The role of lifestyle intervention in the prevention and treatment of gestational diabetes

Obesity during pregnancy is associated with the development of adverse outcomes, including gestational diabetes mellitus (GDM). GDM is highly associated with obesity and independently increases the risk of both complications during pregnancy and future impaired glycemic control and risk factors for cardiovascular disease for both the mother and child. Despite extensive research evaluating the effectiveness of lifestyle interventions incorporating diet and/or exercise, there remains a lack of definitive consensus on their overall efficacy alone or in combination for both the prevention and treatment of GDM. Combination of diet and physical activity/exercise interventions for GDM prevention demonstrates limited success, whereas exercise-only interventions report of risk reductions ranging from 3 to 49%. Similarly, combination therapy of diet and exercise is the first-line treatment of GDM, with positive effects on maternal weight gain and the prevalence of infants born large-for-gestational age. Yet, there is inconclusive evidence on the effects of diet or exercise as standalone therapies for GDM treatment. In clinical care, women with GDM should be treated with a multidisciplinary approach, starting with lifestyle modification and escalating to pharmacotherapy if needed. Several key knowledge gaps remain, including how lifestyle interventions can be optimized during pregnancy, and whether intervention during preconception is effective for preventing the rising prevalence of GDM

Effect of a physical activity and sleep m-health intervention on a composite activity-sleep behaviour score and mental health: A mediation analysis of two randomised controlled trials

Background: To examine if a composite activity-sleep behaviour index (ASI) mediates the effects of a combined physical activity and sleep intervention on symptoms of depression, anxiety, or stress, quality of life (QOL), energy and fatigue in adults. Methods: This analysis used data pooled from two studies: Synergy and Refresh. Synergy: Physically inactive adults (18–65 years) who reported poor sleep quality were recruited for a two-arm Randomised Controlled Trial (RCT) (Physical Activity and Sleep Health (PAS; n = 80), or Wait-list Control (CON; n = 80) groups). Refresh: Physically inactive adults (40–65 years) who reported poor sleep quality were recruited for a three-arm RCT (PAS (n = 110), Sleep Health-Only (SO; n = 110) or CON (n = 55) groups). The SO group was omitted from this study. The PAS groups received a pedometer, and accessed a smartphone/tablet “app” using behaviour change strategies (e.g., self-monitoring, goal setting, action planning), with additional email/SMS support. The ASI score comprised self-reported moderate-to-vigorous-intensity physical activity, resistance training, sitting time, sleep duration, efficiency, quality and timing. Outcomes were assessed using DASS-21 (depression, anxiety, stress), SF-12 (QOL-physical, QOL-mental) and SF-36 (Energy & Fatigue). Assessments were conducted at baseline, 3 months (primary time-point), and 6 months. Mediation effects were examined using Structural Equation Modelling and the product of coefficients approach (AB), with significance set at 0.05. Results: At 3 months there were no direct intervention effects on mental health, QOL or energy and fatigue (all p > 0.05), and the intervention significantly improved the ASI (all p < 0.05). A more favourable ASI score was associated with improved symptoms of depression, anxiety, stress, QOL-mental and of energy and fatigue (all p < 0.05). The intervention effects on symptoms of depression ([AB; 95%CI] -0.31; − 0.60,-0.11), anxiety (− 0.11; − 0.27,-0.01), stress (− 0.37; − 0.65,-0.174), QOL-mental (0.53; 0.22, 1.01) and ratings of energy and fatigue (0.85; 0.33, 1.63) were mediated by ASI. At 6 months the magnitude of association was larger although the overall pattern of results remained similar. Conclusions: Improvements in the overall physical activity and sleep behaviours of adults partially mediated the intervention effects on mental health and quality of life outcomes. This highlights the potential benefit of improving the overall pattern of physical activity and sleep on these outcomes. Trial registration: Australian New Zealand Clinical Trial Registry: ACTRN12617000680369; ACTRN12617000376347. Universal Trial number: U1111–1194-2680; U1111–1186-6588. Human Research Ethics Committee Approval: H-2016-0267; H-2016–0181

Additional file 1 of Association between the use of weight management strategies and weight change among Australian adults over 12 months: an observational study

Supplementary Material

Associations between app usage and behaviour change in a m-health intervention to improve physical activity and sleep health in adults: Secondary analyses from two randomised controlled trials

Background To examine associations between user engagement and activity-sleep patterns in a 12-week m-health behavioural intervention targeting physical activity and sleep. Methods This secondary analysis used data pooled from two Randomised Control Trials (RCT, [Synergy and Refresh]) that aimed to improve physical activity and sleep (PAS) among physically inactive adults with poor sleep. Both RCTs include a PAS intervention group (n = 190 [Synergy n = 80; Refresh n = 110]) and a wait list Control (CON n = 135 [Synergy n = 80; Refresh n = 55]). The PAS groups received a pedometer and accessed a smartphone/tablet “app” with behaviour change strategies, and email/SMS support. Activity-sleep patterns were quantified using the activity-sleep behaviour index (ASI) based on self-report measures. Intervention usage was quantified as a composite score of the frequency, intensity and duration of app usage during intervention (range: 0–30). Assessments were conducted at baseline, 3 and 6 months. Relationships between usage and ASI were examined using generalised linear models. Differences in ASI between the control group and intervention usage groups (Low [0–10.0], Mid [10.1–20.0], High [20.1–30.0]) were examined using generalised linear mixed models adjusted for baseline values of the outcome. Trial Registration: ACTRN12617000376347; ACTRN12617000680369. Results During the 3-month intervention, the mean (± sd) usage score was 18.9 ± 9.5. At 3 months (regression coefficient [95%CI]: 0.45 [0.22, 0.68]) and 6 months (0.48 [0.22, 0.74]) there was a weak association between usage score and ASI in the intervention group. At 3 months, ASI scores in the Mid (Mean [95%CI] = 57.51 [53.99, 61.04]) and High (60.09 [57.52, 62.67]) usage groups were significantly higher (better) than the control group (51.91 [49.58, 54.24]), but not the Low usage group (47.49 [41.87, 53.12]). Only differences between the high usage and control group remained at 6 months. Conclusion These findings suggests that while higher intervention usage is associated with improvements in behaviour, the weak magnitude of this association suggests that other factors are also likely to influence behaviour change in m-health interventions

Female athlete health domains: a supplement to the International Olympic Committee consensus statement on methods for recording and reporting epidemiological data on injury and illness in sport

The IOC made recommendations for recording and reporting epidemiological data on injuries and illness in sports in 2020, but with little, if any, focus on female athletes. Therefore, the aims of this supplement to the IOC consensus statement are to (i) propose a taxonomy for categorisation of female athlete health problems across the lifespan; (ii) make recommendations for data capture to inform consistent recording and reporting of symptoms, injuries, illnesses and other health outcomes in sports injury epidemiology and (iii) make recommendations for specifications when applying the Strengthening the Reporting of Observational Studies in Epidemiology-Sport Injury and Illness Surveillance (STROBE-SIIS) to female athlete health data. In May 2021, five researchers and clinicians with expertise in sports medicine, epidemiology and female athlete health convened to form a consensus working group, which identified key themes. Twenty additional experts were invited and an iterative process involving all authors was then used to extend the IOC consensus statement, to include issues which affect female athletes. Ten domains of female health for categorising health problems according to biological, life stage or environmental factors that affect females in sport were identified: menstrual and gynaecological health; preconception and assisted reproduction; pregnancy; postpartum; menopause; breast health; pelvic floor health; breast feeding, parenting and caregiving; mental health and sport environments. This paper extends the IOC consensus statement to include 10 domains of female health, which may affect female athletes across the lifespan, from adolescence through young adulthood, to mid-age and older age. Our recommendations for data capture relating to female athlete population characteristics, and injuries, illnesses and other health consequences, will improve the quality of epidemiological studies, to inform better injury and illness prevention strategies.</p