Sociodemographic and Health Indicators of Diet Quality in Pre-Frail Older Adults in New Zealand

Author:

Tay Esther1,Barnett Daniel2,Rowland Maisie3,Kerse Ngaire1ORCID,Edlin Richard4,Waters Debra L.567,Connolly Martin8,Pillai Avinesh2,Tupou Evelingi1,Teh Ruth1ORCID

Affiliation:

1. Department of General Practice and Primary Health Care, School of Population Health, University of Auckland, Auckland 1023, New Zealand

2. Department of Statistics, Faculty of Science, University of Auckland, Auckland 1010, New Zealand

3. Human Nutrition Research Centre, Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne NE2 4HH, UK

4. Health Systems Group, School of Population Health, University of Auckland, Auckland 1023, New Zealand

5. Department of Medicine, University of Otago, Dunedin 9016, New Zealand

6. Centre for Health, Activity and Rehabilitation Research, School of Physiotherapy, University of Otago, Dunedin 9016, New Zealand

7. Department of Internal Medicine, University of New Mexico, Albuquerque, NM 87106, USA

8. Department of Geriatric Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand

Abstract

This study aimed to identify sociodemographic and health indicators of diet quality in pre-frail community-dwelling older adults. Pre-frail older adults are those at risk of progression to clinical manifestations of frailty and are targets for preventative intervention. We previously reported that pre-frail older adults have reasonably good overall diet quality. However, further analyses found a low intake of energy, protein and several micronutrients. Methods: We collected detailed dietary intake from pre-frail (FRAIL scale 1–2) older adults using NZ Intake24, an online version of 24 h multiple pass dietary recall. Diet quality was ascertained with the Diet Quality Index-International (DQI-I). We used regression generalized linear models to determine predictors of diet quality as well as classification and regression tree (CART) analysis to examine the complex relationships between predictors and identified profiles of sub-groups of older adults that predict diet quality. Results: The median age in this sample (n = 468) was 80.0 years (77.0–84.0). Living with others, a high deprivation index and a higher BMI were independent predictors of poorer diet quality. With CART analysis, we found that those with a BMI > 29 kg/m2, living with others and younger than 80 years were likely to have a lower diet quality. Conclusions: We found that BMI, living arrangement and socioeconomic status were independent predictors of diet quality in pre-frail older adults, with BMI being the most important variable in this sample when the interaction of these variables was considered. Future research is needed to determine the similarities and/or differences in the profile of subgroups of older adults with poorer diet quality.

Funder

Ageing Well, Ministry of Business Innovation and Employment

Health Research Council New Zealand

Publisher

MDPI AG

Subject

Food Science,Nutrition and Dietetics

Reference56 articles.

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