Exploration of Women's Experiences and Technology Usage Before, During, and After Pregnancy in Singapore

Pregnancy Related Clinical Trial

Official title:

Exploration of Women's Experiences and Technology Usage Before, During, and After Pregnancy in Singapore

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05099900
• Other study ID #: 2021/00034
• Status: Recruiting
• Start date: November 8, 2021
• Est. completion date: November 8, 2022

Study information

• Verified date: November 2021
• Source: National University, Singapore
• Contact: Xavier Tadeo, PhD
• Phone: +65 66017766
• Email: lsixtc[@]nus.edu.sg
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This study seek to understand the motivations and contextual influences that can induce and sustain behaviour change to inform future interventions for women before, during and after pregnancy, through a qualitative interview-based assessment of 60 participants. As digital health intervention in pregnant women has been shown to be cost-effective and scalable, the current study also aims to understand women's usage of technology throughout the process of trying to conceive, being pregnant and being a new mother within the local Singapore context.

Description:

Maternal overweight and obesity is a growing public health concern in Singapore. A recent Singaporean prospective cohort study examined 724 pregnant women and reported that 26.2% of the women had a total gestational weight gain (GWG) which exceed the Institute of Medicine's (IOM) 2009 guidelines. When examined based on body mass index (BMI), overweight and obese women had significantly increased risk of gaining gestational weight above IOM recommendations, compared to normal weight women. Higher GWG have previously been linked to adverse maternal and infant outcomes including higher rates of gestational diabetes mellitus (GDM) and primary caesarean delivery, large for age (LGA) infant, macrosomia and increased risk of childhood overweight/obesity. Given the impact of maternal GWG on pregnancy and infant outcomes, there is a need for a targeted behavioural intervention. As effective health behaviour change requires early initiation and maintenance of change, women before, during and after pregnancy should be targeted. Furthermore, high pre-pregnancy BMI have been shown to be linked with increased risk of GDM and type 2 diabetes post-delivery, and higher infant birthweight, child obesity and atypical child neurodevelopment. Accordingly, this highlights the need for early behavioural intervention beginning with women trying to get pregnant. Current studies have focused predominantly on individual factors contributing to maternal obesity in relation to infant outcomes, both immediately postpartum and prospectively into early childhood. Based on Bronfenbrenner's ecological model, key contextual factors involving the micro-, meso-, exo-, macro- and chronosystem are important factors contributing to the efficacy of digital means on health behavioural change among pregnant women. From this theoretical orientation, understanding individual factors involving motivation and contextual influences is central to facilitating health behaviour change. Specifically, elucidating the proximal (e.g. peers, family) and distal factors (e.g. community, health services) embedded within specific cultural contexts ensure sustainability of behaviour change among pregnant women.

As Singapore is a culturally diverse society, there is a need to understand the impact of cultural factors on maternal behaviours and decision making. Accordingly, the current study will consist of a qualitative assessment of 60 participants who will undergo semi-structured interviews with the aim to understand motivations and contextual influences that induce and sustain behaviour change, so as to inform future interventions for women before, during and after pregnancy. As digital health intervention in pregnant women has been shown to be cost-effective and scalable, the current study also aims to understand women's usage of technology throughout the process of trying to conceive, being pregnant and being a new mother within the local Singapore context.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 60
• Est. completion date: November 8, 2022
• Est. primary completion date: May 8, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 21 Years to 45 Years

Eligibility

Inclusion Criteria:

• English fluency;

• Aged 21 years and above;

• Actively trying to conceive (pre-pregnancy) or currently in first to third trimester of pregnancy (during pregnancy) or have a child aged 0-2 years (post-pregnancy).

Exclusion Criteria:

• Evidence/diagnosis of cognitive impairment (e.g. history of dementia, intellectual disability, traumatic brain injury);

• Current diagnosis of psychiatric disorder (e.g. severe anxiety, depression, schizophrenia);

• Significant hearing impairment;

• Inability to complete the study at the judgement of the clinician investigators;

• Women requiring or who had any form of assisted conception.

Related Conditions & MeSH terms

• Pregnancy Related

Locations

Country	Name	City	State
Singapore	National University Hospital	Singapore
Singapore	The N.1 Institute for Health (N.1), NUS, Singapore	Singapore

Sponsors (1)

Lead Sponsor	Collaborator
National University, Singapore

Country where clinical trial is conducted

Singapore, 

References & Publications (15)

Evans WD, Abroms LC, Poropatich R, Nielsen PE, Wallace JL. Mobile health evaluation methods: the Text4baby case study. J Health Commun. 2012;17 Suppl 1:22-9. doi: 10.1080/10810730.2011.649157. — View Citation

Frederick IO, Williams MA, Sales AE, Martin DP, Killien M. Pre-pregnancy body mass index, gestational weight gain, and other maternal characteristics in relation to infant birth weight. Matern Child Health J. 2008 Sep;12(5):557-67. Epub 2007 Aug 23. — View Citation

Gaillard R, Santos S, Duijts L, Felix JF. Childhood Health Consequences of Maternal Obesity during Pregnancy: A Narrative Review. Ann Nutr Metab. 2016;69(3-4):171-180. doi: 10.1159/000453077. Epub 2016 Nov 18. Review. — View Citation

Goldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, Li N, Hu G, Corrado F, Rode L, Kim YJ, Haugen M, Song WO, Kim MH, Bogaerts A, Devlieger R, Chung JH, Teede HJ. Association of Gestational Weight Gain With Maternal and Infant Outcomes: A Systematic Review and Meta-analysis. JAMA. 2017 Jun 6;317(21):2207-2225. doi: 10.1001/jama.2017.3635. Review. — View Citation

He S, Allen JC, Razali NS, Win NM, Zhang JJ, Ng MJ, Yeo GSH, Chern BSM, Tan KH. Are women in Singapore gaining weight appropriately during pregnancy: a prospective cohort study. BMC Pregnancy Childbirth. 2019 Aug 13;19(1):290. doi: 10.1186/s12884-019-2443-z. — View Citation

Heslehurst N, Vieira R, Akhter Z, Bailey H, Slack E, Ngongalah L, Pemu A, Rankin J. The association between maternal body mass index and child obesity: A systematic review and meta-analysis. PLoS Med. 2019 Jun 11;16(6):e1002817. doi: 10.1371/journal.pmed.1002817. eCollection 2019 Jun. — View Citation

Hung TH, Hsieh TT. Pregestational body mass index, gestational weight gain, and risks for adverse pregnancy outcomes among Taiwanese women: A retrospective cohort study. Taiwan J Obstet Gynecol. 2016 Aug;55(4):575-81. doi: 10.1016/j.tjog.2016.06.016. — View Citation

International Weight Management in Pregnancy (i-WIP) Collaborative Group. Effect of diet and physical activity based interventions in pregnancy on gestational weight gain and pregnancy outcomes: meta-analysis of individual participant data from randomised trials. BMJ. 2017 Jul 19;358:j3119. doi: 10.1136/bmj.j3119. Review. Erratum in: BMJ. 2017 Aug 23;358:j3991. — View Citation

Lindlof TR, Taylor BC. Sensemaking: Qualitative data analysis and interpretation. Qualitative communication research methods. 2011;3(1):241-81.

Redman LM, Gilmore LA, Breaux J, Thomas DM, Elkind-Hirsch K, Stewart T, Hsia DS, Burton J, Apolzan JW, Cain LE, Altazan AD, Ragusa S, Brady H, Davis A, Tilford JM, Sutton EF, Martin CK. Effectiveness of SmartMoms, a Novel eHealth Intervention for Management of Gestational Weight Gain: Randomized Controlled Pilot Trial. JMIR Mhealth Uhealth. 2017 Sep 13;5(9):e133. doi: 10.2196/mhealth.8228. — View Citation

Sanchez CE, Barry C, Sabhlok A, Russell K, Majors A, Kollins SH, Fuemmeler BF. Maternal pre-pregnancy obesity and child neurodevelopmental outcomes: a meta-analysis. Obes Rev. 2018 Apr;19(4):464-484. doi: 10.1111/obr.12643. Epub 2017 Nov 22. — View Citation

Singapore Department of Statistics. (2010). Census of Population 2010. Retrieved from: http:// www.singstat.gov.sg/publications/publications-and-papers/population/census10_admin

Torloni MR, Betrán AP, Horta BL, Nakamura MU, Atallah AN, Moron AF, Valente O. Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obes Rev. 2009 Mar;10(2):194-203. doi: 10.1111/j.1467-789X.2008.00541.x. Epub 2008 Nov 24. Review. — View Citation

Voerman E, Santos S, Patro Golab B, Amiano P, Ballester F, Barros H, Bergström A, Charles MA, Chatzi L, Chevrier C, Chrousos GP, Corpeleijn E, Costet N, Crozier S, Devereux G, Eggesbø M, Ekström S, Fantini MP, Farchi S, Forastiere F, Georgiu V, Godfrey KM, Gori D, Grote V, Hanke W, Hertz-Picciotto I, Heude B, Hryhorczuk D, Huang RC, Inskip H, Iszatt N, Karvonen AM, Kenny LC, Koletzko B, Küpers LK, Lagström H, Lehmann I, Magnus P, Majewska R, Mäkelä J, Manios Y, McAuliffe FM, McDonald SW, Mehegan J, Mommers M, Morgen CS, Mori TA, Moschonis G, Murray D, Chaoimh CN, Nohr EA, Nybo Andersen AM, Oken E, Oostvogels AJJM, Pac A, Papadopoulou E, Pekkanen J, Pizzi C, Polanska K, Porta D, Richiardi L, Rifas-Shiman SL, Ronfani L, Santos AC, Standl M, Stoltenberg C, Thiering E, Thijs C, Torrent M, Tough SC, Trnovec T, Turner S, van Rossem L, von Berg A, Vrijheid M, Vrijkotte TGM, West J, Wijga A, Wright J, Zvinchuk O, Sørensen TIA, Lawlor DA, Gaillard R, Jaddoe VWV. Maternal body mass index, gestational weight gain, and the risk of overweight and obesity across childhood: An individual participant data meta-analysis. PLoS Med. 2019 Feb 11;16(2):e1002744. doi: 10.1371/journal.pmed.1002744. eCollection 2019 Feb. — View Citation

Wang X, Zhang X, Zhou M, Juan J, Wang X. Association of prepregnancy body mass index, rate of gestational weight gain with pregnancy outcomes in Chinese urban women. Nutr Metab (Lond). 2019 Aug 19;16:54. doi: 10.1186/s12986-019-0386-z. eCollection 2019. — View Citation

* Note: There are 15 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Experiences and technology usage among women	We will conduct semi-structured interviews with women who are either trying to conceive, pregnant or have a child aged 0 to 2 years. The interviews seek to find out about the participants' pregnancy/maternal-related experiences including the challenges faced, lifestyle changes made, support systems and experience with the health system. Participants will also be asked about their previous and current experiences with technology usage, such as the type of technology used and the purposes of technology usage. Additionally, there will also be two questionnaires, conducted pre- and post-interview, on the participants' demographics and their opinions on technical aspects of digital platforms.	1 year