Publications by authors named "B N Radford"

Aims: Investigate the effects of breakfast timing on postprandial glycaemia in adults with type 2 diabetes (T2D), and the impact of a 20-min walk after breakfast.

Methods: Eleven adults with T2D (57 ± 7 y; HbA1c 7.4 ± 1%) participated in a six-week randomised crossover controlled trial comprising three 4-day conditions: Early (0700 h), Mid (0930 h) and Delayed (1200 h).

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Background: Intermittent fasting (IF) is an effective energy restricted dietary strategy to reduce body and fat mass and improve metabolic health in individuals with either an overweight or obese status. However, dietary energy restriction may impair muscle protein synthesis (MPS) resulting in a concomitant decline in lean body mass. Due to periods of prolonged fasting combined with irregular meal intake, we hypothesised that IF would reduce rates of MPS compared to an energy balanced diet with regular meal patterns.

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Aims: To test the efficacy of time-restricted eating (TRE) in comparison to dietitian-led individualised dietary guidance to improve HbA1c in people with Type 2 diabetes mellitus.

Methods: In a parallel groups design, 51 adults (35-65 y) with Type 2 diabetes mellitus and overweight/obesity (HbA1c ≥6.5% (48 mmol/mol), BMI ≥25-≤40 kg/m) commenced a six-month intervention.

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The extent to which juvenile abundance can predict future populations of lethrinids at Ningaloo Reef was assessed using size frequency data collected over 13 consecutive years. Annual abundance of juvenile lethrinids (<5 cm TL) was highest in northern Ningaloo during La Niña years, when seawater is warmer and oceanic currents stronger. Juvenile lethrinid abundance explained 35% of the variance in 1-2 year-old Lethrinus nebulosus abundance the following year, a steeper relationship in the north suggesting greater survival of juveniles.

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Background: Accurate predictions of animal occurrence in time and space are crucial for informing and implementing science-based management strategies for threatened species.

Methods: We compiled known, available satellite tracking data for pygmy blue whales in the Eastern Indian Ocean (n = 38), applied movement models to define low (foraging and reproduction) and high (migratory) move persistence underlying location estimates and matched these with environmental data. We then used machine learning models to identify the relationship between whale occurrence and environment, and predict foraging and migration habitat suitability in Australia and Southeast Asia.

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