The obtained results can contribute to a better understanding of Romanian consumers’ preferences, acting as a knowledge stage in the adoption of a healthy eating style.In recent years, people have become increasingly interested in adopting a healthy diet, which also extends to healthy snacks, such as chips. Understanding the interplay of factors that influence the preference decisions concerning food products is very helpful in market segmentation for identifying specific groups of consumers with similar needs. This study aims to obtain a better understanding of Romanian consumers’ preference for organic vegetable chips vs. classic potato chips. The research involved a transversal, cross-sectional, descriptive, exploratory, and correlational design. Data were collected based on a questionnaire (1060 participants) and processed with the SPSS 22 program, using the Pearson chi-square test and binary logistic regression as statistical procedures. Significant differences were found regarding the distribution of the respondents who prefer organic vegetable chips vs. classic potato chips based on socio-demographic characteristics. The results of the binary logistic regression analysis (χ The world’s population exceeds 8 billion people, and, as Lelieveld noted in 2012, we all want to be happy, have enough to eat, have families, and prosper ,4. UnderOne of the most applied models in the consumer behavior approach is based on the Theory of Planned Behavior (TPB), which is also used to predict the intention and actual behavior of a consumer regarding the purchase of commodities ,6,7,8. OResearchers, such as Tomić Maksan and Jelić (2022) , consideA buying habit is defined as a behavior that is performed automatically and without the individual’s awareness ,12, whilThe shift towards healthy consumption practices encourages the development of products that correspond to sustainable diets, defined by the FAO  as “protResearch highlights that, although there is no formal definition for “healthy snacks”, dietary guidelines refer to snacks as highly nutritional, which ensures a “healthy life” and an “active lifestyle” . It has An increasing number of consumers and, consequently, producers are concerned about food products’ quality and safety. This has led to a rise in consumer demand for organic processed products that keep more of their original fresh plant characteristics . One of In the context of the increasing consumption of organic products, the buying and consumption behavior has become a matter of interest for producers and market/retailers . MoreoveOrganic foods are no longer products that belong to specific groups of producers or consumers . In deveSimilar to worldwide trends, the Romanian food market is undergoing continuous changes as consumers increasingly prioritize health and environmental concerns . They opThe expanding demand for healthy products with functional properties has prompted extensive research into the use of new ingredients rich in bioactive compounds, including nuts, fruits, fruit byproducts, vegetables, and vegetable byproducts . VegetabThe Romanian market for organic vegetable chips, obtained through a gentle drying process, relies on imported products from countries such as Belgium, Holland, Ecuador, etc., and these chips can be purchased from specialized stores or ordered online.To obtain vegetable chips that can be considered healthy snacks, the use of organic vegetables is crucial in the proper processing technique, such as drying and freeze-drying. These drying techniques offer several benefits, including extended shelf life, reduced microbial development, and decreased postharvest waste, resulting in significant savings in storage and transportation costs ,53,54.Given the current state of the art in the field, this study aims to contribute to the existing literature by providing insights into Romanian consumer preference towards vegetable chips, with a focus on organic vegetable chips vs. classic potato chips. The study is justified by the need to understand the Romanian consumers’ appetite for a healthy lifestyle and their choice of food products with high nutritional value.To study Romanian consumers’ preference towards organic vegetable chips, an online survey was conducted based on a cross-sectional observational questionnaire ,28,55,56The questionnaire was structured in two sections: the first section collected information relating to the socio-demographic characteristics of the respondents . The second section focused on the consumer profile, including items related to the preference for organic vegetable chips. This item included ‘preference for organic vegetable chips vs. classic potato chips’, ‘consumption frequency of dried vegetables or products based on dried vegetables’, and ‘reason for replacing potato chips with a different type of organic vegetable chips’.O1. Identifying differences in socio-demographic factors  and their impact on the preference for organic vegetable chips  vs. classic potato chips.O2. Identifying the influence of (a) socio-demographic factors, (b) the consumption frequency of dried vegetables or products based on dried vegetables, and (c) the reason for replacing classic potatoes with the preferred chips (organic vegetable chips vs. classic potato chips).The research employed a cross-sectional, descriptive, exploratory, and correlational design. To achieve the main objective of understanding Romanian consumers’ preferences for a healthy lifestyle by selecting food products with high nutritional value, particularly organic vegetable chips, the following specific objectives were formulated:RQ1. Do socio-demographic variables differentiate chips preference (organic vegetable chips vs. classic potato chips) in Romania?RQ2. Is the chips preference (organic vegetable chips vs. classic potato chips) influenced by?(a)Socio-demographic variables;(b)The frequency of consuming dried vegetables or products based on dried vegetables;(c)Reason for replacing potato chips with different types of organic vegetable chips .As a confirmatory, descriptive, and exploratory research, the following questions were formulated:Socio-demographic factors are important parameters for manufacturers and marketing departments. The reason behind testing these hypotheses was to identify the specific consumer segment that these departments should specifically address in the process of promoting organic vegetable chips.To answer these questions, two general hypotheses were formulated, out of which hypothesis 1 had 5 specific hypotheses:H1. It is expected that the socio-demographic variables will differentiate preferences for organic vegetable chips and classic potato chips in Romania.H1.1. More female consumers compared to males will prefer organic vegetable chips;H1.2. More older consumers compared to younger ones will prefer organic vegetable chips;H1.3. More married consumers than single ones will prefer organic vegetable chips;H1.4. More consumers with a higher education level compared to those with a lower education level will prefer organic vegetable chips;H1.5. More consumers with high incomes compared to those with low incomes will prefer organic vegetable chips.H2. It is expected that chips preference (organic vegetable chips vs. classic potato chips) in Romania (dependent variable-DV) will be influenced by the following independent variables (IV):(a)Gender, age, marital status, education level, household monthly net income;(b)The consumption frequency of dried vegetables or products based on dried vegetables;(c)The reason for replacing classic potato chips with different types of organic vegetable chips.The data were automatically recorded using Google Forms, imported to Microsoft Excel, and then to IBM SPSS 22, where processing and analysis were carried out. Frequency analysis, including percentage, was used for the qualitative data. Following the preliminary analysis, no missing data were reported.Pearson’s chi-square test (Cramer’s V effect size) was used to identify if socio-demographic factors  differentiate chips preference. For gender, the Yates correction was applied due to the 2 × 2 table.Binary logistic regression was used to identify the influence of the independent variables (IV), including socio-demographic factors, frequency of consuming dried vegetables or products based on dried vegetables, and reasons for replacing potato chips with different types of organic vegetable chips on the dependent variable (DV), which represented chips preference. The dependent variable was a dummy variable, with participants who prefer classic potato chips defined as 0, and participants who prefer organic vegetable chips defined as 1.The statistical procedures used to test the hypotheses were:(a)The gender variable had 2 categories: “male (1)” and “female (2)”;(b)The age variable had 6 categories: “18–24 years (1)”, “25–34 years (2)”, “35–44 years (3)”, “45–54 years (4)”, “55–64 years (5)”, and “over 65 years (6)”;(c)The marital status variable had 3 categories: “single (1)”, “in a relationship (2)”, and “married (3)”;(d)The education level variable had 5 categories: “middle school education (1)”, “high school education (2)”, “post-secondary education (3)”, “university education (4), and “postgraduate education (5)”;(e)The household monthly net income variable had 7 categories: “under 2500 lei (1)”, between “2501–3500 lei (2)”, “3501–4500 lei (3)”, “4501–5500 lei (4)”, “5501–6500 lei (5)”, “6501–8500 lei (6)”, and “over 8500 lei (7)”;(f)The consumption frequency of dried vegetables or products based on the dried vegetables variable had 4 categories: „occasionally (1)”, “once a week (2)”, “3–4 times a week (3)”, and “daily (4)”;(g)The reason for replacing classic potato chips with a different type of organic vegetable chips variable had 2 categories: “only if they are healthier (1)” and “other reasons (2)”.The independent variables in the binary logistic regression were categorical:p-values (Sig.), the odds ratio (Exp(β)), and the confidence interval (CI for Exp(β)); Wald test; Hosmer–Lemeshow for goodness of fit for logistic regression models and Nagelkerke R2.The resulting model included the coefficients (β), their standard error (S.E.), the associated p-value was considered significant when p < 0.05 [For both Pearson’s chi-square test and for binary logistic regression, the p < 0.05 .The descriptive analysis was carried out to establish the respondent’s profile in relation to the studied variables, which included socio-demographic factors, preference for chips, the frequency of consuming dried vegetables or products based on dried vegetables, and the reason for replacing classic potato chips with a different type of organic vegetable chips.Among the 1060 respondents, 699 (65.9%) reported that they prefer classic potato chips, 210 (19.8%) reported that they prefer organic vegetable chips, and 151 (14.2%) reported that they do not consume chips.The analysis presented in this study focuses only on those 909 participants who consume chips, regardless of the type of chips consumed; the others were not included.Of the 909 respondents, 699 (76.9%) prefer only classic potato chips, and 210 (23.1%) prefer organic vegetable chips. The prevalence of respondents who prefer classic potato chips can be observed in comparison to those who prefer organic vegetable chips.Regarding the consumption frequency of dried vegetables or products based on dried vegetables: 649 (71.4%) consume occasionally, 135 (14.9%) once a week, 90 (9.9%) 3–4 times a week, and 35 (3.9%) daily. The prevalence of respondents who occasionally consume dried vegetables or products based on dried vegetables can be observed, along with the low percentage of daily consumers.The reason for replacing classic potato chips with another variety was: “only if they are healthier” for 303 (33.3%) respondents, and other reasons for 606 (66.7%) respondents. The prevalence of respondents who replace classic potato chips with another variety for reasons other than health can be observed.The sample structure of the 909 participants included in the study consisted of 288 (31.7%) males and 621 (68.3%) females, distributed as follows by age: 399 (43.9%) were between 18 and 24 years old, 203 (22.3%) were between 25 and 34 years old, 148 (16.3%) were between 35 and 44 years old, 115 (12.7 %) were between 45 and 54 years old, 33 (3.6 %) were between 55 and 64 years old, and 11 (1.2 %) were over 65 years old. In terms of marital status, the research participants have the following situation: 263 (28.9%) were single, 331 (36.4%) were in a relationship, and 315 (34.7%) were married.In terms of education level, 1 (0.1%) had a middle school level education, 94 (10.3%) had a high school education, 23 (2.5%) had a post-secondary education, 569 (62.7%) had a university degree, and 222 (24.4%) were postgraduates.Regarding household monthly net income categories, they were as follows: under 2500 RON , between 2501 and 3500 RON , between 3501 and 4500 RON , between 4501 and 5500 RON , between 5501 and 6500 RON , between 6501 and 8500 RON , and over 8500 .The respondent’s socio-demographic characteristics are presented in To test the hypotheses regarding the differences in the distribution of respondents who prefer organic vegetable chips and those who prefer classic potato chips based on the socio-demographic characteristics, six analyses were run using the Pearson chi-square test.2(1) = 0.725, p = 0.394; Cramer’s V effect size = 0.031). The distribution of female and male respondents in relation to their preference for organic vegetable chips vs. classic potato chips is not statistically significant.The first specific hypothesis of H1  is not supported by the results  × 2 (gender), shows that: (i) among male consumers, 21.2% prefer organic vegetable chips compared to 78.8% who prefer classic potato chips; (ii) among female consumers, 24.0% prefer organic vegetable chips compared to 76.0% who prefer classic potato chips .2(5) = 36.670 and p < 0.001, Cramer’s V effect size = 0.201). The contingency table, 2 (preference for chips) × 6 (age ranges), shows the following: (i) among consumers aged 18–24 years, 14.3% prefer organic vegetable chips compared to 85.7% who prefer classic potato chips; (ii) among consumers aged 25–34 years, 28.6% prefer organic vegetable chips compared to 71.4% who prefer classic potato chips; (iii) among consumers aged 35–44 years, 29.7% prefer organic vegetable chips compared to 70.3% who prefer classic potato chips; (iv) among consumers aged 45–54 years, 29.6% prefer organic vegetable chips compared to 70.4% who prefer classic potato chips; (v) among consumers aged 55–64 years, 45.5% prefer organic vegetable chips compared to 54.5% who prefer classic potato chips; and (vi) among consumers over 65 years old, 18.2% prefer organic vegetable chips compared to 81.8% who prefer classic potato chips. The prevalence of preference for classic potato chips is observed in all age categories, but it is most noticeable in those aged between 18 and 24 years and those over 65 years old. Moreover, the preference for organic vegetable chips increases with respondent age, except for those over 65 years old (The second specific hypothesis of H1 (H1.2. “More older consumers compared to young ones will prefer organic vegetable chips”) is supported by the obtained results. The association between age and preference for organic vegetable chips vs. classic potato chips is statistically significant (χears old .2(2) = 10.329 and p = 0.006, Cramer’s V effect size = 0.107). The contingency table, 2 (preference for chips) × 3 , shows the following: (i) among single consumers, 17.9% prefer organic vegetable chips compared to 82.1% who prefer classic potato chips; (ii) among consumers in a relationship, 21.8% prefer organic vegetable chips compared to 78.2% who prefer classic potato chips; (iii) among married consumers, 28.9% prefer organic vegetable chips compared to 71.1% who prefer classic potato chips. The prevalence of preference for classic potato chips is observed in all categories of respondents, but it is most pronounced among those who are single. An increased preference for organic vegetable chips can be observed when consumers are married (The third specific hypothesis of H1 (H1.3. “More married consumers than single ones will prefer organic vegetable chips”) is supported by the results. The association between marital status and preference for organic vegetable chips vs. classic potato chips is statistically significant (χ married .2(4) = 36.046 and p < 0.001, Cramer’s V effect size = 0.199). The contingency table, 2 (preference for chips) × 5 , shows the following: (i) there is only one consumer with a middle school level of education, and they prefer organic vegetable chips; (ii) among consumers with a high school education, 12.8% prefer organic vegetable chips compared to 87.2% who prefer classic potato chips; (iii) among consumers with post-secondary education, 13.0% prefer organic vegetable chips compared to 87.0% who prefer classic potato chips; (iv) among consumers with a university education, 19.9% prefer organic vegetable chips compared to 80.1% who prefer classic potato chips; and (v) among consumers with postgraduate education, 36.5% prefer organic vegetable chips compared to 63.5% who prefer classic potato chips. The prevalence of the preference for classic potato chips is observed in all categories but is pronounced among respondents with high school and post-high school education. Among respondents with university and postgraduate education, the preference for organic vegetable chips is increased, indicating that consumer preference is influenced by education level  is supported by the results. The association between the preference for organic vegetable chips vs. classic potato chips and educational level is statistically significant (χon level .2(6) = 22.018 and p = 0.001, Cramer’s V effect size = 0.156. The contingency table, 2 (preference for chips) × 7 (household monthly net income), shows the following: (i) among consumers with an income below 2500 RON, 14.1% organic vegetable chips compared to 85.9% who prefer classic potato chips; (ii) among consumers with an income between 2501 and 3500 RON, 18.4% organic vegetable chips compared to 81.6% prefer who classic potato chips; (iii) among consumers with an income between 3501 and 4500 RON, 23.2% prefer organic vegetable chips compared to 76.8% who prefer classic potato chips; (iv) among consumers with an income between 4501 and 5500 RON, 27.2% prefer organic vegetable chips compared to 72.8% who prefer classic potato chips; (v) among consumers with an income between 5501 and 6500 RON, 29.8% prefer organic vegetable chips compared to 70.2% who prefer classic potato chips; (vi) among consumers with an income between 6501 and 8500 RON, 35.7% prefer organic vegetable chips compared to 64.3% who prefer classic potato chips; and (vii) among consumers with an income over 8500, 26.4% prefer organic vegetable chips compared to 73.6% who prefer classic potato chips. The preference for classic potato chips is prevalent in all income categories, but it is most present among those with an income below 2500 and those between 2501 and 3500 RON. It can be observed that a higher household income influences the consumer preference regarding organic vegetable chips (The fifth specific hypothesis of H1 (H1.5. “More consumers with high incomes compared to those with low incomes will prefer organic vegetable chips”) is supported by the results. The association between the preference for organic vegetable chips vs. classic potato chips and household income is statistically significant, indicated by the coefficient χle chips .To answer the second research question (RQ2), we formulated the general hypothesis H2. To test this hypothesis, a binary logistic regression analysis was conducted, with chip preference (organic vegetable chips vs. classic potato chips) as the dependent variable (DV) and the following independent variables (IV): gender, age, marital status, education level, household monthly net income, the frequency of consuming dried vegetables or products based on dried vegetables, and the reason for replacing classic potato chips with a different type of organic vegetable chips.2 = 102,906 with 22 degrees of freedom and a sample size of 909 (N = 909), demonstrates statistical significance at a p-value of less than 0.001. This implies that the independent variables (IVs) significantly influence the variation observed in the dependent variable (DV). The Nagelkerke R² index is 0.162, suggesting that the entire model explains 16.2% of the variation in the results. The p-value in the Hosmer and Lemeshow test was 0.949 , indicating that there is no difference between the observed and predicted model (p > 0.05). A non-significant Chi-square indicates that the data fit the model well.The result of the binary logistic regression analysis, indicated by χp < 0.05).It was observed that only education level  and frequency of consumption of dried vegetables or products based on dried vegetables  have a statistical impact on the preference for chips (DV) , while education level (university education) has the weakest statistically significant impact . Meanwhile, the other categories have no significant impact on preference for organic vegetable chips (Sig. > 0.05).The frequency of consumption of dried vegetables or products based on dried vegetables  has the highest statistically significant impact on the preference for organic vegetable chips , odds ratio (Exp(β)), and confidence interval (CI for Exp(β)).As anticipated at the beginning of the study, the logistic model results indicate that certain categories of the analyzed variables significantly impact the preference for organic chips: “the frequency of consumption of dried vegetable products”  and “education level” .We expected gender to be one of the variables influencing the preference for organic vegetable chips; however, the coefficient for gender in the binary regression model was not significant. We started with the premise that females are often more interested in health and nutrition, especially organic vegetables, based on data from previous studies . For exaSocial status is another variable for which the results of the binary regression model indicate that it does not influence the preference for organic vegetable chips, as the coefficient’s significance is not statistically meaningful (for none of the categories of the variable). In hypothesis testing for the differentiation of chips preference (organic vegetable chips vs. classic potato chips), the result was statistically significant. In the case of “married” consumers, the observed frequency was higher than the theoretical frequency, and the percentage difference between the observed and theoretical frequencies was the highest compared to what was determined for the “single” and “in a relationship” categories. For both “single” and “in a relationship” respondents, the observed frequency was lower than the theoretical frequency. The percentage difference  decreases from “single” to “in a relationship”, suggesting that “in a relationship” respondents have a stronger preference for organic vegetable chips compared to “single” respondents. The obtained results align with data presented in another study, which states that women with children represent a factor in the profile of actual consumers of organic products .The results obtained from hypothesis testing for the other socio-demographic factors can play an important role in promoting organic vegetable chips to different consumer segments, considering factors such as age, educational level, and monthly income. We formulate this statement based on the significant statistical differences identified between the categories of each listed socio-demographic factor.For example, in the case of hypothesis testing aimed at identifying age-related differences in preference for chips (organic vegetable chips vs. classic potato chips), the results were statistically significant. For the age categories “18–24 years” and “above 65 years”, the observed frequency was lower compared to the theoretical frequency, indicating that in these groups, the preference for organic vegetable chips might be lower than expected. For the other age categories, the situation is different; the observed frequencies are higher than the theoretical frequency, suggesting that in these age groups, the preference for organic vegetable chips is higher than theoretically anticipated. Considering the percentage differences  for each age category and the situations where observed frequencies were higher or lower compared to theoretical frequencies, we can infer that the results indicate a fluctuation in the preference for organic vegetable chips. Additionally, we can deduce that respondents in the “55–64 years” age group exhibit the highest preference for organic vegetable chips compared to any other age category. In the binary regression model, the only category for which the coefficient had a positive value was “55–64 years.” We can state that respondents in this category might have a slight preference for organic vegetable chips. However, the result is not significant enough to draw firm conclusions.For the education level variable, the coefficient value from the binary logistic regression is statistically significant for two of the categories. For example, in the case of “high school education”, the significance level of the coefficient B indicates significant differences between this category and the reference category “postgraduate education”. The negative coefficient value suggests that respondents with a „high school education” are less likely to prefer organic vegetable chips compared to those with a „postgraduate education”. Similarly, for the „university education”, we can observe significant differences between this category and the reference category („postgraduate education”), supported by the statistically significant coefficient value. Again, the negative coefficient value suggests that respondents with „university education” are less likely to prefer organic vegetable chips compared to those with „postgraduate education”.The differentiation in chips preference (organic vegetable vs. classic potato) based on the educational level variable was statistically significant. When comparing observed frequencies to theoretical frequencies for the category of consumers of organic vegetable chips, a trend emerges: as the educational level increases, the preference for organic vegetable chips also increases. This is evident in the decreasing percentage differences between observed and theoretical frequencies as educational levels rise, particularly when observed frequencies are lower than theoretical ones. In the case of respondents in the “postgraduate education” category, the observed frequency exceeded the theoretical frequency.Price is an important factor in the consumer purchasing decisions, as they often evaluate the price-to-quality ratio. In this sense, we can identify two categories of consumers: a category willing to pay more for organic vegetable chips or higher quality potato chips and those who can opt for more affordable options. Consumers who choose organic vegetable chips are more interested in using natural or organic ingredients in their food. This interest may stem from the fact that organic vegetable chips are obtained from raw materials that come from organic agriculture; hence, they are perceived as more environmentally friendly.Considering these aspects, we found it relevant to identify how net income differentiates the preference for organic vegetable chips vs. classic potato chips, and our hypothesis is statistically supported. Taking into account the percentage differences  corresponding to each “net income” category and considering situations where observed frequencies were lower or higher than theoretically estimated frequencies, we can conclude that respondents with a higher net income have a stronger inclination towards preferring organic vegetable chips.The results of the binary regression model indicate that none of the coefficients of the net income categories are statistically significant. For the income categories: “4501–5500 lei”, “5501–6500 lei”, and “6501–8500 lei”, the positive coefficients’ values suggest that respondents in these net income categories could have a slight preference for organic vegetable chips. However, the result is not statistically significant enough to assert this as a conclusive finding. Our study’s findings are in line with some previous studies  and contIn the binary regression model, the variable “frequency of consumption of dried vegetables and products based on dried vegetables” had the highest Wald value, and the coefficient value was statistically significant for the “occasionally” category. The negative coefficient value suggests that respondents who consume dried vegetables and products based on dried vegetables “occasionally” are less likely to prefer organic vegetable chips compared to those who consume them “daily”.We expected that the “reason for replacing potato chips with a different type of organic vegetable chips”, specifically the category “only if they are healthier”, would influence the preference for organic vegetable chips. Some studies suggest that a significant share of consumers frequently consider organic products as healthier and tastier, valuing them for their higher vitamin and mineral content . HoweverConsumer preferences are important aspects of consumer behavior. The understanding of factors influencing consumer behavior is currently a frequently studied topic . In the Regardless of the perspective, the study of consumers’ preferences for organic vegetable chips can undoubtedly bring benefits to producers, traders, and consumers alike.Since personal preferences can be influenced by family health consciousness, food safety consciousness, perceiving vegetables as healthy, and enjoying vegetables, the study results can be a valuable source of information for understanding attitudes towards vegetables in the TPB model.The research design was conceived from the perspective of shaping the profile of organic vegetable chips consumer. In this study, we explored potential predictive factors regarding the preference for organic vegetable chips. Regarding chips preference, three categories were identified within the sample: respondents who prefer classic potato chips, respondents who prefer organic vegetable chips, and a third category who do not prefer chips. The latter category was excluded from the sample that constituted the database for this study. Within the analyzed sample, approximately a quarter of the respondents indicated a preference for organic vegetable chips.The hypotheses aimed at identifying differences in preference between organic vegetable chips and classic potato chips based on the socio-demographic characteristics of Romanian consumers. These hypotheses were statistically significant for age, marital status, education level, and net monthly income. The exception is represented by the hypothesis regarding gender differences in the preference for organic vegetable chips vs. classic potato chips.The conclusions obtained from the SPSS 22 analysis (binary logistic regression) indicate that, regarding education level, respondents who fall into the “high school education” and “university education” categories are less likely to prefer organic vegetable chips compared to those in the “postgraduate education” category (reference category).Another conclusion drawn from the same analysis highlights that individuals who consume dried vegetables or products based on dried vegetables “occasionally” have fewer chances of preferring organic vegetable chips compared to those who consume dried vegetables or related products “daily”.The obtained results in this study provide valuable insights into the factors that influence the preference for organic vegetable chips in Romania. This information can be useful for both chip manufacturers and retailers in developing effective strategies to promote the consumption of healthy snacks. These insights can enhance the awareness of chip producers regarding consumption preference patterns for snacks in Romania, which is a relatively less-explored niche. This study is among the few that enable a better understanding of Romanian preferences, and these preferences represent important factors that encourage the consumption of healthy snacks.


The results showed that N, P and K fertilizer had different effects on fruit yield and quality, among which K fertilizer was the most important factor. Fertilization could significantly improve the yield and fruit quality of blueberry, and the average yield of fertilization treatment was 37.78% higher than that of the control group (CK). Even the treatment with the worst results F6 (N2P3K1), its single fruit weight, anthocyanins, total phenols, soluble solids and soluble protein content were 1.09, 1.32, 1.23, 1.08 and 1.21 times higher than the control (CK), respectively. Based on the comprehensive evaluation of principal component analysis and multi factor analysis of variance, the best fertilization combination for high-yield and good-quality blueberries was N1P2K2 (F2), that is, the best fertilization effect was that including N 100 g/plant, P2O5 25 g/plant, K2O 25 g/plant, applied in the form of ammonium sulfate (472 g/plant), superphosphate (41 g/plant) and potassium sulfate (40 g/plant), respectively.Through the application ratio of nitrogen (N), phosphorus (P) and potassium (K) in the field, L Nitrogen (N), phosphorus (P) and potassium (K) fertilizers are well-known mineral elements necessary for plant growth and development , and the2 and a yield of 85,000 tons, but the yield per unit area and quality are not high Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: PartlyReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: YesReviewer #2: I Don't Know**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: No**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This is a good attempt to show the importance of major nutrients in production and quality. However, some important aspects are missing in this manuscript which is mentioned below;• What is the basis of selection of three levels of N, P and K , as no information was provided on the soil properties.• Beside the primary nutrients, micro nutrient affects the quality very significantly, but nothing was informed in this aspect.• The experiment was carried out in 2014, the result presented is based on one year data or else.• The work was related with the application of N,P, K on yield and quality, but use/ recommendation of these fertilizer only, may create the imbalance of micro nutrient and other soil properties. Which is an important aspect that is missing• Based upon comprehensive score, it was recommended that F2 is superior but F4 and F9 is second and third, F2 and F4 were very similar. So it is better to compare the benefit cost ratio also before recommendation.Reviewer #2: In general, the manuscript needs extensive revision for English language and grammar by a professional.Page 1, title: … and potassium fertilization formula on the yield and berry quality of blueberry Page 2, line 21: …of nitrogen (N), phosphorus (P) and potassium (P) in the field…From this line and throughout the rest text you must use the above abbreviations for the three nutrients.Page 2, line 23: blueberry fruits, aiming to optimize the amount of supplied fertilizers.Page 2, line 28: Even the treatment with the worst results (N2P3K1), …Page 2, line 30: times higher than the control, respectively.Page 2, line 33: blueberries was that including N 100 g/plant, P2O5 25 g/plant, K2O 25 g/plant, applied in the form of ammonium sulfate (472 g/plant), superphosphate (41 g/plant) and potassium sulfate (40 g/plant), respectively.Page 3, line 41: potassium are well-known mineral … development [1], and the application of fertilizers containing these elements can significantly improve the yield…Page 3, line 43-51: These lines must be written better.Page 3, line 58: but the yield per unit area and quality are notPage 4, line 65: “…they often have the problem …potassium fertilizer.” What do you mean?Page 4, line 68: fertilization affect negatively the yieldPage 4, line 69: and the vegetative growth of blueberries is inhibited, or even damaged [17].Page 4, line 71: cannotPage 4, line 72: but also it can improve their fruit…Page 4, line 76: have pointed out that applying conventional fertilizers without guidance, can lead to salt accumulation in soil and reduce…Page 4, line 77-78: Delete ‘by applying … habits [8].’Page 4, line 80: single-nutrient fertilizerPage 5, line 83: rabbiteye blueberry cv. Brilliant plants, exploresPage 5, line 85: test, aiming to optimize thePage 5, line 90: 2.1 Experimental sitePage 5, line 91: Please add the geographic coordinates of this sitePage 5, line 92: which is characterized by subtropicalPage 5, line 95: Delete the second ‘soil’Page 5, line 96-97: Express these values as per cent (%)Page 5, line 98: 2.2 Plant materialRefer whether these plants were self-rooted or grafted. Were the trees trained with a single trunk or what?Page 5, line 99: Five -year-old rabbiteye blueberry (Vaccinium ??? cv. Brightwell) plantsPage 5, line 101: 2.3 Experimental designRefer the plantation distances among trees.Page 5, line 102: …under field conditions. Fertilizers containing N, P and K doses at three doses for each nutrient, three …Page 6, line 104: Non fertilized plants were used …Page 6, line 105: The fertilizers used were…Page 6, line 106-107: Delete the words ‘content’Page 6, line 107: …63.2%), all of high chemical purity.Page 6, line 109: Do you mean ‘fruit production’ or ‘fruit set’?Page 6, line 110: What do you mean with ‘reducing fertilizer’?Page 6, line 110-111: The fertilizers were applied into a trenchPage 6, line 112-113: canopy projection, mixed and covered with soil.Page 6, line 113: What you mean with ‘carried out’?Page 6, line 114: the experimental design is shownPage 7, Table 2: There are not all possible N-P-K combinations (eg N3P3K3). How did you select these ones?Page 7, Table 2: You have not explained what is orthogonal test design. Provide a relative reference.Page 7, Table 2: I propose the following legend: Table 2. Treatments of N, P and K with their dosages on blueberry plants.Page 7, Table 2: It would be better to present the doses of nutrients  not the form of fertilizers (ammonium sulfate etc)Page 7, line 126: The Folin-Ciocalteu…method was used to establish …. (mg/g FW)[23].Page 7, line 128: Replace ‘modified’ with ‘determined’Page 8, line 133: Refer the type and model of the refractometer device.Page 8, line 136: …SPSS 18 software programs were used…Page 8, line 137: ANOVA and least significant difference (LSD) were applied to comparePage 8, line 138: Delete ‘calculate’Page 8, line 139: levels was calculated and the principal …evaluation was used.Page 8, line 141: Results and DiscussionPage 8, line 145: effects were observed in F2 and F6 treatments, which gave 8.67% higher values than ...Page 8, line 149: best fertilizer combinations affecting the fruit weight and yield…Page 9, line 152: P>K>N, respectively…Page 9, line 154: Delete ‘This is the’Page 9, line 156: The Table 3 repeats data that has been alreadyPage 9-10, line 156: In the Table 3, column ‘Code’, there is a Chinese character.Page 10, line 160, 166, 168: sugar: acidPage 10, line 172: titratable acidity of otherPage 10, line 173: but there is noPage 11, line 174: effect was that of treatment F9 (17.07% lower than CK)Page 11, line 176-177: F2 was significantly higher (36.36%) than CK…Page 11, line 180: is significantly higher (20.59%) than CK…Page 11, line 187: on the above-mentioned parameters of blueberry fruit is…Page 11, line 189: ,respectively. According…Page 12, line 194: Effect of different fertilization formulas on blueberry fruit quality.Page 12, line 196: Table 4. Orthogonal test on …Page 13, line 199: yield and berry quality of blueberry.Page 13, line 200: The yield and berry quality of blueberry were evaluated…Page 14, line 203-204: Limit to two decimal digitsPage 14, line 212: treatments F2…Page 14, line 213: were ranked second.Page 14, line 215, 216, 218: and berry qualityPage 14, line 217: Delete ‘which was also F2’Page 15, table 5: Titratable acidityPage 16, line 223: yield and fruit quality of trees. Studies have shown that these nutrients and their…Page 16, line 226: improve fruit qualityPage 16, line 233-234: it is not clear what you mean. Was the level of soil K in the region low?Page 16, line 235-236: In accordance, we found that …Page 16, line 237: titratable acidity in blueberry fruit. There was also significant improvement of the flavonoid…Page 16, line 238 and 240: sugar: acidPage 16, line 239: Delete ‘on’Page 16, line 240: fertilizer on the soluble sugar content, Vc and sugar: acid ratio.Page 16, line 241: Since K can participate in…Page 17, line 242: sugar, it increases the sugar…promotes …Page 17, line 246: Replace ‘extremely’ with ‘highly’Page 17, line 246: It was shown that thePage 17, line 248: It was shown that thePage 17, line 252: and applying more (but not excessive) P fertilizer …Page 17, line 252-253: Delete ‘but it does not mean adding excessive phosphorus fertilizer.’Page 17, line 256: low demands forPage 17, line 259: and berry quality of blueberry, it is necessary to determine and apply the appropriate…Page 18, line 263: fertilization formulaPage 18, line 264: but all treatments could improve them by …Page 18, line 265-266: Moreover, heavy fertilization does not mean …Page 18, line 267: Replace ‘obtain’ with ‘result in’Page 18, line 271-275: Replace ‘that is, the best … potassium sulfate’ with ‘that is, 100, 25 and 25 g/plant of N, P2O5 and K2O, respectively.’Page 18, line 277-281: It is not clear what you mean here. Rewrite it better.Page 19, references: All latin names of plants must be written in italics.**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  28 Jan 2023We have consider all the suggestions and revised the manuscript as per reviewer comments. Response sheet is uploaded in separate file.Attachmentrebuttal letter.docxSubmitted filename: Click here for additional data file. 13 Feb 2023
 PONE-D-22-30089R1

 Effects of nitrogen, phosphorus and potassium formula fertilization on the yield and berry quality of blueberry

 PLOS ONE  Dr. Delu Wang  ,Dear

 Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Mar 30 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at
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For instructions see: We look forward to receiving your revised manuscript.Kind regards,Sushanta Kumar Naik, PhDAcademic EditorPLOS ONEJournal Requirements:Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.[Note: HTML markup is below. Please do not edit.]Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author  1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: (No Response)Reviewer #2: (No Response)**********  2. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: (No Response)Reviewer #2: Partly**********  3. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: (No Response)Reviewer #2: Yes**********  4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: (No Response)Reviewer #2: Yes**********  5. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: (No Response)Reviewer #2: Yes**********  6. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: 1. Soil pH is in acidic range, so significant amount of the applied phosphorus may be fixed in soil, so, whether, it is good to apply P fertilizer directly without any soil amendments.2. It is still not mentioned in the manuscript that, what is the basis of selection of three levels of N, P and K, How you get the level 1.3. Selection of recommendation should be based on the B: C ratio or some other important aspects. Which is not mentioned clearly?4. Presentation of results in figures and tables are complicated, so, if possible convert it in simple form.Reviewer #2: There are yet some missing data as follows:Materials and methodsIf available, provide the mean nutrient status of tested plants before fertilizer treatments took place.If available, provide data about the fertilizers applied the year before experimentation.What were the plantation distances and plantation density (number of trees/ha) of the tested orchard?What about canopy training system and mean height of the tested trees?Page 2, line 11: Replace ‘plants’ with ‘species’Page 2, bottom line: Replace ‘was’ with ‘were’Discussion, line 11: Delete the word ‘fertilizer’Conclusion, first line: …formula on blueberry fruit …Conclusion, bottom line: …of increasing N and K, and stabilizing P.**********  7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: No**********https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  16 Feb 2023Dear Editor: Sushanta Kumar Naik and Dear reviewers:Thank you very much for your useful comments and professional advice on our manuscript. We wish to give a sincere gratitude to referees for reviewing our paper carefully. These opinions help to improve academic rigor of our article, and we apologize for any inconveniences caused by these errors. We have modified the manuscript accordingly, and the response to the referees’ comments are listed point by point below:Reviewer #1: Comment 1: Soil pH is in acidic range, so significant amount of the applied phosphorus may be fixed in soil, so, whether, it is good to apply P fertilizer directly without any soil amendments.Reply 1: Thanks for your comment. Although a large amount of phosphorus is fixed in the soil under acidic conditions, it is not the best to apply phosphorus fertilizer directly. Because applying only one kind of fertilizer will aggravate the imbalance of soil nutrients. Moreover, a large number of studies have shown that compared with the single application of nitrogen, phosphorus and potassium, the combined application can significantly improve the fertilizer utilization rate.Comment 2: It is still not mentioned in the manuscript that, what is the basis of selection of three levels of N, P and K, How you get the level 1.Reply 2: Thanks for your comment. Select the three factors of N fertilizer, P fertilizer and K fertilizer, and set three levels for each factor according to Guo et al. [22], which has been added to line 3 on page 3 of the manuscript. Comment 3: Selection of recommendation should be based on the B: C ratio or some other important aspects. Which is not mentioned clearly?Reply 3: Thanks for your suggestion. It is calculated that the benefit-cost ratio of treating F2 and F4 is 16.13 and 15.87, respectively, so F2 processing is more recommended, and the relevant content has been added to lines 16-17 on page 6 of the manuscript. Comment 4: Presentation of results in figures and tables are complicated, so, if possible convert it in simple form.Reply 4: Thanks for your suggestion. But I can't find a better way to show the results. If possible, I hope you can give me some advice. Reviewer #2: There are yet some missing data as follows: Materials and methodsComment 1: If available, provide the mean nutrient status of tested plants before fertilizer treatments took place.If available, provide data about the fertilizers applied the year before experimentation.What were the plantation distances and plantation density (number of trees/ha) of the tested orchard?What about canopy training system and mean height of the tested trees?Reply 1: Thanks for your suggestion. We regret that we did not provide enough complete material information in the materials and methods section. First of all, the plantation distances and plantation density (number of trees/ha) of the tested orchard are 1.5m and 4444 trees/ha respectively, which have been added to lines 4-5 on page 3 of the manuscript. Secondly, as a response to the reviewers, we regret the lack of detailed data on the mean nutrient status of tested plants before fertilizer treatments took place, the data of the fertilizers applied the year before experimentation, the canopy training system and mean height of the tested trees. This is a mistake in our research. We will be more cautious in future research.Comment 2: Page 2, line 11: Replace ‘plants’ with ‘species’.Reply 2: Thanks for your suggestion. The relevant content has been revised in line 10 on page 2 of the manuscript.Comment 3: Page 2, bottom line: Replace ‘was’ with ‘were’.Reply 3: Thanks for your suggestion. The last line on page 2 of the manuscript has been revised.Comment 4: Discussion, line 11: Delete the word ‘fertilizer’.Reply 4: Thanks for your suggestion. Relevant contents are modified in line 11 of the discussion part of the manuscript.Comment 5: Conclusion, first line: …formula on blueberry fruit …Reply 5: Thanks for your suggestion. Relevant contents are revised in the first line of the conclusion of the manuscript.Comment 6: Conclusion, bottom line: …of increasing N and K, and stabilizing P.Reply 6: Thanks for your suggestion. The last line of the conclusion of the manuscript has been revised.Attachmentrebuttal letter.docxSubmitted filename: Click here for additional data file. 3 Mar 2023Effects of nitrogen, phosphorus and potassium formula fertilization on the yield and berry quality of blueberryPONE-D-22-30089R2Dear Dr. Wang,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. 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For more information, please contact Kind regards,Sushanta Kumar Naik, PhDAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author  1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.Reviewer #1: (No Response)**********  2. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: (No Response)**********  3. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: (No Response)**********  4. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: (No Response)**********  5. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: (No Response)**********  6. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: (No Response)**********  7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: No********** 7 Mar 2023PONE-D-22-30089R2 Effects of nitrogen, phosphorus and potassium formula fertilization on the yield and berry quality of blueberry Dear Dr. Wang:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Sushanta Kumar Naik Academic EditorPLOS ONE


This corrects the article “Social media for research discourse, dissemination, and collaboration in rheumatology” in volume 3 on page 172.In the December 2022 issue of Rheumatology and Immunology Research, the paper titled “Social media for research discourse, dissemination, and collaboration in rheumatology” :169-179. doi: 10.2478/rir-2022-0031), was published with some errors:The sentence “Among 26 rheumatology journal families, 21 (81%) have active Twitter accounts.” should be changed to “Among 28 rheumatology journal families, 24 (86%) have Twitter accounts.” The sentence, “However, only 5 (19%) have a designated social media editor,” should be changed to “However, only 10 (36%) have a designated social media editor.”In The errors do not change the scientific conclusions of the article. The authors would like to apologize for the errors and any inconvenience caused.


COVID-19 has profoundly impacted the mental health and well-being of pregnant women worldwide. In Ethiopia, the poor sleep quality and suicidal ideation among pregnant women has increased due to the COVID-19 pandemic. This study aimed to provide comprehensive evidence on the prevalence and associated factors of poor sleep quality and suicidal ideation among pregnant women during COVID-19 in Ethiopia.2was used to check heterogeneity. Stata 14.0  was used for statistical analysis.This study is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendation. Data were searched from PubMed, Google Scholar, and African Journals Online from the occurrence of the COVID-19 pandemic to February 2023. Two researchers extracted the data and performed the methodological quality assessment independently. Random-effect model was used to estimate the pooled effect size and I2 = 98.1%, p < 0.001). Age ≥ 30 years pooled AOR = 1.95, 95% CI , 3rd trimester pooled AOR = 3.20, 95% CI , substance use pooled AOR = 2.51, 95% CI , depression pooled AOR = 2.97, 95% CI  and stress pooled AOR 2.14, 95% CI  were associated factors of poor sleep quality. Three studies reported about suicidal ideation and pooled prevalence was 11% . Depression pooled AOR = 3.19, 95% CI  was the only associated factor of suicidal ideation.From six studies the pooled prevalence of poor sleep quality was 55% (95% CI [0.42–0.69], IDue to COVID-19 pregnant women in Ethiopia were affected by poor sleep quality and suicidal ideation. Thus, suitable and well designed programs proposing awareness of COVID-19, mental health counseling and involvement should be designed to improve the general mental health of pregnant women.PROSPERO registration number CRD42023389896. Pregnancy is an event in a woman’s life and can lead to psychological disorders. Several risk factors (such as disasters and pandemics) have been correlated with prevalence of mental disorders during pregnancy . The COVPregnant women are vulnerable and suffered great psychological problems . PregnanSleep is an important biological function in humans. Physical body dynamics during pregnancy lead to the deterioration of both sleep quality and sleep duration. According to the Pittsburgh Sleep Quality Index (PSQI) a score of above 5 is considered poor sleep quality . On the The COVID-19 pandemic was expected to have terrible effects on mothers’ mental health in Africa because of the continent’s poor health systems, inadequate mental health policies, and unreliable maternal care . StudiesIn addition, suicidal ideation is communal in the third trimester, particularly in pregnant women with poor marital satisfaction, high attachment anxiety, prenatal depression and prenatal anxiety . The preTherefore, a thorough investigation is necessary, as is the provision of thorough evidence regarding the prevalence and contributing variables of poor sleep quality and suicidal ideation among pregnant women in Ethiopia. No study reported in a summarized way. By measuring the combined prevalence of poor sleep quality, suicidal ideation, and their associated factors, this study intends to close this gap. This study offers proof of mental health issues in pregnant women to academics and decision-makers.• This study aimed to provide comprehensive evidence on the prevalence and associated factors of poor sleep quality and suicidal ideation among pregnant women during COVID-19 in Ethiopia. •To estimate the pooled prevalence of poor sleep quality and suicidal ideation during COVID-19 among pregnant women in Ethiopia. •To review associated factors of poor sleep quality and suicidal ideation during the COVID-19 pandemic among pregnant women in Ethiopia.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 recommendation was used . The proSearching of literature was done by two authors (AAH and AAS) individually. Disagreement was handled by discussion with third author (AAM). Databases PubMed, African Journals Online and Google Scholar, and papers published from the occurrence of the COVID-19 pandemic to February 2023 were searched. Observational studies that evaluated sleep quality and suicidal ideation during COVID-19 among pregnant women in Ethiopia were considered. Systematic searches were carried out by combining every possible predefined search term determined by Medical Subject Headings (MeSH) and keywords. The following key words were used: “COVID-19”, “2019 novel coronavirus disease”, “2019 novel coronavirus infection”,“2019 ncov disease”, “2019 ncov infection”, “covid 19 pandemic”, “covid 19 pandemics”, “covid 19 virus disease”, “covid 19 virus infection”, “COVID19”, “coronavirus disease 2019”, “coronavirus disease 19”, “sars coronavirus 2 infection”, “sars cov 2 infection”, “severe acute respiratory syndrome coronavirus 2 infection”, “SARS-CoV-2”, “2019 novel coronavirus”, “2019 novel coronavirus”, “2019- nCoV”, “covid 19 virus”, “covid19 virus”, “Coronavirus disease 2019 virus”, “SARS coronavirus 2”, “SARS cov 2 virus”, “severe acute respiratory syndrome coronavirus 2”, “Wuhan coronavirus”, “Wuhan seafood market pneumonia virus”, “mental illness”, “psychiatric problem”, “psychology problem”, “mental health effect”, “psychological disturbance”, “mental disorder”, “psychiatric illness”, “psychiatric diseases”, “psychiatric disorders”, “behavior disorders”, “severe mental disorder”, “sleeping quality”, “poor sleep quality”, “insomnia”, “suicide”, “suicidal ideations”, “pregnant women”, “prenatal”, “ perinatal”, “postpartum”, “antenatal”, “postnatal”, “puerperal”, “lactating women”, “reproductive age women”, “child bearing women” and “Ethiopia”.Reference lists of full text articles included in the review were checked to identify any potentially eligible study for this review. Systematic procedure confirmed that the literature search encompasses all published studies on the prevalence and associated factors of poor sleep quality and suicidal ideation during COVID-19 among pregnant women in Ethiopia. From the searched results duplicates were detached by using Mendeley . The seaFor this systematic review and meta-analysis, observational studies that focused on the prevalence and associated factors of poor sleep quality and suicidal ideation among pregnant women during COVID-19 in Ethiopia were considered. Moreover, the inclusion criteria is stated as:Setting: Studies in Ethiopia were the main emphasis of this review.Population: All pregnant women in Ethiopia.Study design: Observational studies on prevalence of poor sleep quality and suicidal ideation and their associated factors.Language: Only English.The following types of studies were excluded: studies focusing on the whole population, studies that do not report prevalence and associated factors of poor sleep quality and suicidal ideation among pregnant women, systematic reviews and meta-analyses, randomized controlled trials, editorials, and conference abstracts and opinions.The primary outcomes were the pooled prevalence of prevalence of poor sleep quality and suicidal ideation among pregnant women during COVID-19 in Ethiopia. The secondary outcomes were factors associated with poor sleep quality and suicidal ideation among pregnant women during COVID-19 in Ethiopia.Two researchers (AAH and AAS) reviewed studies based on eligibility criteria. Firstly, the authors evaluated titles and abstracts of the studies from the searched databases. Then, full-text screening was done to screen the full texts. We have provided a reasoning for inclusion and exclusion of studies in the PRISMA diagram. The list of suitable studies for this systematic review and meta-analysis were arranged.The data were collected by two researchers (AAH and AAS) individually. Pretest on the data extraction form by using two pilot surveyed studies was done. This helps ensure the collection of all necessary data for this systematic review and meta-analysis is accurate. Disagreements were handled by deep discussion among the investigators. All relevant data were collected from the original studies. Specifically, author’s last name, year of publication, region, study design, number of cases, sample size, sampling design, instrument, study population, average age and prevalence of poor sleep quality and suicidal ideation with their associated factors were extracted.Two researchers (AAH and AAM) separately evaluated the quality of the included studies by the Newcastle-Ottawa Scale (NOS) . Accordi2 and Cochran’s Q-statistics . The pooled prevalence with 95% confidence interval (CI) by using random-effect model with the generic inverse variance method was computed. Evaluation of heterogeneity was checked by Iatistics . Subgrouatistics   for cross-sectional studies quality assessment was presented in Table 1ne study  that wasne study  that werp-value =0.531), i.e., there is minor asymmetry, but the Egger’s test result is not statistically significant with p-value =0.531, indicating that no significant influence was found on the pooled result and therefore no publication bias. For suicidal ideation studies . i.e, both LFK index and Egger’s test supports no publication bias . As shown in Six studies reported the prevalence of anxiety, and the pooled prevalence of poor sleep quality was found to be 55% .From the subgroup analysis of prevalence of poor sleep quality by region, the pooled prevalence of poor sleep quality in Oromiya, Amhara and South West Ethiopia was 44%, 58% and 71% respectively . The preQ = 0.2, df = 1, p = 0.657) implies that there was no significant heterogeneity in years of publication on the prevalence of poor sleep quality.From the subgroup analysis by publication year , the pooQ = 20.60, df = 3, p = 0.000) implies that there is statistically significant heterogeneity among sampling methods.From the subgroup analysis by sampling method  the poolI2 = 48.2%, p = 0.145) as shown in Fig. 7. No significant heterogeneity was observed. We did not do subgroup analysis to assess the source of heterogeneity.From three studies, the pooled prevalence of suicidal ideation was 11% , suggesting that the odds of developing poor sleep quality among pregnant women with substance use was 2.51 times more than women without substance use. The pooled adjusted odds ratio of pregnant women with depression was 2.97, 95% CI  suggests that the odds of developing poor sleep quality among pregnant women with depression was 2.97 times more than women with out depression. Likewise, the pooled adjusted odds ratio of pregnant women with stress was 2.14, 95% CI  suggests that the odds of developing poor sleep quality among pregnant women with stress was 2.14 times more than women without stress.We also reported pooled effect size of associated factors of suicidal ideation among pregnant women. The pooled adjusted odds ratio of pregnant women with depression was 3.19, 95% CI  suggesting that the odds of developing suicidal ideation among pregnant women with depression was 3.19 times more than women that do not have depression. The pooled adjusted odds ratio of pregnant women with intimate partner violence was 3.83, 95% CI  suggesting that the odds of developing suicidal ideation among pregnant women with intimate partner violence was 3.83 times more than women without intimate partner violence. Due to the limited number of studies available, pooling the adjusted odds ratio for the associated factors of poor sleep quality and suicidal ideation is not possible. Hence, we summarized the collected findings in In this comprehensive study we have shown that due to the COVID-19 pandemic poor sleep quality and suicidal ideation increased among pregnant women in Ethiopia. This study is the first of its kind that assessed the pooled prevalence and associated factors of poor sleep quality and suicidal ideation among pregnant women during COVID-19 in Ethiopia. This study includes nine papers. We supposed that all of the included studies are conducted with in ethical guidelines. The pooled prevalence of poor sleep quality and suicidal ideation with their associated factors were discussed.The pooled prevalence of poor sleep quality was found to be 55%. This is higher than the study in Changsha, China where the prevalence was 37.65% , the stuThe pooled prevalence of suicidal ideation was 11%. The result is consistent with a systematic review result prevalence worldwide that was between 2.73% to 18% . The resOur study also demonstrated the associated risk factor for poor sleep quality and suicidal ideation among pregnant women during COVID-19 in Ethiopia. For pregnant women age >= 30, the odds of developing poor sleep quality are more than with age <30 years. Gestational age in the 3rd trimester increases the odds of developing poor sleep quality among pregnant women more so women in the 2nd or 1st trimester. This finding is supported by study results from However, this study has both strengths and weaknesses. Two researchers (AAH and AAS) independently conducted the searching, screening, data extraction, and methodological quality assessment. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale. Lack of adequate number of studies on the psychological effects of COVID-19 across all regions of Ethiopia was the limitation of this study.Due to the COVID-19 pandemic pregnant women in Ethiopia were affected by poor sleep quality and suicidal ideation. The prevalence of poor sleep quality and suicidal ideation among pregnant women were high. Providing pregnant women with appropriate and well-designed programs that promote awareness of COVID-19, mental health counseling, and involvement to improve their general mental health is crucial. This review has helped to highlight the need of interventions that can be taken during COVID-19. Further studies could be conducted on the mental health effects of COVID-19 among pregnant women in Ethiopia.10.7717/peerj.16038/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16038/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.16038/supp-3Supplemental Information 3Click here for additional data file.


As a primary source of added sugars in the US diet, sugar-sweetened beverage (SSB) consumption is presumed to contribute to obesity prevalence and poor oral health. We systematically synthesized and quantified evidence from US-based natural experiments concerning the impact of SSB taxes on beverage prices, sales, purchases, and consumption.A keyword and reference search was performed in PubMed, Web of Science, Cochrane Library, Scopus, and EconLit from the inception of an electronic bibliographic database to Oct 31, 2022. Meta-analysis was conducted to estimate the pooled effect of soda taxes on SSB consumption, prices, passthrough rate, and purchases.Twenty-six natural experiments, all adopting a difference-in-differences approach, were included. Studies assessed soda taxes in Berkeley, Oakland, and San Francisco in California, Philadelphia in Pennsylvania, Boulder in Colorado, Seattle in Washington, and Cook County in Illinois. Tax rates ranged from 1 to 2 ¢/oz. The imposition of the soda tax was associated with a 1.06 ¢/oz.  increase in SSB prices and a 27.3%  decrease in SSB purchases. The soda tax passthrough rate was 79.7% . A 1 ¢/oz. increase in soda tax rate was associated with increased prices of SSBs by 0.84 ¢/oz .Soda taxes could be effective policy leverage to nudge people toward purchasing and consuming fewer SSBs. Future research should examine evidence-based classifications of SSBs, targeted use of revenues generated by taxes to reduce health and income disparities, and the feasibility of redesigning the soda tax to improve efficiency. In the Recent policy interventions aimed at combating obesity have utilized economic incentives to “nudge”  people toward a healthier diet choice . For exaPreliminary evidence suggests that soda taxes are associated with weight loss, reduced body mass index (BMI), and decreased risks of overweight and obesity , 15. AddExisting research on the effect of soda taxes in the US can be categorized into three groups: “proxy,” “modeling,” and “local” studies. The “proxy” studies have used state sales tax on soda, candy, and other qualified groceries as a substitute for a soda tax due to the absence of soda taxes or related data . HoweverThis study aims to systematically synthesize and quantify the impacts of soda taxes on beverage prices, sales, purchases, and consumption in the US. By exclusively relying on natural experiments , our review provides robust causal inferences regarding the effectiveness of soda taxes. Moreover, our approach goes beyond a narrative review by providing quantitative estimates of the magnitude of the tax effect. The findings of this study can inform local, state, and federal policymakers in designing or revising soda tax-related legislation and implementation strategies to effectively combat obesity.2.The present study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses .2.1.Studies meeting all of the following criteria were eligible for the review: (1) Participants: consumers, stores, or beverage items within US taxing jurisdictions that implemented a soda tax; (2) Interventions: soda tax (SSB excise tax); (3) Comparisons: consumers, stores, or beverage items within and outside the US taxing jurisdictions that implemented a soda tax; (4) Outcomes: beverage prices, sales, purchases, and consumption; (5) Study design: natural experiment; (6) Article type: peer-reviewed original study; (7) Time window of search: from the inception of an electronic bibliographic database to Oct 31, 2022; and (8) Language: English.2.2.A keyword search was performed in five electronic bibliographic databases: PubMed, Web of Science, Cochrane Library, Scopus, and EconLit. The search algorithm included all keywords from two groups: (1) “tax,” “taxes,” “taxation,” “taxed,” “taxing,” “pre-taxation,” “post-taxation,” “pre-tax,” “post-tax,” “excise,” or “excises”; and (2) “beverage,” “beverages,” “drink,” “drinks,” “soda,” “sodas,” “cola,” “coke,” “SSB,” or “SSBs.” The search algorithm used in PubMed was reported in 2.3.I2 index, which allows us to quantify the degree of variability between study estimates (I2 index was interpreted as modest (I2 ≤ 25%), moderate (25% < I2 ≤ 50%), substantial (50% < I2 ≤ 75%), or considerable (I2 > 75%)  change in prices of taxed beverages ; (2) change in prices of untaxed beverages; (3) change in purchases of taxed beverages; (4) change in purchases of untaxed beverages; (5) change in the consumption of taxed beverages; and (6) tax passthrough rate. Out of the 40 studies, 13 were excluded from the meta-analyses due to: (1) non-overlapping outcome measures , 56–63, stimates . The I2 2 > 75%) . Based o2 > 75%) . Additio2.4.Following Littell et al.  and An e3.3.1.3.2.To account for cross-border estimation across studies within the same jurisdiction, we employed a variety of methods to explain the variation in findings. These methods included considering differences in sample size, sample characteristics, statistical approaches, and outcome measures. By examining these factors, we aimed to provide insights into the varying effects observed within jurisdictions. 3.3.p < 0.01) in Oakland 10 months post-tax and 1 ¢/oz.  in San Francisco 4 months post-tax in comparison to Richmond and San Jose , a reduction in the purchases of taxed beverages by 27.3% . The soda tax passthrough rate was estimated to be 79.7% . Following the soda tax implementation, the prices of untaxed beverages were estimated to increase by 0.08 ¢/oz. . No changes were observed for the purchases of untaxed beverages and the consumption of taxed beverages (p > 0.05). Meta-regression found that 1 ¢/oz. increase in the soda tax rate was associated with an increase in the prices of taxed beverages by 0.84 ¢/oz. . No dose–response effect of soda taxes was found for purchases and consumption of taxed beverages or the tax passthrough rate (ps > 0.05). No publication bias was identified by Egger’s or Begg’s tests (ps > 0.05).3.5.n = 36) had comparable intervention and control groups. Additionally, most studies had a sample representative of the stores or the population residing in the taxing jurisdiction (n = 17) (n = 28) (n = 22) (n = 11) as outcomes (n = 17) ((n = 17) , 74, 75.(n = 28) , 75–81, (n = 22) , 79–81 aoutcomes , 58, 60.(n = 17) , 79–81. (n = 17) , and nin(n = 17) , 79. To 4.A few previous meta-analyses estimated the effect of soda taxes or SSB prices on beverage sales, purchases, and consumption. Nakhimovsky et al. reviewed studies in middle-income countries and estimated that a 10% increase in SSB prices was associated with a reduction in SSB consumption from 1.2–9.3 kcal per person per day . Teng etSeveral main criticisms of soda taxes are worth noting. First, substitution effects in addition to cross-border shopping may partially offset the intended effect of soda taxes on SSB purchases and consumption , 50. A nDespite the various criticisms of soda taxes, it remains a potentially effective policy leverage to nudge people toward reducing purchases and consumption of SSBs. There is also preliminary evidence linking soda tax to reduced body weight, BMI, and overweight or obesity , 15. TheThis study comprehensively reviews evidence reported from natural experiments on soda taxes implemented across US local taxing jurisdictions. The strengths of the included studies encompassed real-world policy interventions providing causal inferences, the inclusion of comparison cities, an elucidation of the local soda tax implementation processes, and longitudinal changes in price, consumption, and purchases pre-and post-tax. However, a few limitations of this review and the included studies should be noted. First, all studies were natural experiments focused on one or a few US cities implementing a soda tax. The limited geographic coverage and nationally non-representative sample have confined the generalizability of study findings. Therefore, caution should be exercised when extrapolating the results to other cities or jurisdictions. Second, DID is a quantitative method to reveal causal references, but the resulting evidence should not be overstated due to lacking a randomization design . Third, 5.This study systematically reviewed evidence from natural experiments regarding the impact of soda taxes on beverage prices, sales, purchases, and consumption in the US. Soda tax implementation was associated with increased prices of taxed beverages and reduced purchases and consumption of taxed beverages. Soda taxes could be effective policy leverage to nudge people toward purchasing and consuming fewer SSBs. Future research should examine evidence-based classifications of SSBs, more targeted use of revenues generated by taxes to reduce health and income disparities, and the feasibility of redesigning the soda tax to improve its efficiency.The original contributions presented in the study are included in the article/JS, JW, and RA designed the study, wrote the manuscript, and revised the manuscript. JW, FY, and JS jointly designed the search algorithm and screened articles. JS and JW performed data extraction and constructed the summary tables. All authors contributed to the article and approved the submitted version.This research was funded by the Fundamental Research Funds for the Central Universities, CUGB, grant number 2-9-2020-036; The Ministry of Education of Humanities and Social Science project, grant number 19YJC890044.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Dysregulation in the secretion of adipokines or adipocytokines plays a significant role in triggering a pro-inflammatory state, leading to endothelial dysfunction and insulin resistance, and ultimately elevating the risk of atherosclerosis and coronary artery disease (CAD). Previous studies have shown a link between NOV/CCN3 (an adipokine) and obesity, insulin resistance, and inflammation. However, no research has explored the relationship between CCN3 serum levels and CAD. Therefore, we conducted the first investigation to examine the correlation between CCN3 and CAD risk factors in patients.In a case-control study, we measured the serum levels of CCN3, IL-6, adiponectin, and TNF-α in 88 angiography-confirmed CAD patients and 88 control individuals using ELISA kits. Additionally, we used an auto analyzer and commercial kits to measure the biochemical parameters.In patients with CAD, the serum levels of CCN3, TNF-α, and IL-6 were significantly higher compared to the control group, whereas lower levels of adiponectin were observed in the CAD group (P < 0.0001). A positive correlation was found between CCN3 and IL-6 and TNF-α in the CAD group . A binary logistic regression analysis showed the risk of CAD in the model adjusted (OR [95% CI] = 1.29 [1.19 − 1.41]), (P < 0.0001). We determined a cut-off value of CCN3 (3169.6 pg/mL) to distinguish CAD patients from the control group, with good sensitivity and specificity obtained for this finding .This study provides evidence of a positive association between CCN3 serum levels and CAD, as well as inflammation markers such as IL-6 and TNF-α. These findings suggest that CCN3 may serve as a potential biomarker for CAD, and further investigations are necessary to validate this association and explore its potential use in clinical settings. According to the World Health Organization’s report, the mortality rate attributable to cardiovascular diseases (CVD) is currently estimated to exceed 17 million individuals annually, with a projected increase to 23 million by 2023  = 1.29 [1.188 − 1.41]) and adjusted models accounting for age, gender, and BMI (OR [95% CI] = 1.29 [1.190 − 1.41]) (P < 0.0001)  was identified to distinguish between CAD patients and the control group, demonstrating good sensitivity and specificity . The area under the curve was calculated as 0.87 [0.81–0.93 (95% Cl)] and exhibited a significant p value of less than 0.0001  with metabolic disorders, encompassing conditions such as obesity, diabetes, insulin resistance, and non-alcoholic fatty liver –26. To tThe results of our study demonstrate a significant upregulation of serum CCN3 in CAD patients compared to controls, with a positive correlation between CCN3 levels and CAD risk. Notably, a CCN3 cutoff value was established with good sensitivity and specificity (> 80%). Additionally, we observed a positive correlation between CCN3 and IL-6 and TNF-α in CAD patients, consistent with previous studies highlighting the regulatory role of CCN3 in metabolic processes. For instance, Martinerie et al.  demonstrPakradouni et al.  conducteNotably, our findings lend support to the notion that CCN3 levels in CAD patients increase independently of obesity and insulin resistance parameters, thus potentially serving as a robust biomarker for the prognosis of CAD patients. Nonetheless, to draw a more definitive conclusion, a larger cohort of subjects must be studied.Our findings reveal, for the first time, a notable elevation in serum CCN3 levels among CAD patients in comparison to the control group. Notably, our study indicates a significant positive association between CCN3 serum levels and the progression of CAD. Furthermore, the identification of a significant correlation between CCN3 and inflammatory cytokines (IL-6 and TNF-α) provides further insight into the potential role of CCN3 in the pathogenesis of CAD. Given the robust association between CCN3 and CAD, which persists even after controlling for variables such as age, gender, and BMI, it is plausible that CCN3 may serve as a promising prognostic marker for CAD.