Factors influencing health behaviour change during pregnancy: a systematic review and meta-synthesis.
Lauren RockliffeSarah PetersAlexander Edward HeazellDebbie M SmithPublished in: Health psychology review (2021)
Pregnancy is an opportune time for women to make healthy changes to their lifestyle, however, many women struggle to do so. Multiple reasons have been posited as to why this may be. This review aimed to synthesise this literature by identifying factors that influence women's health behaviour during pregnancy, specifically in relation to dietary behaviour, physical activity, smoking, and alcohol use. Bibliographic databases (MEDLINE, PsycINFO, CINAHL-P, MIDIRS) were systematically searched to retrieve studies reporting qualitative data regarding women's experiences or perceptions of pregnancy-related behaviour change relating to the four key behaviours. Based on the eligibility criteria, 30,852 records were identified and 92 studies were included. Study quality was assessed using the CASP tool and data were thematically synthesised. Three overarching themes were generated from the data. These were (1) A time to think about 'me', (2) Adopting the 'good mother' role, and (3) Beyond mother and baby. These findings provide an improved understanding of the various internal and external factors influencing women's health behaviour during the antenatal period. This knowledge provides the foundations from which future pregnancy-specific theories of behaviour change can be developed and highlights the importance of taking a holistic approach to maternal behaviour change in clinical practice.
Keyphrases
- pregnancy outcomes
- polycystic ovary syndrome
- healthcare
- pregnant women
- physical activity
- public health
- mental health
- preterm birth
- cervical cancer screening
- electronic health record
- clinical practice
- big data
- breast cancer risk
- primary care
- metabolic syndrome
- health information
- body mass index
- cardiovascular disease
- emergency department
- risk assessment
- type diabetes
- social media
- deep learning
- weight loss
- case control
- depressive symptoms
- human health
- artificial intelligence