Peer-Reviewed Journal Details
Mandatory Fields
Harrington, JM;Dahly, DL;Fitzgerald, AP;Gilthorpe, MS;Perry, IJ
2014
December
Public health nutrition
Capturing changes in dietary patterns among older adults: a latent class analysis of an ageing Irish cohort
Validated
Optional Fields
CORONARY-HEART-DISEASE LIFE-STYLE FACTORS MIDDLE-AGED WOMEN CLUSTER-ANALYSIS EATING PATTERNS INSULIN-RESISTANCE METABOLIC SYNDROME RISK HEALTH FOOD
17
2674
2686
Objective: Data-driven approaches to dietary patterns are under-utilized; latent class analyses (LCA) are particularly rare. The present study used an LCA to identify subgroups of people with similar dietary patterns, explore changes in dietary patterns over a 10-year period and relate these dynamics to sociodemographic factors and health outcomes. Design: The 1998 baseline and 2008 follow-up of the Cork and Kerry Diabetes and Heart Disease Study. Diets were assessed with a standard FFQ. LCA, under the assumption of conditional independence, was used to identify mutually exclusive subgroups with different dietary patterns, based on food group consumption. Setting: Republic of Ireland. Subjects: Men and women aged 50-69 years at baseline (n 923) and at 10-year follow-up (n 320). Results: Three dietary classes emerged: Western, Healthy and Low-Energy. Significant differences in demographic, lifestyle and health outcomes were associated with class membership. Between baseline and follow-up most people remained 'stable' in their dietary class. Most of those who changed class moved to the Healthy class. Higher education was associated with transition to a healthy diet; lower education was associated with stability in an unhealthy pattern. Transition to a healthy diet was associated with higher CVD risk factors at baseline: respondents were significantly more likely to be smokers, centrally obese and to have hypertension (non-significant). Conclusions: LCA is useful for exploring dietary patterns transitions. Understanding the predictors of longitudinal stability/transitions in dietary patterns will help target public health initiatives by identifying subgroups most/least likely to change and most/least likely to sustain a change.
CAMBRIDGE
1368-9800
10.1017/S1368980014000111
Grant Details