationship of nutritional risk with diet quality and health outcomes in community-dwelling older adults

ObjectivesTo identify early nutritional risk in older populations, simple screening approaches are needed. This study aimed to compare nutrition risk scores, calculated from a short checklist, with diet quality and health outcomes, both at baseline and prospectively over a 2.5-year follow-up period; the association between baseline scores and risk of mortality over the follow-up period was assessed.MethodsThe study included 86 community-dwelling older adults in Southampton, UK, recruited from outpatient clinics. At both assessments, hand grip strength was measured using a Jamar dynamometer. Diet was assessed using a short validated food frequency questionnaire; derived ‘prudent’ diet scores described diet quality. Body mass index (BMI) was calculated and weight loss was self-reported. Nutrition risk scores were calculated from a checklist adapted from the DETERMINE (range 0–17).ResultsThe mean age of participants at baseline (n = 86) was 78 (SD 8) years; half (53%) scored ‘moderate’ or ‘high’ nutritional risk, using the checklist adapted from DETERMINE. In cross-sectional analyses, after adjusting for age, sex and education, higher nutrition risk scores were associated with lower grip strength [difference in grip strength: − 0.09, 95% CI (− 0.17, − 0.02) SD per unit increase in nutrition risk score, p = 0.017] and poorer diet quality [prudent diet score: − 0.12, 95% CI (− 0.21, − 0.02) SD, p = 0.013]. The association with diet quality was robust to further adjustment for number of comorbidities, whereas the association with grip strength was attenuated. Nutrition risk scores were not related to reported weight loss or BMI at baseline. In longitudinal analyses there was an association between baseline nutrition risk score and lower grip strength at follow-up [fully-adjusted model: − 0.12, 95% CI (− 0.23, − 0.02) SD, p = 0.024]. Baseline nutrition risk score was also associated with greater risk of mortality [unadjusted hazard ratio per unit increase in score: 1.29 (1.01, 1.63), p = 0.039]; however, this association was attenuated after adjustment for sex and age.ConclusionsCross-sectional associations between higher nutrition risk scores, assessed from a short checklist, and poorer diet quality suggest that this approach may hold promise as a simple way of screening older populations. Further larger prospective studies are needed to explore the predictive ability of this screening approach and its potential to detect nutritional risk in older adults.

P119 Examining Predictors for Diet Quality: A Needs Assessment of Rural Versus Urban Older Adults

<h3>Background</h3> Diet and lifestyle are vital in maintaining good health and independence for older adults. Older adults living in rural areas are at risk for chronic disease, poor diet quality, and limited accessibility to healthy foods, compared to their urban counterparts. <h3>Objective</h3> To assess rural and urban older adults' diet quality, food insecurity, and self-efficacy for overcoming barriers to making informed nutrition-related decisions. <h3>Study Design, Setting, Participants</h3> From April-August 2019, a cross-sectional design was used to administer a self-reported survey to adults ≥50 years, in Maryland, participating at senior/community sites. Using convenience sampling, 475 participants (Urban, n = 215; Rural n = 260) were recruited prior to a nutrition education program (44 sites, 19 counties). <h3>Measurable Outcome/Analysis</h3> Participants completed a self-administered survey (41 items). Measurable constructs included diet quality assessed by the validated Dietary Screening Tool (DST), which evaluates nutritional risk in older adults. Perceived self-efficacy for healthy eating behaviors, food insecurity, and sociodemographic factors were also measured. Data were analyzed using SPSS version 24.0 (t-test, chi-square, and multiple linear regression). <h3>Results</h3> Participants were predominantly women (83.6%), white (54.0%), who lived alone (53.6%), with a median age of 74.5 (SD ± 8.5), and reported their health as somewhat good/very good (53.1%). Those in rural areas had significantly lower DST scores (<i>P</i> = .024), higher average number of chronic diseases (<i>P</i> = .004), and lower self-efficacy for changing health behaviors (<i>P</i> = .001) compared to urban/suburban counterparts. After controlling for other variables, rural and urban dwelling was not a significant predictor of diet quality. However, DST scores were significant predictors of food insecurity (B = −3.097, <i>P</i> < .05) and perceived self-efficacy (B = 1.145, <i>P</i> < .001) among older adults. <h3>Conclusions</h3> Although previous research reported rural dwellers were more at risk for poor diet quality than urban dwellers, our findings did not support this. However, our results did support the need for nutrition interventions addressing food insecurity and barriers to self-efficacy for improving diet quality of all older adults.

Validation of the Dietary Screening Tool in a Middle‐Aged Appalachian Population

The Dietary Screening Tool (DST) was developed and validated to assess nutritional risk in older adults. The purpose of this study was to assess its use in a middle‐aged sample of adults (45–64 years) in Appalachia (n=87) and evaluate its validity with multiple 24‐hour dietary recalls and selected biomarkers. Adults identified by the DST as at nutrition risk (&lt;60 of 100 points) were significantly more likely to have had lower overall diet quality as assessed by the Healthy Eating Index (64 vs 50, p&lt;0.001) when compared to those not at risk or potential risk. Dietary intakes of vitamin A, vitamin C, vitamin B2, vitamin B6, niacin, vitamin K and potassium were also lower among those considered at risk, despite having significantly higher energy intakes (1653 ± 106 vs 2032 ± 76 kcal/day, p=0.004). The diets of those at risk were higher in added sugars (33 g vs 23 g, P=0.005) and lower in dietary fiber (10 g vs 12 g, p&lt;0.001) than those not at risk. The at risk group had significantly lower serum levels of 13‐cis‐β carotene (40 vs 60 nmol/L, p&lt;0.02), trans‐ β‐carotene (395 vs 711 nmol/L, p&lt;0.001), β‐Cryptoxanthin (137 vs 198 nmol/L, p&lt;0.01), cis‐Lutein (90 vs 137 nmol/L, p&lt;0.02), and trans‐lutein (292 vs 424 nmol/L, p=0.01). The DST was able to identify middle‐aged adults at nutritional risk.Support or Funding InformationUSDA Northeast Regional Project NE1439, WVU Health Disparities Mountains of Excellence Flash Funding and the WVU ADVANCE Sponsorship Program.

FACTORS ASSOCIATED WITH NUTRITIONAL RISK AMONG HOMEBOUND OLDER ADULTS WITH DEPRESSIVE SYMPTOMS

This study used the Evans model of public health determinants to identify factors associated with nutritional risk in older adults. The Evans model domains (physical and mental well-being, social/environmental statuses, individual choice, and economic security) were measured in a sample of homebound older adults. Regularized logistic regression analysis with LASSO penalty function was used to determine the strongest domain of the Evans model. Using traditional logistic regression, individual variables across all domains were compared to identify the significant predictors. Older adults receiving home meal services were referred to the study by community program staff. Participants included 164 homebound older adults (age > 60) who endorsed at least one gateway symptom of depression. Measurements: Nutritional risk was determined using the Mini Nutritional Assessment. Domains of the Evans model were measured using the MAI Medical Condition Checklist, items from the IADL scale, the Structured Clinical Interview for DSM-IV Axis I Disorders, the Duke Social Support Index, living arrangements, marital status, the Alcohol Use Disorders Identification Test, items from the SCID Screening Module, and a self-report of perceived financial security. Poor mental well-being, defined by a diagnosis of major depressive disorder, was identified as the strongest Evans model domain in the prediction of nutritional risk. When each variable was independently evaluated across domains, instrumental support (Wald’s Z=-2.24, p=0.03) and a history of drug use (Wald’s Z=-2.40, p=0.02) were significant predictors. The Evans model is a useful conceptual framework for understanding nutritional health, with the mental domain found to be the strongest domain predictor of nutritional risk. Among individual variables across domains, having someone to help with shopping and food preparation and a history of drug use were associated with lower nutritional risk. These analyses highlight potential targets of intervention for nutritional risk among older adults.

Farmers' Market Produce Delivery Program for Mitigating Nutritional Risk in Older Adults

Community-dwelling older adults in disadvantaged neighborhoods may face nutritional risks not mitigated by existing programs. The Senior Market Basket Program, administered by nonprofit organization P.E.E.R., Inc., is a unique approach to serving community-dwelling senior adults and a valuable model for integrating targeted social services into local food systems. The program ensures access to fresh produce during the growing season for a defined target population.

Relative validity of the Geisinger Rural Aging Study food frequency questionnaire.

To determine the relative validity of a population specific food frequency questionnaire (FFQ) and evaluate the effectiveness of the instrument for assessing nutritional risk in older adults. A cross-over design with participants completing two different dietary assessment instruments in random order. The Geisinger Rural Aging Study (GRAS), a longitudinal study of over 20,000 adults living in the central, northern and eastern counties of Pennsylvania. A subset of GRAS consisting of 245 older adults (60% women) ranging in age from 70 to 95 years. Energy and nutrient intakes were assessed from two instruments: a population specific food frequency questionnaire (FFQ) and four 24-hour dietary recalls conducted over a two week period. Pearson correlation coefficients between the FFQ and dietary recalls for most nutrients were 0.5 or higher which suggests that the FFQ provided relatively valid estimates of macro and micronutrient intakes examined. Bland-Altman plots were generated to examine the agreement between instruments. Data are shown for energy, folate and zinc with close agreement at lower intakes indicative of risk for folate and zinc. Sensitivity results also showed that the FFQ was able to correctly classify individuals adequately at risk for most nutrients examined. This population specific FFQ appears to be a valid instrument for use in in evaluating risk for many nutrients that are of particular concern in older adults residing throughout many predominately rural counties in Pennsylvania.

Dietary screening tool identifies nutritional risk in older adults

Background:No rapid methods exist for screening overall dietary intakes in older adults. Objective:The purpose of this study was to develop and evaluate a scoring system for a diet screening tool to identify nutritional risk in community-dwelling older adults. Design:This cross-sectional study in older adults (n = 204) who reside in rural areas examined nutrition status by using an in-person interview, biochemical measures, and four 24-h recalls that included the use of dietary supplements. Results:The dietary screening tool was able to characterize 3 levels of nutritional risk: at risk, possible risk, and not at risk. Individuals classified as at nutritional risk had significantly lower indicators of diet quality (Healthy Eating Index and Mean Adequacy Ratio) and intakes of protein, most micronutrients, dietary fiber, fruit, and vegetables. The at-risk group had higher intakes of fats and oils and refined grains. The at-risk group also had the lowest serum vitamin B-12, folate,β-cryptoxanthin, lutein, and zeaxanthin concentrations. The not-at-nutritional-risk group had significantly higher lycopene andβ-carotene and lower homocysteine and methylmalonic acid concentrations. Conclusion:The dietary screening tool is a simple and practical tool that can help to detect nutritional risk in older adults.

Persistent oral health problems associated with comorbidity and impaired diet quality in older adults

Chewing, swallowing, and mouth pain (CSP) are identified as indictors of nutritional risk in older adults. Previous research has shown that oral health problems in community-living older rural adults were associated with increased hospitalization. The purpose of this study was to characterize older adults with self-reported persistent CSP problems at baseline and one-year follow-up. Participants were from the Geisinger Rural Aging Study, either with persistent oral problems (PCSP; n=22) or without problems (NCSP; n=125). Demographic, health, and anthropometric data were collected via home visit; diet information was assessed by five, 24-hour recalls collected over 10 months. PCSP subjects reported almost twice the number of medications (4.2 vs 2.6, respectively, P=.008) and diseases (7.0 vs 4.2, respectively, P=.001), with higher occurrence of type 2 diabetes mellitus, peptic ulcers/gastritis, and angina. PCSP participants had lower Healthy Eating Index scores (66.6 vs 70.6, respectively, P=.04), significantly lower intakes of vitamin A, and higher prevalence of inadequate intakes of vitamins B-6 and A. These results indicate that impaired intake of certain foods and nutrients is associated with persistent oral health problems. Oral status is an important component of overall health and should be monitored for intervention.

Evaluation of nutrition screening initiative screening tools in characterizing nutritional status of older adults

The Nutrition Screening Initiative (NSI) has developed screening tools for use in identifying nutritional risk and presence of factors associated with malnutrition in older Americans. However, these tools have not yet been adequately assessed for reliability or validity. This study was conducted to determine if the NSI screening tools are effective in identifying older adults who are at increased nutritional risk. The study used a nonprobability convenience sampling procedure where subjects were recruited from various agencies frequented by older adults (i.e., congregate meals, senior centers, etc.). Eligibility criteria included age 60 or older, independently living and signed consent. All subjects filled out the NSI DETERMINE Your Nutritional Health Checklist and a demographic questionnaire; the dietitian completed the NSI Level I Screen and measured height and weight for each subject. 106 older adults (73 females and 33 males) were surveyed. Demographics of the subjects were as follows: mean age was 76.1 years; 33% were married, 47% were widowed and 20% were single; 53% lived alone, 33% lived with their spouse and 14% lived with others; 15% had total household income less than $6000/yr, 32% had income between $6000 and 10,000, 12% had income between $10,000 and 15,000 and 41% had income greater than $15,000/yr; mean number of medications was 2.8 per day. Based on the NSI DETERMINE Checklist, 33% of the sample were at moderate nutritional risk (score of 3–5) while 26% were at high nutritional risk (score of 6 or more). Scores on the DETERMINE Checklist were highly correlated (r=.72, P .001) with the number of risk factors identified on the NSI Level I Screen. In addition, 75% of the subjects identified as being underweight were classified as at nutritional risk by the DETERMINE Checklist. In summary, the DETERMINE Checklist was effective as an early screening tool for nutritional risk in older adults. It can be used as an educational tool to alert older adults of potential health problems before malnutrition occurs.