Temporal eating patterns: a latent class analysis approach

Leech, Rebecca M.; Worsley, Anthony; Timperio, Anna; McNaughton, Sarah A.

doi:10.1186/s12966-016-0459-6

Cited by 49 publications

(79 citation statements)

References 36 publications

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“…The LCA procedures and a description of the resulting patterns have been described in detail previously (18). In brief, binary variables that indicate whether or not an EO (minimum energy content of $210 kJ) had occurred within each hour of the day were used as the input variables for the LCA.…”

Section: Temporal Eating Patternsmentioning

confidence: 99%

“…Final class numbers were determined by the evaluation of model fit indexes (e.g., Akaike information criterion and Bayesian information criterion), likelihood ratio tests comparing k with k 21 class models, and pattern interpretability (48). After these procedures, a 3-class solution was identified as the most appropriate fit for the data (18). The reliability of the 3-class solution was then examined by repeating the LCA procedures with the use of 1 recall day of dietary intake, and similar temporal eating patterns were found when comparing results with the use of 1 compared with 2 d of dietary intake (18).…”

Section: Temporal Eating Patternsmentioning

confidence: 99%

“…After these procedures, a 3-class solution was identified as the most appropriate fit for the data (18). The reliability of the 3-class solution was then examined by repeating the LCA procedures with the use of 1 recall day of dietary intake, and similar temporal eating patterns were found when comparing results with the use of 1 compared with 2 d of dietary intake (18).…”

Section: Temporal Eating Patternsmentioning

confidence: 99%

“…Two studies found distinct temporal eating patterns across the day when applying a data-driven analytic approach, including cluster analysis (17) and latent class analysis (LCA) (18). These methods were used to discover unknown patterns in the data on the basis of an observed set of indicators (e.g., time of EO consumption).…”

Section: Introductionmentioning

confidence: 99%

“…These methods were used to discover unknown patterns in the data on the basis of an observed set of indicators (e.g., time of EO consumption). Examples of patterns found by using these methods include a "conventional" pattern, where meals and snacks were evenly spaced and consumed at conventional times in the United States and Australia, and a pattern characterized by a higher eating frequency with less definitive meal occasions (17,18). These patterns were associated with differences in sociodemographic and health characteristics (17,18) and diet quality (17).…”

Section: Introductionmentioning

confidence: 99%

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Temporal eating patterns: associations with nutrient intakes, diet quality, and measures of adiposity

Leech

Timperio

Livingstone

et al. 2017

The American Journal of Clinical Nutrition

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Background: Some evidence suggests that higher energy intake (EI) later in the day is associated with poor diet quality and obesity. However, EI at one eating occasion (EO) is also dependent on EI at surrounding EOs. Studies that examine the distribution of EOs across the day are rare. Objective: The aim of this study was to examine associations between temporal eating patterns, nutrient intakes, diet quality, and measures of adiposity in a representative sample of Australian adults. Design: Dietary data from two 24-h recalls collected during the crosssectional 2011-2012 Australian National Nutrition and Physical Activity Survey were analyzed (n = 4544 adults, aged $19 y). Temporal eating patterns, based on the distribution of EOs across the day, were determined by using latent class analysis. Diet quality estimated adherence to healthy eating recommendations and was assessed by using the 2013 Dietary Guidelines Index (DGI). Multivariate regression models assessed associations between temporal eating patterns, nutrient intakes, diet quality, and adiposity (body mass index, waist circumference, weight status, and central weight status). Models were adjusted for potential confounders and energy misreporting. Results: Three patterns, labeled "conventional," "later lunch," and "grazing," were identified. Compared with a "conventional" or "later lunch" pattern, men and women with a "grazing" pattern had lower DGI scores and higher intakes of discretionary (noncore) foods (P , 0.05). Among women, the "grazing" pattern was associated with overweight or obesity (OR: 1.57; 95% CI: 1.15, 2.13) and central overweight or obesity (OR: 1.73; 95% CI: 1.19, 2.50). These associations were attenuated after the exclusion of energy misreporters and adjustment for total EI. Conclusions: This study found that a "grazing" temporal eating pattern was modestly but significantly associated with poorer diet quality and adiposity among women, after adjustment for covariates and energy misreporting. Future research should consider the impact of energy misreporting on the relation between temporal eating patterns and adiposity. This secondary analysis was registered at anzctr.org.au as ACTRN12617001029381.

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Section: Temporal Eating Patternsmentioning

confidence: 99%

Section: Temporal Eating Patternsmentioning

confidence: 99%

Section: Temporal Eating Patternsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Temporal eating patterns: associations with nutrient intakes, diet quality, and measures of adiposity

Leech

Timperio

Livingstone

et al. 2017

The American Journal of Clinical Nutrition

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Eating patterns of Australian adults: associations with blood pressure and hypertension prevalence

et al. 2018

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Purpose Eating patterns have been linked to obesity, an established risk factor for hypertension; however, their contribution to hypertension is poorly understood. This study aimed to examine associations of frequency of meals, snacks and all eating occasions (EO), and temporal eating patterns, with blood pressure (BP) and hypertension. Methods Dietary data collected via two 24-h recalls during the 2011-2012 Australian National Nutrition and Physical Activity Survey (n = 4482 adults, ≥ 19 years) were analysed. Frequencies of EO, meals, and snacks were calculated. Temporal eating patterns were determined using latent class analysis. Multivariate regression models assessed associations of eating patterns with systolic BP (SBP), diastolic BP (DBP), and hypertension prevalence. Results Among men, a higher snack frequency was inversely associated with DBP [β = − 0.59, 95% confidence interval (CI) (− 1.12, − 0.07)] and hypertension [odds ratio (OR) 0.86, 95% CI (0.75, 0.98)] after adjustment for covariates and BMI. However, these associations disappeared after additional adjustment for total energy intake and overall diet quality. Among women, a temporal eating pattern characterized by a later "lunch" meal was associated with SBP [β = 2.45, 95% CI (0.05, 4.84)], DBP [β = 1.69, 95% CI (0.25, 3.13)], and hypertension [OR = 1.49, 95% CI (1.00, 2.22)], when compared to a "conventional" eating pattern. Conclusions In this study, an inverse association found between snack frequency and BP among men disappeared after adjustment for dietary factors and a "later lunch" pattern was associated with higher BP in women. Future research is needed to understand the relationship and potential mechanistic pathways between eating patterns and BP.

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The association between diet quality and chrononutritional patterns in young adults

Wang,

Chan,

Allman-Farinelli

et al. 2024

Eur J Nutr

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Purpose Young adults eat erratically and later in the day which may impact weight and cardiometabolic health. This cross-sectional study examined relationships between chrononutritional patterns and diet quality in two young adult populations: a university and community sample. Methods Three days of dietary data were collected including food images captured using wearable cameras. Chrononutritional variables were extracted: time of first and last eating occasions, caloric midpoint (time at which 50% of daily energy was consumed), number of eating occasions per day, eating window, day-to-day variability of the above metrics, and evening eating (≥20:00h). The Healthy Eating Index for Australian Adults scored diet quality. Statistical analyses controlled for gender, body mass index, and socio-economic status. Results No significant associations between chrononutritional patterns and diet quality were found for all participants (n = 95). However, differences in diet quality were found between university (n = 54) and community (n = 41) samples with average diet quality scores of 59.1 (SD 9.7) and 47.3 (SD 14.4), respectively. Of those who extended eating ≥20:00 h, university participants had better diet quality (62.9±SE 2.5 vs. 44.3±SE 2.3, p < 0.001) and discretionary scores (7.9±SE 0.9 vs. 1.6±SE 0.6, p < 0.001) than community participants. University participants consumed predominately healthful dinners and fruit ≥20:00h whereas community participants consumed predominately discretionary foods. Conclusion For the general young adult population, meal timing needs to be considered. Food choices made by this cohort may be poorer during evenings when the desire for energy-dense nutrient-poor foods is stronger. However, meal timing may be less relevant for young adults who already engage in healthy dietary patterns.

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Temporal eating patterns: a latent class analysis approach

Cited by 49 publications

References 36 publications

Temporal eating patterns: associations with nutrient intakes, diet quality, and measures of adiposity

Temporal eating patterns: associations with nutrient intakes, diet quality, and measures of adiposity

Eating patterns of Australian adults: associations with blood pressure and hypertension prevalence

The association between diet quality and chrononutritional patterns in young adults

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