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Fecundability in reproductive aged women at risk of sexual dysfunction and associated risk factors: a prospective preconception cohort study



Female sexual dysfunction (FSD) is a prevalent problem, affecting up to 41% of reproductive aged women worldwide. However, the association between female sexual function (FSF) and fecundability in women attempting to conceive remains unclear. We aimed 1) to examine the association between FSF in reproductive-aged preconception Asian women and fecundability, as measured by time-to-pregnancy in menstrual cycles, and 2) to examine lifestyle and behavioral factors associated with FSF.


From the Singapore PREconception Study of long-Term maternal and child Outcomes (S-PRESTO) prospective cohort, we evaluated FSF using the 6-item Female Sexual Function Index (FSFI-6) and ascertained time-to-pregnancy within a year of baseline assessment. We estimated fecundability ratio (FR) and 95% confidence interval (CI) using the discrete-time proportional hazards model, accounting for left-truncation and right censoring. We used multivariable logistic and linear regression models to identify potential factors related to FSF.


Among 513 participants, 58.9% had low FSF as defined by a total FSFI-6 score at or below the median value of 22. Compared to women with high FSF, those with low FSF had a 27% reduction in fecundability (FR 0.73; 95% CI 0.54, 0.99), with adjustment for age, ethnicity, education, parity and body mass index. Overall, the FRs generally reduced with decreasing FSFI-6 scores. Physical activity, obesity, absence of probable depression and anxiety were independently associated with reduced odds of low FSF and increased FSFI-6 scores, after adjusting for sociodemographic characteristics.


Low FSF is associated with a longer time-to-pregnancy. Early evaluation and optimization of FSF through increased physical activity and optimal mental health may help to improve female fecundity. The finding of obese women having improved FSF remains uncertain which warrants further investigations on plausibly mechanisms. In general, the current finding highlights the importance of addressing FSF in preconception care service for general women, which is currently lacking as part of the fertility promotion effort in the country.

Peer Review reports


Female sexual dysfunction (FSD) affects many women in the reproductive age group, with an estimated prevalence of 41% worldwide, making it a significant public health problem [1]. According to the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-V), FSD is a heterogeneous combination of disorders that entails sexual interest/ arousal disorder, orgasmic disorder and genito-pelvic pain/ penetration disorder [2]. FSD is multifactorial in etiology, with biological, physical, behavioral and psychosocial issues contributing to the spectrum of FSD [3]. Specifically, factors such as women’s age at marriage, physical health, emotion, exercise, body image, sex education and partner’s sexual health have been consistently associated with female sexual function (FSF) in various populations [3]. Most importantly, impairment in sexual function has been shown to adversely affect a woman’s quality of life and wellbeing [4], with implications for fertility and reproductive health [5].

Sex is a natural and spontaneous expression of intimacy. However, when sex becomes an instrument for conception rather than pleasure, it may create unnecessary pressure and lead to frustration, potentially impeding sexual satisfaction and resulting in a downward spiral [6]. Indeed, situational sexual dysfunction and loss of a couple’s intimacy can happen as a result of timed intercourse around the time of ovulation, or the “fertile period”, where couples only think about conception during intercourse [7]. This is commonly reported by infertile couples who experience infertility-related stress after attempting to conceive for a long period of time, resulting in the need for fertility treatment [7, 8]. There is also evidence showing that attempts to conceive are positively associated with FSF in subfertile women, where the desire for pregnancy could prevail over emotional stress [9]. Taken together, these contradictory findings of the impact of subfertility on FSF may reflect varying responses (i.e. stress vs. desire) of the women while attempting to conceive.

Among women of reproductive age from the general population who are trying to conceive, the association between FSF and fecundability is poorly understood. In particular, it remains unknown to what extent female fecundability (the per-cycle probability of conception) is influenced by FSF. Using data from the Singapore PREconception Study of long-Term maternal and child Outcomes (S-PRESTO) cohort study, we examined FSF and explored its relationship with fecundability as measured by time-to-pregnancy (TTP), in Asian women of reproductive age during the preconception period. We hypothesized that women with low FSF would have a reduced fecundability with longer TTP in 1 year of trying to conceive. In addition, we also examined lifestyle and behavioral factors that could be associated with FSF. Understanding potential risk factors of FSF, particularly modifiable characteristics, may help to devise approaches to reduce FSD, with potential beneficial effect for improved fecundability.

Materials and methods


Data were drawn from the S-PRESTO (, NCT03531658), a prospective preconception cohort study that was designed to investigate the long-term influences of events occurring before and during early pregnancy on mother-offspring metabolic and mental health. Asian women of Chinese, Malay or Indian ethnicity attempting to conceive within the next 12 months and aged between 18 and 45 years were enrolled. Ineligibility criteria were known Type 1 diabetes or Type 2 diabetes, had been taking anticonvulsant medication, oral steroids or receiving assisted fertility treatment in the past 1 month. This study was conducted according to the guidelines laid down in the Declaration of Helsinki. The Singhealth Centralized Institute Review Board approved the study protocol (reference 2014/692/D). All participants provided written informed consent.

Study procedure

Details of the study protocol have been described elsewhere [10]. Briefly, at the recruitment visit (baseline), research staff interviewed women about their sociodemographic characteristics, obstetric histories and lifestyle factors, and performed weight and height measurements in the S-PRESTO cohort center, KK Women’s and Children’s hospital (KKH). At the end of clinic visit, research staff reminded women to perform a pregnancy test using the provided home urinary pregnancy test kits (Biotron Diagnostics, USA) detecting the beta subunit of human chorionic gonadotropin, if their menstrual periods were late for 3–4 days, or 2 weeks after unprotected intercourse. Women were also being reminded to contact the research staff if they had a positive pregnancy test. This was followed by an ultrasound scan to confirm clinical pregnancy. In the absence of any update within 6, 9 and 12 months of recruitment, research staff conducted a follow-up contact by telephone to determine the woman’s pregnancy status. All women were followed for up to 1 year while attempting to conceive.

Data collection

General questionnaire

At baseline, women provided data on date of birth (for age calculation), ethnicity, educational level, parity, menstrual cycle length and regularity, contraception method, date of last menstrual period (LMP) and the number of months of attempting to conceive at study entry. For physical activity assessment, women were asked about their frequency and intensity of physical activity using the short form of the International Physical Activity Questionnaire [11] and were classified into three groups (inactive, minimally active and active) based on the metabolic equivalent task scores in minutes (MET-minutes) [12]. For mental health assessment, women self-administered the Edinburg Postnatal Depression Scale (EPDS) and the state-anxiety subscale of the State Trait Anxiety Inventory (S-STAI), with respective scores of ≥13 indicate probable depression [13] and scores of > 40 indicate probable state anxiety [14, 15]. Although the EPDS has mainly been used in pregnant and postpartum women, it has been found suitable as a depression screener among adults in the community [16]. By calculating the Cronbach’s alpha, high internal consistencies were observed for EPDS (0.83) and S-STAI (0.93) [17].

Assessment of FSF

At baseline, women self-administered the 6-item Female Sexual Function Index (FSFI-6) [18], which is a short version of the original 19-item FSFI (FSFI-19) [19]. It is a screening tool that aims to identify women at risk of FSD [18]. The FSFI-6 comprises six questions with each item derived from one of the six domains of the FSFI-19: desire (original item #2), arousal (original item #4), lubrication (original item #7), orgasm (original item #11), satisfaction (original item #16) and pain (original item #17) [18]. Each question provides a score varying from 0 to 5, whose sum yields a total FSFI-6 score ranging from 2 to 30. Higher total score indicates better sexual function. We defined women as low FSF if their total FSFI-6 scores were at or below the median value of 22. This median score approach had been previously adopted by other groups [20, 21]. In this cohort, the internal consistency of the FSFI-6 as measured by Cronbach’s alpha was 0.78.

Assessment of TTP

We estimated TTP based on the number of menstrual cycles required to achieve a pregnancy over 12 months of follow-up. We determined pregnancy based on a positive urinary pregnancy test as confirmed by the presence of an intrauterine gestational sac from an ultrasound scan after 6 weeks of gestation. In the event where an ultrasound scan was not available or inconclusive, the diagnosis of pregnancy was made clinically. We calculated the interval between the dates of LMP at recruitment and before conception (for pregnant women) or last follow-up call (for censored women). The interval was converted to cycles by dividing with the average cycle length, which was obtained from the reported minimum and maximum lengths of usual cycles at baseline. TTP was calculated as the total discrete cycles at risk of pregnancy: (days of conception attempt at study entry/ average cycle length) + [(date of LMP before conception or the most recent follow-up) − (date of LMP at recruitment)]/ average cycle length. For women who became pregnant, one more conception cycle was added [10].

Statistical analysis

We compared baseline characteristics between women with low and high FSF using the Pearson’s chi-squared test for categorical variables and Mann-Whitney test for continuous variables. We used discrete-time proportional hazards model which analyzed TTP as a discrete scale based on the number of menstrual cycles, to estimate fecundability ratio (FR) and 95% confidence interval (CI) [22, 23], accounting for left truncation and right censoring. The FR represents the cycle-specific probability of conception in one group of women, relative to a control group. A FR < 1 indicates reduced fecundability with longer TTP, while a FR > 1 indicates increased fecundability with shorter TTP. To account for left truncation, we based risk sets only on observed cycles at risk, i.e. cycles of conception attempt while participating in the study. For example, if a woman had been attempting to conceive for two cycles at study entry and reported a pregnancy after six cycles of total attempt time, only four cycles (i.e. 3rd to 6th cycles) as observed in the study contributed to the analysis. Data were censored when women (i) had not conceived after 12 months from the recruitment, (ii) initiated fertility treatment, or (iii) were lost to follow-up or withdrew from the study, whichever occurred first.

Potential confounders that have been commonly reported to influence both FSF and fecundability were determined from the literature [3, 10, 24, 25], based on clinical judgement and using a directed acyclic graph. Those that minimally altered the effect estimates were not included in the final models [26, 27]. The selected potential confounders were age, ethnicity, education, parity and BMI. In view of the possibility that women who had been attempting to conceive for a long period might be suffering from infertility issue, we performed sub-analysis by restricting samples to women who had been attempting conception for ≤12 months at study entry. This would help to exclude potential cases with underlying pathologies in female and male fertility, given that 12-month is a typical length of time after which couples would seek infertility treatment. As FSF might be lower with increasing conception attempts, we performed a sensitivity analysis stratifying on duration attempting to conceive at study entry (dichotomized as ≤6 cycles and ≥ 7 cycles). Owing to the possibility that polycystic ovarian syndrome (PCOS) could affect self-image with potential negative repercussions on sexuality [28], we performed additional analyses excluding women with self-reported PCOS.

We used multivariable logistic and linear regression models to examine potential lifestyle and behavioral factors associated with low FSF (binary dependent variable) and total FSFI-6 scores (continuous dependent variable), respectively. All factors were included simultaneously in the models, except for probable depression and anxiety as both of them were highly correlated with each other (r = 0.74; p < 0.001). The models were adjusted for women’s age, ethnicity, education and parity. We used multiple imputation by chained equation to account for missing data, including parity (n = 1 woman), BMI (n = 3), physical activity (n = 2), EPDS scores (n = 46) and STAI scores (n = 54) [29, 30]. Fifty datasets were generated and the results of the 50 analyses were pooled using Rubin’s rule [31]. We performed sensitivity analyses on the complete-case sample. Statistical analyses were conducted using the SPSS Statistics Version 20 (IBM Corp, Armonk, NY, USA) and Stata Statistical Software, Release 16 (StataCorp, College Station, TX, USA).


Study participants

Of 1032 women recruited into the S-PRESTO study, only about the first half of women were invited to complete the FSFI-6. Subsequently, considering overall participant burden and time, the study protocol was modified to reduce measurements and FSFI-6 was removed from the study. As shown in Fig. 1, among 556 women with FSFI-6 administration, 13 were excluded due to incomplete data or reported no sexual activity over the previous 4 weeks (FSFI-6 only captured sexual activities in the past month). Of the remaining 543 women with valid FSFI-6 data, 30 were excluded due to missing and/ or incomplete data for the primary analysis in examining association between FSF and fecundability.

Fig. 1
figure 1

Flowchart showing participants included in the present study

Characteristics of participants

We included a final sample of 513 women in this study, where 194 women conceived naturally during the 12 months follow-up and 319 women were censored (293 did not conceive, 10 initiated fertility treatment, 16 self-withdrawal). Of these women, 141 (27.5%) achieved a pregnancy within six cycles of follow-up, and 184 (35.9%) within 12 cycles. Compared with excluded women (n = 519), included women were similar in ethnicity, parity, BMI, probable depression and anxiety, but older, of lower education and less physically active (see Additional file 1).

Table 1 shows the baseline characteristics of women classified as either non-FSD or probable FSD. Using the FSFI-6 median cut-off score of ≤22, 302 women (58.9%) were found to have low FSF. These women were more likely to be of Chinese ethnicity (80.1% vs. 60.2%), to be underweight (11.6% vs. 6.2%), to be physically inactive (22.8% vs. 18.0%), to exhibit probable depression (17.2% vs. 5.7%) and probable anxiety (25.8% vs. 13.7%), compared to women with high FSF. No differences in age, education, parity, prior use of hormonal contraceptives and amount of time spent trying to conceive at study entry were observed between both groups of women.

Table 1 Characteristics between women with low and high FSF from the S-PRESTO study, 2015–2018

Distribution of participants by types of FSD

Figure 2 presents proportions of women reporting scores ≤2 for each item of FSFI-6. Rarely reaching orgasm (a few times/ almost never/ never; 18.3%) and low sexual desire (low/ very low/ none; 14.8%) were the most frequently reported FSD, followed by low sexual arousal (low/ very low/ none; 8.2%), painful intercourse (most times/ almost always/ always discomfort or pain; 8.2%), rarely experienced sexual lubrication (a few times/ almost never/ never; 7.2%) and sexual dissatisfaction (moderately or very dissatisfied with overall sexual life; 4.7%). Individual scores obtained for each item in the FSFI-6 are presented in Additional file 2.

Fig. 2
figure 2

Proportions of women reporting scores ≤2 based on each item of the 6-item Female Sexual Function Index (n = 513). Low desire included low, very low or none at all in terms of sexual desire or interest; low arousal included low, very low or none at all in terms of sexual arousal; lubrication rarely included a few times, almost never or never experienced lubrication; rarely orgasm included a few times, almost never or never reach orgasm; dissatisfaction included moderately or very dissatisfied with sexual life; always in pain included most times, almost always or always experienced discomfort or pain during vaginal penetration

Association between FSF and fecundability

Table 2 shows the association between low FSF and fecundability as measured by TTP in cycles. Compared to women with high FSF, women with low FSF had a lower FR of 0.73 (95% CI 0.54, 0.99). This association was primarily driven by lower FRs in women reporting lack of lubrication (FR 0.61, 95% CI 0.43, 0.85) and low sexual desire (FR 0.70, 95% CI 0.49, 1.02) (see Additional file 3). Across the range of FSFI-6 total scores, women in the lowest quartile showed the lowest FR of 0.66 (95% CI 0.45, 0.96), compared with women in the highest quartile. Overall, there was a trend of reducing FRs with decreasing FSFI-6 scores (Fig. 3).

Table 2 Association between low FSF and fecundability in women from the S-PRESTO study
Fig. 3
figure 3

Association between FSFI-6 scores and fecundability. FSFI-6 total scores are divided into quartiles, with the highest quartile as the reference category. The line graph is adjusted for age, ethnicity, education, parity and body mass index. The error bars denote 95% confidence intervals

In the sub-analysis including only women who had been attempting conception for ≤12 months at study entry (n = 390), the results were similar where low FSF was still found to have a similarly reduced FR of 0.72 (0.52, 0.99). The Kaplan-Meier curves in Fig. 4 show the cumulative pregnancy probabilities by FSF status in the group of women who had been attempting conception for ≤12 months at study entry. When a sensitivity analysis was performed stratifying on duration attempting to conceive at study entry (dichotomized as ≤6 cycles vs. ≥7 cycles), the association between FSF and fecundability was somewhat stronger in women with ≥7 cycles attempting to conceive at study entry, albeit with overlapping 95% confidence limits (see Additional file 4). When additional analysis was performed by excluding women who reported PCOS (n = 7), no substantial change in FR was observed, after adjustment of confounders (0.71; 0.52, 0.95).

Fig. 4
figure 4

Kaplan-Meier, pregnancy probability curves by female sexual function (FSF) status in women with pregnancy attempt ≤12 months at study entry (n = 390). The curves are adjusted for age, ethnicity, education, parity and body mass index

Associations of lifestyle and behavioral factors with FSF

Table 3 shows the potential lifestyle and behavioral factors associated with low FSF and the total FSFI-6 scores. With adjustment for socio-demographic characteristics, being physically active and obesity were associated with reduced odds of low FSF; while probable depression and probable anxiety were associated with increased odds of low FSF. Similar findings were observed when the outcome was based on the total FSFI-6 scores. Inclusion of women with complete dataset in the sensitivity analysis (n = 455) revealed similar findings (see Additional file 5).

Table 3 Lifestyle and behavioral factors associated with female sexual function in preconception women from the S-PRESTO study


In this prospective preconception cohort study, we examined the association between FSF and fecundability based on TTP among reproductive aged Asian women in Singapore. We found that low FSF, specifically lack of lubrication and low sexual desire, were associated with a reduction in fecundability. In general, worsening FSF as indicated by decreasing FSFI-6 scores was associated with reduced fecundability. This was particularly evident in women who had been attempting to conceive for a longer period at study entry. To investigate potential risk factors of low FSF, we focused on lifestyle and behavioral factors which are modifiable. Women who were physically active and obese were less likely to have low FSF; whereas women with depression and anxiety symptoms were more likely to have low FSF. Taken together, these findings suggest that increasing physical activity and promoting mental wellness may have the potential to improve FSF during the preconception period, leading to shorter TTP and improved fecundability in women of reproductive age. The finding of obese women having improved FSF remains uncertain which warrants further investigations on plausibly mechanisms.

The FSFI-19 is an established instrument used to assess FSF in populations of different ethnicities with good reliability [4]. The Cronbach’s alpha of 0.78 for FSFI-6 in our samples was similar to that presented in the first validation study of the FSFI-6 by Isidori and colleagues [18], reporting Cronbach’s alpha of 0.79. The FSFI-6 was created as a simpler and quicker alternative for easy use in clinical fields and epidemiological studies, with the same psychometric properties [18]. Using the FSFI-6, a cut-off score of ≤19 has been initially proposed to identify women at risk of FSD among Italian women [18]. Considering social and cultural disparities that can influence sexual attitudes [32], studies from other countries have applied different approaches to derive different cut-offs of FSFI-6 to identify high-risk women for FSD [20, 21, 33]. By validating FSFI-6 against FSFI-19, cut-off score of ≤21 was proposed for Korean women [33]. In this study, we utilized a cut-off point based on the FSFI-6 median score of ≤22, similar to the approach used by others for Ecuadorian [20] and Brazilian women [21] with respective ≤20 and ≤ 21 scores reported. Compared to Chedraui et al. [20] and Dall’Agno et al. [21], our women had a slightly higher median score for FSFI-6. This could be explained by the participants almost all being married (98.8%) with presumably stable relationships, potentially placing them in a better psychosocial environment which may contribute to a better sexual function [20, 34].

Nevertheless, to clarify the definite cut-off score for FSD, validation of FSFI-6 against a more accurate test (i.e. sex therapist consultation and physical examination followed by the FSFI-19 assessment) [17] to identify absolute certainty women with and without FSD in our population should be performed. Using the median score approach, we showed that 58.6% of Asian women in Singapore displayed scores ≤22, suggesting at-risk of FSD. Among the six items of sexual disorders as examined in the FSFI-6, low sexual desire and rarely reaching orgasm were more commonly reported in these women. This is consistent with previous reports showing hypoactive sexual desire disorder and orgasmic disorder as major female sexual problems [1, 35]. A study from Malaysia [36], a country that shares many cultural similarities and in close geographical proximity with Singapore, reported that lack of sexual desire was the most common FSD, while lack of sexual satisfaction and lubrication were less commonly reported in Malaysian women, which is in agreement with our findings. Nevertheless, lack of lubrication and low sexual desire were the major FSF issues associated with reduced fecundability among women in this study.

Women with low FSF exhibited a 27% reduction in fecundability compared to women with high FSF, adjusting for confounders. Biologic mechanisms by which FSD could influence fecundability are uncertain. We postulate that the reduced fecundability in women with low FSF may be related to the low coital frequency due to unpleasant sexual experience and difficulties with intimacy [7, 9]. Psychological distress resulting from FSD may also induce an inflammatory response [37], whilst stress-related glucocorticoid changes mediate disruption in reproductive hormonal balance such as luteinizing hormone and melatonin, which in turn can interfere with ovulation [38, 39], contributing to the delayed TTP [24]. In view of the low total fertility rate in Singapore (1.10 in 2020) [40], greater efforts are required with targeted strategies to enhance pregnancy rates. Our findings of the association between FSF and reduced fecundability in preconception women highlights the importance of addressing sexual function in preconception care programs, as part of the fertility promotion effort to increase chances of conception. Importantly, evaluating sexual function during the preconception period provides an opportunity to intervene and resolve sexual dysfunction issues with women and their partners early, resulting in improved preconception quality of life and overall health for the women, with potential beneficial effect on pregnancy outcomes [41].

To manage FSD, it is essential to be aware of related risk factors, in particular those modifiable factors. Consistent with a previous review [3], we observed that women who were physically inactive, and those with depression or anxiety symptoms had a poorer sexual function. An inactive lifestyle and emotional disorder may reduce sexual desire, arousal and satisfaction through disruption of nervous system activity and endocrine factors [42, 43]. Although our study does not demonstrate causal-effect relationships between these factors and FSF, the present findings revealed that physical activity and mental well-being are important lifestyle and behavioral components that must be addressed in sexual health management for preconception women.

To date, the effect of obesity on sexual life in women remains inconclusive. Some studies found no association between BMI and FSF [44, 45]; while others showed increased FSD in overweight and obese women [46,47,48]. Herein, we observed obese women had improved FSF. We speculate that this may be arisen from the high androgen levels in obese individuals that can increase sexual desire, arousal, orgasm and satisfaction [49]. Indeed, this is supported by Smith et al. [50] demonstrating obese women were more likely than normal weight women to report extreme physical pleasure in sex. The authors pointed out that being obese is not necessarily detrimental to sexual functioning in women [50]. Alternatively, the impact of BMI on coital frequency may account for our result. It is possible that low coital frequency, which has been commonly reported in obese women [51], may translate to a greater desire and arousal, leading to a better FSF. Besides, the present finding could also be partly explained by a lesser degree of obesity (median BMI of 31.3 kg/m2 in obese group) in our women, compared with previous reports involving a majority of women from Western clinical setting with a more severe degree of obesity and related comorbid conditions [46, 47, 52]. This is aligned with evidence showing excessive obesity was more likely to be associated with FSD, when different obesity categories were compared [53]. Given that nearly 90% of our women had BMI less than 30 kg/m2, obesity-related sex hormone imbalance effects on sexual function may not be readily detectable as the magnitude of FSD may be less pronounced. Overall, these contradictory findings suggest that weight status may not be a good clinical evaluation measure when managing FSD.

There are several limitations that need to be taken into account when interpreting the results. Firstly, FSFI-6 only ascertained sexual activities over the past 4 weeks. Although its validity has not been established in our population, FSFI-6 is proven to have a strong criterion validity across studies and to be a good screening tool for FSD [54]. Secondly, we did not collect information on ovulation, coital timing and frequency throughout the follow-up period to verify the cycles at-risk, which is a limitation of this study. Thirdly, we did not consecutively collect variables which could be varied through the study, such as cycle length, and thus not able to verify the accuracy of self-reported cycle length at baseline. In addition, we did not collect information related to women’s ovarian condition, such as ovarian reserve, which could affect fecundability. Fourthly, we had no information about male partners’ characteristics (e.g. age and education) and sexual functions (e.g. erectile dysfunction and premature ejaculation) which may affect FSF [20, 55].

Finally, the extent to which the present findings could be generalizable to other populations remains to be established, as this study was restricted to planned pregnancies among Asian women in Singapore. Our cohort showed that 35.9% of women spontaneously conceived after 12 cycles of pregnancy attempts, which is lower than the reported rates of 70% or more in some other studies [25, 56, 57]. However, a study of Chinese women had shown similarly a conception rate of 42% after 12 cycles of natural conception [58]. This lower conception rate may help to explain the relatively low total fertility rate in Singapore of 1.10 [40]. In this study, although the recruited women expressed their intention to conceive and were encouraged to engage in sexual intercourse for 2–3 times per week, their frequency of sexual activity might be overestimated, resulting in a low pregnancy rate. Owing to other issues in lifestyle or medical conditions, it was possible that some women might have temporary stopped or delayed their pregnancy attempts during the study without informing the study staff. Additionally, although we excluded women with potential subfertility (conception attempt > 12 months at study entry), we cannot rule out having recruited a group with lower fertility than the general population. Further, differences observed in some characteristics between included and excluded women could potentially reflect an element of selection bias. Nonetheless, this study provides a useful reference for future fertility related studies or family planning interventions for considering FSD assessment and associated factors. This is important as the majority of women are not likely to seek treatment and discuss their sexual problems with physicians unless they are asked [59].


We observed that low FSF in preconception women of reproductive age is associated with a reduction in fecundability and longer time-to-pregnancy. Early evaluation and optimization of FSF through physical activity and mental wellness promotion in family planning interventions or preconception care can be a pivotal strategy to improve female fecundability. Improving FSF not only helps to shorten TTP, but also improves the overall quality of life in women, resulting in a conducive maternal environment in preparation for pregnancy.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.



Body mass index


Confidence interval


Edinburg Postnatal Depression Scale


Fecundability ratio


Female sexual dysfunction


Female sexual function


6-item Female Sexual Function Index


19-item Female Sexual Function Index


KK Women’s and Children’s hospital


Last menstrual period


Polycystic ovarian syndrome


Singapore PREconception Study of long-Term maternal and child Outcomes


State-anxiety subscale of the State Trait Anxiety Inventory




  1. McCool ME, Zuelke A, Theurich MA, Knuettel H, Ricci C, Apfelbacher C. Prevalence of female sexual dysfunction among premenopausal women: a systematic review and meta-analysis of observational studies. Sex Med Rev. 2016;4(3):197–212.

    Article  PubMed  Google Scholar 

  2. American Psychiatric Association. Diagnostic and statistical manual of mental health disorders. 5th ed. Arlington: American Psychiatric Association; 2013.

    Book  Google Scholar 

  3. McCool-Myers M, Theurich M, Zuelke A, Knuettel H, Apfelbacher C. Predictors of female sexual dysfunction: a systematic review and qualitative analysis through gender inequality paradigms. BMC Womens Health. 2018;18(1):108.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chedraui P, Pérez-López FR. Assessing sexual problems in women at midlife using the short version of the female sexual function index. Maturitas. 2015;82(3):299–303.

    Article  PubMed  Google Scholar 

  5. Bayoumi RR, van der Poel SZ, Koert E, Boivin J. Fertility awareness and quality of life: assessment and impact of fertility problems and infertility. Glob Reprod Health. 2018;3(4):e21.

    Article  Google Scholar 

  6. Read J. Sexual problems associated with infertility, pregnancy, and ageing. BMJ. 2004;329(7465):559–61.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Luk BHK, Loke AY. Sexual satisfaction, intimacy and relationship of couples undergoing infertility treatment. J Reprod Infant Psychol. 2019;37(2):108–22.

    Article  PubMed  Google Scholar 

  8. de Mendonça CR, Arruda JT, Noll M, Campoli PMO, Amaral WND. Sexual dysfunction in infertile women: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2017;215:153–63.

    Article  PubMed  Google Scholar 

  9. Maseroli E, Fanni E, Mannucci E, Fambrini M, Jannini EA, Maggi M, et al. Which are the male factors associated with female sexual dysfunction (FSD)? Andrology. 2016;4(5):911–20.

    Article  CAS  PubMed  Google Scholar 

  10. Loy SL, Cheung YB, Soh SE, Ng S, Tint MT, Aris IM, et al. Female adiposity and time-to-pregnancy: a multiethnic prospective cohort. Hum Reprod. 2018;33(11):2141–9.

    Article  CAS  PubMed  Google Scholar 

  11. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire:12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.

    Article  PubMed  Google Scholar 

  12. IPAQ Research committee. Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ), 2005. Accessed 9 Aug 2020.

  13. Cox JL, Holden JM, Sagovsky R. Detection of postnatal depression: development of the 10-item Edinburgh postnatal depression scale. Br J Psychiatry. 1987;150(6):782–6.

    Article  CAS  PubMed  Google Scholar 

  14. Spielberger CD, Gorsuch RL, Lushene RE. Manual for the state-trait anxiety inventory. PaloAlto, CA: Consulting Psychologists Press; 1970.

    Google Scholar 

  15. Julian LJ. Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res (Hoboken). 2011;63(Suppl 11):S467–2.

    Article  Google Scholar 

  16. Matijasevich A, Munhoz TN, Tavares BF, Barbosa AP, da Silva DM, Abitante MS, et al. Validation of the Edinburgh postnatal depression scale (EPDS) for screening of major depressive episode among adults from the general population. BMC Psychiatry. 2014;14(1):284.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kee MZL, Ponmudi S, Phua DY, Rifkin-Graboi a, Chong YS, Tan KH, et al. Preconception origins of perinatal maternal mental health. Arch Womens Ment Health. 2021 (in press). doi:

  18. Isidori AM, Pozza C, Esposito K, Giugliano D, Morano S, Vignozzi L, et al. Development and validation of a 6-item version of the female sexual function index (FSFI) as a diagnostic tool for female sexual dysfunction. J Sex Med. 2010;7(3):1139–46.

    Article  PubMed  Google Scholar 

  19. Rosen R, Brown C, Heiman J, Leiblum S, Meston C, Shabsigh R, et al. The female sexual function index (FSFI): a multidimensional self-report instrument for the assessment of female sexual function. J Sex Marital Ther. 2000;26(2):191–208.

    Article  CAS  PubMed  Google Scholar 

  20. Chedraui P, Pérez-López FR, Sánchez H, Aguirre W, Martínez N, Miranda O, et al. Assessment of sexual function of mid-aged Ecuadorian women with the 6-item Female Sexual Function Index. Maturitas. 2012;71(4):407–12.

    Article  PubMed  Google Scholar 

  21. Dall'Agno ML, Ferreira CF, Ferreira FV, Pérez-López FR, Wender MCO. Validation of the six-item female sexual function index in middle-aged Brazilian women. Rev Bras Ginecol Obstet. 2019;41(07):432–9.

    Article  PubMed  Google Scholar 

  22. Spira A. The use of fecundability in epidemiological surveys. Hum Reprod. 1998;13(7):1753–6.

    Article  CAS  PubMed  Google Scholar 

  23. Wilcox AJ. Fertility and pregnancy: an epidemiologic perspective. United Kingdom: Oxford University Press; 2010.

    Google Scholar 

  24. Lynch CD, Sundaram R, Maisog JM, Sweeney AM, Buck Louis GM. Preconception stress increases the risk of infertility: results from a couple-based prospective cohort study--the LIFE study. Hum Reprod. 2014;29(5):1067–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. CJ MK, Hatch EE, Rothman KJ, Mikkelsen EM, Wesselink AK, Hahn KA, et al. A. Body mass index, physical activity and fecundability in a North American preconception cohort study. Fertil Steril. 2016;106:451–9.

    Article  Google Scholar 

  26. Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol. 1989;129(1):125–37.

    Article  CAS  PubMed  Google Scholar 

  27. Maldonado G, Greenland S. Simulation study of confounder-selection strategies. Am J Epidemiol. 1993;138(11):923–36.

    Article  CAS  PubMed  Google Scholar 

  28. Firmino Murgel AC, Santos Simões R, Maciel GAR, Soares JM Jr, Baracat EC. Sexual dysfunction in women with polycystic ovary syndrome: systematic review and meta-analysis. J Sex Med. 2019;16(4):542–50.

    Article  PubMed  Google Scholar 

  29. Royston P. Multiple imputation of missing values. Stata J. 2004;4:227e41.

    Google Scholar 

  30. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–99.

    Article  PubMed  Google Scholar 

  31. Rubin DB. Multiple imputation for nonresponse in surveys. New York, USA: Wiley; 2004.

    Google Scholar 

  32. Ahrold TK, Meston CM. Ethnic differences in sexual attitudes of U.S. college students: gender, acculturation, and religiosity factors. Arch Sex Behav. 2010;39(1):190–202.

    Article  PubMed  Google Scholar 

  33. Lee Y, Lim MC, Joo J, Park K, Lee S, Seo S, et al. Development and validation of the Korean version of the female sexual function Index-6 (FSFI-6K). Yonsei Med J. 2014;55(5):1442–6.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Mozafari M, Khajavikhan J, Jaafarpour M, Khani A, Direkvand-Moghadam A, Najafi F. Association of body weight and female sexual dysfunction: a case control study. Iran Red Crescent Med J. 2015;17(1):e24685.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Rellini AH, Clifton J. Female orgasmic disorder. Adv Psychosom Med. 2011;31:35–56.

    Article  PubMed  Google Scholar 

  36. Sidi H, Puteh SE, Abdullah N, Midin M. The prevalence of sexual dysfunction and potential risk factors that may impair sexual function in Malaysian women. J Sex Med. 2007;4(2):311–21.

    Article  PubMed  Google Scholar 

  37. Haimovici F, Anderson JL, Bates GW, Racowsky C, Ginsburg ES, Simovici D, et al. Stress, anxiety, and depression of both partners in infertile couples are associated with cytokine levels and adverse IVF outcome. Am J Reprod Immunol. 2018;79(4):e12832.

    Article  CAS  PubMed  Google Scholar 

  38. Schenker JG, Meirow D, Schenker E. Stress and human reproduction. Eur J Obstet Gynecol Reprod Biol. 1992;45(1):1–8.

    Article  CAS  PubMed  Google Scholar 

  39. Ferin M. Clinical review 105: stress and the reproductive cycle. J Clin Endocrinol Metab. 1999;84(6):1768–74.

    Article  CAS  PubMed  Google Scholar 

  40. Singapore Department of Statistics. Age-specific fertility and total fertility rate. Accessed 5 Apr 2021.

  41. Hemsing N, Greaves L, Poole N. Preconception health care interventions: a scoping review. Sex Reprod Healthc. 2017;14:24–32.

    Article  PubMed  Google Scholar 

  42. Brotto L, Atallah S, Johnson-Agbakwu C, Rosenbaum T, Abdo C, Byers ES, et al. Psychological and interpersonal dimensions of sexual function and dysfunction. J Sex Med. 2016;13(4):538–71.

    Article  PubMed  Google Scholar 

  43. Stanton AM, Handy AB, Meston CM. The effects of exercise on sexual function in women. Sex Med Rev. 2018;6(4):548–57.

    Article  PubMed  Google Scholar 

  44. Adolfsson B, Elofsson S, Rössner S, Undén AL. Are sexual dissatisfaction and sexual abuse associated with obesity? A population-based study. Obes Res. 2004;12(10):1702–9.

    Article  PubMed  Google Scholar 

  45. Kadioglu P, Yetkin DO, Sanli O, Yalin AS, Onem K, Kadioglu A. Obesity might not be a risk factor for female sexual dysfunction. BJU Int. 2010;106(9):1357–61.

    Article  PubMed  Google Scholar 

  46. Kolotkin RL, Zunker C, Østbye T. Sexual functioning and obesity: a review. Obesity (Silver Spring). 2012;20(12):2325–33.

    Article  Google Scholar 

  47. Ranjbar H, Azmoude E. The relationship between body mass index and sexual dysfunction among women: a meta-analysis. Shiraz E-Med J. 2018;19(In Press):e14409.

    Article  Google Scholar 

  48. Bates JN, Pastuszak AW, Khera M. Effect of body weight on sexual function in men and women. Curr Sex Health Rep. 2019;11(1):52–9.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Yaylali GF, Tekekoglu S, Akin F. Sexual dysfunction in obese and overweight women. Int J Impot Res. 2010;22(4):220–6.

    Article  CAS  PubMed  Google Scholar 

  50. Smith AM, Patrick K, Heywood W, Pitts MK, Richters J, Shelley JM, et al. Body mass index, sexual difficulties and sexual satisfaction among people in regular heterosexual relationships: a population-based study. Intern Med J. 2012;42(6):641–51.

    Article  CAS  PubMed  Google Scholar 

  51. Costa RM, Brody S. Obesity, overweight, female sexual function, and penile-vaginal intercourse frequency. J Sex Marital Ther. 2016;42(4):293–6.

    Article  PubMed  Google Scholar 

  52. Ponholzer A, Temml C, Rauchenwald M, Marszalek M, Madersbacher S. Is the metabolic syndrome a risk factor for female sexual dysfunction in sexually active women? Int J Impot Res. 2008;20(1):100–4.

    Article  CAS  PubMed  Google Scholar 

  53. Elnashar AR, Ibrahim NH, Ahmed HE, Hassanin AM, Elgawady MA. Assessment of female sexual function in a group of uncircumcised obese Egyptian women. Int J Impot Res. 2015;27(5):178–81.

    Article  CAS  PubMed  Google Scholar 

  54. Neijenhuijs KI, Hooghiemstra N, Holtmaat K, Aaronson NK, Groenvold M, Holzner B, et al. The female sexual function index (FSFI)-a systematic review of measurement properties. J Sex Med. 2019;16(5):640–60.

    Article  PubMed  Google Scholar 

  55. Pérez-López FR, Fernández-Alonso AM, Trabalón-Pastor M, Vara C, Chedraui P, MenopAuse RIsk assessment (MARIA) research group. Menopause Assessment of sexual function and related factors in mid-aged sexually active Spanish women with the six-item Female Sex Function Index. Menopause. 2012;19(11):1224–30.

    Article  PubMed  Google Scholar 

  56. Taylor A. ABC of subfertility: extent of the problem. BMJ. 2003;327(7412):434–6.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Wise LA, Palmer JR, Rosenberg L. Body size and time-to-pregnancy in black women. Human Reprod. 2013; 28: 2856–2864. doi: org/

  58. Zhao J, Hong X, Zhang H, Dai Q, Huang K, Zhang X, et al. Pre-pregnancy maternal fasting plasma glucose levels in relation to time to pregnancy among the couples attempting first pregnancy. Human Reprod. 2019;34, 1325–1333. doi:org/

  59. Armstrong C. ACOG guideline on sexual dysfunction in women. Obstet Gynecol. 2011;84:705–7.

    Google Scholar 

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We thank the S-PRESTO staff, participants and the study group, including Anne Eng Neo Goh, Anne Rifkin-Graboi, Anqi Qiu, Bee Wah Lee, Bernard Chern, Bobby Cheon, Christiani Jeyakumar Henry, Ciaran Gerard Forde, Claudia Chi, Doris Fok, Elaine Quah, Elizabeth Tham, Evelyn Chung Ning Law, Evelyn Xiu Ling Loo, Faidon Magkos, Falk Mueller-Riemenschneider, George Seow Heong Yeo, Heng Hao Tan, Hugo P S van Bever, Izzuddin Bin Mohd Aris, Joanne Yoong, Joao N. Ferreira., Jonathan Tze Liang Choo, Kenneth Kwek, Kuan Jin Lee, Lieng Hsi Ling, Ling Wei Chen, Lourdes Mary Daniel, Marielle V. Fortier, Mary Foong-Fong Chong, Mei Chien Chua, Melvin Leow, Michael Meaney, Mya Thway Tint, Neerja Karnani, Ngee Lek, Oon Hoe Teoh, Peter D. Gluckman, Queenie Ling Jun Li, Sendhil Velan, Sharon Ng, Shephali Tagore, Shirong Cai, Shu E Soh, Sok Bee Lim, Stella Tsotsi, Stephen Chin-Ying Hsu, Sue Anne Toh, Teng Hong Tan, Tong Wei Yew, Victor Samuel Rajadurai, Wee Meng Han, Wei Wei Pang, Yiong Huak Chan, Yung Seng Lee.


This research is supported by the Singapore National Research Foundation under its Translational and Clinical Research (TCR) Flagship Programme and administered by the Singapore Ministry of Health’s National Medical Research Council (NMRC), Singapore - NMRC/TCR/004-NUS/2008; NMRC/TCR/012-NUHS/2014. Additional funding is provided by the Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore. YBC is supported by a Clinician Scientist Award from the Singapore NMRC (NMRC/CSA/0039/2012). KMG is supported by the UK Medical Research Council (MC_UU_12011/4) and the National Institute for Health Research (NIHR Senior Investigator (NF-SI-0515-10042)) and NIHR Southampton Biomedical Research Centre (IS-BRC-1215-20004). SYC is supported by a Clinician Scientist Award from the Singapore NMRC (NMRC/CSA-INV/0010/2016). JKYC is supported by a Clinician Scientist Award from the Singapore NMRC (CSA(SI)/008/2016). The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Author information

Authors and Affiliations



KMG, YSC, LPCS, KHT, FKYP, SYC and JKYC designed and led the S-PRESTO cohort study. SLL designed the present study. SLL and JYB contributed to data management and cleaning. YBC advised on the statistical analysis. SLL performed data analysis and drafted the manuscript. CWK, YBC, JYB, HYC and TYT interpreted the findings. CWK, KMG, YBC, JYB, HYC, SYC, TYT and JKYC revised the manuscript critically. All authors reviewed, read and approved the final manuscript.

Corresponding authors

Correspondence to See Ling Loy or Jerry Kok Yen Chan.

Ethics declarations

Ethics approval and consent to participate

This study was reviewed and approved by the Singhealth Centralized Institute Review Board (reference 2014/692/D). All participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

KMG, YSC and FKYP have received reimbursement to speak at conferences sponsored by companies selling nutritional products. KMG, YSC and SYC are part of an academic consortium who have received research funding from Abbott, Nutrition, Nestle and Danone. Other authors declare that they have no competing interests.

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Supplementary Information

Additional file 1

Women’s characteristics according to their inclusion status in the present analysis from the S-PRESTO study (n = 1032)

Additional file 2

Total and each item scores of the 6-item female sexual function index (n = 513).

Additional file 3.

Associations between types of female sexual function (based on each ≤2 scores in FSFI-6) with fecundability. The line graph is adjusted for age, ethnicity, education, parity and body mass index. The error bars denote 95% confidence intervals. FSFI-6, 6-item Female Sexual Function Index.

Additional file 4

Association between female sexual function and fecundability in women from the S-PRESTO study, stratified by cycles of conception attempt at study entry (n = 513).

Additional file 5

Lifestyle and behavioral factors associated with probable FSD and total FSFI-6 scores in preconception women based on complete dataset (n = 455).

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Loy, S.L., Ku, C.W., Cheung, Y.B. et al. Fecundability in reproductive aged women at risk of sexual dysfunction and associated risk factors: a prospective preconception cohort study. BMC Pregnancy Childbirth 21, 444 (2021).

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