Design of a Remote Time-Restricted Eating and Mindfulness Intervention to Reduce Risk Factors Associated with Early-Onset Colorectal Cancer Development among Young Adults.
Manoela Lima OliveiraAlana BiggersVanessa M OddoKeith B NaylorZhengjia ChenAlyshia HammLacey B PezleyBeatriz Peñalver BernabéKelsey GabelLisa K SharpLisa Marie Tussing-HumphreysPublished in: Nutrients (2024)
Early-onset colorectal cancer (EOCRC) is defined as a diagnosis of colorectal cancer (CRC) in individuals younger than 50 years of age. While overall CRC rates in the United States (US) decreased between 2001 and 2018, EOCRC rates have increased. This research project aims to evaluate the feasibility and acceptability of Time-Restricted Eating (TRE), Mindfulness, or TRE combined with Mindfulness among young to middle-aged adults at risk of EOCRC. Forty-eight participants will be randomly assigned to one of four groups: TRE, Mindfulness, TRE and Mindfulness, or Control. Data on feasibility, adherence, and acceptability will be collected. Measures assessed at baseline and post-intervention will include body weight, body composition, dietary intake, physical activity, sleep behavior, circulating biomarkers, hair cortisol, and the gut microbiome. The effects of the intervention on the following will be examined: (1) acceptability and feasibility; (2) body weight, body composition, and adherence to TRE; (3) circulating metabolic, inflammation, and oxidative stress biomarkers; (4) intestinal inflammation; and (5) the gut microbiome. TRE, combined with Mindfulness, holds promise for stress reduction and weight management among individuals at risk of EOCRC. The results of this pilot study will inform the design and development of larger trials aimed at preventing risk factors associated with EOCRC.
Keyphrases
- body composition
- early onset
- body weight
- physical activity
- oxidative stress
- chronic pain
- late onset
- resistance training
- randomized controlled trial
- bone mineral density
- middle aged
- young adults
- weight loss
- body mass index
- dna damage
- type diabetes
- machine learning
- metabolic syndrome
- depressive symptoms
- quality improvement
- glycemic control
- artificial intelligence