Are antenatal interventions effective in improving multiple health behaviours among pregnant women? A systematic review protocol

Clustering of health risk behaviors in pregnant individuals: Data from the Brazilian risk factor surveillance system for non-communicable chronic diseases

OBJECTIVE

To investigate the clustering of health risk behaviors (HRB) and its association with demographics, physical exercise, overweight, perception of health, and diseases in Brazilian pregnant people.

STUDY DESIGN

This is a cross-sectional study using data from the Risk Factor Surveillance System for Non-communicable Chronic Diseases by Telephone Survey (VIGITEL), the main health survey in Brazil.

METHODS

We used data on fruit and vegetable consumption, TV time, tobacco, and alcohol abuse in individuals who reported being pregnant (n = 4553). We used latent class analysis to identify optimal HRB clustering among participants. Multinomial regression (odds ratio [OR] and 95% confidence intervals [95%CI]) was applied to identify factors associated with HRB cluster.

RESULTS

Three clustering classes were identified: "without HRB cluster" (i.e., least unhealthy behaviors) (n = 2402, 52,8%), "moderate HRB cluster" (n = 1983, 43,5%), and "high HRB cluster" (i.e., most unhealthy behaviors) (n = 168, 3,7%). Pregnant people aged 35-50 years (OR = 1.89, 95%CI = 1.01; 3.52) who did not practice physical exercise (OR = 1.94, 95%CI 1.11; 3.39) were more likely to be classified as "high HRB cluster". Participants with 9-11 years (OR = 0.11, 95%CI = 0.07; 0.17) and ≥ 12 (OR = 0.05, 95%CI = 0.02; 0.11) years of education had a lower likelihood of being in the "high HRB cluster".

CONCLUSION

Three HRB clustering patterns were found in this study. Greater maternal age, low education, and absence of physical exercises increased the chances of being in the high HRB cluster group. Participants with higher educational levels were less likely to be in the High HRB cluster.

Cigarette Smoking Cessation Counselling in Pregnant Smokers with Mental Illness/Substance Use Disorders

Our objective was to determine if past-year mental illness and substance use disorders (SUD) among pregnant smokers predicted the probability of receipt of counselling for cigarette smoking cessation. A secondary analysis of data from the National Survey on Drug Use and Health 2016-2019 was conducted. We found that approximately 83% of pregnant smokers (N = 373) received screening for cigarette smoking, and 65% received cessation counselling. Having mental illness predicted the probability of receipt of counselling for smoking cessation in pregnant smokers (adjusted odds ratio [AOR]: 3.75; 95% confidence interval [CI]: 1.25-11.27). However, having SUD (alcohol [AOR: 2.30; 95%CI: 0.57-9.26] or illicit drug use [AOR: 1.32; 95%CI: 0.26-6.82]) or comorbid mental illness and SUD (AOR: 0.23; 95%CI: 0.03-2.03) was not associated with receipt of counselling for smoking cessation. Practice guidelines and policy initiatives are needed to reduce cigarette use and its related adverse health outcomes in pregnant smokers with SUD.

Identifying target behaviors for weight management interventions for women who are overweight during pregnancy and the postpartum period: a qualitative study informed by the Behaviour Change Wheel

Background

Maternal overweight is increasing, and it is associated with several risk factors for both the mother and child. Healthy lifestyle behaviors adopted during pregnancy are likely to impact women’s health positively after pregnancy. The study’s aim was to identify and describe weight management behaviors in terms of the Capability, Opportunity and Motivation Behaviour (COM-B) -model to target weight management interventions from both the perspectives of women who are overweight and maternity care professionals.

Methods

This qualitative, descriptive study was conducted between 2019 and 2020. Individual interviews with pregnant and postpartum women who were overweight (n = 11) and focus group interviews with public health nurses (n = 5) were undertaken in two public maternity clinics in Southwest Finland. The data were analyzed using deductive content analysis consistent with the COM-B model.

Results

In the capability category, the women and the public health nurses thought that there was a need to find consistent ways to approach overweight, as it had often become a feature of the women’s identities. The use of health technology was considered to be an element of antenatal care that could be used to approach the subject of weight and weight management. Smart wearables could also support an evaluation of the women’s lifestyles. The opportunity category highlighted the lack of resources for support during perinatal care, especially after birth. Both groups felt that support from the family was the most important facilitating factor besides motivation. The women also expressed a conflict between pregnancy as an excuse to engage in unhealthy habits and pregnancy as a motivational period for a change of lifestyle. Furthermore, the women wanted to be offered a more robust stance on weight management and discreet counseling.

Conclusions

Our findings offer a theoretical basis on which future research can define intervention and implementation strategies. Such interventions may offer clear advice and non-judgmental support during pregnancy and after delivery by targeting women’s capabilities, opportunities, and motivation. Health technology could be a valuable component of intervention, as well as an implementation strategy, as they provide ways during maternity care to approach this topic and support women.