Skip to main content
Download PDF

Development, validation, and reliability of the Chrononutrition Profile Questionnaire-Pregnancy (CPQ-P)

BMC Pregnancy and Childbirth volume 24, Article number: 217 (2024) Cite this article

Abstract

Background

During pregnancy, physiological changes can affect eating and sleeping habits, which may eventually have negative consequences for maternal and foetal health. To better understand these changes, it is essential to develop a reliable questionnaire that addresses lifestyle habits such as snacking and daytime napping. This study aimed to determine the validity and reliability of the Chrononutrition Profile Questionnaire-Pregnancy (CPQ-P).

Methods

A total of 399 women in their second and third trimester of pregnancy were recruited from government maternal and child health clinics in Kuala Lumpur and Putrajaya and completed a self-administered online questionnaire. Content validity was conducted with an expert panel consisting of 4 members. Confirmatory factor analysis (CFA) using maximum likelihood was conducted to determine the construct validity. Internal consistency was determined by Cronbach’s alpha coefficient (CAC), while the test-retest reliability was conducted using intraclass correlation coefficient (ICC).

Results

The questionnaire had an appropriate content validity index of 0.91. The CPQ-P consists of 22 items, measuring 5 constructs, including morning chrono-habits, sleeping habits, evening eating, temporal eating, and pregnancy symptoms. The factor model showed good fit with χ2/df = 2.486, GFI = 0.893, CFI = 0.912, and RMSEA = 0.065. The 22 items in CPQ-P showed fair to excellent test-retest reliability (ICC: 0.42 to 0.98). The 5 constructs in CPQ-P were found to have a good to excellent internal consistency (α = 0.612–0.963).

Conclusions

The CPQ-P is a valid and reliable tool for assessing lifestyle habits during pregnancy. The questionnaire can be used to identify areas where pregnant women may need additional support or intervention to adopt healthy behaviours and reduce the risk of adverse maternal and foetal outcomes.

Trial registration

NCT05700136 (clinicaltrials.gov). Trial registration date: 26/01/2023.

Peer Review reports

Introduction

In recent years, chrononutrition has been studied to investigate the impact of meal timing and circadian rhythms on health. It has been shown that the timing of food intake is equally important as the quantity and amount of food consumed because our circadian rhythms dictate the metabolism of nutrients in our body [1]. Our circadian clock in the hypothalamic suprachiasmatic nuclei (SCN) is entrained by external cues such as the 24-hour light dark cycle and timing of food intake [2]. When there is a disruption or misalignment of our circadian rhythm, it may cause adverse effects on our metabolic health. Metabolic rhythms such as glucose homeostasis, insulin regulation, and energy expenditure are in favour of earlier meal timings, characterized by larger distribution of energy intake on the earlier window of the day [2].

Pregnancy is a crucial period of development for both the mother and the growing fetus. Adequate nutrition plays a vital role in supporting maternal health, promoting optimal fetal growth, and reducing the risk of complications such as preterm birth, low birth weight, and small for gestational age [3]. The Barker’s hypothesis states that a malnutrition state during pregnancy would lead to unfavourable health outcomes of the offspring during adulthood such as obesity and cardiovascular diseases [4]. Hence, the nutritional status of the mother during pregnancy can have long-lasting effects on the health and well-being of both mother and child.

Chrononutrition practices such as breakfast skipping, long eating window, and night eating have been found to influence both the mother and the infant’s outcomes [5, 6]. As diet is one of the factors synchronizing our circadian rhythm, irregular meal timings can be disruptive to the alignment of environmental oscillators with the central pacemaker, resulting in adverse health outcomes [7]. For example, breakfast skipping among pregnant women in Japan has been associated with low infant birth weight compared to those who eat breakfast daily [8]. Eating window and breakfast skipping was also associated with melatonin and cortisol rhythm, which ultimately increases the risk of pre-eclampsia and intrauterine growth retardation [9]. A study done in Singapore had reported that pregnant women had a high prevalence of meal skipping and meal delaying (28% and 29% respectively), and it has been correlated with poor sleep and emotion [10]. Notably, breakfast skipping was examined in most chrononutrition-related studies, as it is the first meal taken in a day [11, 12]. Gontijo et al. (2018) reported that an earlier breakfast, longer eating window, and more meals taken in a day was associated with improved diet quality during pregnancy, which can be favourable for the mother’s health [13].

Pregnant women also experience changes in sleep patterns and disorders such as insomnia, nocturnal awakening, and restless legs syndrome due to hormonal changes [14]. These factors causing sleep disturbance may negatively affect the sleep quality of pregnant women and leads to adverse outcomes such as preterm birth, maternal psychological distress, and increased risk of childhood obesity [15, 16]. Sleep disturbances during pregnancy has also affected the sleep and wake time, which subsequently affects their meal timings throughout the day [17]. In order to overcome the sleep debt from disturbed night time sleep, many pregnant women take naps during the day and it was found that napping for approximately 1.5 h during pregnancy could contribute to reduce risk of low birth weight infants [18]. Hence, pregnancy symptoms should be considered to fully represent the pregnancy condition which affects lifestyle habits.

Despite the growing interest in the field of chrononutrition, there is a lack of standardized questionnaires specifically designed to capture meal timing data and its relationship with pregnancy-related variables. Most studies use a diet record and sleep questionnaire to capture meal and sleep timings during pregnancy [9, 19]. This may reduce the compliance rate as the questionnaires are lengthy and may require trained interviewers to capture the accurate data. To investigate the association between meal timing and pregnancy outcomes, it is essential to have a reliable and validated tool to assess chrononutrition patterns in pregnant women. Developing and validating such a questionnaire would enable researchers and healthcare providers to comprehensively assess chrononutrition patterns in pregnant women. To the best of our knowledge, this was the first validated questionnaire to determine the chrononutrition profile of pregnant women. The current CPQ-P addresses additional items related to pregnancy symptoms, snacking habits, and daytime napping which may represent changes during the gestation period. Hence, this study aims to modify, validate, and test the reliability of the Chrononutrition Profile Questionnaire-Pregnancy (CPQ-P). This is part of a longitudinal study (clinicaltrials.gov: NCT05700136, registered on 26/01/2023) which objective is to determine the prenatal and postnatal factors associated with infant circadian rhythm, growth, and temperament.

Methodology

Study design and population

A total of 399 participants were recruited from government maternity clinics in Kuala Lumpur and Putrajaya, Malaysia. The inclusion criteria were Malaysian pregnant women aged 18–39 years old, literate in English or Bahasa Malaysia, not having any health comorbidities before or during pregnancy, and not under any medications related to sleep. Participants were excluded if they were working night shift or diagnosed with any sleep disorders. The recruitment commenced from 6th September 2022 to 17th November 2022. The study protocol was reviewed and approved by the Medical Research Ethics Committee (NMRR ID-22-00182-HIP (IIR)).

Sample size calculation

For overall testing and analysis, at least 10 participants were required for each item included in the questionnaire [20]. Hence, a total of 290 participants was required for the validation of the current questionnaire.

$$N=10 \times 29\,items=290\,participants$$

To compensate for 20% non-compliance and non-response rate, the total sample size required was 348 participants.

To perform test-retest analysis, with an effect size of 0.8, alpha level of 0.05, the minimum sample size required was 22 subjects to obtain ICC value of 0.5. After accounting for 20% dropout rate, a total of 28 subjects should be recruited [21].

Study procedures

Participants were recruited from nine government Maternal and Child Health (MCH) clinics in Kuala Lumpur and Putrajaya. Participants were explained about the study and an informed consent was signed. A set of questionnaires in an electronic form was then administered to the participants. The questionnaire consists of two sections: (1) Sociodemographic details such as age, race, educational level, household income level, gestation week, and medical conditions, and (2) CPQ-P, which is the studied questionnaire provided in both English and Malay language. After two weeks of initial completion, the participants were contacted to complete the same CPQ-P again for test-retest analysis. The responses collected were checked for completeness and the participants were contacted if there were any missing answers. The validation of the CPQ-P consists of content validity, construct validity, and reliability (internal consistency and test-retest reliability) analyses.

Modification of the questionnaire

The original CPQ was developed by Veronda and colleagues after conducting a literature search of existing measures of chrononutrition profile [22]. It consists of 19 items addressing the 6 main behaviours of chrononutrition, namely breakfast skipping, largest meal, evening eating, evening latency, night eating, and eating window. The CPQ has incorporated elements of sleep-wake rhythm synchronization between workdays and freedays from the Munich ChronoType Questionnaire (MCTQ) and preferences items from the Composite Scale of Morningness (CSM) [22]. To modify the original CPQ so that it can be relevant among the pregnant women population, we have conducted a literature search of factors affecting sleep-wake rhythms during pregnancy. The National Sleep Foundation’s Women and Sleep Survey reported that 78% of pregnant women experience disturbed sleep [23]. This condition may be due to pregnancy-related physical symptoms such as nausea, backpain, insomnia, and leg cramps, and will worsen as the women enters the third trimester of pregnancy [24, 25]. Therefore, a section asking on whether pregnancy-related symptoms would affect wake time, sleep time, and first meal time was added into the questionnaire. Additionally, daytime sleepiness was reported with a high prevalence of up to 84% during pregnancy and this may affect sleep timing [26, 27]. Pregnant women should have sufficient rest and at the same time do not exceed the recommended duration to avoid nocturnal sleep disturbance [28]. As sleep quality affects mother and infant outcome such as increased risk of caesarean section and preterm birth, we should consider sleep as an important element and explore the underlying factors causing poor sleep quality, including daytime napping [14]. Excessive daytime sleepiness has resulted in napping habits among pregnant women, therefore, the CPQ-P included questions about daytime napping to assess the frequency, timing, and duration of naps. Lastly, the Recommended Nutrient Intakes of Malaysia states that pregnant women should increase energy intake by 80–470 kcal per day across trimesters to accommodate for the growth of fetus and maternal tissues as well as maintaining a healthy gestational weight [29]. Pregnant women were recommended to consume small frequent meals while making healthier choices in selecting nutrient-dense foods [30]. Kebbe et al. (2021) reported that pregnant women tend to snack more frequently, especially closer to bedtime compared to before pregnancy [31]. Night time snacking has been found to negatively affect blood glucose regulation, sleep quality, and birth outcomes [32, 33]. Therefore, items addressing snacking frequency and timing were added into the CPQ-P.

Content validity

Content validity is the extent to which the instrument fully represents the measured construct [34]. A content validation form was prepared, and four experts were invited to evaluate the items in the questionnaire in terms of their relevance, clarity, simplicity, and ambiguity. The expert panel consists of two nutritionists proficient in maternal and infant nutrition, and two nutritionist specializing in chrononutrition research. An online video meeting was conducted to present the questionnaire and collect feedback from the experts. The experts were required to fill in the content validation form which consists of a four-point Likert scale for the four categories of evaluation stated above for each item. The content validity index (CVI) was calculated from the responses collected, which ranges from one (not relevant) to four (very relevant). A score of 1 and 2 indicates the item was identified as not relevant, while score of 3 and 4 were classified as relevant items and suitable to be included in the questionnaire. A score of ‘1’ was assigned for items that were classified as relevant and achieved universal agreement (UA). Comments for each item was also collected from the experts and the questionnaire was revised accordingly.

Construct validity

To determine the construct structure, present in the CPQ-P, an exploratory factor analysis (EFA) with varimax rotation was conducted for 29 items included in the questionnaire. Each item was labelled accordingly to identify its factor as shown in Table 1. The variables which indicate time (A1, A2, A3, A4, A5, A6, B1, B2, B3, C1, C2, C3, C4, C5, G1, and G2) which original format was hh:mm was multiplied by 24 h to show the numerical form of timing for the analysis. Variables A7, D3, D4, E1, E2, E3 were reported as frequency of days per week, while D1 and D2 were reported as frequency per day. Largest meal was reported as binary variables with each response separated into three categories: F1, F2, and F3.

Table 1 Items included in the questionnaire labelled according to their constructs
Full size table

Then, a confirmatory factor analysis (CFA) using maximum likelihood method was conducted to determine the construct validity. A path diagram was constructed using Amos Version 24 to test the goodness of fit of the hypothesized model, which specified correlated factors, and factor loading of item with highest discrepancy in each constructs set to 1.

Reliability testing

An instrument is considered reliable if it produces a consistent result across time [35]. For this study, reliability was tested by determining the internal consistency and test-retest reliability. The Cronbach’s alpha indicates the internal consistency which is the extent of which the items in the questionnaire are inter-correlated, or consistent in measuring the same construct [36]. Test-retest reliability was conducted to determine the stability of responses from participants over a period of time across a repeated administration of the same questionnaire [36]. Test-retest reliability was evaluated using intraclass correlation coefficient (ICC).

Statistical analysis

Sociodemographic data were presented as frequency (percentage) for categorical data and mean ± standard deviation for continuous data. For content validity, the S-CVI/UA was calculated by getting the average of universal agreement scores across all items. Shi et al. recommends that the cut off value for S-CVI/UA should be of 0.9 or higher for excellent content validity [37].

Exploratory Factor Analysis (EFA) was deployed to determine the structure presence across the items in CPQ-P. The Bartlett’s Test of Sphericity and Kaiser-Meyer-Olkin Measure of Sampling Adequacy were used to confirm the data was suitable for factor analysis. EFA was performed using varimax rotation, using maximum likelihood extraction and eigenvalues > 1. Items that loaded on different factors were grouped into theoretical relevant factors, while items with factor loading less than 0.298 were deleted from the questionnaire [38]. After determining the factor structure, confirmatory factor analysis using the maximum likelihood method was conducted to confirm the fit of variable into the model structure [39]. Model fit was determined using the following indices: normed chi-square (χ2/df); goodness of fit index (GFI); comparative fit index (CFI); and root mean-square error of approximation (RMSEA). A value of below 0.3 indicates a good fitting model, while values more than 0.9 for CFI and GFI is ideal to represent good fitting of the model [40]. RMSEA values should be more than 0.8 to be acceptable for the model fit [41]. Factor loading was also assessed for the model, which items with factor loading less than 0.298 will be considered for removal [38].

For reliability testing, internal consistency was determined using Cronbach’s alpha while test-retest reliability was examined using intraclass correlation coefficient. Cronbach’s alpha value above 0.60 is considered to have moderate level of reliability [42]. Intraclass correlation coefficient values less than 0.40 indicates poor reliability, while values between 0.40 and 0.59 indicates fair reliability, values between 0.60 and 0.74 indicates good reliability, and values above 0.75 were considered to have excellent reliability [43].

Results

In total, 399 pregnant women participated in the validation of the CPQ-P. Most of them were Malay (85.5%), had tertiary level education (79.5%), and from moderate household income families (58.5%) as shown in Table 2. The mean pre-pregnancy body mass index (BMI) was 25.16 ± 6.01 kg/m2, with 48% of the pregnant women having normal pre-pregnancy BMI.

Table 2 Sociodemographic details of participants (N = 399)
Full size table

Content validity

Table 3 shows the summary of content validity and modification of the questionnaire based on comments from the expert panel. From the original questionnaire, there is a lack of examples for the term ‘eating event’, hence a cultural relevant example was added at the end of the sentence which is “(e.g. Nasi lemak, biscuit/cookies, a cup of Milo)”. The expert panels also find it hard to identify “What time do you fall asleep?” since it may be unintentional. Hence, the panel has agreed to change the question to “What time do you go to bed?”. Other than that, the panel has found it confusing to give a standard answer for questions with regards to timing. Hence, an additional instruction “Please indicate A.M./P.M. as part of your response.” was added for items that required time as an answer. The comments from the expert panels had been addressed and modified accordingly in the CPQ-P questionnaire, hence an overall CVI of 1 was deemed appropriate for content validity (Table 4).

Table 3 Summary of comments and modifications for content and face validation based on comments from experts and target population (n = 4)
Full size table
Table 4 Content validity index for CPQ-P by expert panel (n = 4)
Full size table

Construct validity

The summary of construct validity of 25 items in the CPQ-P using exploratory factor analysis with varimax rotation is shown in Table 5. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.713 and the Bartlett’s Test of Sphericity was significant (p < 0.001), indicating that the data was suitable for EFA with sufficient samples and variables are correlated with each other. CPQ-P presented six factors and explained overall variances by 59.51% [44]. The six factors were labelled as Morning chrono-habits, Sleeping habits, Evening eating, Temporal eating habits, Pregnancy symptoms, and Largest meal. In the rotated factor matrix, the percentage of variances ranged from 6.59 to 19.21%. Each factor explained as shown: Factor 1 (Morning chrono-habits), 15.71%, Factor 2 (Sleeping habits), 10.93%, Factor 3 (Evening eating), 8.84%, Factor 4 (Temporal eating habits), 8.26%, Factor 5 (Pregnancy symptoms), 8.02%, and Factor 6 (Largest meal), 7.76%.

Table 5 Summary of the construct validity using EFA with varimax rotation (n = 358)
Full size table

Lunch time variables for weekdays and weekends did not have enough factor loading, with 0.253 and 0.167 respectively. Additionally, variables related to daytime napping such as ‘Daytime napping frequency’ and ‘Daytime napping duration’ had a factor loading of 0.188 and 0.142 respectively. This indicated that the lunch time variables and daytime napping variables were not meaningful enough did not strongly represent the any of the extracted factors, thus, it was removed from the CPQ-P.

Next, the hypothesized model was inserted for model fit testing using confirmatory factor analysis and showed poor fit (χ2/df = 4.408, CFI = 0.788, RMSEA = 0.098), and the factor loadings for the factor F, ‘Largest meal’ was weak (< 0.298). Hence, a second model which includes the correlations between errors was constructed for the 7 pairs with highest modification indices (e6-e5, e2-e4, e1-e4, e1-e5, e25-e6, e10-e14, e17-e18). Factor F, ‘Largest meal’, was also removed from the model. Figure 1 shows the factor model of the Chrononutrition Profile Questionnaire-Pregnancy (CPQ-P). In this model, the normed chi-square (χ2/df) was 2.486, indicating a good fit in the model as it was below 3. The GFI was 0.893, which was close to 0.9 and acceptable for research purposes [45]. The CFI of the model was 0.912, which is above cut-off of 0.9, indicating good fit of the model. Lastly, the RMSEA was 0.065, indicating optimal goodness of fit to the model. Notably, all variables had sufficient factor loadings except for A7- Breakfast skipping with factor loading close to cut-off of 0.289 (0.27) and C5- Weekend last eating event time with weak factor loading of 0.19.

Fig. 1
figure 1

Factor model of the Chrononutrition Profile Questionnaire-Pregnancy (CPQ-P) with standardized estimates

Full size image

Reliability testing

A summary of internal consistency and test-retest reliability was depicted in Table 6. Cronbach’s alpha reliability coefficient (CAC) was examined to test the internal consistency of CPQ-P. The current CPQ-P had shown CAC of 0.612 to 0.923 among the 6 constructs of the questionnaire, indicating moderate to excellent internal consistency [42].

Table 6 Summary of test-retest reliability and internal consistency for CPQ-P after duration of 14 days
Full size table

For test-retest reliability, a total of 30 participants completed the first CPQ-P and second CPQ-P within an average of 14.40 ± 1.89 days. The intraclass correlation coefficient (ICC) was calculated to examine the reliability of the administered CPQ-P over a period of 14 days. From morning chrono-habits, variables on wake time and first eating event time had excellent reliability (ICC = 0.947–0.984), except for weekday first eating event time and breakfast skipping with good reliability (ICC = 0.620 and 0.661). In terms of sleeping habits, the ICC of variables for sleep time and bedtime had excellent reliability (0.818–0.889). From the evening eating construct, variables for dinner time and last eating event time had excellent reliability with ICC ranging from 0.793 to 0.919, except for weekend dinner time and weekend last eating event time with fair reliability of ICC value of 0.540 and 0.552 respectively.

In temporal eating habits, variables had good to excellent reliability, with night eating (ICC = 0.790) of excellent reliability, and weekday snacking frequency (ICC = 0.620) and weekend snacking frequency (ICC = 0.731) with good reliability, while snacking after last meal was found to have fair reliability (ICC = 0.422). In terms of the pregnancy symptoms construct, the ICC values ranges from fair to excellent reliability. Pregnancy effect on sleep time had excellent reliability (ICC = 0.892), pregnancy effect on wake time had good reliability (ICC = 0.637), while pregnancy effect on first meal time had fair reliability (ICC = 0.492). From the Largest meal construct, breakfast and dinner variables were found to have good to excellent reliability (ICC = 0.288 and 0.725), while lunch variables had poor reliability with ICC value of 0.390.

Discussion

Determining chrononutrition variables have always been unstandardized and requires a lot of resources to collect. The study revealed that the CPQ-P had successfully determined the various chrononutrition profile of pregnant women and had good validity with 22 items in a 5 factor structure. It has also shown moderate to excellent internal consistency and test-retest reliability for the scales. The current CPQ-P was also in line with established literature on chrononutrition during pregnancy. Habits such as snacking frequency has been added into the questionnaire which was relevant for pregnant women. Based on Recommended Nutrient Intakes for Malaysia, pregnant women are required to increase their energy intake to provide for the growth of fetus, placenta, and various maternal tissues, as well as changes for the maternal metabolism [29]. Hence, snacking was recommended for pregnant women to increase their energy intake, and at the same time improving their diet quality with regular food intake and healthy food choices [46].

From the construct validation analysis, daytime napping variables did not have sufficient factor loading to be considered significant in construct validity. From our current sample, we have observed majority of the pregnant women (70.5%) took naps on one day in a week only. This may be due to work and social commitments, in which the pregnant women could only rest more on their ‘free days’. Hence, future assessments should also consider including preferred nap time, and segregating the daytime nap habits into weekdays and weekends to specify daytime napping habits among pregnant women. Additionally, lunch time variables did not load in our 6 factor structure and hence excluded from the questionnaire. This may be due to the variability of lunch timing among pregnant women in Malaysia. Based on the findings from the Malaysian Adults Nutrition Survey (MANS), lunch was defined as the meal between breakfast and dinner eaten during mid-day between 12:30 h to 2:30 h [47]. However, our current sample had a lunchtime ranging from 11:00 h to 16:00 h, suggesting great variability for lunch time among pregnant women.

The original CPQ developed by Veronda et al. (2020) had determined 6 chrononutrition behaviours, namely breakfast skipping, largest meal, evening eating, night eating, and eating window [22]. It has shown good convergent validity with the Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) in reporting chrononutrition behaviours with strong correlation (r = 0.28 to 0.44). However, largest meal was shown to have poor validity in the original CPQ (Kappa = 0.012), which we had consistent results from CFA which factor loadings were not presented in the factor structure. It was recommended to include two or more questions measuring a single latent factor to provide meaningful statistical information on shared variance [48]. Hence, more information about largest meal consumption in a week could be collected by asking questions such as ‘In a 7-day period, how often do you take breakfast as your largest meal’, to provide a more comprehensive information about this construct. Other than that, breakfast skipping was also found to have low factor loading from our CFA, despite obtaining a good fit in the model (CFI = 0.912). Breakfast eating may not be accurate in this study as ‘breakfast’ was not well defined in the questionnaire. The amount of food and time period should be stated clearly to capture accurate data of breakfast eating, such as ‘first meal of the day consumed within 2 to 3 hours of waking, and comprised of food from at least 1 food group’ [49]. From the test-retest reliability analysis of the original CPQ showed low correlation among free days variables, suggesting that chrononutrition habits during free days are more variable over time. This finding was also evident in our analysis as weekend last eating event time had a weak factor loading of 0.19 and fair level of reliability with ICC value 0.552. Despite the weak statistical fitness of the variables in the CFA, the authors mutually agree that largest meal, breakfast skipping, and weekend last eating event time should be retained in the CPQ-P as it is of substantive interest to address chrononutrition habits [50]. Considering the significance of caloric intake in the day with weight status and diet quality during pregnancy [51], these items contribute meaningful information for healthcare providers to assess the pregnant women chrononutrition habits and further conduct personalized interventions.

In terms of night snacking, there were various contributing factors reported, such as hunger, thirst, nausea, altered sleep patterns, and fetal movements, which were common experiences during pregnancy [52]. These pregnancy symptoms may not be consistent and pregnant women reported to have consumed food whenever they chose to instead of following a proper meal schedule. Hence, the erratic meal timings during late night had contributed to the fair consistency of night snacking in this questionnaire (ICC = 0.422).

The CPQ-P had addressed several factors related specifically to the pregnant population, such as snacking and pregnancy effects on chrononutrition behaviours. It has also been mutually agreed that the items were relevant to pregnant women and examples given in the questionnaire were culturally acceptable. This questionnaire could be applied in clinical settings to determine the chrononutrition profile of pregnant women. The data obtained could be used to identify problem areas of the pregnant women’s lifestyle habits such as late night snacking and breakfast skipping, as well as addressing pregnancy-related symptoms. Chrononutrition emerge as a modifiable risk factor by altering the timing of food intake, with past literature reporting the different effects of caloric intake on weight loss and metabolic responses at different times of the day [53]. Health issues can be addressed by determining the association of chrononutrition habits with gestational weight gain, biomedical markers, and nutritional status during pregnancy via data collected from this questionnaire and routine antenatal check-ups.

Despite that, several limitations of the study are worth noting. Firstly, the current study did not engage with participants to conduct face validity and criterion validity, thus, its accuracy to capture data may be affected. Next, this study was conducted among pregnant women in the urban area of Kuala Lumpur and Putrajaya only, hence it may not be generalized to other pregnant women in suburban and rural areas. Although daytime napping did not show significant associations in this questionnaire, it has established a basis for future research to deploy different techniques in determining daytime napping habits among pregnant women.

It was suggested that more research should be conducted to further examine the validity and reproducibility of the CPQ-P among pregnant women from countries with seasonal variation where light-dark cycles may vary and affect the lifestyle of pregnant women. Other than that, it would be ideal to assess the convergent validity of pregnancy-related symptoms with diet records with meal timing.

Conclusion

In conclusion, the Chrononutrition Profile Questionnaire - Pregnancy (CPQ-P) has demonstrated preliminary feasibility for addressing chrononutrition habits during pregnancy. CPQ-P was established with positive comments from experts, produced good construct validity, moderate to good internal consistency, and moderate test-retest reliability. Future studies can be conducted to translate and test the reliability of the CPQ-P among pregnant women from different countries of various cultural background. CPQ-P can be applied in future research to explore novel findings related to the circadian rhythms and pregnancy outcomes.

Data availability

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not publicly available. Data are however available from the corresponding author upon reasonable request.

References

  1. Kaur S, Ng CM, Tang SY, Kok EY. Weight status of working adults: the effects of eating misalignment, chronotype, and eating jetlag during mandatory confinement. Chronobiol Int. 2023;:1–10.

  2. Flanagan A, Bechtold DA, Pot GK, Johnston JD. Chrono-nutrition: from molecular and neuronal mechanisms to human epidemiology and timed feeding patterns. J Neurochem. 2021;157:53–72.

    Article  CAS  PubMed  Google Scholar 

  3. Marshall NE, Abrams B, Barbour LA, Catalano P, Christian P, Friedman JE, et al. The importance of nutrition in pregnancy and lactation: lifelong consequences. Am J Obstet Gynecol. 2022;226:607–32.

    Article  PubMed  Google Scholar 

  4. Almond D, Currie J. Killing me softly: the fetal origins Hypothesis. J Econ Perspect J Am Econ Assoc. 2011;25:153–72.

    Article  Google Scholar 

  5. Loy SL, Loo RSX, Godfrey KM, Chong YS, Shek LPC, Tan KH, et al. Chrononutrition during pregnancy: a review on maternal night-time eating. Nutrients. 2020;12:1–16.

    Article  Google Scholar 

  6. Dong J-Y, Ikehara S, Kimura T, Cui M, Kawanishi Y, Kimura T, et al. Skipping breakfast before and during early pregnancy and incidence of gestational diabetes mellitus: the Japan Environment and Children’s study. Am J Clin Nutr. 2020;111:829–34.

    Article  PubMed  Google Scholar 

  7. Franzago M, Alessandrelli E, Notarangelo S, Stuppia L, Vitacolonna E. Chrono-Nutrition: Circadian Rhythm and Personalized Nutrition. Int J Mol Sci. 2023;24.

  8. Aizawa M, Murakami K, Takahashi I, Onuma T, Noda A, Ueno F, et al. Association between frequency of breakfast intake before and during pregnancy and infant birth weight: the Tohoku Medical Megabank Project Birth and three-generation cohort study. BMC Pregnancy Childbirth. 2023;23:268.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Teoh AN, Kaur S, Shafie SR, Mohd Shukri NH, Ahmad Bustami N, Takahashi M et al. Chrononutrition is associated with melatonin and cortisol rhythm during pregnancy: findings from MY-CARE cohort study. Front Nutr. 2023;9.

  10. Loo RSX, Yap F, Ku CW, Cheung YB, Tan KH, Chan JKY, et al. Maternal meal irregularities during pregnancy and lifestyle correlates. Appetite. 2022;168:105747.

    Article  PubMed  Google Scholar 

  11. Shiraishi M, Haruna M, Matsuzaki M. Effects of skipping breakfast on dietary intake and circulating and urinary nutrients during pregnancy. Asia Pac J Clin Nutr. 2019;28:99–105.

    CAS  PubMed  Google Scholar 

  12. Aizawa M, Murakami K, Takahashi I, Onuma T, Noda A, Ueno F, et al. Skipping breakfast during pregnancy and hypertensive disorders of pregnancy in Japanese women: the Tohoku medical megabank project birth and three-generation cohort study. Nutr J. 2022;21:71.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Gontijo CA, Cabral BBM, Balieiro LCT, Teixeira GP, Fahmy WM, de Maia YC. Time-related eating patterns and chronotype are associated with diet quality in pregnant women. Chronobiol Int. 2019;36:75–84.

    Article  PubMed  Google Scholar 

  14. Shaliha F, Mozaffari M, Ramezani F, Hajnasiri H, Moafi F. Daytime Napping and Nighttime Sleep during pregnancy and Preterm Birth in Iran. J Prev Med Public Health. 2021;54:182–9.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Chang JJ, Pien GW, Duntley SPMG. Sleep deprivation during pregnancy and maternal and fetal outcomes: is there a relationship? Sleep Med Rev. 2010;14:107–14.

    Article  PubMed  Google Scholar 

  16. Harskamp-van Ginkel MW, Ierodiakonou D, Margetaki K, Vafeiadi M, Karachaliou M, Kogevinas M et al. Gestational sleep deprivation is associated with higher offspring body mass index and blood pressure. Sleep. 2020;43.

  17. Umeno S, Kato C, Nagaura Y, Kondo H, Eto H. Characteristics of sleep/wake problems and delivery outcomes among pregnant Japanese women without gestational complications. BMC Pregnancy Childbirth. 2020;20:179.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Song L, Shen L, Li H, Liu B, Zheng X, Zhang L, et al. Afternoon napping during pregnancy and low birth weight: the healthy baby cohort study. Sleep Med. 2018;48:35–41.

    Article  PubMed  Google Scholar 

  19. Ku CW, Loo RSX, Tiong MMY, Eng SYC, Cheung YB, Ong LS et al. Nocturnal lifestyle behaviours and risk of poor sleep during pregnancy. Nutrients. 2022;14.

  20. Kline RB. Principles and practice of structural equation modeling. 3rd ed. New York, NY, US: Guilford Press; 2011.

    Google Scholar 

  21. Bujang MA. A simplified guide to determination of sample size requirements for estimating the value of intraclass correlation coefficient: a review. Arch Orofac Sci. 2017;12:1–11.

    Google Scholar 

  22. Veronda AC, Allison KC, Crosby RD, Irish LA. Development, validation and reliability of the Chrononutrition Profile - Questionnaire. Chronobiol Int. 2020;37:375–94.

    Article  PubMed  Google Scholar 

  23. Hashmi AM, Bhatia SK, Bhatia SK, Khawaja IS. Insomnia during pregnancy: diagnosis and rational interventions. Pakistan J Med Sci. 2016;32:1030.

    Google Scholar 

  24. Smyka M, Kosińska-Kaczyńska K, Sochacki-Wójcicka N, Zgliczyńska M, Wielgoś M. Sleep problems in Pregnancy-A cross-sectional study in over 7000 pregnant women in Poland. Int J Environ Res Public Health. 2020;17:5306.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kızılırmak A, Timur S, Kartal B. Insomnia in pregnancy and factors related to insomnia. ScientificWorldJournal. 2012;2012:197093.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Lopes EA, de Carvalho LBC, Seguro PB, da Mattar C, Silva R, Prado AB et al. LBF do,. Sleep disorders in pregnancy. Arq Neuropsiquiatr. 2004;62:217–21.

  27. Cai X-H, Xie Y-P, Li X-C, Qu W-L, Li T, Wang H-X, et al. The prevalence and associated risk factors of sleep disorder-related symptoms in pregnant women in China. Sleep Breath. 2013;17:951–6.

    Article  PubMed  Google Scholar 

  28. Ebert RM, Wood A, Okun ML. Minimal effect of daytime napping behavior on nocturnal sleep in pregnant women. J Clin Sleep Med. 2015;11:635–43.

    Article  PubMed  PubMed Central  Google Scholar 

  29. National Coordinating Committee on Food and Nutrition (NCCFN). RNI recommended nutrient intakes for Malaysia. A report of the Technical Working Group on Nutritional guidelines. Putrajaya, Malaysia.;; 2017.

  30. Liu H. Correlation between snacking and blood glucose level of pregnant women in their second trimester. Chin J Heal Manag. 2017;11.

  31. Kebbe M, Flanagan EW, Sparks JR, Redman LM. Eating behaviors and dietary patterns of women during pregnancy: optimizing the Universal Teachable Moment. Nutrients. 2021;13.

  32. Loy SL, Cheung YB, Cai S, Colega MT, Godfrey KM, Chong Y-S, et al. Maternal night-time eating and sleep duration in relation to length of gestation and preterm birth. Clin Nutr. 2020;39:1935–42.

    Article  PubMed  Google Scholar 

  33. Loy SL, Chan JKY, Wee PH, Colega MT, Cheung YB, Godfrey KM, et al. Maternal circadian eating time and frequency are Associated with blood glucose concentrations during Pregnancy1. J Nutr. 2017;147:70–7.

    Article  CAS  PubMed  Google Scholar 

  34. Yusoff MSB. ABC of Content Validation and Content Validity Index calculation. Educ Med J. 2019;11:49–54.

    Article  Google Scholar 

  35. Revicki D. In: Michalos AC, editor. Internal consistency reliability BT - Encyclopedia of Quality of Life and Well-Being Research. Dordrecht: Springer Netherlands; 2014. pp. 3305–6.

    Chapter  Google Scholar 

  36. Tsang S, Royse CF, Terkawi AS. Guidelines for developing, translating, and validating a questionnaire in perioperative and pain medicine. Saudi J Anaesth. 2017;11(Suppl 1):S80–9.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Shi J, Mo X, Sun Z. Content validity index in scale development. J Cent South Univ Sci. 2012;37:152–5.

    Google Scholar 

  38. Field A. Discovering statistics using SPSS. Third. London: SAGE; 2009.

    Google Scholar 

  39. Price LR. Confirmatory factor analysis: foundations and extensions. In: Tierney RJ, Rizvi F, Ercikan K, editors. International Encyclopedia of Education (Fourth Edition). Fourth Edition. Oxford: Elsevier; 2023. pp. 607–18.

    Chapter  Google Scholar 

  40. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Model Multidiscip J. 1999;6:1–55.

    Article  Google Scholar 

  41. Browne MW, Cudeck R. Alternative ways of assessing Model Fit. Sociol Methods Res. 1992;21:230–58.

    Article  Google Scholar 

  42. Nawi FAM, A.Tambi AM, Samat MF, Mustapha WMW. A review on the internal consistency of a scale: the empirical example of the influence of human capital investment on Malcom Baldrige quality principles in TVET institutions. Asian People J. 2020;3 1 SE-Articles.

  43. Fleiss J, Levin B, Paik M. Statistical methods for rates and proportions. Hoboken (NJ): Wiley; 2013.

    Google Scholar 

  44. Taherdoost H, Sahibuddin S, Jalaliyoon N. Advances in Applied and pure Mathematics. WSEAS; 2014.

  45. Rodríguez-Santero J, Torres-Gordillo JJ, Gil-Flores J. Confirmatory Factor Analysis of a questionnaire for evaluating online training in the Workplace. Sustainability. 2020;12.

  46. Schwedhelm C, Lipsky LM, Temmen CD, Nansel TR. Eating patterns during pregnancy and Postpartum and their association with Diet Quality and Energy Intake. Nutrients. 2022;14.

  47. Wan Abdul Manan WM, Nur Firdaus I, Safiah MY, Siti Haslinda MD, Poh BK, Norimah AK, et al. Meal patterns of Malaysian adults: findings from the Malaysian adults nutrition survey (MANS). Malays J Nutr. 2012;18:221–30.

    CAS  PubMed  Google Scholar 

  48. El-Den S, Schneider C, Mirzaei A, Carter S. How to measure a latent construct: psychometric principles for the development and validation of measurement instruments. Int J Pharm Pract. 2020;28:326–36.

    Article  PubMed  Google Scholar 

  49. O’Neil CE, Byrd-Bredbenner C, Hayes D, Jana L, Klinger SE, Stephenson-Martin S. The role of breakfast in Health: definition and criteria for a quality breakfast. J Acad Nutr Diet. 2014;114:S8–26.

    Article  PubMed  Google Scholar 

  50. Enders CK, Tofighi D. Centering predictor variables in cross-sectional multilevel models: a new look at an old issue. Psychol Methods. 2007;12:121–38.

    Article  PubMed  Google Scholar 

  51. Gontijo CA, Balieiro LCT, Teixeira GP, Fahmy WM, Crispim CA, de Maia YC. Effects of timing of food intake on eating patterns, diet quality and weight gain during pregnancy. Br J Nutr. 2020;123:922–33.

    Article  CAS  PubMed  Google Scholar 

  52. Kroeger EN, Carson TL, Baskin ML, Langaigne A, Schneider CR, Bertrand B, et al. Reasons for late-night eating and willingness to change:a qualitative study in pregnant Black women. J Nutr Educ Behav. 2019;51:598–607.

    Article  PubMed  Google Scholar 

  53. Pot GK. Chrono-nutrition – an emerging, modifiable risk factor for chronic disease? Nutr Bull. 2021;46:114–9.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to all pregnant women who participated in this study.

Funding

This study is funded by UCSI University Research Excellence and Innovation Grant (REIG-FAS-2023/004).

Author information

Authors and Affiliations

  1. Department of Food Science and Nutrition, Faculty of Applied Sciences, UCSI University, Cheras, Wilayah Persekutuan Kuala Lumpur, 56000, Malaysia

    Ee Yin Kok, Satvinder Kaur & Nurliyana Abdul Razak

  2. Department of Nutrition, Faculty of Medicine and Health Sciences, University Putra Malaysia, Selangor, Malaysia

    Nurul Husna Mohd Shukri

  3. Institute for Liberal Arts, Tokyo Institute of Technology, Tokyo, Japan

    Masaki Takahashi

Authors
  1. Ee Yin Kok
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Satvinder Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nurul Husna Mohd Shukri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nurliyana Abdul Razak
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Masaki Takahashi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SK, EYK, NHMS, LAR, and MT contributed to conceptualization and design of the study framework. EYK drafted this manuscript. All authors substantially revised and approved the final manuscript.

Corresponding author

Correspondence to Satvinder Kaur.

Ethics declarations

Ethical approval and consent to participate

The study was approved by Medical and Research Ethics Committee (NMRR ID-22-00182-HIP (IIR)). Written consent is obtained from the pregnant women to participate in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kok, E.Y., Kaur, S., Mohd Shukri, N.H. et al. Development, validation, and reliability of the Chrononutrition Profile Questionnaire-Pregnancy (CPQ-P). BMC Pregnancy Childbirth 24, 217 (2024). https://0-doi-org.brum.beds.ac.uk/10.1186/s12884-024-06403-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12884-024-06403-4

Keywords

BMC Pregnancy and Childbirth

ISSN: 1471-2393

Contact us