Breaking the Habit: A Systematic Review and Meta-Analysis of Pregnancy-Related Smoking Cessation Randomized Controlled Trials

Abstract

Background: Smoking during pregnancy is a significant issue because of its impact on maternal and fetal health. This study aimed to ascertain the effects of smoking cessation programs on the smoking abstinence rate and pregnancy outcomes. Methods: A meta-analysis of twenty-one RCTs was carried out in accordance with PRISMA standards. The meta-analysis comprised 8149 pregnant smokers. With RevMan (version 5.4), the pooled effect of RR for different smoking cessation interventions was determined. Using the GRADE approach, the certainty was evaluated. I² statistics and sensitivity analysis were utilized to measure heterogeneity. Egger's test and funnel plot analysis were used to assess publication bias. Results: The pregnant women who received cognitive-behavioral counseling and financial incentives recognized a significant rise in their smoking abstinence rate (RR: 1.14, 95% CI: 1.02-1.28, p = 0.03 and RR: 2.37, 95% CI: 1.92-2.93, p < 0.001), but there was no significant difference observed among other behavioral approaches or pharmaceutical therapy. Fetuses born to women in the intervention group had significantly larger birth weights (MD = 94.73, 95% CI = (41.18-58.27), p < 0.001. Conclusions: Pregnant women respond well to cognitive-behavioral counseling and financial incentives for quitting smoking, which improve pregnancy outcomes like birth weight.

Keywords: RCT; behavioral approaches; pharmacological therapies; pregnancy; smoking.

Figure 1

PRISMA search flow diagram.

Figure 2

Forest plot of pharmacological smoking abstinence rate Kranzler et al., 2021 [35], Nanovskaya et al., 2017 [34], Stotts et al., 2015 [33], Berlin et al., 2014 [30], Coleman et al., 2012 [28], El-Mohandes et al., 2013 [29], Iyen et al., 2019 [31], Oncken et al., 2019 [32].

Figure 3

Funnel plot for pharmacological interventions.

Figure 4

Forest plot of behavioral smoking abstinence rate Emery et al., 2024 [36], King et al., 2022 [37], Pollak et al., 2020 [38], Berlin et al., 2021 [39], Kurti 2020 [40], Tappin et al., 2022 [42], Tappin et al., 2015 [41], Bradizza et al., 2017 [43], Jin et al., 2018 [44], Lee et al., 2015 [45], Loukopoulou et al., 2018 [46], Patten et al., 2020 [47], Ussher et al., 2015 [20].

Figure 5

Funnel plot for behavioral interventions.

Figure 6

Forest plot of Caesarean section Berlin et al., 2014 [30], Coleman et al., 2012 [28], Jin et al., 2018 [44], Lee et al., 2015 [45], Ussher et al., 2015 [20].

Figure 7

Forest plot of birth weight (gm) Berlin et al., 2014 [30], Jin et al., 2018 [44], Kranzler et al., 2021 [35], Nanovskaya et al., 2017 [34], Stotts et al., 2015 [33], Tappin et al., 2015 [41].

Figure 8

Forest plot of low birth weight: Coleman et al., 2012 [28], El-Mohandes et al., 2013 [29], Iyen et al., 2019 [31], Jin et al., 2018 [44], Oncken et al., 2019 [32], Ussher et al., 2015 [20].

Figure 9

Forest plot of Apgar score < 7 (5 min): Coleman et al., 2012 [28], Jin et al., 2018 [44], Kranzler et al., 2021 [35], Ussher et al., 2015 [20].

Figure 10

Forest plot of preterm birth: Berlin et al., 2014 [30], Coleman et al., 2012 [28], El-Mohandes et al., 2013 [29], Jin et al., 2018 [44], Nanovskaya et al., 2017 [34], Oncken et al., 2019 [32], Tappin et al., 2022 [42], Tappin et al., 2015 [41], Ussher et al., 2015 [20].

Figure 11

Forest plot of stillbirth: Berlin et al., 2014 [30], Coleman et al., 2012 [28], Jin et al., 2018 [44], Tappin et al., 2015 [41], Ussher et al., 2015 [20].