ArticleThe international journal of behavioral nutrition and physical activity2016
A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians.
Article in The international journal of behavioral nutrition and physical activity, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
66 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of food-based fortification on nutritional outcomes and acceptability in older adults: systematic literature review.Frontiers in nutrition · 2023Pooled it
- Identification of Distinct Arterial Waveform Clusters and a Longitudinal Evaluation of Their Clinical Usefulness.Hypertension (Dallas, Tex. : 1979) · 2019Trial
- Cluster Analysis Reveals Subgroups with Different Risk Profiles and Sickness Absence Patterns in an Occupational Health Cohort.Journal of occupational rehabilitation · 2026Article
- Dietary Patterns and Food Choices of Adults With Inflammatory Bowel Disease in New Zealand.JGH open : an open access journal of gastroenterology and hepatology · 2026Article
- Assessment of Serbian adults' diet through cluster analysis approach - findings from the national health interview survey.BMC public health · 2026Article
- The Impact of Healthcare Professionals' Characteristics on the Evaluation of Clinical Decision Support Systems: Insights from a Cross-Country Usability and Technology Acceptance Study of the iCARE Tool.Journal of medical systems · 2026Article
- Unveiling the relationship between dietary patterns and sleep quality: cross-sectional evidence from university students in Jinan city.Frontiers in nutrition · 2026Article
- Gut microbe's consortium in pregnant women influenced by diet of North-east India: A metagenomic study.Journal of family medicine and primary care · 2025Article
- Dietary patterns of Filipino older adults and associated factors: analysis of the 2013 National nutrition survey and 2018-2019 expanded National nutrition survey.BMC geriatrics · 2025Article
- Identifying and predicting dietary patterns in the Dutch population using machine learning.European journal of nutrition · 2025Article
- Examining the influence of socio-economic factors on ultra-processed food consumption patterns of UK adolescents.Public health nutrition · 2025Article
- Medical service performance evaluation of tertiary general hospitals in Sichuan Province in China based on diagnosis-related groups.BMC health services research · 2025Article
- From Steps to Context: Optimizing Digital Phenotyping for Physical Activity Monitoring in Older Adults by Integrating Wearable Data and Ecological Momentary Assessment.Sensors (Basel, Switzerland) · 2025Article
- Health Deficits Among People Experiencing Homelessness in an Australian Capital City: An Observational Study.International journal of environmental research and public health · 2025Observational
- Gender differences in dietary patterns and physical activity: an insight with principal component analysis (PCA).Journal of translational medicine · 2024Article
- Article
- Household structure and dietary diversity among older adults in rural and urban China: a cross-sectional study.BMC public health · 2024Article
- Health Status of People Who Are and Are Not Experiencing Homelessness: Opportunities for Improvement.International journal of environmental research and public health · 2024Article
- Mediterranean Diet and Ultra-Processed Food Intake in Older Australian Adults-Associations with Frailty and Cardiometabolic Conditions.Nutrients · 2024Article
- Major dietary patterns and their associations with total weight loss and weight loss composition 2-4 years after sleeve gastrectomy.European journal of medical research · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDespite increased use of dietary pattern methods in nutritional epidemiology, there have been few direct comparisons of methods. Older adults are a particularly understudied population in the dietary pattern literature. This study aimed to compare dietary patterns derived by principal component analysis (PCA) and cluster analysis (CA) in older adults and to examine their associations with socio-demographic and health behaviours.
methodsMen (n = 1888) and women (n = 2071) aged 55-65 years completed a 111-item food frequency questionnaire in 2010. Food items were collapsed into 52 food groups and dietary patterns were determined by PCA and CA. Associations between dietary patterns and participant characteristics were examined using Chi-square analysis. The standardised PCA-derived dietary patterns were compared across the clusters using one-way ANOVA.
resultsPCA identified four dietary patterns in men and two dietary patterns in women. CA identified three dietary patterns in both men and women. Men in cluster 1 (fruit, vegetables, wholegrains, fish and poultry) scored higher on PCA factor 1 (vegetable dishes, fruit, fish and poultry) and factor 4 (vegetables) compared to factor 2 (spreads, biscuits, cakes and confectionery) and factor 3 (red meat, processed meat, white-bread and hot chips) (mean, 95% CI; 0.92, 0.82-1.02 vs. 0.74, 0.63-0.84 vs. -0.43, -0.50- -0.35 vs. 0.60 0.46-0.74, respectively). Women in cluster 1 (fruit, vegetables and fish) scored highest on PCA factor 1 (fruit, vegetables and fish) compared to factor 2 (processed meat, hot chips cakes and confectionery) (1.05, 0.97-1.14 vs. -0.14, -0.21- -0.07, respectively). Cluster 3 (small eaters) in both men and women had negative factor scores for all the identified PCA dietary patterns. Those with dietary patterns characterised by higher consumption of red and processed meat and refined grains were more likely to be Australian-born, have a lower level of education, a higher BMI, smoke and did not meet physical activity recommendations (all P < 0.05).
conclusionsPCA and CA identified comparable dietary patterns within older Australians. However, PCA may provide some advantages compared to CA with respect to interpretability of the resulting dietary patterns. Older adults with poor dietary patterns also displayed other negative lifestyle behaviours. Food-based dietary pattern methods may inform dietary advice that is understood by the community.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.