The Relationship of Education Level, Lifestyle, and Personality to BMI and Obesity Differs Between Men and Women

Abstract

Obesity has become a major global health concern, but few studies have examined the determinants of body mass index (BMI, kg/m²) and overweight/obesity (BMI ≥ 25) specifically in women. This study investigated the roles of education, lifestyle, and personality using data from a questionnaire survey of 4276 Japanese adults (2215 women and 2061 men) aged 30–79 years. Multiple regression and logistic regression analyses were conducted to identify factors associated with BMI (continuous) and obesity (BMI ≥ 25) in women. The multiple regression results indicated that educational attainment, rest, diet, and conscientiousness were negatively associated with BMI, whereas extraversion and openness were positively associated with BMI. Logistic regression further showed that higher education, regular exercise, sufficient rest, and conscientiousness were associated with non-obesity (BMI < 25), while openness was associated with obesity (BMI ≥ 25). To our knowledge, this is the first study to identify determinants of BMI and obesity in women with a simultaneous focus on education, lifestyle, and personality traits.

1. Introduction

Obesity affects an estimated 650 million adults worldwide [1]. It is a major risk factor for numerous diseases, including hypertension, cardiovascular disease, Alzheimer’s disease, asthma, metabolic syndrome, fatty liver disease, gallbladder disease, osteoarthritis, obstructive sleep apnea, several cancers, hypercholesterolemia, and type 2 diabetes [2]. Musculoskeletal problems, such as joint and muscle pain, are also frequently linked to obesity. In Japan, the prevalence of overweight and obesity (BMI ≥ 25 kg/m²) among adults aged 20 years and older is 31.7% in men and 21.0% in women [3]. However, in most countries, obesity is more common in women than in men [4]. For example, in the United States, approximately two-thirds of women are overweight or obese [5].

Overweight and obesity impose significant burdens on both individuals and society. Reviews indicate that the incidence of cancers associated with excess weight is higher in women than in men, with 55% of weight-related cancers occurring in women compared to 24% in men [6]. These include endometrial, ovarian, and postmenopausal breast cancers [7]. Sex differences have also been reported in other obesity-related comorbidities, such as hormone-dependent malignancies, type 2 diabetes, non-alcoholic fatty liver disease, obesity hypoventilation syndrome, impaired lung function, psychiatric disorders, and reduced health-related quality of life, with women more frequently affected than men. Moreover, overweight women have been found to have nearly twice the mortality risk of overweight men [7].

Obesity in women is also closely linked to emotional and psychological difficulties [7]. Several systematic reviews have reported that obesity and depression co-occur more frequently in women than in men [8,9]. Earlier studies indicated that obesity carries a greater stigma for women than for men [10], and subsequent research has shown that women experience higher levels of weight-related prejudice and discrimination [11,12,13]. For instance, women with a higher BMI have fewer opportunities for dating and marriage compared to women with a lower BMI—a relationship not observed among men [11]. In addition, overweight women tend to earn lower incomes than women of normal weight, with this effect being more pronounced in women than in men [11].

The mechanisms underlying obesity have long been a subject of research interest, with previous studies examining its associations with educational level, lifestyle, and personality. However, relatively few investigations have considered these factors simultaneously, and even fewer have specifically focused on gender differences. Given that women face distinct and disproportionate disadvantages related to weight gain and obesity, it is particularly valuable to focus on this population. Accordingly, the present study aims to clarify the integrated relationships between education, lifestyle, and personality in women.

Therefore, this study analyzed data from a questionnaire survey of more than 4000 women and men across Japan to identify the determinants of BMI (as a continuous variable) and obesity (BMI ≥ 25) in women, with a particular focus on educational attainment, lifestyle, and personality. Specifically, we hypothesized that educational attainment, selected lifestyle factors (rest, diet, and exercise), and three of the Big Five personality traits (conscientiousness, extraversion, and openness) would predict women’s BMI and obesity. This hypothesis was tested using cross-sectional analyses. Other lifestyle factors (healthcare, social life, learning, and environment), remaining personality traits (agreeableness and neuroticism), and demographic variables (age, income, marital status, and parenthood) were included as control variables. For comparison, the same analyses were also conducted in a male sample to highlight the distinct features of the associations between BMI/obesity and the predictors in women. The overarching aim of this study is to provide a foundation for developing feasible strategies to improve women’s BMI and reduce obesity in the future. Figure 1 illustrates the conceptual framework of the current research.

2. Literature Review and Hypotheses

2.1. Education

It has been argued that individuals with higher levels of education possess greater health literacy and access to resources, which promote healthier lifestyles and reduce the risk of obesity [14]. Empirical studies support this view, showing that socioeconomic status, particularly educational attainment, is negatively correlated with BMI—an association that is generally stronger in women than in men [15,16]. Similarly, a meta-analysis of adults in Organization for Economic Cooperation and Development (OECD) countries confirmed that the relationship between lower education and obesity was more pronounced among women [17].

According to the theory of social determinants of health, women are often perceived as healthier and more attractive when they weigh less [18]. However, this weight-related ideal may be difficult to achieve for women with lower socioeconomic status who have limited access to health resources [19]. Education may serve as an empowering factor by enabling individuals, especially young women, to acquire, internalize, and apply health-related knowledge [20]. The enhanced self-awareness and agency fostered through education may help reduce the risk of entering a vicious cycle of obesity [21]. Thus, education is expected to exert a protective effect against weight gain and obesity in women [17].

Hypothesis 1 (H1).

Education and BMI/obesity.

H1a. 

Education is negatively correlated with BMI in women.

H1b. 

Education is positively associated with non-obesity (BMI < 25) in women.

2.2. Lifestyle

The relationship between lifestyle and obesity has been primarily examined with a focus on rest, diet, and exercise.

2.2.1. Rest and Diet

In recent years, rest has attracted increasing attention as a factor linked to obesity. Delayed bedtime and altered sleep phases may heighten the risk of circadian misalignment. Studies of night-shift workers, in particular, suggest that circadian disruption is a key contributor to abdominal obesity [22,23]. One mechanism may be the greater exposure to light at night, which suppresses melatonin secretion over the long term, thereby weakening or desynchronizing circadian rhythms [24].

Circadian misalignment has been associated with reduced leptin levels, elevated plasma glucose and corticosteroids, and increased systemic inflammation—all of which are linked to impaired metabolic health [24]. A meta-analysis confirmed a significant association between self-reported short sleep duration and the development of obesity [25]. Furthermore, a large cross-sectional study of over 130,000 participants from 26 countries reported that both late bedtime and short nighttime sleep were independently associated with higher risks of total abdominal obesity, even after controlling for a wide range of confounders [26].

Of note, Tse et al. [26] reported that the association between sleep and obesity was stronger in women than in men. Gender differences may partly reflect chronotype: women are more likely than men to be classified as morning types and may therefore be more vulnerable to circadian rhythm disruptions caused by delayed bedtimes [27,28]. In addition, hormonal and physiological changes across the female lifespan influence sleep quality. For example, sleep disorders are common during pregnancy and tend to increase as pregnancy progresses [29]. These biological mechanisms may help explain why women with later bedtimes or delayed sleep phases are at greater risk of obesity compared to men.

Consistent with this view, Li [30] found that short and long sleep durations were associated with 2.59-fold and 1.70-fold higher odds of obesity, respectively, among women. In contrast, no significant association between sleep duration and weight status was observed in men.

Hur et al. [31] conducted a study in Korea and found that women with poor sleep quality had approximately twice the risk of obesity compared with women who reported good sleep quality, but only among those whose diet quality was below the population median. This finding suggests that the association between sleep quality and obesity may be moderated by diet quality. Importantly, this relationship was not observed in men.

Short sleep duration has also been linked to reduced dietary fiber intake and increased consumption of carbohydrates, total sugar, total cholesterol, and saturated fat [32]. Such dietary patterns contribute to excessive caloric intake and an imbalance between energy intake and expenditure [31]. In addition, physiological differences in fat distribution and lipid metabolism may make women more susceptible than men to overweight and obesity [33]. Taken together, these findings suggest that rest and diet are closely associated with women’s BMI and obesity.

Hypothesis 2 (H2).

Rest and BMI/obesity.

H2a. 

Rest is negatively correlated with BMI in women.

H2b. 

Rest is positively associated with non-obesity (BMI < 25) in women.

Hypothesis 3 (H3).

Diet and BMI/obesity.

H3a. 

Diet is negatively correlated with BMI in women.

H3b. 

Diet is positively associated with non-obesity (BMI < 25) in women.

2.2.2. Exercise

Evidence indicates that women and men respond differently to physical activity. Women tend to benefit more from low-to-moderate aerobic exercise, whereas men appear to gain greater health benefits from more intense exercise [34,35]. These differences likely reflect physiological variations between the sexes. For example, men generally have higher VO₂max, greater red blood cell counts, lower resting heart rates, and superior lung function, which enable them to sustain higher-intensity exercise [34,35]. In contrast, women rely more on fat metabolism, whereas men rely more on carbohydrates and protein to fuel activity [34,35].

Consequently, women may experience greater health benefits from relatively low-intensity activity compared to men [36]. Supporting this view, an observational study found that for every 0.5 h of daily walking, men lost 0.15 kg/year while women lost 0.29 kg/year [37]. A 12-month randomized controlled trial further reported that 60 min of moderate-to-vigorous daily exercise, six days per week, produced cumulative weight loss of 1.8 kg in men and 1.4 kg in women [38]. More recent studies have also shown that men are more successful in weight loss through vigorous physical activity, whereas women are more successful with low-intensity activity [39].

Taken together, these findings suggest that exercise is particularly relevant to women’s BMI and obesity status.

Hypothesis 4 (H4).

Exercise and BMI/obesity.

H4a. 

Exercise is negatively correlated with BMI in women.

H4b. 

Exercise is positively associated with non-obesity (BMI < 25) in women.

2.3. Personality

While numerous studies employing the Big Five personality framework have demonstrated that conscientiousness predicts lower BMI in both men and women [40,41], the associations between other personality traits and body weight are less consistent [41]. Consequently, relatively little is known about how these personality–weight relationships differ by gender [41,42].

2.3.1. Conscientiousness

Individuals with high conscientiousness tend to exhibit better cognitive functioning [43,44], stronger self-control, and a greater tendency to plan their actions rather than act impulsively, making them generally more reliable [45]. A multivariate meta-analysis of 107 studies demonstrated that self-control is positively associated with healthier behaviors, including physical activity, diet, and sleep [46,47]. Consequently, conscientious individuals are at lower risk of obesity [41,48] and are more likely to transition from obesity to non-obesity [49], maintaining a healthy weight by adhering to healthier eating habits [40]. Therefore, conscientiousness is hypothesized to be negatively associated with BMI in women.

Hypothesis 5 (H5).

Conscientiousness and BMI/obesity.

H5a. 

Conscientiousness is negatively associated with BMI in women.

H5b. 

Conscientiousness is associated with non-obesity (BMI < 25) in women.

2.3.2. Extraversion and Openness

Compared with conscientiousness, the relationships between other personality traits and obesity appear more complex. Stephan et al. [50] reported that extraversion and openness are associated with participation in a wide range of physical, cognitive, and social activities. These traits, however, can exert both positive and negative influences.

Openness, characterized by curiosity and receptivity to new ideas, may encourage active engagement in diverse activities [50]. At the same time, individuals high in openness are more likely to adopt novel eating habits—both healthy and unhealthy [40]. Extraversion, in contrast, is marked by optimism, sociability, and sensation-seeking. Highly extraverted individuals often eat in social settings and consume non-recommended foods more frequently, which may negatively affect their health [51]. Armon et al. [48] further argued that individuals with consistently positive moods may become careless and more prone to unhealthy behaviors that increase obesity risk.

Gender differences may further shape these associations. Women tend to value social rewards, such as relationships, more than intrinsic rewards, such as autonomy [52], and they are more likely to engage in group activities and conversations [53]. In a study of more than 30,000 Indonesians, Pristyna et al. [53] found that extraversion was positively correlated with both recommended and non-recommended food intake as well as obesity, with stronger effects in women than in men. Similarly, Kim [42] observed that openness was negatively associated with obesity in men but positively associated with obesity in women. Taken together, these findings suggest that both extraversion and openness may be positively associated with BMI and obesity in women.

Hypothesis 6 (H6).

Extraversion and BMI/obesity.

H6a. 

Extraversion is positively correlated with BMI in women.

H6b. 

Extraversion is associated with obesity (BMI ≥ 25) in women.

Hypothesis 7 (H7).

Openness and BMI/obesity.

H7a. 

Openness is positively correlated with BMI in women.

H7b. 

Openness is associated with obesity (BMI ≥ 25) in women.

Based on the above literature, this study tests the following overarching hypothesis: educational attainment (H1), rest (H2), diet (H3), exercise (H4), and conscientiousness (H5) are negatively associated with BMI and/or linked to a lower likelihood of obesity (BMI < 25). In contrast, extraversion (H6) and openness (H7) are expected to be positively associated with BMI and/or linked to a higher likelihood of obesity (BMI ≥ 25).

3. Methods

3.1. Participants

From 25 to 27 December 2023, an online survey was administered via a commercial internet survey company to individuals residing across Japan. Eligible participants were company panel members aged 30 to 80 years. No additional inclusion or exclusion criteria were applied. A total of 5155 individuals responded, of whom 879 were excluded due to missing information on personal income. The final analytic sample comprised 4276 respondents (2061 women and 2215 men) aged 30 to 79 years. This study was approved by the Ethics Committee of Institute of Science Tokyo (Approval Number 2023137, 29 October 2021) and was conducted following the institute’s guidelines and regulations. All participants provided written informed consent before participation, and their anonymity was maintained.

3.2. Scale

3.2.1. Lifestyle

Lifestyle was assessed using the Japanese version of the BHQ Actions Scale, which consists of seven subscales: healthcare, social life, learning, exercise, environment, rest, and diet [54]. These subscales were developed based on well-established measures with confirmed reliability and validity, and their associations with the Big Five personality traits, emotional intelligence, and cultural intelligence have been previously demonstrated [54]. All items are rated on a 5-point Likert scale, with higher scores indicating healthier behaviors.

In this study, rest, diet, and exercise were the primary variables for hypothesis testing, while the remaining four subscales (healthcare, social life, learning, and environment) were included as control variables.

Exercise: Participants were asked, “How many times a week do you exercise for 30 min or more?” Response options were: (1) Never, (2) Once a week, (3) Twice a week, (4) Three times a week, and (5) Every day or almost every day. Scores ranged from 1 to 5.

Rest: Participants were asked, “Please tell us whether the following applied to your sleep during the last week.” Items included: (1) I fall asleep easily, (2) I sleep soundly until morning, (3) I don’t take naps or they are short (<30 min), (4) I sleep at the same time each day, and (5) I sleep 7–8 h per night. Scores ranged from 1 to 5.

Diet: Participants were asked, “Please select the number of the following items that applied to your meals during the past week.” Fifteen items were presented (e.g., “I ate green leafy vegetables six or more times a week”), with dichotomous response options (Applies/Does not apply). Scores were assigned as follows: 1 = ≤3 items, 2 = 4–6 items, 3 = 7–8 items, 4 = 9–12 items, 5 = ≥13 items. In this study, Cronbach’s alpha for the Diet scale was 0.691.

Details of the other lifestyle subscales—healthcare (health concerns), social life (human relationships), learning (engagement in hobbies or learning), and environment (frequency of going outdoors and experiencing nature)—are provided in Kokubun et al. [54].

3.2.2. Personality

Personality traits were assessed using the Japanese version [55] of the Ten-Item Personality Inventory (TIPI-J), originally developed by Gosling et al. [56]. This instrument is widely used because of its brevity and ease of administration, and it has demonstrated good convergent validity with longer Big Five inventories, satisfactory test–retest reliability, and adequate psychometric properties [57,58].

The TIPI-J consists of 10 items, paired to represent the five Big Five dimensions. In the present study, the following Pearson correlation coefficients and p-values were observed between paired items: extraversion (r = −0.318, p < 0.001), agreeableness (r = −0.212, p < 0.001), conscientiousness (r = −0.298, p < 0.001), neuroticism (r = −0.293, p < 0.001), and openness to experience (r = −0.238, p < 0.001). All items were rated on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). Each trait score was calculated as the mean of its two corresponding items.

3.2.3. Education

Educational attainment was assessed with the question: “How many years of schooling have you completed (total years including elementary school, junior high school, high school, vocational school, university, graduate school, and any other formal education)?” Participants reported the exact number of years, and these self-reported values were used directly in the analyses.

3.2.4. BMI and Obesity

Body mass index (BMI; kg/m²) was calculated from self-reported height and weight. Obesity was operationalized as a binary variable, coded as 1 = obese/overweight (BMI ≥ 25) and 0 = non-obese (BMI < 25).

3.2.5. Moderator

Gender was included as a moderation variable and coded as 1 = male and 0 = female.

3.2.6. Control Variables

Several demographic characteristics were included as control variables. Age was recorded as the exact self-reported value in years. Income was measured as annual personal income in Japanese yen, with respondents selecting from nine categories (see

Table 1. Respondent attributes.

	Male		Female
	N	%	N	%
Marriage	(2215)		(2061)
Unmarried	723	32.6	661	32.1
Married	1492	67.4	1400	67.9
Child	(2215)		(2061)
No children	840	37.9	629	30.5
With children	1375	62.1	1432	69.5
Area	(2215)		(2061)
Hokkaido	121	5.5	106	5.1
Tohoku	117	5.3	115	5.6
Kanto	855	38.6	733	35.6
Chubu	363	16.4	326	15.8
Kinki	410	18.5	425	20.6
Chugoku	110	5	114	5.5
Shikoku	59	2.7	46	2.2
Kyushu	180	8.1	196	9.5
Household annual income (Japanese Yen)	(2215)		(2061)
Less than 2 million	183	8.3	251	12.2
2 million to less than 4 million	538	24.3	536	26
4 million to <6 million	538	24.3	460	22.3
6 million to <8 million	412	18.6	265	12.9
8 million to <10 million	232	10.5	158	7.7
10 million to <12 million	113	5.1	93	4.5
12 million to <15 million	70	3.2	40	1.9
15 million to <20 million	28	1.3	29	1.4
20 million or more	25	1.1	12	0.6
Don’t know	74	3.3	206	10
No answer	2	0.1	11	0.5
Personal annual income (Japanese Yen)	(2215)		(2061)
Less than 2 million	384	17.3	1463	71
2 million to less than 4 million	724	32.7	430	20.9
4 million to <6 million	563	25.4	133	6.5
6 million to <8 million	288	13	20	1
8 million to <10 million	152	6.9	11	0.5
10 million to <12 million	52	2.3	1	0
12 million to <15 million	25	1.1	1	0
15 million to <20 million	10	0.5	1	0
20 million or more	17	0.8	1	0
Occupation	(2215)		(2061)
Civil servant	112	5.1	31	1.5
Manager/executive	66	3	3	0.1
Company employee (administrative)	308	13.9	224	10.9
Company employee (technical)	408	18.4	55	2.7
Company employee (other)	416	18.8	128	6.2
Self-employed	172	7.8	51	2.5
Freelance	55	2.5	37	1.8
Full-time housewife/househusband	14	0.6	782	37.9
Part-time/casual work	139	6.3	490	23.8
Student	3	0.1	3	0.1
Other	69	3.1	50	2.4
Unemployed	453	20.5	207	10
Age	(2215)		(2061)
30 to 34 years old	172	7.8	141	6.8
35 to 39 years old	216	9.8	180	8.7
40 to 44 years old	215	9.7	210	10.2
45 to 49 years old	280	12.6	215	10.4
50 to 54 years old	247	11.2	224	10.9
55 to 59 years old	222	10	186	9
Over 60 years old	863	39	905	43.9
Education	(2215)		(2061)
12 years or less	639	28.8	758	36.8
14 years or less	251	11.3	567	27.5
16 years or less	1054	47.6	631	30.6
17 years or more	271	12.2	105	5.1
BMI	(2215)		(2061)
Less than 25 (non-obesity)	1610	72.7	1771	85.9
25 or over (obesity)	605	27.3	290	14.1

). Marital status was coded as 1 = married and 0 = unmarried, while parental status was coded as 1 = with children and 0 = without children.

4. Data Analysis

In the multiple regression model, a total of 13 facets related to education, lifestyle, and personality were included as independent variables. Of these, education, rest, diet, exercise, conscientiousness, extraversion, and openness were treated as the main predictors, while healthcare, social life, learning, environment, agreeableness, and neuroticism were entered as control variables. The dependent variable was BMI.

Additionally, four demographic variables—age, personal annual income, marital status, and parental status—were included as control variables. For reference, analyses were also conducted separately for male samples, and gender differences in the regression coefficients were examined by including interaction terms in the combined sample of men and women. The interaction variable was calculated as the product of the mean-centered independent variable and the binary variable distinguishing females (0) from males (1).

The above analytical procedures were also applied in logistic regression analyses, where the dependent variable was a binary variable indicating obesity (BMI ≥ 25 = 1) versus non-obesity (BMI < 25 = 0).

Given the large sample size, the threshold for statistical significance was set at 1% (p < 0.01). All analyses were performed using IBM SPSS Statistics/AMOS Version 28 (IBM Corp., Armonk, NY, USA).

5. Results

Table 2. Descriptive statistics.

			Male	Female		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	BMI	Mean	23.4388	21.56283	r		−0.117 **	−0.011	−0.032	−0.092 **	−0.102 **	−0.025	−0.082 **	−0.032	−0.009	−0.030	−0.146 **	0.042	0.066 *	−0.036	0.051
		SD	3.89015	4.175526	p		0.000	0.603	0.146	0.000	0.000	0.250	0.000	0.149	0.694	0.176	0.000	0.056	0.003	0.099	0.019
2	Education (H1)	Mean	14.860	13.920	r	0.008		−0.135 **	0.217 **	0.029	0.061 *	0.039	0.088 **	0.071 *	0.117 **	0.049	0.012	−0.009	0.008	0.03	−0.038
		SD	3.143	2.160	p	0.719		0.000	0.000	0.189	0.006	0.080	0.000	0.001	0.000	0.027	0.591	0.681	0.729	0.167	0.085
3	Age	Mean	54.510	55.800	r	−0.046	−0.016		−0.178 **	0.125 **	0.264 **	0.271 **	0.269 **	−0.152 **	0.142 **	0.310 **	0.247 **	0.116 **	0.027	0.229 **	−0.268 **
		SD	13.505	13.778	p	0.032	0.442		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.219	0.000	0.000
4	Income	Mean	2.780	1.400	r	−0.013	0.169 **	−0.130 **		0.020	−0.018	0.006	0.045	0.058 *	−0.014	−0.046	0.056	0.095 **	0.080 **	−0.030	−0.054
		SD	1.469	0.751	p	0.555	0.000	0.000		0.359	0.404	0.791	0.042	0.008	0.528	0.036	0.010	0.000	0.000	0.168	0.013
5	Rest (H2)	Mean	2.930	3.010	r	−0.079 **	0.049	0.125 **	0.085 **		0.205 **	0.119 **	0.142 **	0.087 **	0.083 **	0.162 **	0.146 **	0.092 **	0.039	0.130 **	−0.231 **
		SD	1.325	1.350	p	0.000	0.021	0.000	0.000		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.076	0.000	0.000
6	Diet (H3)	Mean	2.620	2.940	r	−0.070 *	0.088 **	0.199 **	0.078 **	0.262 **		0.205 **	0.354 **	0.098 **	0.188 **	0.284 **	0.185 **	0.082 **	0.054	0.193 **	−0.173 **
		SD	1.076	1.015	p	0.001	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.015	0.000	0.000
7	Exercise (H4)	Mean	2.430	2.310	r	−0.117 **	0.033	0.129 **	0.003	0.142 **	0.254 **		0.344 **	−0.018	0.296 **	0.483 **	0.151 **	0.139 **	0.133 **	0.110 **	−0.139 **
		SD	1.501	1.518	p	0.000	0.121	0.000	0.882	0.000	0.000		0.000	0.425	0.000	0.000	0.000	0.000	0.000	0.000	0.000
8	Healthcare	Mean	3.500	3.740	r	−0.076 **	0.061 *	0.228 **	0.102 **	0.190 **	0.356 **	0.369 **		0.082 **	0.253 **	0.343 **	0.202 **	0.134 **	0.110 **	0.201 **	−0.128 **
		SD	1.415	1.330	p	0.000	0.004	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
9	Social life	Mean	2.660	2.560	r	−0.021	0.100 **	−0.084 **	0.348 **	0.163 **	0.196 **	0.076 **	0.187 **		0.003	0.055	0.001	0.152 **	0.051	0.076 *	−0.043
		SD	1.151	1.025	p	0.327	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.900	0.012	0.979	0.000	0.021	0.001	0.053
10	Learning	Mean	2.920	2.940	r	−0.048	0.072 *	0.048	0.006	0.120 **	0.199 **	0.309 **	0.257 **	0.049		0.279 **	0.070 *	0.075 *	0.111 **	0.114 **	−0.119 **
		SD	1.302	1.392	p	0.023	0.001	0.024	0.771	0.000	0.000	0.000	0.000	0.020		0.000	0.001	0.001	0.000	0.000	0.000
11	Environment	Mean	2.660	2.590	r	−0.094 **	0.024	0.250 **	0.009	0.196 **	0.266 **	0.497 **	0.321 **	0.153 **	0.249 **		0.165 **	0.139 **	0.103 **	0.150 **	−0.155 **
		SD	1.467	1.447	p	0.000	0.259	0.000	0.686	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000	0.000
12	Conscientiousness (H5)	Mean	4.014	4.134	r	−0.149 **	0.05	0.148 **	0.102 **	0.140 **	0.127 **	0.133 **	0.203 **	0.080 **	0.121 **	0.117 **		0.219 **	0.205 **	0.288 **	−0.340 **
		SD	1.041	1.121	p	0.000	0.019	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.000	0.000	0.000	0.000
13	Extraversion (H6)	Mean	3.628	3.752	r	0.017	−0.007	0.014	0.164 **	0.107 **	0.080 **	0.081 **	0.129 **	0.245 **	0.014	0.082 **	0.172 **		0.355 **	−0.021	−0.271 **
		SD	1.130	1.268	p	0.410	0.745	0.509	0.000	0.000	0.000	0.000	0.000	0.000	0.520	0.000	0.000		0.000	0.333	0.000
14	Openness (H7)	Mean	3.863	3.626	r	−0.039	−0.009	0.066 *	0.093 **	0.088 **	0.111 **	0.094 **	0.167 **	0.101 **	0.127 **	0.106 **	0.304 **	0.355 **		0.023	−0.188 **
		SD	1.007	1.095	p	0.064	0.681	0.002	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000		0.305	0.000
15	Agreeableness	Mean	4.547	4.855	r	−0.104 **	0.017	0.170 **	0.039	0.144 **	0.132 **	0.112 **	0.173 **	0.084 **	0.115 **	0.144 **	0.252 **	−0.067 *	0.103 **		−0.287 **
		SD	0.970	1.011	p	0.000	0.432	0.000	0.065	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.002	0.000		0.000
16	Neuroticism	Mean	3.998	4.157	r	0.065 *	−0.044	−0.151 **	−0.144 **	−0.213 **	−0.161 **	−0.112 **	−0.141 **	−0.146 **	−0.112 **	−0.143 **	−0.376 **	−0.240 **	−0.270 **	−0.341 **
		SD	0.994	1.139	p	0.002	0.039	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

Note: n = 4276. Below the diagonal are Pearson’s correlation coefficients for men (n = 2215), and above the diagonal are Pearson’s correlation coefficients for women (n = 2061). ** p < 0.001, * p < 0.01. Age = years. Education = years of schooling. Income = nine categories of personal annual income shown in Table 1 (Japanese yen). Values in the “Male” and “Female” columns are means (top) and standard deviations (SD; bottom).

presents the descriptive statistics, and

Table 3. Results of multiple regression analysis. (Dependent variable: BMI).

	Male			Female			Interaction
	β	ρ		β	ρ		β	ρ
Demographic
Education (H1a)	0.025	0.247		−0.104	0.000	**	0.133	0.000	**
Age	0.039	0.120		0.027	0.313		−0.032	0.127
Income	0.007	0.750		0.002	0.939		0.062	0.083
Married	−0.059	0.068		0.017	0.502		−0.055	0.081
With children	−0.042	0.186		−0.001	0.979		−0.053	0.082
Lifestyle
Rest (H2a)	−0.039	0.079		−0.062	0.006	*	0.029	0.211
Diet (H3a)	−0.021	0.383		−0.065	0.008	*	0.026	0.233
Exercise (H4a)	−0.079	0.002	*	−0.003	0.912		0.029	0.211
Healthcare	−0.002	0.947		−0.046	0.065		0.014	0.533
Social life	0.047	0.083		−0.029	0.260		0.029	0.211
Learning	0.000	0.996		0.021	0.378		0.029	0.211
Environment	−0.024	0.345		0.012	0.634		0.029	0.211
Personality
Conscientiousness (H5a)	−0.127	0.000	**	−0.162	0.000	**	0.000	0.990
Extraversion (H6a)	0.050	0.036		0.065	0.008	*	−0.016	0.430
Openness (H7a)	0.000	0.995		0.087	0.000	**	−0.055	0.007	*
Agreeableness	−0.054	0.021		0.039	0.103		−0.049	0.018

Note: n = 4276, including men (n = 2215) and women (n = 2061). ** p < 0.001, * p < 0.01. Age = years. Education = years of schooling. Income = nine categories of personal annual income shown in Table 1 (Japanese yen). β = standardized partial regression coefficients.

summarizes the results of the multiple regression analysis.

In the model for women, Education (β = −0.104, p < 0.001), Rest (β = −0.062, p = 0.006), Diet (β = −0.065, p = 0.008), and Conscientiousness (β = −0.162, p < 0.001) were all negatively and significantly associated with BMI at the 1% level. In contrast, Extraversion (β = 0.065, p = 0.008) and Openness (β = 0.087, p < 0.001) were positively and significantly associated with BMI at the 1% level. Exercise (β = −0.003, p = 0.912), however, showed no significant association with BMI in the female sample. These findings support H1a, H2a, H3a, H5a, H6a, and H7a, but not H4a.

For reference, in the model for men, Exercise (β = −0.079, p = 0.002) and Conscientiousness (β = −0.127, p < 0.001) were negatively and significantly associated with BMI at the 1% level. In the combined analysis including interaction terms, Education (β = 0.133, p < 0.001) and Openness (β = −0.055, p = 0.007) were significant, indicating that the effects of these variables on BMI differed significantly between women and men.

To further understand the significance of the interaction terms, we divided the female and male samples into high and low Education and Openness groups, one standard deviation above and below the mean, respectively [59]. The difference in BMI between female and male samples is shown graphically in Figure 2 for Education and Figure 3 for Openness.

Table 4. Results of logistic regression analysis. (Dependent variable: obesity).

	Male			Female			Interaction
	OR	ρ		OR	ρ		OR	ρ
Demographic
Education (H1b)	1.029	0.056		0.892	0.000	**	1.166	0.000	**
Age	1.006	0.142		1.008	0.161		0.994	0.327
Income	1.010	0.790		1.041	0.690		1.133	0.211
Married	0.811	0.186		1.128	0.459		0.865	0.419
With children	0.739	0.043		0.829	0.248		0.872	0.434
Lifestyle
Rest (H2b)	0.939	0.106		0.831	0.000	**	1.147	0.078
Diet (H3b)	1.029	0.572		0.906	0.169		1.156	0.062
Exercise (H4b)	0.891	0.005	*	0.826	0.001	*	1.147	0.078
Healthcare	0.965	0.372		0.970	0.577		1.043	0.470
Social life	1.076	0.192		0.883	0.107		1.147	0.078
Learning	0.966	0.397		0.976	0.628		1.147	0.078
Environment	0.958	0.293		1.042	0.466		1.147	0.078
Personality
Conscientiousness (H5b)	0.807	0.000	**	0.686	0.000	**	1.098	0.215
Extraversion (H6b)	1.078	0.130		1.105	0.088		0.976	0.716
Openness (H7b)	1.000	0.999		1.220	0.002	*	0.861	0.050
Agreeableness	0.916	0.115		1.102	0.177		0.905	0.221
Neuroticism	1.036	0.545		1.050	0.459		0.979	0.785

Note: n = 4276, including men (n = 2215) and women (n = 2061). ** p < 0.001, * p < 0.01. Age = years old. Education = years of schooling. Income = nine categories of personal annual income shown in Table 1 (Japanese yen). OR = Odds ratio.

presents the results of the logistic regression analysis, where obesity (BMI ≥ 25 = 1, BMI < 25 = 0) was used as the dependent variable.

In women, Education (OR = 0.892, p < 0.001), Exercise (OR = 0.826, p = 0.001), Rest (OR = 0.831, p < 0.001), and Conscientiousness (OR = 0.686, p < 0.001) were all significant at the 1% level, with ORs less than 1.0, indicating that these variables were associated with a reduced likelihood of obesity. By contrast, Openness (OR = 1.220, p = 0.002) was significant at the 1% level with an OR greater than 1.0, indicating an increased likelihood of obesity. Diet (OR = 0.906, p = 0.169) and Extraversion (OR = 1.105, p = 0.088) were not significant. These results support H1b, H2b, H4b, H5b, and H7b, but not H3b or H6b.

For reference, in men, Exercise (OR = 0.891, p = 0.005) and Conscientiousness (OR = 0.807, p < 0.001) were significant at the 1% level, both with ORs less than 1.0, indicating a protective effect against obesity. For the interaction term, Education (OR = 1.166, p < 0.001) was significant at the 1% level with an OR greater than 1.0, suggesting a gender difference in the association between education and obesity.

Overall, the pattern of findings is broadly consistent with the multiple regression results presented in Table 3. However, for women, some differences emerged in the logistic regression model: Diet and Extraversion were no longer significant, while Exercise became significant.

6. Discussion

Previous studies have demonstrated that the relationships between educational level, lifestyle factors (such as rest, diet, and exercise), personality traits (such as conscientiousness, extraversion, and openness), and BMI or obesity differ by gender. However, this study is the first to integrate these factors in order to identify the determinants of BMI and obesity specifically in women. The analyses revealed that education, rest, diet, and conscientiousness were negatively and significantly correlated with BMI, whereas extraversion and openness were positively and significantly correlated with BMI. Furthermore, education, exercise, rest, and conscientiousness were significantly associated with non-obesity (BMI < 25), while openness was significantly associated with obesity (BMI ≥ 25).

These findings suggest that, beyond education, engaging in healthy lifestyle practices (exercise, rest, and diet) and possessing certain personality traits (notably conscientiousness) are linked to lower BMI and a reduced likelihood of obesity, whereas other traits (extraversion and openness) are linked to higher BMI and an increased likelihood of obesity. By contrast, among men, although exercise and conscientiousness were associated with lower BMI and reduced odds of obesity, fewer variables reached statistical significance than in women. Notably, the associations of education and openness with BMI, and of education with obesity, differed significantly between men and women at the 1% level.

In other words, the results indicate that higher education exerts a stronger protective effect against elevated BMI and obesity in women than in men, while lower openness appears more protective for women’s BMI. Taken together, these findings suggest that a greater number of—and more consistent—factors are associated with BMI and obesity in women than in men. Accordingly, targeted interventions may yield larger benefits for women, whereas inaction may pose greater risks. In the following sections, we discuss these implications in detail.

First, regarding educational attainment, previous studies have suggested that women with lower levels of education are more likely to develop obesity, in part because thinness is regarded as the ideal body image [18] and achieving this ideal requires greater educational and health-related resources [19]. In terms of lifestyle, prior research has indicated that women are more prone than men to sleep problems [27,28], which in turn increases the likelihood of disordered eating behaviors [32]. Consequently, sleep and eating difficulties are more likely to contribute to obesity in women than in men [26,30,31]. The present findings are consistent with this line of evidence, demonstrating that higher BMI in women is associated with lower educational attainment as well as poorer rest and dietary habits.

On the other hand, previous research has suggested that women are more likely to lose weight through light exercise, whereas men tend to benefit more from vigorous exercise [36,37,38,39]. Given that men have a more developed cardiovascular system than women [34,35], they may require more frequent and sustained physical activity to achieve weight loss effects. Consistent with this, the multiple regression analysis in the present study showed that exercise was significantly and negatively associated with BMI in men, but no comparable relationship was observed in women. However, the logistic regression analysis indicated that exercise, alongside rest, contributes to preventing obesity and maintaining a healthy weight in women. This suggests that exercise may provide greater benefits for women in terms of obesity prevention rather than weight reduction.

With regard to personality, recent studies have shown that conscientiousness tends to promote the consumption of recommended foods, whereas extraversion and openness are associated with the consumption of both recommended and non-recommended foods [53]. Moreover, extraversion and openness appear to be more strongly related to obesity in women than in men [42,53]. Thus, while conscientiousness has been consistently identified as a protective factor against obesity [40,41], extraversion and openness can function as “double-edged swords,” exerting either beneficial or detrimental effects depending on how they shape behavioral choices. The findings of the present study align with this body of evidence, further suggesting that the negative effects of certain personality traits are particularly pronounced among women.

Personality was once thought to remain largely static across the lifespan; however, recent evidence suggests that it can change in response to environmental influences and intentional interventions [60,61]. Accordingly, the results of this study may contribute to the development of novel weight management strategies that take personality into account. For example, interventions could focus on helping women with obesity consciously moderate extraversion and openness while fostering conscientiousness and regulating overly positive moods that may promote careless and unhealthy behaviors [48]. Notably, the negative association between conscientiousness and both BMI and obesity was also observed among men in this study, suggesting that enhancing conscientiousness may represent a promising intervention target across genders.

Overall, improved dietary quality was associated with weight reduction, while exercise habits were associated with the prevention of obesity in women. Better rest quality contributed to both weight reduction and obesity prevention. Conscientiousness emerged as a protective factor for maintaining lower body weight and avoiding obesity, whereas openness appeared to increase the risk; thus, managing openness may help reduce both BMI and the likelihood of obesity. Extraversion, in contrast, was linked to higher body weight. Taken together, these findings suggest that better rest quality, higher conscientiousness, and lower openness are effective for both weight reduction and obesity prevention. Additionally, better diet quality and lower extraversion appear to be particularly important for weight reduction, while regular exercise habits are especially effective for obesity prevention.

The results of this study provide important insights into developing gender-specific programs for weight and obesity management. Women, who face numerous disadvantages from weight gain and obesity, also appear more sensitive to a range of potential countermeasures. Given that relatively few studies have investigated the determinants of BMI and obesity specifically in women—and that women have consistently been reported to experience greater disadvantages than men, including comorbid conditions and social discrimination—this study makes a novel contribution by identifying women-specific predictors using large-scale data. By examining education, lifestyle, and personality together, this research is the first to clarify factors uniquely associated with women’s BMI and obesity. These findings offer valuable implications for both advancing scientific understanding and informing practical, gender-tailored interventions in society.

7. Limitations

First, although the sample size of this study was larger than that of previous research, the participants were limited to Japanese residents in Japan. Caution is therefore required when generalizing the findings to expatriates, international students, or non-Japanese populations living abroad. In addition, the survey was conducted over only two days, which raises the possibility that time-specific and uncontrollable factors may have influenced the results. Moreover, because this was a cross-sectional study, causal relationships between the variables cannot be established. Future research should extend and validate these findings by conducting intervention studies and including participants from diverse cultural and national backgrounds.

8. Conclusions

The present findings, based on a questionnaire survey of more than 4,000 women and men across Japan, suggest that the determinants of women’s body weight and obesity are multifaceted and distinct. This indicates that women may benefit more substantially from appropriate interventions but are also at greater risk of adverse outcomes if no action is taken. To our knowledge, this is the first study to comprehensively identify the determinants of BMI and obesity in women by simultaneously examining education, lifestyle, and personality.