Are weight control and food waste a trade-off?: A clustering of appropriate amount of food choice and plate-clearing behaviors among Japanese adult consumers

Are weight control and food waste a trade-off?: A clustering of appropriate amount of food choice and plate-clearing behaviors among Japanese adult consumers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Are weight control and food waste a trade-off?: A clustering of appropriate amount of food choice and plate-clearing behaviors among Japanese adult consumers Yui Kawasaki, Sayaka Nagao-Sato, Misa Shimpo, Rie Akamatsu, Yoko Fujiwara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3371761/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background/Objectives Plate-clearing behavior (PCB), in which individuals eat more food than is appropriate for them regarding excessive portion size choices, is considered to cause weight gain. However, the appropriate amount of food choice behavior (ACB) to avoid the trade-off between weight gain and food waste has been overlooked in previous studies. This study aimed to identify patterns of ACB and PCB in various meal situations and describe the demographic, anthropometric, psychological, and lifestyle-related characteristics of those who follow each pattern. Subjects/Methods In total, 1,707 Japanese participants responded to a web-based anonymous questionnaire in February 2023 and were included in this study. Cluster analysis was performed to identify patterns in the ACB and PCB. Multiple logistic regression analysis was used on clusters of participant characteristic variables. Results The median age of the participants was 40 (25th and 75th percentile: 30, 50) years (female = 865, 50.7%). Four clusters with independent predictors were identified: low ACB and high PCB, moderate ACB and high PCB, moderate ACB and low PCB, and high ACB and low PCB. The independent predictors of high ACB and low PCB were being female [1.550 (1.177–2.041), p = 0.002]; having low BMI (< 18.5) [1.735 (1.273–2.365), p < 0.001]; much interest in health [1.042 (1.018–1.066), p < 0.001], attitude toward avoiding food waste [1.133 (1.077–1.191), p < 0.001], gratitude for food [1.106 (1.060–1.154), p < 0.001], and the need for an appropriate amount of food choice [1.046 (1.014–1.080), p = 0.005]. Conclusion This study identifies appropriate consumer behaviors to maintain health and develop a strategy for food-choice and PCBs. Health sciences/Health care/Weight management Health sciences/Health care/Public health food waste weight control plate-clearing behaviors behavioral pattern consumer appropriate amount of food choice Figures Figure 1 Introduction Food waste increases environmental impacts and adversely affects global environment and health, causing greenhouse gas emissions, burdening waste management systems, and increasing hunger and malnutrition due to population growth and food distribution imbalances 1,2 . To reduce food waste, it is necessary to reduce consumer leftovers. However, researchers, mainly in Western countries, have identified that plate-clearing behavior (PCB), in which individuals force themselves to eat more food than is appropriate for them regarding choice of excessive portion sizes, causes weight gain 3–6 . Food-choice behaviors have been overlooked in previous studies examining the relationship between PCBs and body weight 3–7 . If the appropriate amount of food choice behaviors (ACBs) is implemented, individuals’ body weight may not increase without leaving leftovers; however, if more than the appropriate amount of food is selected, there is a trade-off between food waste and weight gain due to PCB. Numerous studies have shown that the selection of an appropriate portion size can prevent weight gain and/or maintain individuals’ weights 8,9 . However, the effect of a combination of PCBs and ACBs on body weight has not yet been examined. Although previous studies have reported that people with stronger food waste concerns are more likely to implement PCBs 4 , little has been done to examine health awareness, which is expected to affect PCBs and ACBs, as well as food waste concerns. Lifestyle-related factors, such as the frequency of eating out and cooking, may also influence behavioral patterns, as they relate to the frequency of ACB and PCB implementation. In addition, there are various situations in which ACBs and PCBs are present, such as at home (home cooking and/or eating prepared foods) and at restaurants. Although it is assumed that the frequency of the appropriate amount of food choice varies slightly depending on the situation, little research has been conducted on these behaviors in these scenarios. Moreover, Japanese people have unique norms regarding food waste avoidance behaviors, such as eating what you are served without leaving any leftovers, following Buddhism and Shintoism 7,10 . These norms are based on Japanese “Gratitude for Food” 7,10 . Exploratory data-driven methods, such as cluster analysis, can be used to gain insight into behavioral patterns 11 . By identifying the patterns of ACBs and PCBs in various dietary behavioral scenarios and describing the demographic, psychological, and lifestyle characteristics of those who follow each pattern, it is possible to make policy recommendations to promote food waste reduction while aiming to maintain individuals’ appropriate body weights. Therefore, this study aims to ( 1 ) identify patterns of ACBs and PCBs in various meal situations and ( 2 ) describe the demographic, anthropometric, psychological, and lifestyle-related characteristics of those who follow each pattern. Materials and Methods Study Design This study was part of a larger longitudinal online survey that aimed to examine the association between ACBs, PCBs, and weight control in 2023. The detailed data collection process is reported in a previous study. 12 Baseline data of 1,800 men and women aged 18–59 years living in Japan were used in the present study. In addition, to examine the test-retest reliability and criterion validity of the original items in this study, we used data from 1,380 individuals who responded to a follow-up survey conducted one week after the baseline survey. 12 This study was performed in accordance with the Declaration of Helsinki and approved by the Research Ethics Board (no. 2022 − 166). All participants were informed of the study’s aim and voluntarily provided their consent online. Variables To measure ACBs, eight questions were developed. Each item was divided into eating scenes, such as eating out and at home. With respect to ACBs, five items concerning the eating-out scenario (AE-1–5; Table 1 ) were developed based on the present study to describe the characteristics of people who order the appropriate amount of food at restaurants 13 . Three items concerning the home scenario (AH-1–3) were developed considering the food choice scenario listed in a previous study. 12 A 6-point Likert scale was used (1 = not at all, 6 = always). Criterion validity and repeat reliability of these items were described at Table 1 . Table 1 Items of questions developed for the survey. Items of questions by each theme Options Appropriate amount of food choice behavior a 1: Not at all – 6: always AE-1. I check the serving size of food before entering a restaurant or choose a restaurant where I already know the serving size of food or can adjust the serving size of food. AE-2. When I select a menu, I check weather I can eat the food I want to order without difficulty. AE-3. When I want to order food with a larger serving size than I can eat without difficulty, I choose a smaller size if the menu has a range of sizes. AE-4. When I want to order food with a larger serving size than I can eat without difficulty, I order food with smaller serving size if it is available in smaller sizes and if I can get a discount by reducing the size. AE-5. When I want to order food with a larger serving size than I can eat without difficulty, I ask the waiter for a smaller portion even if the menu does not have a size range. AH-1. When I feel that the amount of food I prepare is more than I can eat without difficulty, I reduce it to the amount I can eat. AH-2. When I feel that the amount of food (e.g. lunch boxes, prepared foods, or instant foods) I purchased is more than I can eat without difficulty, I reduce the amount until I can finish the food. AH-3. When the amount of food prepared by family members is more than I can eat without difficulty, I reduce the amount until I can finish the food. Plate clearing behavior b 1: I eat more than twice as much food as I can eat without difficulty – 6: I do not eat (leave/take home) more than I can eat without difficulty on the spot, regardless of serving size of the food PE-1. When eating out, if you feel that the amount of food is too much (there is more food in front of you than you can eat without difficulty), do you eat up the meal, even if you have to push yourself? 1: I do not eat (leave/take home) more than I can eat without difficulty on the spot, regardless of how much the menu offers – 6: I eat more than twice as much food as I can eat without difficulty. PH-1. When I cook and prepare (serve) a larger amount of food than I can eat without difficulty, … PH-2. When I feel that the amount of food I purchased (e.g. lunchbox and prepared food) or instant food I cooked is more than I can eat without difficulty, … PH-3. When the amount of food prepared by my family members was more than I could reasonably eat, … Frequency of the need to choose the appropriate amount of food 1: Not at all – 6: always 1. When I eat out, I would like to order food that has larger portion sizes than I can eat without difficulty. 2. I cook larger amounts of food than I can eat without difficulty. 3. I buy larger amounts of food for a meal (e.g. lunch boxes, prepared foods, or cooked instant foods) than I can eat without difficulty. 4. My family member(s) prepare meals that are larger than I can eat without difficulty. a Criterion validity of these items was confirmed by the correlation coefficients of each item with “eating selected foods without leaving any leftovers”, which is one of the sustainable and healthy dietary behaviors developed in a previous study (ρ=-0.125– -0.201, p < 0.001) 7 . Repeat reliability was also tested using the same items asked in a one-week follow-up survey (Spearman’s correlation coefficient: ρ = 0.452–0.664, p < 0.001 for each item). b The criterion validity of these items was confirmed by the correlation coefficients of each item with hunger and satiety cues (three items, 4-point Likert scale; Cronbach’s alpha = 0.657), a subscale of the expanded mindful eating scale developed in a previous study (ρ=-0.237– -0.306, p < 0.001). 15 Test-retest reliability was also confirmed using the same items (PE-1 and PH-1–3) in the one-week follow-up survey (Spearman’s correlation coefficients: ρ = 0.675–0.739, p < 0.001 for each item). Eight original question items were developed for PCBs (Table 1 ). Participants were asked whether they eat all foods that they cannot eat with difficulty in four food choice scenarios: eating out (PE-1), home cooking (PH-1), eating prepared food (PH-2), and eating food prepared by their family member (PH-3). 14 Criterion validity and test-retest reliability was also described in Table 1 . Additionally, to consider interpersonal variability in the frequency of the need to choose an appropriate amount of food, four items were developed based on the aforementioned four scenarios, such as eating out, home cooking, eating prepared food, and eating food prepared by family members (1 = not at all, 6 = always; Table 1 ). Participants’ demographic, anthropometric, psychological, and lifestyle-related data, as well as ACBs and PCBs, were used in the present study. Demographic and anthropometric variables were described in the previous study 12 . Interest in health, attitudes toward avoiding food waste, and gratitude for food were included in the psychological data. The Interest in Health Scale, which was developed for Japanese adults and comprises a 12-item scale with three factors, was used 15 . Respondents answered using a 4-point Likert scale (1 = strongly disagree, 4 = strongly agree). The Cronbach’s alpha for the 12-item scale was 0.881 in this study. With respect to attitudes toward food waste, three items used in previous study were selected to measure participants’ attitudes 16 . A 6-point Likert scale (1 = strongly disagree, 6 = strongly agree) was used; the Cronbach’s alpha was 0.836 in this study. In addition, gratitude for food was measured using the Gratitude for Food Scale for Adults (GFS-A) 12 . This scale comprises one factor and five items scored on a 4-point Likert scale (1 = strongly disagree, 4 = strongly agree). Cronbach’s alpha for the present data was 0.919. Lifestyle-related variables included eating habits, the need for an adequate amount of food choice, and physical activity. With respect to eating habits, the participants were asked about their frequency of eating out, preparing meals, eating together, and cooking, while considering items to measure ACBs and PCBs. A 6-point Likert scale was used (1 = once in a month or less, 6 = twice a day or more), except for cooking frequency (1 = not at all, 8 = three times a day or more). Moreover, the short form of the International Physical Activity Questionnaire (IPAQ) was adopted. This scale examines the duration and frequency of three types of activities: walking, moderate-intensity activity, and vigorous-intensity activity 17 . Analysis Since it is difficult for participants who do not recognize the needs of ACBs to answer the questions concerning ACBs and PCBs, 93 participants who answered 1=“not at all” to all four questions concerning the frequency of needing to choose the appropriate amount of food were excluded from baseline data (n = 1,800). Therefore, 1,707 (baseline) and 1,338 (1-week follow-up) participants were included in the analysis. The BMI of each participant was calculated based on their height and weight. The total scores for each psychological variable (interest in health, attitude toward avoiding food waste, and gratitude for food) and the frequency of the need to choose the appropriate amount of food were calculated. Total scores of interests in health (score range is 12–48), attitude toward avoiding food waste ( 3 – 18 ), and the GFS-A ( 5 – 20 ), were calculated. The total metabolic equivalents in minutes per week were calculated based on the guidelines of the IPAQ and according to participants' responses. The Shapiro–Wilk test indicated that the samples were not normally distributed ( p < 0.05); therefore, non-parametric analysis was performed. Data are presented as medians and 25th and 75th percentiles. A cluster analysis was performed to identify patterns of ACBs and PCBs implementation, and 12 items related to ACBs and PCBs were incorporated as indicators. A two-step cluster analysis was applied to establish cluster groups. In the first step, small clusters were created based on distance, as in K-means, and in the second step, the small clusters were combined in a stepwise manner, as in hierarchical cluster analysis. To describe the characteristics of the identified cluster groups of participants, the mean frequencies of each item in relation to ACBs and PCBs in each cluster were described. Kruskal–Wallis and chi-square tests were used to describe participants’ backgrounds, such as their demographic, anthropometric, psychological, and lifestyle-related factors, by each cluster. Multiple logistic regression analysis with a stepwise method was used to calculate the odds ratios (ORs) and 95% confidence indices (CIs) assigned to clusters based on the background variables. Collinearity was tested using the Variance Inflation Factor (VIF). As the VIF values for all covariates were small (< 5), no evidence of multicollinearity was found. Bonferroni-Holm correction was applied to adjust for multiple tests 18 . All statistical analyses were performed using SPSS for Windows (version 29; SPSS Inc.). The tests were two-tailed, and the results were considered statistically significant at p < 0.05. The validity of the sample size for factor analysis in this study has been confirmed by previous studies. Vergouwe et al. have suggested a minimum of 100 events and 100 nonevents as external validation samples for a logistic regression analysis with adequate power 19 . Further, a power analysis calculation indicated that for an effect size of 0.5 (Kruskal-Wallis test) and 0.3 (chi-square test) and power of 0.8, four groups with at least 128 and 210 participants for Kruskal-Wallis and chi-square tests, respectively, would be required. Results Participants’ characteristics The study sample comprised 842 males (49.3%) and 865 females (50.7%) (Table 2 ). Median (25th, 75th percentiles) age and BMI were 40 (30, 50) years and 21.2 (19.2, 23.7), respectively. More than half of the participants lived with their family members from other generations, such as parents or children (55.7%). Table 2 Differences of characteristics of the study participants by each cluster (n = 1,707) Total LA*HP (n = 319) MA*HP (n = 400) MA*LP (n = 593) HA*LP (n = 395) p value n/Median %/ 25th, 75th percentile n/Median %/ 25th, 75th percentile n/Median %/ 25th, 75th percentile n/Median %/ 25th, 75th percentile n/Median %/ 25th, 75th percentile Demographic and anthropometric variables Age (year) 40 30, 50 38 hi 29, 48 38 j 29, 48 42 hj 32, 52 41 i 31, 51 < 0.001 m Sex Male 842 49.3 217 68.0 262 65.5 225 37.9 138 34.9 < 0.001 n Female 865 50.7 102 32.0 138 34.5 368 62.1 257 65.1 Body Mass Index 21.2 19.2, 23.7 22.2 hi 19.7, 25.0 21.5 jk 19.9, 24.2 21 hjl 19.1, 23.5 20.4 ikl 18.5, 22.5 < 0.001 m < 18.5 279 16.3 43 13.5 36 9.0 100 16.9 100 25.3 25.0 297 17.4 78 24.5 82 20.5 92 15.5 45 11.4 Living status a 2 1, 2 2 d 0, 2 2 1, 2 2 d 1, 2 2 1, 2 0.040 m Household income [JPY (USD)] < 2,000,000 (15,000) 213 12.5 39 12.2 46 11.5 74 12.5 54 13.7 0.961 n 2,000,000–4,000,000 (15,000–30,000) 356 20.9 77 24.1 88 22.0 121 20.4 70 17.7 4,000,000–6,000,000 (30,000–45,000) 426 25.0 80 25.1 101 25.3 147 24.8 98 24.8 6,000,000–8,000,000 (45,000–60,000) 278 16.3 48 15.0 66 16.5 99 16.7 65 16.5 8,000,000–10,000,000 (60,000–75,000) 198 11.6 32 10.0 47 11.8 72 12.1 47 11.9 > 10,000,000 (75,000) 236 13.8 43 13.5 52 13.0 80 13.5 61 15.4 Education Elementary or/ junior high school 40 2.3 6 1.9 11 2.8 14 2.4 9 2.3 0.082 n High school 409 24.0 85 26.6 88 22.0 149 25.1 87 22.0 Junior college or vocational school 334 19.6 56 17.6 61 15.3 134 22.6 83 21.0 College, university or graduate school 924 54.1 172 53.9 240 60.0 296 49.9 216 54.7 Psychological variables (range) Interest in health (12–48) b 33 29, 36 29 hij 25, 34 33 ik 29, 36 33 hl 29, 36 35 jkl 31, 39 < 0.001 m Attitude toward avoiding food waste (3–18) c 14 12, 16 14 h 12, 16 14 i 12, 16 14 j 12, 16 15 hij 13, 17 < 0.001 m Gratitude for food (5–20) d 15 11, 15 13 hij 10, 15 15 ik 12, 15 14 hl 11, 15 15 jkl 13, 17 < 0.001 m Lifestyle-related variables Frequency of eating out e 2 1, 3 2 h 1, 3 2 i 1, 3 2 i 1, 2 2 h 1, 2 < 0.001 m Frequency of home-meal replacement/prepared meal e 2 2, 3 3 1, 3 2 2, 3 2 1, 3 3 2, 3 0.137 n Frequency of eating together e 5 2, 6 4 hi 1, 5 4 2, 5 5 h 3, 6 5 i 3, 6 < 0.001 m Frequency of cooking f 5 2, 7 4 hi 2, 6 4 j 2, 6 5 hk 2, 7 6 ijk 4, 7 < 0.001 m Needs for adequate amount of food choice (4–16) 13 10, 15 11 hij 9, 13 13 hk 12, 16 13 ikl 11, 15 13 jl 10, 16 < 0.001 m Eating out g 3 2, 4 3 hi 2, 5 4 jk 3, 4 3 hj 2, 4 3 ik 2, 5 < 0.001 m Cooking g 3 3, 4 3 hij 2, 4 4 h 3, 4 4 i 3, 4 4 j 2, 4 < 0.001 m Prepared food g 3 2, 4 2 hij 1, 3 3 h 2, 4 3 i 3, 4 3 j 2, 4 < 0.001 m Prepared meal by family member g 3 2, 4 2 hij 1, 3 3 h 2, 4 3 i 3, 4 3 j 2, 4 < 0.001 m Physical activity level (METs/week) d 792 0, 2079 742 h 0, 1950 972 i 107, 2555 594 ij 0, 1823 990 hj 132, 2385 < 0.001 m LA*LP: Low appropriate amount of food choice behavior* low plate clearing behaviors; MA*HP: Moderate appropriate amount of food choice behavior* high plate clearing behaviors; MA*LP: moderate appropriate amount of food choice behavior* low plate clearing behaviors; HA*HP: high appropriate amount of food choice behavior* high plate clearing behaviors. a 0: living alone; 3: three-generation family. b Interest in Health Scale (Ozawa, et al. 2021). Higher score represents higher interest in health. c Items used in previous study (Stancu, et al. 2016). Higher score represents higher attitude toward avoiding food waste. d Gratitude for food scale for adults (GFS-A; Kawasaki, et al. under review). Higher score represents higher gratitude for food. e 1: once in a month or less–6: twice in a day or more. f 1: not at all–8: three times in a day or more. g 1: Not at all − 6: always. Letters (h-l) represent significant statistical differences between each group by using the Bonferroni’s multiple comparison test (adjusted p < 0.05). m Kruskal-Wallis test. n Chi-square test. Cluster description The cluster analysis identified four groups. Figure 1 shows the mean frequencies of ACBs and PCBs for each cluster. These behavioral patterns were referred to as follows: low frequency of ACBs and high PCBs (LA*HP; n = 319, 18.7%), moderate frequency of ACBs and high PCBs (MA*HP; n = 400, 23.4%), moderate frequency of ACBs and low PCBs (MA*LP; n = 593, 34.7%), and high frequency of ACBs and low PCBs (HA*LP; n = 395, 23.1%). Differences of characteristics of the study participants by each cluster Table 2 shows the differences in the characteristics of the study participants for each cluster. Almost all variables differed for each cluster, except for household income, education, and frequency of prepared meals ( p = 0.961, 0.082, and 0.137, respectively). More than 60% of the participants were male for LA*HP (68.0%) and MA*HP (65.5%), while most were female for MA*LP (62.1%) and HA*LP (65.1%). The proportion of obese and overweight (BMI > 25.0) participants was largest in the LA*HP cluster (24.5%) and lowest in the HA*LP cluster (11.4%), whereas the proportion of underweight (BMI < 18.5) participants was largest in the HA*LP cluster (25.3%) and lowest in the LA*HP cluster (13.5%) in all four clusters. All three psychological variables, such as interest in health, attitude toward avoiding food waste, and gratitude for food, differed by cluster, and the total scores were highest for HA*LP and lowest for LA*HP ( p < 0.001). Odds ratios of cluster allocation based on participants’ demographic, anthropometric, psychological, and lifestyle-related variables The ORs of cluster allocation based on the participants’ demographic, anthropometric, psychological, and lifestyle-related variables are described in Table 3 . Being young, male, having a high household income, living with simple generations, and having low scores of interest in health and gratitude for food were associated with allocation for LA*HP cluster. Individuals were more likely to be in the MA*HP cluster if they were young, male, had high BMI (< 18.5), frequently ate out, and realized needs for adequate amount of food choice. Those belonging to the MA*LP cluster were more likely to be old, female, live with multiple generations, have low scores of attitude toward of food waste, and less frequently eat out and cook. Finally, individuals belonging to the HA*LP cluster were more likely to be female; have lower BMIs (< 18.5); have higher interest in health scores, attitude toward avoiding food waste, and gratitude for food; and realize the needs for adequate amount of food choice. Table 3 Odds ratios for cluster allocation based on participants’ demographic, anthropometric, psychological, and lifestyle variables a LA*HP (n = 319) MA*HP (n = 400) MA*LP (n = 593) HA*LP (n = 395) OR 95%CI b p c OR 95%CI b p c OR 95%CI b p c OR 95%CI b p c Demographic and anthropometric variables Age (year) 0.984 0.972–0.996 0.008 0.985 0.975–0.996 0.006 1.023 1.013–1.033 < 0.001 - Sex Male 1 1 1 1 Female 0.424 0.321–0.561 < 0.001 0.454 0.355–0.581 < 0.001 2.575 2.022–3.279 < 0.001 1.550 1.177–2.041 0.002 Body Mass Index 0.007 < 0.001 < 18.5 - 0.547 0.371–0.807 0.002 - 1.735 1.273–2.365 25.0 1.042 0.770–1.411 0.789 0.791 0.546–1.146 0.215 Living status (0: living alone; 3: three-generation family) 0.803 0.687–0.938 0.006 - 1.189 1.048–1.348 0.007 - Household income (JPY) 1.104 1.007–1.211 0.034 - - - Education - - - - Psychological variables Interest in health (12–48) 0.912 0.889–0.935 < 0.001 - - 1.042 1.018–1.066 < 0.001 Attitude toward avoiding food waste (3–18) - - 0.905 0.871–0.940 < 0.001 1.133 1.077–1.191 < 0.001 Gratitude for food (5–20) 0.922 0.885–0.961 < 0.001 - - 1.106 1.060–1.154 < 0.001 Lifestyle-related variables Frequency of eating out (1: once in a month or less–6: twice in a day or more) - 1.190 1.052–1.346 0.006 0.876 0.777–0.988 0.031 - Frequency of home-meal replacement/prepared meal (1: once in a month or less–6: twice in a day or more) - - - - Frequency of eating together (1: once in a month or less–6: twice in a day or more) - - - - Frequency of cooking (1: not at all–8: 3 times in a day or more) - - 0.923 0.877–0.972 0.002 1.073 1.009–1.141 0.025 Needs for appropriate amount of food choice (4–24) 0.884 0.850–0.919 < 0.001 1.063 1.031–1.096 < 0.001 - 1.046 1.014–1.080 0.005 Physical activity level (METs/week) - - - - Adjusted R 2 0.208 0.088 0.094 0.168 HA: high frequency of appropriate amount of food choice behaviors; HP: high frequency of plate clearing behaviors; LA: low frequency of appropriate amount of food choice behaviors; LP: low frequency of plate clearing behaviors; MA: moderate frequency of appropriate amount of food choice behaviors; OR, odds ratio; CI, confidence index. a Stepwise method. b Cells with a hyphen indicate that they were not entered into the regression equation by the Stepwise method. c Significant differences by Bonferoni-Holm correction are highlighted bold (Holm, 1979). Discussion In this study, four clustering patterns concerning ACBs and PCBs and their independent predictors were identified: LA*HP, MA*HP, MA*LP, and HA*LP. The LA*HP cluster that associates being younger, male, and less frequently feeling the need for ACBs had the highest proportion of obese individuals among the four clusters. Psychological factors, such as interest in health, attitude toward avoiding food waste, and gratitude for food, were described as independent predictors of several clusters. Participants in the HA*LP group were more likely to be female, have a BMI of less than 18.5, and have a higher level of health concern and attitude toward avoiding food waste and gratitude for food. This result is consistent with the results of a previous study that reported the characteristics of those who order an appropriate amount of food, such as being female and having a high subjective health status 13 . Although this cluster was expected to be a sustainable and healthy pattern that did not cause a trade–off between obesity and food waste due to having the highest frequency of ACBs of the four clusters, a significantly higher proportion had a BMI below 18.5, which is an unhealthy outcome. In Japan, thinness is serious problem, often caused by undernutrition, and raises future health risks; moreover, obesity and the prevalence of thinness is particularly high among young females 20,21 . Some of the participants in the HA*LP group may have had incorrect perceptions of their own appropriate portion size to maintain their health. In a previous study, female participants who had a low BMI and higher scores for restrained eating tended to estimate lower portion sizes 22 . The results suggest that to achieve both weight control and food waste reduction, it is necessary to promote education to correctly recognize the appropriate amount of food for oneself, as well as to promote ACBs and PCBs from the perspective of improving individual and global health. It is reasonable that the LA*HP cluster, which had a lower frequency of ACBs and a higher frequency of PCBs, had the highest percentage of obese persons among the four clusters. This result indicates the need for nutritional education to increase and encourage the use of ACBs. Being younger, male, and feeling the need for ACBs less frequently were independently associated with the LA*HP cluster. It is reasonable to assume that the frequency of ACBs is naturally low if the frequency of feeling the need for ACBs is also low. However, in a previous study describing the serving sizes of fixed meal offerings in the restaurant industry, many of the target meals exceeded the Japanese standard for the appropriate amount of food in a meal for an adult (450–650 or 650–850 kcal by Smart meal; Ministry of Health, 2019; Saiki et al., 2019). Psychological factors such as interest in health, attitude toward avoiding food waste, and gratitude for food were independent predictors of several clusters in the present study. The results of this study support the findings of many previous studies that psychological factors are involved in healthy dietary and food waste avoidance behaviors 16,25–27 . Although a number of previous studies have evaluated the associations of interest in health with health behaviors and food waste concerns with food waste avoidance behaviors, no studies have examined these factors simultaneously. The finding that psychological factors concerning both higher health and food waste concerns were independently associated with the HA*LP cluster indicates that parallel education on health and food waste avoidance promotion may be necessary to implement sustainable and healthy dietary behaviors without making a tradeoff between poor weight control and increased food waste. Several limitations of this study were reported in the previous study. 12 Additionally, several items used in this study, such as ACBs and PCBs, were not validated. The PCB scale developed in a previous study was not used in the present study 6 . This is because culturally, the Japanese have a strong norm warning people to “not leave food uneaten” 7,10 . The authors’ previous research with Japanese participants found response bias and ceiling effects in many of the items regarding “eating without leaving food” 7,12 , making it difficult to accurately measure PCBs with the questionnaire items developed for Westerners. However, both the ACB and PCB items used in this study showed significant correlations with the validated questionnaire items, and test-retest reliability was confirmed in the questionnaire setting. Despite these limitations, the present study is the first to examine the clustering patterns of ACBs and PCBs, considering their dietary behavioral scenarios and characteristics. Four clustering patterns concerning ACBs and PCBs and their independent predictors were identified. This study contributes to identifying appropriate consumer behaviors to maintain physical and planetary health and develop an implementation strategy for ACBs and PCBs. Further research is needed to determine the longitudinal impact of these patterns on health and environmental outcomes such as BMI and the amount of individual food waste. Declarations Acknowledgments We thank the study participants for their contribution, Editage for English language editing, and ASMARQ Co. Ltd. for data collection. Author Contributions Yui Kawasaki: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review & Editing, Project administration, Funding acquisition; Sayaka Nagao-Sato: Methodology, Writing - Review & Editing; Misa Shimpo: Methodology, Writing - Review & Editing; Rie Akamatsu: Conceptualization, Writing - Review & Editing; Yoko Fujiwara: Writing - Review & Editing, Funding acquisition, Supervision Ethical Approval Approval for this research was granted by Ochanomizu University’s Research Ethics Board (no. 2022-166). Funding This study was supported by the Institute for SDGs Promotion, Ochanomizu University [no grant number], grants-in-aid from the Nakatani Suzuyo Memorial Fund for Nutrition and Dietetics, Tokyo, Japan [no grant number], and the Ochanomizu University Nagase Research Scholarship [no grant number]. The sponsor played no role in the study design; data collection, analysis, or interpretation; writing of the report; or decision to submit the article for publication. Declarations of Interest The authors have no conflicts of interest to declare. Data availability Data will be made available on reasonable request. References United Nations Environment Programme, Forbes H, Quested T, O’Connor C. Food Waste Index Report 2021. Nairobi, 2021 https://wedocs.unep.org/bitstream/handle/20.500.11822/35280/FoodWaste.pdf (accessed 20 September 2023). Xue L, Liu G, Parfitt J, Liu X, Van Herpen E, Stenmarck Å et al. Missing Food, Missing Data? A Critical Review of Global Food Losses and Food Waste Data. Environ Sci Technol 2017; 51: 6618–6633. Sheen F, Hardman CA, Robinson E. Plate-clearing tendencies and portion size are independently associated with main meal food intake in women: A laboratory study. Appetite 2018; 127: 223–229. Sheen F, Hardman CA, Robinson E. Food waste concerns, eating behaviour and body weight. Appetite 2020; 151. doi: 10.1016/j.appet.2020.104692 . Robinson E, Hardman CA. Empty plates and larger waists: A cross-sectional study of factors associated with plate clearing habits and body weight. Eur J Clin Nutr 2016; 70: 750–752. Robinson E, Aveyard P, Jebb SA. Is Plate Clearing a Risk Factor for Obesity? A Cross-Sectional Study of Self-Reported Data in US Adults. Obesity (Silver Spring) 2015; 23: 301. Kawasaki Y, Akamatsu R, Warschburger P. The relationship between traditional and common Japanese childhood education and adulthood towards avoiding food waste behaviors. Waste Manag 2022; 145: 1–9. Higgins KA, Hudson JL, Hayes AMR, Braun E, Cheon E, Couture SC et al. Systematic Review and Meta-Analysis on the Effect of Portion Size and Ingestive Frequency on Energy Intake and Body Weight among Adults in Randomized Controlled Feeding Trials. Adv Nutr 2022; 13: 248–268. Robinson E, McFarland-Lesser I, Patel Z, Jones A. Downsizing food: a systematic review and meta-analysis examining the effect of reducing served food portion sizes on daily energy intake and body weight. Br J Nutr 2023; 129: 888–903. Ambros. Partaking of Life: Buddhism, Meat-Eating, and Sacrificial Discourses of Gratitude in Contemporary Japan. Religions 2019; 10: 279. Hofstetter H, Dusseldorp E, van Empelen P, Paulussen TWGM. A primer on the use of cluster analysis or factor analysis to assess co-occurrence of risk behaviors. Prev Med (Baltim) 2014; 67: 141–146. Kawasaki Y, Nagao-Sato S, Shimpo M, Akamatsu R, Fujiwara Y. Development and validation of the gratitude for food scale for Japanese adults. unpublished article . Nishida I, Akamatsu R, Tonsho N. Characteristics of People Who Order the Appropriate Amount of Food at Restaurants. Japanese J Nutr Diet 2023; 81: 68–74. [Japanese] Kawasaki Y, Nagao-Sato S, Shimpo M, Fujisaki K, Yoshii E, Boehnke J et al. Understanding sustainable dietary behaviors in Japanese and German adults: A qualitative analysis and cross-cultural comparison. unpublished article . Ozawa C, Ishikawa H, Kato M, Fukuda Y. Development of the Interest in Health Scale to understand the “population indifferent to health”. Japanese J Heal Educ Promot 2021; 29: 266–277. [Japanese] Stancu V, Haugaard P, Lähteenmäki L. Determinants of consumer food waste behaviour: Two routes to food waste. Appetite 2016; 96: 7–17. Murase N, Katsumura T, Ueda C, Inoue S ST. Validity and reliability of Japanese version of International Physical Activity Questionnaire. J Heal Welf Stat 2002; 49: 1–9. [Japanese] Holm S. A Simple Sequentially Rejective Multiple Test Procedure. Scand J Stat 1979; 6: 65–70. Vergouwe Y, Steyerberg EW, Eijkemans MJC, Habbema JDF. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005; 58: 475–483. Ministry of Health L and W. National Health and Nutrition Survey in 2015. 2015 https://www.mhlw.go.jp/toukei/chousahyo/dl/h27_tyousahyou_seikatu.pdf . Tsugane S, Sasaki S, Tsubono Y. Under- and overweight impact on mortality among middle-aged Japanese men and women: A 10-y follow-up of JPHC Study cohort i. Int J Obes 2002; 26: 529–537. Duszka K, Hechenberger M, Dolak I, Kobiljak D, König J. Gender, Age, Hunger, and Body Mass Index as Factors Influencing Portion Size Estimation and Ideal Portion Sizes. Front Psychol 2022; 13. doi: 10.3389/FPSYG.2022.873835 . Ministry of Health Labor and Welfare. What is smart meal. 2019. https://smartmeal.jp/smartmealkijun.html . Saiki M, Shimpo M, Akamatsu R, Fujisaki K. Do Fixed Meals Meet the Criterion of the Smart Meal? A Case Study of Restaurants in Tokyo, Kanagawa, and Saitama Prefectures. Japanese J Nutr Diet 2019; 77: 193–200. [Japanese] Stangherlin I do C, de Barcellos MD. Drivers and barriers to food waste reduction. Br Food J 2018; 120: 2364–2387. Kirks BA, Wolff HK. A comparison of methods for plate waste determinations. J Am Diet Assoc 1985; 85: 328–31. Stok FM, Hoffmann S, Volkert D, Boeing H, Ensenauer R, Stelmach-Mardas M et al. The DONE framework: Creation, evaluation, and updating of an interdisciplinary, dynamic framework 2.0 of determinants of nutrition and eating. PLoS One 2017; 12: e0171077. Additional Declarations There is NO conflict of interest to disclose. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3371761","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":269412542,"identity":"7ffa5723-40d3-4812-83ca-f0a4c533e300","order_by":0,"name":"Yui Kawasaki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACCQiVwAMiD3wAsiACPERqOTiDFC1gkpkHrgUPkGw/Y/jxR02aDINEjuFhmwq7PP4G5ocfGGTu4NQizZNjLM1zLIcHqMXgcM6Z5GKJA2zGEgw8z3BqkWPI3SDNwFYB0ZLbdiCx4QCDGdAvh3Fr4X+7+eePf1AtlkAt8w+wf8OrRVoid5sEbxvUYYxALRsO8OC3RXLG+2/WvH1pPGw8zwoO9pxJTtx4mKdYIgGPXyTOpyXf/PEt2Z6fPXnzhx8Vdonzjrdv/PCxB3eIwQGbQAKUxQzEiT0HCGth4EdR9IMYLaNgFIyCUTBCAAC8IlM1GkibNQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3641-4534","institution":"Ochanomizu University","correspondingAuthor":true,"prefix":"","firstName":"Yui","middleName":"","lastName":"Kawasaki","suffix":""},{"id":269412543,"identity":"c503c273-4bfb-409d-8623-7a34046e6ba3","order_by":1,"name":"Sayaka Nagao-Sato","email":"","orcid":"https://orcid.org/0000-0001-6721-0422","institution":"Ochanomizu University","correspondingAuthor":false,"prefix":"","firstName":"Sayaka","middleName":"","lastName":"Nagao-Sato","suffix":""},{"id":269412544,"identity":"48b15289-cc82-4da3-920a-cf12c5c10557","order_by":2,"name":"Misa Shimpo","email":"","orcid":"","institution":"The University of Nagano","correspondingAuthor":false,"prefix":"","firstName":"Misa","middleName":"","lastName":"Shimpo","suffix":""},{"id":269412545,"identity":"39536312-46fa-4db9-9b11-b6aa070fedda","order_by":3,"name":"Rie Akamatsu","email":"","orcid":"","institution":"Ochanomizu University","correspondingAuthor":false,"prefix":"","firstName":"Rie","middleName":"","lastName":"Akamatsu","suffix":""},{"id":269412546,"identity":"b8ae25bf-3b73-4db3-bfe5-e112ff77a2d3","order_by":4,"name":"Yoko Fujiwara","email":"","orcid":"","institution":"Ochanomizu University","correspondingAuthor":false,"prefix":"","firstName":"Yoko","middleName":"","lastName":"Fujiwara","suffix":""}],"badges":[],"createdAt":"2023-09-20 07:31:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3371761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3371761/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50384132,"identity":"923fb679-ad6a-47a9-889a-4dce3a959300","added_by":"auto","created_at":"2024-01-30 17:28:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11086,"visible":true,"origin":"","legend":"\u003cp\u003eMean frequencies of appropriate amount of food choice and plate clearing behaviors by each cluster (n=1,707)\u003c/p\u003e\n\u003cp\u003eAE: appropriate amount of food choice behavior when eating out; AH: appropriate amount of food choice behavior at home; HA: high frequency of appropriate amount of food choice behaviors; HP: high frequency of plate clearing behaviors; LA: low frequency of appropriate amount of food choice behaviors; LP: low frequency of plate clearing behaviors; MA: moderate frequency of appropriate amount of food choice behaviors; PE: plate clearing behavior when eating out; PH: plate clearing behavior at home.\u003c/p\u003e\n\u003cp\u003eAE1-5, AH1-3: 1: Not at all – 6: always\u003c/p\u003e\n\u003cp\u003ePE1, PH1-3: 1: I do not eat (leave/take home) more than I can eat without difficulty on the spot, regardless of how much the menu offers - 6: I eat more than twice as much food as I can eat without difficulty. (Reversed scores from the questionnaire; Table 1)\u003c/p\u003e","description":"","filename":"FigureClusterEJCN.png","url":"https://assets-eu.researchsquare.com/files/rs-3371761/v1/8d6979aa4e9594473435a121.png"},{"id":51538118,"identity":"fdf04fa5-4dea-40dd-996f-9d44298736f9","added_by":"auto","created_at":"2024-02-23 10:24:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":622572,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3371761/v1/eb1fc985-e2ba-4a0f-abc5-ab40542e6da5.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Are weight control and food waste a trade-off?: A clustering of appropriate amount of food choice and plate-clearing behaviors among Japanese adult consumers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFood waste increases environmental impacts and adversely affects global environment and health, causing greenhouse gas emissions, burdening waste management systems, and increasing hunger and malnutrition due to population growth and food distribution imbalances \u003csup\u003e1,2\u003c/sup\u003e. To reduce food waste, it is necessary to reduce consumer leftovers. However, researchers, mainly in Western countries, have identified that plate-clearing behavior (PCB), in which individuals force themselves to eat more food than is appropriate for them regarding choice of excessive portion sizes, causes weight gain \u003csup\u003e3\u0026ndash;6\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFood-choice behaviors have been overlooked in previous studies examining the relationship between PCBs and body weight \u003csup\u003e3\u0026ndash;7\u003c/sup\u003e. If the appropriate amount of food choice behaviors (ACBs) is implemented, individuals\u0026rsquo; body weight may not increase without leaving leftovers; however, if more than the appropriate amount of food is selected, there is a trade-off between food waste and weight gain due to PCB. Numerous studies have shown that the selection of an appropriate portion size can prevent weight gain and/or maintain individuals\u0026rsquo; weights \u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, the effect of a combination of PCBs and ACBs on body weight has not yet been examined. Although previous studies have reported that people with stronger food waste concerns are more likely to implement PCBs \u003csup\u003e4\u003c/sup\u003e, little has been done to examine health awareness, which is expected to affect PCBs and ACBs, as well as food waste concerns. Lifestyle-related factors, such as the frequency of eating out and cooking, may also influence behavioral patterns, as they relate to the frequency of ACB and PCB implementation. In addition, there are various situations in which ACBs and PCBs are present, such as at home (home cooking and/or eating prepared foods) and at restaurants. Although it is assumed that the frequency of the appropriate amount of food choice varies slightly depending on the situation, little research has been conducted on these behaviors in these scenarios. Moreover, Japanese people have unique norms regarding food waste avoidance behaviors, such as eating what you are served without leaving any leftovers, following Buddhism and Shintoism \u003csup\u003e7,10\u003c/sup\u003e. These norms are based on Japanese \u0026ldquo;Gratitude for Food\u0026rdquo; \u003csup\u003e7,10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExploratory data-driven methods, such as cluster analysis, can be used to gain insight into behavioral patterns \u003csup\u003e11\u003c/sup\u003e. By identifying the patterns of ACBs and PCBs in various dietary behavioral scenarios and describing the demographic, psychological, and lifestyle characteristics of those who follow each pattern, it is possible to make policy recommendations to promote food waste reduction while aiming to maintain individuals\u0026rsquo; appropriate body weights. Therefore, this study aims to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) identify patterns of ACBs and PCBs in various meal situations and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) describe the demographic, anthropometric, psychological, and lifestyle-related characteristics of those who follow each pattern.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study was part of a larger longitudinal online survey that aimed to examine the association between ACBs, PCBs, and weight control in 2023. The detailed data collection process is reported in a previous study.\u003csup\u003e12\u003c/sup\u003e Baseline data of 1,800 men and women aged 18\u0026ndash;59 years living in Japan were used in the present study. In addition, to examine the test-retest reliability and criterion validity of the original items in this study, we used data from 1,380 individuals who responded to a follow-up survey conducted one week after the baseline survey. \u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e This study was performed in accordance with the Declaration of Helsinki and approved by the Research Ethics Board (no. 2022\u0026thinsp;\u0026minus;\u0026thinsp;166). All participants were informed of the study\u0026rsquo;s aim and voluntarily provided their consent online.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVariables\u003c/h2\u003e \u003cp\u003eTo measure ACBs, eight questions were developed. Each item was divided into eating scenes, such as eating out and at home. With respect to ACBs, five items concerning the eating-out scenario (AE-1\u0026ndash;5; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were developed based on the present study to describe the characteristics of people who order the appropriate amount of food at restaurants \u003csup\u003e13\u003c/sup\u003e. Three items concerning the home scenario (AH-1\u0026ndash;3) were developed considering the food choice scenario listed in a previous study. \u003csup\u003e12\u003c/sup\u003e A 6-point Likert scale was used (1\u0026thinsp;=\u0026thinsp;not at all, 6\u0026thinsp;=\u0026thinsp;always). Criterion validity and repeat reliability of these items were described at Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItems of questions developed for the survey.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eItems of questions by each theme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAppropriate amount of food choice behavior\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003e1: Not at all \u0026ndash; 6: always\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;Eating out\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE-1. I check the serving size of food before entering a restaurant or choose a restaurant where I already know the serving size of food or can adjust the serving size of food.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE-2. When I select a menu, I check weather I can eat the food I want to order without difficulty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE-3. When I want to order food with a larger serving size than I can eat without difficulty, I choose a smaller size if the menu has a range of sizes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE-4. When I want to order food with a larger serving size than I can eat without difficulty, I order food with smaller serving size if it is available in smaller sizes and if I can get a discount by reducing the size.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAE-5. When I want to order food with a larger serving size than I can eat without difficulty, I ask the waiter for a smaller portion even if the menu does not have a size range.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;Home\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH-1. When I feel that the amount of food I prepare is more than I can eat without difficulty, I reduce it to the amount I can eat.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH-2. When I feel that the amount of food (e.g. lunch boxes, prepared foods, or instant foods) I purchased is more than I can eat without difficulty, I reduce the amount until I can finish the food.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAH-3. When the amount of food prepared by family members is more than I can eat without difficulty, I reduce the amount until I can finish the food.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlate clearing behavior\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;Eating out\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1: I eat more than twice as much food as I can eat without difficulty \u0026ndash; 6: I do not eat (leave/take home) more than I can eat without difficulty on the spot, regardless of serving size of the food\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePE-1. When eating out, if you feel that the amount of food is too much (there is more food in front of you than you can eat without difficulty), do you eat up the meal, even if you have to push yourself?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026lt;Home\u0026gt;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1: I do not eat (leave/take home) more than I can eat without difficulty on the spot, regardless of how much the menu offers \u0026ndash; 6: I eat more than twice as much food as I can eat without difficulty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH-1. When I cook and prepare (serve) a larger amount of food than I can eat without difficulty, \u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH-2. When I feel that the amount of food I purchased (e.g. lunchbox and prepared food) or instant food I cooked is more than I can eat without difficulty, \u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePH-3. When the amount of food prepared by my family members was more than I could reasonably eat, \u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of the need to choose the appropriate amount of food\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1: Not at all \u0026ndash; 6: always\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. When I eat out, I would like to order food that has larger portion sizes than I can eat without difficulty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2. I cook larger amounts of food than I can eat without difficulty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3. I buy larger amounts of food for a meal (e.g. lunch boxes, prepared foods, or cooked instant foods) than I can eat without difficulty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4. My family member(s) prepare meals that are larger than I can eat without difficulty.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e Criterion validity of these items was confirmed by the correlation coefficients of each item with \u0026ldquo;eating selected foods without leaving any leftovers\u0026rdquo;, which is one of the sustainable and healthy dietary behaviors developed in a previous study (ρ=-0.125\u0026ndash; -0.201, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003csup\u003e7\u003c/sup\u003e. Repeat reliability was also tested using the same items asked in a one-week follow-up survey (Spearman\u0026rsquo;s correlation coefficient: ρ\u0026thinsp;=\u0026thinsp;0.452\u0026ndash;0.664, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for each item).\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e The criterion validity of these items was confirmed by the correlation coefficients of each item with hunger and satiety cues (three items, 4-point Likert scale; Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.657), a subscale of the expanded mindful eating scale developed in a previous study (ρ=-0.237\u0026ndash; -0.306, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003csup\u003e15\u003c/sup\u003e Test-retest reliability was also confirmed using the same items (PE-1 and PH-1\u0026ndash;3) in the one-week follow-up survey (Spearman\u0026rsquo;s correlation coefficients: ρ\u0026thinsp;=\u0026thinsp;0.675\u0026ndash;0.739, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for each item).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eEight original question items were developed for PCBs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants were asked whether they eat all foods that they cannot eat with difficulty in four food choice scenarios: eating out (PE-1), home cooking (PH-1), eating prepared food (PH-2), and eating food prepared by their family member (PH-3).\u003csup\u003e14\u003c/sup\u003e Criterion validity and test-retest reliability was also described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Additionally, to consider interpersonal variability in the frequency of the need to choose an appropriate amount of food, four items were developed based on the aforementioned four scenarios, such as eating out, home cooking, eating prepared food, and eating food prepared by family members (1\u0026thinsp;=\u0026thinsp;not at all, 6\u0026thinsp;=\u0026thinsp;always; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eParticipants\u0026rsquo; demographic, anthropometric, psychological, and lifestyle-related data, as well as ACBs and PCBs, were used in the present study. Demographic and anthropometric variables were described in the previous study\u003csup\u003e12\u003c/sup\u003e. Interest in health, attitudes toward avoiding food waste, and gratitude for food were included in the psychological data. The Interest in Health Scale, which was developed for Japanese adults and comprises a 12-item scale with three factors, was used \u003csup\u003e15\u003c/sup\u003e. Respondents answered using a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 4\u0026thinsp;=\u0026thinsp;strongly agree). The Cronbach\u0026rsquo;s alpha for the 12-item scale was 0.881 in this study. With respect to attitudes toward food waste, three items used in previous study were selected to measure participants\u0026rsquo; attitudes \u003csup\u003e16\u003c/sup\u003e. A 6-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 6\u0026thinsp;=\u0026thinsp;strongly agree) was used; the Cronbach\u0026rsquo;s alpha was 0.836 in this study. In addition, gratitude for food was measured using the Gratitude for Food Scale for Adults (GFS-A)\u003csup\u003e12\u003c/sup\u003e. This scale comprises one factor and five items scored on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 4\u0026thinsp;=\u0026thinsp;strongly agree). Cronbach\u0026rsquo;s alpha for the present data was 0.919.\u003c/p\u003e \u003cp\u003eLifestyle-related variables included eating habits, the need for an adequate amount of food choice, and physical activity. With respect to eating habits, the participants were asked about their frequency of eating out, preparing meals, eating together, and cooking, while considering items to measure ACBs and PCBs. A 6-point Likert scale was used (1\u0026thinsp;=\u0026thinsp;once in a month or less, 6\u0026thinsp;=\u0026thinsp;twice a day or more), except for cooking frequency (1\u0026thinsp;=\u0026thinsp;not at all, 8\u0026thinsp;=\u0026thinsp;three times a day or more). Moreover, the short form of the International Physical Activity Questionnaire (IPAQ) was adopted. This scale examines the duration and frequency of three types of activities: walking, moderate-intensity activity, and vigorous-intensity activity \u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eSince it is difficult for participants who do not recognize the needs of ACBs to answer the questions concerning ACBs and PCBs, 93 participants who answered 1=\u0026ldquo;not at all\u0026rdquo; to all four questions concerning the frequency of needing to choose the appropriate amount of food were excluded from baseline data (n\u0026thinsp;=\u0026thinsp;1,800). Therefore, 1,707 (baseline) and 1,338 (1-week follow-up) participants were included in the analysis.\u003c/p\u003e \u003cp\u003eThe BMI of each participant was calculated based on their height and weight. The total scores for each psychological variable (interest in health, attitude toward avoiding food waste, and gratitude for food) and the frequency of the need to choose the appropriate amount of food were calculated. Total scores of interests in health (score range is 12\u0026ndash;48), attitude toward avoiding food waste (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and the GFS-A (\u003cspan additionalcitationids=\"CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), were calculated. The total metabolic equivalents in minutes per week were calculated based on the guidelines of the IPAQ and according to participants' responses.\u003c/p\u003e \u003cp\u003eThe Shapiro\u0026ndash;Wilk test indicated that the samples were not normally distributed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); therefore, non-parametric analysis was performed. Data are presented as medians and 25th and 75th percentiles. A cluster analysis was performed to identify patterns of ACBs and PCBs implementation, and 12 items related to ACBs and PCBs were incorporated as indicators. A two-step cluster analysis was applied to establish cluster groups. In the first step, small clusters were created based on distance, as in K-means, and in the second step, the small clusters were combined in a stepwise manner, as in hierarchical cluster analysis.\u003c/p\u003e \u003cp\u003eTo describe the characteristics of the identified cluster groups of participants, the mean frequencies of each item in relation to ACBs and PCBs in each cluster were described. Kruskal\u0026ndash;Wallis and chi-square tests were used to describe participants\u0026rsquo; backgrounds, such as their demographic, anthropometric, psychological, and lifestyle-related factors, by each cluster.\u003c/p\u003e \u003cp\u003eMultiple logistic regression analysis with a stepwise method was used to calculate the odds ratios (ORs) and 95% confidence indices (CIs) assigned to clusters based on the background variables. Collinearity was tested using the Variance Inflation Factor (VIF). As the VIF values for all covariates were small (\u0026lt;\u0026thinsp;5), no evidence of multicollinearity was found. Bonferroni-Holm correction was applied to adjust for multiple tests \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS for Windows (version 29; SPSS Inc.). The tests were two-tailed, and the results were considered statistically significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe validity of the sample size for factor analysis in this study has been confirmed by previous studies. Vergouwe et al. have suggested a minimum of 100 events and 100 nonevents as external validation samples for a logistic regression analysis with adequate power\u003csup\u003e19\u003c/sup\u003e. Further, a power analysis calculation indicated that for an effect size of 0.5 (Kruskal-Wallis test) and 0.3 (chi-square test) and power of 0.8, four groups with at least 128 and 210 participants for Kruskal-Wallis and chi-square tests, respectively, would be required.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u0026rsquo; characteristics\u003c/h2\u003e \u003cp\u003eThe study sample comprised 842 males (49.3%) and 865 females (50.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Median (25th, 75th percentiles) age and BMI were 40 (30, 50) years and 21.2 (19.2, 23.7), respectively. More than half of the participants lived with their family members from other generations, such as parents or children (55.7%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences of characteristics of the study participants by each cluster (n\u0026thinsp;=\u0026thinsp;1,707)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLA*HP (n\u0026thinsp;=\u0026thinsp;319)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMA*HP (n\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eMA*LP (n\u0026thinsp;=\u0026thinsp;593)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eHA*LP (n\u0026thinsp;=\u0026thinsp;395)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en/Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%/ 25th, 75th percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003en/Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%/ 25th, 75th percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003en/Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%/ 25th, 75th percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003en/Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e%/ 25th, 75th percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003en/Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e%/ 25th, 75th percentile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic and anthropometric variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30, 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 \u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29, 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38 \u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29, 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e42 \u003csup\u003ehj\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e32, 52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e41 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e31, 51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003en\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.2, 23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.2 \u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.7, 25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21.5 \u003csup\u003ejk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.9, 24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21 \u003csup\u003ehjl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e19.1, 23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e20.4 \u003csup\u003eikl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e18.5, 22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003en\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e70.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e67.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e63.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving status \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.040 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income [JPY (USD)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2,000,000 (15,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.961 \u003csup\u003en\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2,000,000\u0026ndash;4,000,000 (15,000\u0026ndash;30,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4,000,000\u0026ndash;6,000,000 (30,000\u0026ndash;45,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6,000,000\u0026ndash;8,000,000 (45,000\u0026ndash;60,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8,000,000\u0026ndash;10,000,000 (60,000\u0026ndash;75,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10,000,000 (75,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary or/ junior high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.082 \u003csup\u003en\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior college or vocational school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e22.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege, university or graduate school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e54.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychological variables (range)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterest in health (12\u0026ndash;48) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29, 36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29 \u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25, 34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33 \u003csup\u003eik\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29, 36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e33 \u003csup\u003ehl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e29, 36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e35 \u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e31, 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude toward avoiding food waste (3\u0026ndash;18) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14 \u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e15 \u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e13, 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGratitude for food (5\u0026ndash;20) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 \u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15 \u003csup\u003eik\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14 \u003csup\u003ehl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e15 \u003csup\u003ejkl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e13, 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle-related variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of eating out \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e2 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of home-meal replacement/prepared meal \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.137 \u003csup\u003en\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of eating together \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 \u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1, 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3, 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e5 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e3, 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of cooking \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 \u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2, 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 \u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5 \u003csup\u003ehk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2, 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6 \u003csup\u003eijk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e4, 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeeds for adequate amount of food choice (4\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 \u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9, 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 \u003csup\u003ehk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13 \u003csup\u003eikl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13 \u003csup\u003ejl\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e10, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEating out \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 \u003csup\u003ehi\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2, 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 \u003csup\u003ejk\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3 \u003csup\u003ehj\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3 \u003csup\u003eik\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2, 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 \u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e4 \u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrepared food \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 \u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3 \u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrepared meal by family member \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 \u003csup\u003ehij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1, 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e3 \u003csup\u003ej\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e2, 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity level (METs/week) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0, 2079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e742 \u003csup\u003eh\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0, 1950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e972 \u003csup\u003ei\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e107, 2555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e594 \u003csup\u003eij\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0, 1823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e990 \u003csup\u003ehj\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e132, 2385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 \u003csup\u003em\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e \u003cp\u003eLA*LP: Low appropriate amount of food choice behavior* low plate clearing behaviors; MA*HP: Moderate appropriate amount of food choice behavior* high plate clearing behaviors; MA*LP: moderate appropriate amount of food choice behavior* low plate clearing behaviors; HA*HP: high appropriate amount of food choice behavior* high plate clearing behaviors.\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e 0: living alone; 3: three-generation family.\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Interest in Health Scale (Ozawa, et al. 2021). Higher score represents higher interest in health. \u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Items used in previous study (Stancu, et al. 2016). Higher score represents higher attitude toward avoiding food waste.\u003c/p\u003e \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Gratitude for food scale for adults (GFS-A; Kawasaki, et al. under review). Higher score represents higher gratitude for food.\u003c/p\u003e \u003cp\u003e\u003csup\u003ee\u003c/sup\u003e 1: once in a month or less\u0026ndash;6: twice in a day or more.\u003c/p\u003e \u003cp\u003e\u003csup\u003ef\u003c/sup\u003e 1: not at all\u0026ndash;8: three times in a day or more.\u003c/p\u003e \u003cp\u003e\u003csup\u003eg\u003c/sup\u003e 1: Not at all \u0026minus;\u0026thinsp;6: always.\u003c/p\u003e \u003cp\u003eLetters (h-l) represent significant statistical differences between each group by using the Bonferroni\u0026rsquo;s multiple comparison test (adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). \u003c/p\u003e \u003cp\u003e\u003csup\u003em\u003c/sup\u003eKruskal-Wallis test.\u003c/p\u003e \u003cp\u003e\u003csup\u003en\u003c/sup\u003e Chi-square test.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCluster description\u003c/h2\u003e \u003cp\u003eThe cluster analysis identified four groups. Figure\u0026nbsp;1 shows the mean frequencies of ACBs and PCBs for each cluster. These behavioral patterns were referred to as follows: low frequency of ACBs and high PCBs (LA*HP; n\u0026thinsp;=\u0026thinsp;319, 18.7%), moderate frequency of ACBs and high PCBs (MA*HP; n\u0026thinsp;=\u0026thinsp;400, 23.4%), moderate frequency of ACBs and low PCBs (MA*LP; n\u0026thinsp;=\u0026thinsp;593, 34.7%), and high frequency of ACBs and low PCBs (HA*LP; n\u0026thinsp;=\u0026thinsp;395, 23.1%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDifferences of characteristics of the study participants by each cluster\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the differences in the characteristics of the study participants for each cluster. Almost all variables differed for each cluster, except for household income, education, and frequency of prepared meals (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.961, 0.082, and 0.137, respectively). More than 60% of the participants were male for LA*HP (68.0%) and MA*HP (65.5%), while most were female for MA*LP (62.1%) and HA*LP (65.1%). The proportion of obese and overweight (BMI\u0026thinsp;\u0026gt;\u0026thinsp;25.0) participants was largest in the LA*HP cluster (24.5%) and lowest in the HA*LP cluster (11.4%), whereas the proportion of underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5) participants was largest in the HA*LP cluster (25.3%) and lowest in the LA*HP cluster (13.5%) in all four clusters. All three psychological variables, such as interest in health, attitude toward avoiding food waste, and gratitude for food, differed by cluster, and the total scores were highest for HA*LP and lowest for LA*HP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eOdds ratios of cluster allocation based on participants\u0026rsquo; demographic, anthropometric, psychological, and lifestyle-related variables\u003c/h2\u003e \u003cp\u003eThe ORs of cluster allocation based on the participants\u0026rsquo; demographic, anthropometric, psychological, and lifestyle-related variables are described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Being young, male, having a high household income, living with simple generations, and having low scores of interest in health and gratitude for food were associated with allocation for LA*HP cluster. Individuals were more likely to be in the MA*HP cluster if they were young, male, had high BMI (\u0026lt;\u0026thinsp;18.5), frequently ate out, and realized needs for adequate amount of food choice. Those belonging to the MA*LP cluster were more likely to be old, female, live with multiple generations, have low scores of attitude toward of food waste, and less frequently eat out and cook. Finally, individuals belonging to the HA*LP cluster were more likely to be female; have lower BMIs (\u0026lt;\u0026thinsp;18.5); have higher interest in health scores, attitude toward avoiding food waste, and gratitude for food; and realize the needs for adequate amount of food choice.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOdds ratios for cluster allocation based on participants\u0026rsquo; demographic, anthropometric, psychological, and lifestyle variables \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLA*HP (n\u0026thinsp;=\u0026thinsp;319)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eMA*HP (n\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eMA*LP (n\u0026thinsp;=\u0026thinsp;593)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eHA*LP (n\u0026thinsp;=\u0026thinsp;395)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%CI \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e95%CI \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e95%CI \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic and anthropometric variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.984\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.972\u0026ndash;0.996\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.985\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.975\u0026ndash;0.996\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.013\u0026ndash;1.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.424\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.321\u0026ndash;0.561\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.454\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.355\u0026ndash;0.581\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2.575\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e2.022\u0026ndash;3.279\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1.550\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.177\u0026ndash;2.041\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"2\" nameend=\"c4\" namest=\"c2\" rowspan=\"3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.547\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.371\u0026ndash;0.807\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" morerows=\"2\" nameend=\"c12\" namest=\"c10\" rowspan=\"3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1.735\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.273\u0026ndash;2.365\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.770\u0026ndash;1.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.546\u0026ndash;1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving status (0: living alone; 3: three-generation family)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.803\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.687\u0026ndash;0.938\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e1.189\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e1.048\u0026ndash;1.348\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income (JPY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.104\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.007\u0026ndash;1.211\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePsychological variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterest in health (12\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.912\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.889\u0026ndash;0.935\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.018\u0026ndash;1.066\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude toward avoiding food waste (3\u0026ndash;18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.905\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.871\u0026ndash;0.940\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1.133\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.077\u0026ndash;1.191\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGratitude for food (5\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.922\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.885\u0026ndash;0.961\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1.106\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.060\u0026ndash;1.154\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle-related variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of eating out (1: once in a month or less\u0026ndash;6: twice in a day or more)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.190\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.052\u0026ndash;1.346\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.876\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.777\u0026ndash;0.988\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of home-meal replacement/prepared meal (1: once in a month or less\u0026ndash;6: twice in a day or more)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of eating together (1: once in a month or less\u0026ndash;6: twice in a day or more)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency of cooking (1: not at all\u0026ndash;8: 3 times in a day or more)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.923\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.877\u0026ndash;0.972\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e1.009\u0026ndash;1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeeds for appropriate amount of food choice (4\u0026ndash;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.884\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.850\u0026ndash;0.919\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.063\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.031\u0026ndash;1.096\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e1.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e1.014\u0026ndash;1.080\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity level (METs/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"16\" nameend=\"c16\" namest=\"c1\"\u003e \u003cp\u003eHA: high frequency of appropriate amount of food choice behaviors; HP: high frequency of plate clearing behaviors; LA: low frequency of appropriate amount of food choice behaviors; LP: low frequency of plate clearing behaviors; MA: moderate frequency of appropriate amount of food choice behaviors; OR, odds ratio; CI, confidence index.\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Stepwise method.\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Cells with a hyphen indicate that they were not entered into the regression equation by the Stepwise method.\u003c/p\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Significant differences by Bonferoni-Holm correction are highlighted bold (Holm, 1979).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, four clustering patterns concerning ACBs and PCBs and their independent predictors were identified: LA*HP, MA*HP, MA*LP, and HA*LP. The LA*HP cluster that associates being younger, male, and less frequently feeling the need for ACBs had the highest proportion of obese individuals among the four clusters. Psychological factors, such as interest in health, attitude toward avoiding food waste, and gratitude for food, were described as independent predictors of several clusters.\u003c/p\u003e \u003cp\u003eParticipants in the HA*LP group were more likely to be female, have a BMI of less than 18.5, and have a higher level of health concern and attitude toward avoiding food waste and gratitude for food. This result is consistent with the results of a previous study that reported the characteristics of those who order an appropriate amount of food, such as being female and having a high subjective health status \u003csup\u003e13\u003c/sup\u003e. Although this cluster was expected to be a sustainable and healthy pattern that did not cause a trade\u0026ndash;off between obesity and food waste due to having the highest frequency of ACBs of the four clusters, a significantly higher proportion had a BMI below 18.5, which is an unhealthy outcome. In Japan, thinness is serious problem, often caused by undernutrition, and raises future health risks; moreover, obesity and the prevalence of thinness is particularly high among young females\u003csup\u003e20,21\u003c/sup\u003e. Some of the participants in the HA*LP group may have had incorrect perceptions of their own appropriate portion size to maintain their health. In a previous study, female participants who had a low BMI and higher scores for restrained eating tended to estimate lower portion sizes \u003csup\u003e22\u003c/sup\u003e. The results suggest that to achieve both weight control and food waste reduction, it is necessary to promote education to correctly recognize the appropriate amount of food for oneself, as well as to promote ACBs and PCBs from the perspective of improving individual and global health.\u003c/p\u003e \u003cp\u003eIt is reasonable that the LA*HP cluster, which had a lower frequency of ACBs and a higher frequency of PCBs, had the highest percentage of obese persons among the four clusters. This result indicates the need for nutritional education to increase and encourage the use of ACBs. Being younger, male, and feeling the need for ACBs less frequently were independently associated with the LA*HP cluster. It is reasonable to assume that the frequency of ACBs is naturally low if the frequency of feeling the need for ACBs is also low. However, in a previous study describing the serving sizes of fixed meal offerings in the restaurant industry, many of the target meals exceeded the Japanese standard for the appropriate amount of food in a meal for an adult (450\u0026ndash;650 or 650\u0026ndash;850 kcal by Smart meal; Ministry of Health, 2019; Saiki et al., 2019).\u003c/p\u003e \u003cp\u003ePsychological factors such as interest in health, attitude toward avoiding food waste, and gratitude for food were independent predictors of several clusters in the present study. The results of this study support the findings of many previous studies that psychological factors are involved in healthy dietary and food waste avoidance behaviors \u003csup\u003e16,25\u0026ndash;27\u003c/sup\u003e. Although a number of previous studies have evaluated the associations of interest in health with health behaviors and food waste concerns with food waste avoidance behaviors, no studies have examined these factors simultaneously. The finding that psychological factors concerning both higher health and food waste concerns were independently associated with the HA*LP cluster indicates that parallel education on health and food waste avoidance promotion may be necessary to implement sustainable and healthy dietary behaviors without making a tradeoff between poor weight control and increased food waste.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study were reported in the previous study.\u003csup\u003e12\u003c/sup\u003e Additionally, several items used in this study, such as ACBs and PCBs, were not validated. The PCB scale developed in a previous study was not used in the present study \u003csup\u003e6\u003c/sup\u003e. This is because culturally, the Japanese have a strong norm warning people to \u0026ldquo;not leave food uneaten\u0026rdquo; \u003csup\u003e7,10\u003c/sup\u003e. The authors\u0026rsquo; previous research with Japanese participants found response bias and ceiling effects in many of the items regarding \u0026ldquo;eating without leaving food\u0026rdquo;\u003csup\u003e7,12\u003c/sup\u003e, making it difficult to accurately measure PCBs with the questionnaire items developed for Westerners. However, both the ACB and PCB items used in this study showed significant correlations with the validated questionnaire items, and test-retest reliability was confirmed in the questionnaire setting.\u003c/p\u003e \u003cp\u003eDespite these limitations, the present study is the first to examine the clustering patterns of ACBs and PCBs, considering their dietary behavioral scenarios and characteristics. Four clustering patterns concerning ACBs and PCBs and their independent predictors were identified. This study contributes to identifying appropriate consumer behaviors to maintain physical and planetary health and develop an implementation strategy for ACBs and PCBs. Further research is needed to determine the longitudinal impact of these patterns on health and environmental outcomes such as BMI and the amount of individual food waste.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the study participants for their contribution, Editage for English language editing, and ASMARQ Co. Ltd. for data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYui Kawasaki:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Project administration, Funding acquisition; \u003cstrong\u003eSayaka Nagao-Sato:\u003c/strong\u003e Methodology, Writing - Review \u0026amp; Editing; \u003cstrong\u003eMisa Shimpo:\u003c/strong\u003e Methodology, Writing - Review \u0026amp; Editing;\u003cstrong\u003e\u0026nbsp;Rie Akamatsu:\u003c/strong\u003e Conceptualization, Writing - Review \u0026amp; Editing; \u003cstrong\u003eYoko Fujiwara:\u003c/strong\u003e Writing - Review \u0026amp; Editing, Funding acquisition, Supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for this research was granted by Ochanomizu University\u0026rsquo;s Research Ethics Board (no. 2022-166).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Institute for SDGs Promotion, Ochanomizu University [no grant number], grants-in-aid from the Nakatani Suzuyo Memorial Fund for Nutrition and Dietetics, Tokyo, Japan [no grant number], and the Ochanomizu University Nagase Research Scholarship [no grant number]. The sponsor played no role in the study design; data collection, analysis, or interpretation; writing of the report; or decision to submit the article for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations Environment Programme, Forbes H, Quested T, O\u0026rsquo;Connor C. Food Waste Index Report 2021. 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Obesity (Silver Spring) 2015; 23: 301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawasaki Y, Akamatsu R, Warschburger P. The relationship between traditional and common Japanese childhood education and adulthood towards avoiding food waste behaviors. Waste Manag 2022; 145: 1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiggins KA, Hudson JL, Hayes AMR, Braun E, Cheon E, Couture SC \u003cem\u003eet al.\u003c/em\u003e Systematic Review and Meta-Analysis on the Effect of Portion Size and Ingestive Frequency on Energy Intake and Body Weight among Adults in Randomized Controlled Feeding Trials. Adv Nutr 2022; 13: 248\u0026ndash;268.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson E, McFarland-Lesser I, Patel Z, Jones A. Downsizing food: a systematic review and meta-analysis examining the effect of reducing served food portion sizes on daily energy intake and body weight. Br J Nutr 2023; 129: 888\u0026ndash;903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmbros. Partaking of Life: Buddhism, Meat-Eating, and Sacrificial Discourses of Gratitude in Contemporary Japan. Religions 2019; 10: 279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHofstetter H, Dusseldorp E, van Empelen P, Paulussen TWGM. A primer on the use of cluster analysis or factor analysis to assess co-occurrence of risk behaviors. Prev Med (Baltim) 2014; 67: 141\u0026ndash;146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawasaki Y, Nagao-Sato S, Shimpo M, Akamatsu R, Fujiwara Y. Development and validation of the gratitude for food scale for Japanese adults. \u003cem\u003eunpublished article\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishida I, Akamatsu R, Tonsho N. Characteristics of People Who Order the Appropriate Amount of Food at Restaurants. Japanese J Nutr Diet 2023; 81: 68\u0026ndash;74. 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Validity and reliability of Japanese version of International Physical Activity Questionnaire. J Heal Welf Stat 2002; 49: 1\u0026ndash;9. [Japanese]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolm S. A Simple Sequentially Rejective Multiple Test Procedure. Scand J Stat 1979; 6: 65\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVergouwe Y, Steyerberg EW, Eijkemans MJC, Habbema JDF. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005; 58: 475\u0026ndash;483.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Health L and W. 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[Japanese]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStangherlin I do C, de Barcellos MD. Drivers and barriers to food waste reduction. Br Food J 2018; 120: 2364\u0026ndash;2387.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirks BA, Wolff HK. A comparison of methods for plate waste determinations. J Am Diet Assoc 1985; 85: 328\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStok FM, Hoffmann S, Volkert D, Boeing H, Ensenauer R, Stelmach-Mardas M \u003cem\u003eet al.\u003c/em\u003e The DONE framework: Creation, evaluation, and updating of an interdisciplinary, dynamic framework 2.0 of determinants of nutrition and eating. PLoS One 2017; 12: e0171077.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"food waste, weight control, plate-clearing behaviors, behavioral pattern, consumer, appropriate amount of food choice","lastPublishedDoi":"10.21203/rs.3.rs-3371761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3371761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground/Objectives\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePlate-clearing behavior (PCB), in which individuals eat more food than is appropriate for them regarding excessive portion size choices, is considered to cause weight gain. However, the appropriate amount of food choice behavior (ACB) to avoid the trade-off between weight gain and food waste has been overlooked in previous studies. This study aimed to identify patterns of ACB and PCB in various meal situations and describe the demographic, anthropometric, psychological, and lifestyle-related characteristics of those who follow each pattern.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubjects/Methods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn total, 1,707 Japanese participants responded to a web-based anonymous questionnaire in February 2023 and were included in this study. Cluster analysis was performed to identify patterns in the ACB and PCB. Multiple logistic regression analysis was used on clusters of participant characteristic variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe median age of the participants was 40 (25th and 75th percentile: 30, 50) years (female\u0026thinsp;=\u0026thinsp;865, 50.7%). Four clusters with independent predictors were identified: low ACB and high PCB, moderate ACB and high PCB, moderate ACB and low PCB, and high ACB and low PCB. The independent predictors of high ACB and low PCB were being female [1.550 (1.177\u0026ndash;2.041), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002]; having low BMI (\u0026lt;\u0026thinsp;18.5) [1.735 (1.273\u0026ndash;2.365), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; much interest in health [1.042 (1.018\u0026ndash;1.066), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], attitude toward avoiding food waste [1.133 (1.077\u0026ndash;1.191), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], gratitude for food [1.106 (1.060\u0026ndash;1.154), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001], and the need for an appropriate amount of food choice [1.046 (1.014\u0026ndash;1.080), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005].\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study identifies appropriate consumer behaviors to maintain health and develop a strategy for food-choice and PCBs.\u003c/p\u003e","manuscriptTitle":"Are weight control and food waste a trade-off?: A clustering of appropriate amount of food choice and plate-clearing behaviors among Japanese adult consumers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 17:28:06","doi":"10.21203/rs.3.rs-3371761/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88fc0634-cc1e-42f5-aff6-ce5642638c8e","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28389426,"name":"Health sciences/Health care/Weight management"},{"id":28389427,"name":"Health sciences/Health care/Public health"}],"tags":[],"updatedAt":"2024-04-13T23:55:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-30 17:28:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3371761","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3371761","identity":"rs-3371761","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}