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Purpose The purpose of the study was to explore self-regulation skills with real-time activity and glucose monitoring among Black women with type 2 diabetes (T2DM). Methods A small acceptability trial was conducted using technology (continuous glucose monitors and Fitbit) to facilitate core behaviors associated with self-regulation (self-monitoring/assessment, learning, mental contrasting [comparing current values with goal values], and goal setting/review). Participants were given continuous glucose monitors and Fitbit activity trackers for self-monitoring of blood glucose and activity. Two sessions of group diabetes education were also offered. Following the intervention, semistructured interviews and subsequent content analyses were conducted to explore how the women’s experiences reflected certain self-regulation behaviors. Results Eight underserved Black women with non-insulin-requiring T2DM were included (age = 68 ± 5.2 years; A1C = 6.6% ± 1.1%; 15.3 ± 7.2 years since diagnosis). Content analysis revealed themes that were consistent with core self-regulation behaviors: experiential learning through self-monitoring, mental contrasting, and impact on behavior (actual behavior change and motivation to change behavior). Conclusions With use of real-time glucose and activity monitoring, underserved Black women with T2DM described how they used the data from the devices to make choices about eating and activity behaviors.

Introduction Sleep disturbances (i.e. poor sleep quality, low sleep efficiency, and Obstructive Sleep Apnea (OSA)) are common during menopausal transition and menopause. These disturbances are associated with increased risk of cardiovascular disease (CVD), obesity, hypertension, and diabetes. However, the association of sleep disturbances and CVD risk among midlife Thai women, living in the US, have not been fully explored. Thus, this study examined the association between sleep characteristics and CVD risk in those women. Methods In this cross-sectional study, 120 midlife Thai women completed the Pittsburgh Sleep Quality Index (PSQI), Berlin questionnaire, and Menopause Rating Scale. The main outcome, 10-year risk of CVD, was determined using the BMI-based Framingham Risk Score (FRS). Descriptive, bivariate, robust regression, and Structural Equation Model (SEM) analysis for mediation model were performed using STATA 15.1. Results Among participants, their mean age was 51.53 years (SD=7.73), length of stay in the USA was 21.77 years (SD=12.96) and CVD risk score was 6.56 % (SD=5.74). Twenty participants (17%) were at high risk of CVD. The mean subjective sleep quality measured by PSQI was 4.18 (SD=3.03). The majority of participants were good sleepers (n=97, 80.83%). The mean sleep efficiency was 95.36% (SD=5.96). A small portion of the participants had a high risk of OSA as assessed by Berlin questionnaire (n=17, 14.17%). Among sleep characteristics, only the increased risk of OSA was significantly associated with higher CVD risk (B=4.601, p< 0.001). Conclusion The findings of this study indicated that an increased risk of OSA is associated with the risk of CVD among midlife women. Sleep duration, sleep quality, or sleep efficiency neither mediated nor moderated CVD risk. Future studies should use objective measures of sleep, such as actigraphy, to better elucidate the effects of sleep parameters on cardiovascular risk. Support (if any)

This study aimed to determine which dietary factors were associated with habitual sleep duration in pre/peri- and post-menopausal women. Data from the 2007–2008 National Health and Nutrition Examination Survey were used ( N = 1,783). Habitual sleep duration was categorized as very short (

The aim of this meta-analysis was to examine the associations among infant feeding types, sleeping habits, and maternal sleep postpartum. Databases including Cumulative Index of Nursing and Allied-Health Literature (CINAHL), PubMed, and Google Scholar were searched in addition to reference lists from selected articles and other key references. A critical review of relevant articles from the data sources was conducted with attention to the infant feeding types and maternal night-time sleep. The methodological quality was assessed systematically. The pooled mean difference was calculated. Narrative summaries were also used. A total of 6,472 participants from seven studies were included in the meta-analysis. A random-effects model demonstrated a significantly higher maternal night-time sleep in breastfeeding mothers than non-breastfeeding mothers with a pooled standardized mean difference of 0.24 h (95% confidence interval 0.03-0.46, p = 0.026). Co-sleeping with infants during the night also increased the sleeping hours in breastfeeding mothers. Homogeneity was observed with a Tau

This review explored the associations between sleep variability and cardiometabolic health. It was performed following PRISMA guidelines. We identified 63 studies. Forty-one studies examined the association between sleep variability and body composition, with 29 examined body mass index (BMI). Thirteen studies used social jet lag (SJL), n = 30,519, with nine reporting a null association. Eight studies used variability in sleep duration (n = 33,029), with five reporting a correlation with BMI. Fourteen studies (n = 133,403) focused on overweight/obesity; significant associations with sleep variability were found in 11 (n = 120,168). Sleep variability was associated with weight gain (seven studies; n = 79,522). Twenty-three studies examined glucose outcomes. The association with hemoglobin A1c (16 studies, n = 11,755) differed depending on populations, while associations with diabetes or glucose were mixed, and none were seen with insulin resistance (five studies; n = 6416). Sixteen studies examined cardiovascular-related outcomes, with inconsistent results. Overall significant associations were found in five studies focusing on metabolic syndrome (n = 7413). In summary, sleep variability was likely associated with obesity, weight gain, and metabolic syndrome. It might be associated with hemoglobin A1c in people with type 1 diabetes. The associations with other outcomes were mixed. This review highlighted the possible association between sleep variability and cardiometabolic health.