Objective: Time‐restricted eating (TRE) restores circadian rhythms in mice, but the evidence to support this in humans is limited. The objective of this study was to investigate the effects of TRE on 24‐hour profiles of plasma metabolites, glucoregulatory hormones, and the subcutaneous adipose tissue (SAT) transcriptome in humans.Methods: Men (n = 15, age = 63 [4] years, BMI 30.5 [2.4] kg/m²) were recruited. A 35‐hour metabolic ward stay was conducted at baseline and after 8 weeks of 10‐hour TRE. Assessment included 24‐hour profiles of plasma glucose, nonesterified fatty acid (NEFA), triglyceride, glucoregulatory hormones, and the SAT transcriptome. Dim light melatonin onset and cortisol area under the curve were calculated.Results: TRE did not alter dim light melatonin onset but reduced morning cortisol area under the curve. TRE altered 24‐hour profiles of insulin, NEFA, triglyceride, and glucose‐dependent insulinotropic peptide and increased transcripts of circadian locomotor output cycles protein kaput (CLOCK) and nuclear receptor subfamily 1 group D member 2 (NR1D2) and decreased period circadian regulator 1 (PER1) and nuclear receptor subfamily 1 group D member 1 (NR1D1) at 12:00 am. The rhythmicity of 450 genes was altered by TRE, which enriched in transcripts for transcription corepressor activity, DNA‐binding transcription factor binding, regulation of chromatin organization, and small GTPase binding pathways. Weighted gene coexpression network analysis revealed eigengenes that were correlated with BMI, insulin, and NEFA.Conclusions: TRE restored 24‐hour profiles in hormones, metabolites, and genes controlling transcriptional regulation in SAT, which could underpin its metabolic health benefit.

BACKGROUND: While there is a recognized role of optimizing lifestyle (diet and physical activity) behaviours in the management of infertility, the best practice remains unknown and factors influencing the lifestyle of people with infertility are not well understood.OBJECTIVE AND RATIONALE: This systematic review evaluated barriers and enablers to a healthy lifestyle in people with infertility, from the perspectives of people with infertility and health professionals, in order to inform optimal behavioural change strategies.SEARCH METHODS: Ovid MEDLINE(R), PsycINFO, EMBASE, EBM Reviews, and CINAHL were searched from inception to 28 August 2023. Eligible studies were qualitative and quantitative primary studies that explored barriers and/or enablers to lifestyle for infertility management. Quality assessment was performed using the Centre for Evidence-Based Management Critical Appraisal of a Survey Tool and the Critical Appraisal Skills Programme Qualitative Checklist. Data were analysed by thematic analysis with themes mapped to the Capability, Opportunity, Motivation and Behaviour (COM-B) model and Theoretical Domains Framework (TDF).OUTCOMES: After screening 12 326 abstracts and 99 full-texts, 27 studies were included (12 quantitative, 6 qualitative and 9 mixed-methods) with 22 studies of women with infertility (n = 2524), 11 studies of men with infertility (n = 1407), and 6 studies of health professionals (n = 372). We identified barriers and enablers relating to capability (e.g. strategies for behaviour change), opportunity (e.g. limited time, resources, and money), and motivation (e.g. interplay between lifestyle and emotional state). Based on the identified themes, suggested intervention components to integrate into lifestyle management of infertility include facilitating development of self-management skills to support lifestyle change (e.g. self-monitoring, action planning, and goal setting) and incorporating mental health strategies (e.g. providing information about the benefits of healthy lifestyle behaviours for mental health and encouraging patients to reframe healthy lifestyle behaviours as self-care strategies).WIDER IMPLICATIONS: The findings have identified important factors that influence lifestyle management in people with infertility and have suggested relevant intervention components to consider when designing interventions. Given the paucity of qualitative studies identified, more research is needed to further understand the complex and interacting factors that shape lifestyle during the fertility journey.

Background: e-Health refers to any health care service delivered through the internet or related technologies, to improve quality of life. Despite the increasing use of e-health interventions to manage type 2 diabetes (T2D), there is a lack of evidence about the effectiveness on diabetes distress and depression, which are common issues in those living with T2D. Purpose: To synthesize and determine the effects of e-health interventions on diabetes distress and depression among patients with T2D. Methods: We systematically searched PubMed, Scopus, Cochrane CENTRAL, and Web of Science for randomized controlled trials (RCTs), non-RCTs and observational cohort studies for the effects of e-health interventions on diabetes distress and depression in patients with T2D up to September 14, 2022. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 recommendations were followed. The risk of bias was assessed according to the Risk-of-Bias 2 tool (RCTs), the Risk Of Bias In Non-randomised Studies-of Interventions (ROBINS-I) (non-RCTs) and the National Institute of Health tool (observational). The standardized mean difference (SMD) and its related 95% confidence intervals (CIs) were estimated with the DerSimonian–Laird method through random-effect models. A pooled raw mean difference (MD) meta-analysis was conducted for RCTs comparing the effects of e-health versus control on diabetes distress screening to display the clinical impact. Results: A total of 41 studies (24 RCTs, 14 non-RCTs, and 3 observational) involving 8,667 individuals were included. The pooled SMD for the effect of e-health versus the control group on diabetes distress was −0.14 (95% CI = −0.24 to −0.04; I ²  = 23.9%; n  = 10 studies), being -0.06 (95% CI = −0.15 to 0.02; I ²  = 7.8%; n  = 16 studies) for depression. The pooled raw MD on diabetes distress screening showed a reduction of −0.54 points (95% CI = −0.81 to −0.27; I ²  = 85.1%; n  = 7 studies). Conclusion: e-Health interventions are effective in diminishing diabetes distress among adults with T2D, inducing clinically meaningful reductions.

Eating architecture is a term that describes meal frequency, meal timing and meal size and the daily variation in each of these. The aim of this study was to determine the relationship between components of eating architecture on body fat and markers of glycaemic control in healthy adults at increased risk of type 2 diabetes (T2DM). Participants (n73, 39 males, age 58·8 (8·1) years, BMI 33·4 (4·4) kg/m²) recorded food intake and wore accelerometers and continuous glucose monitors (CGM) for 7–14 d under free-living conditions. Body fat and glycated Hb (HbA1c) were also measured. The mean and day-to-day variation (calculated as the standard deviation during the monitoring period) of each component of eating architecture were calculated. Multivariable linear regression models were constructed for three separate outcome variables (body fat mass, mean CGM glucose and HbA1c) for each component of eating architecture before and after adjustment for confounders. Higher variability in the time of first meal consumption was associated with increased body fat mass after adjusting for confounders (β= 0·227, 95 % CI: 0·019, 0·434,P= 0·033). Increased variability in the time lag from waking to first meal consumption was also positively associated with increased HbA1c after adjustment (β= 0·285, 95 % CI: 0·040, 0·530,P= 0·023). Low day-to-day variability in first meal consumption was associated with lower body fat and improved glucose control in adults at increased risk of T2DM. Routine consumption of meals may optimise temporal regulation to anticipate and respond appropriately to a glucose challenge.