Introduction

Obesity has become a major public health concern in the last decades since it is an important risk factor for different non-communicable diseases such as cardiovascular diseases, diabetes, various cancers, and musculoskeletal disorders [1]. Bidirectional associations have also been found with psychological disorders such as depression [2].

Socioeconomic characteristics have been described as important factors affecting the prevalence of obesity in high and middle income [3]. The prevalence of obesity is higher among people with only primary school education compared to people with tertiary education [4, 5]. This inverse relationship has been also described for the level of income [3]. Also, recent research has suggested that socioeconomic deprivation during childhood is an important predictor of adult weight status [6]. Finally, people living in deprived neighborhoods are more likely to have obesity than those living in less deprived areas [7].

A number of researchers have proposed a theoretical framework to explain the association of socioeconomic deprivation and poor health outcomes such as obesity [8]. Deprived individuals, or people living in deprived environments, are more frequently exposed to stressful experiences and have fewer resources to overcome them. This stress has an impact on psycho-social factors which then influence in the adoption or maintenance of healthy behaviors. Psycho-social factors are defined as individual characteristics, learned through exposure to the social environment, and influencing individual’s behaviors [9].

Various studies have analyzed the most common psycho-social factors related to obesity, especially in weight-loss and weight maintenance programs, such as self-efficacy (the confidence in one’s own ability to achieve intended results), body perception, body satisfaction, and locus of control (degree to which people believe that they have control over outcomes in their lives, as opposed to external forces beyond their control) [10,11,12,13,14].

However, the studies assessing relationships between socioeconomic characteristics and psycho-social factors related to obesity have yielded inconsistent results. High levels of certain advantageous psycho-social factors such as self-efficacy are less frequently present among individuals with low socioeconomic status [15, 16], while others such as body satisfaction are less frequently present among individuals with high socioeconomic status [17]. Since psycho-social factors are usually correlated, some authors have suggested that it is inappropriate to study obesity-related psycho-social factors independently from each other, and that a tool (e.g., an overall profile) combining various such factors should be developed to assess the overall impact of this family of mechanisms [10].

This study aims to compose a psycho-social construct based on relevant psycho-social factors, to assess its relation with socioeconomic characteristics, using data from a large epidemiological cohort of adults living in the Paris metropolitan area of France. More specifically, our objectives were (1) to describe relevant obesity-related psycho-social factors in our population; (2) to examine whether these psycho-social factors could be aggregated into one construct through the development of obesity-related psycho-social profiles; and (3) to explore the associations between socioeconomic characteristics and these psycho-social profiles.

Our main research hypothesis was that individuals with the most deprived individual socioeconomic characteristics and/or those living in the most deprived neighborhoods would be more likely to have adverse obesity-related psycho-social profiles (i.e., that can be hypothesized to promote obesity).

Methods

Study population

The Residential Environment and Coronary Heart Disease (RECORD) Cohort Study was established to investigate environmental determinants of territorial disparities in health in the Ile de France region, which includes the Paris metropolitan area [18]. The French National Health Insurance Scheme for Salaried Workers offers a free medical examination every 5 years to all working and retired employees and their families. The RECORD Study recruited participants from people engaged in these preventive medical check-ups. Participants had to be aged 30–79 years in 2007–2008 and to reside in one of 10 (out of 20) administrative divisions of Paris or 111 other municipalities of the region. These divisions or municipalities were chosen a priori to include socioeconomically advantaged and disadvantaged neighborhoods, with over-representation of disadvantaged districts, as well as urban and suburban areas. In addition to the health checkup, participants completed questionnaires related to their socio-demographic status, health behavior, family and personal medical history, and psychological characteristics.

The French Data Protection Authority approved the study protocol. There have been two cohort waves so far, the first wave between 2007 and 2008 and the second wave between 2011 and 2014. This study was based on the data from the second wave including 6460 participants. The exclusion criteria were as follows: to be a pregnant woman (n = 12), to have not answered to one or more questions from the RECORD questionnaire related to psycho-social factors or socioeconomic characteristics (n = 1593) and to have incomplete data on the characteristics of their neighborhood (n = 336). The final study population was composed by 4519 individuals.

Psycho-social factors and psycho-social profiles

The RECORD questionnaire was structured in the following parts: demographic and socioeconomic characteristics, self-reported health information, psychological health evaluation, self-reported experience in the residential neighborhood and health-related behavior/psychological, cognitive and attitudinal characteristics. In this part of the questionnaire, different items on psycho-social factors related to body weight perceptions and values and attitudes related to weight and weight control behaviors [18]. Further details on these items are provided in the Supplementary Appendix Table A.

Three steps were followed to compose the psycho-social construct ‘Psycho-social profiles’: explanatory factor analysis, confirmatory factor analysis and cluster analysis. First, to identify the most relevant psycho-social factors in the study population, an exploratory factor analysis was run based on the 24 items coded from the answers of the participants to the part of the questionnaire on health-related behavior/psychological, cognitive and attitudinal characteristics [19]. An oblique rotation was applied since cognitive/psychological factors are expected to be correlated [20]. Only factors with eigenvalues above 1 and having more than one item with an item-loading equal to or higher than 0.32, were kept for the confirmatory factor analysis. After, the retain model was validated using confirmatory factor analysis, the correlations between factors were analyzed, and a score for each psycho-social factors was obtained through least squares regression. Third, a unique construct combining factors, referred to as psycho-social profiles, was defined through hierarchical cluster analysis (Fig. 1). Ward’s method was applied to the standardized psycho-social factor scores to identify two main psycho-social profiles: favorable and adverse [21].

Fig. 1
figure 1

Dendrogram: hierarchical cluster analysis of psychosocial factors using Ward’s method based on Euclidean distances between observations. a Adverse profile characterized by low self-efficacy, poor body image, external weight locus of control and underestimation of impact of weight in quality of life. b Favorable profile characterized by high self-efficacy, good body image, internal weight locus of control and moderate underestimation of impact of weight in quality of life

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Individual socioeconomic factors

As it was previously described, a part of the RECORD questionnaire was dedicated to participant’s socioeconomic characteristics, with 13 items (described in detail in Supplementary Appendix Table B). An explanatory factor analysis followed by a confirmatory factor analysis was performed on these 13 items, to assess the most relevant individual socioeconomic factors. Two main socioeconomic factors were identified. The first factor was current socioeconomic status, based on six items: education level, household income, owning a secondary residence, owning a house/flat put in hiring, having savings over 25,000 €, and being able to raise funds for an urgent expense. The second factor, economic status in childhood, was based on three items retrospectively asked for the childhood period: parents’ ownership of a car, parents’ ownership of a house, and parents’ financial strain. The resulting factor scores for each of the two individual socioeconomic factor were calculated by least squares regression and were then after divided into quartiles: very low status (first quartile), low status (second quartile), high status (third quartile), and very high status (fourth quartile, used as the reference category). For the psycho-social and socioeconomic variables, the psych, lavaan, and stats packages from the R software (R3.3.0, 2016) were used to perform exploratory factor analysis, confirmatory factor analysis, and hierarchical cluster analysis.

Neighborhood socioeconomic level

The residential address of the participants was collected and geocoded using the VERITAS web mapping application. As a proxy for the socioeconomic level of the residential neighborhood, individually geocoded data from the 2010 French National Census were used to assess the proportion of residents aged 20 years or over with upper tertiary education inside a 1000 m street-network buffer defined around each individual’s residential address [22]. This continuous variable was divided into quartiles, with the very high education level (fourth quartile) used as the reference category.

Other variables

During the medical examination, a nurse measured participants’ height and weight. Body Mass Index (BMI) was calculated as the ratio of weight (kg) to the square of height (m2). Three weight status categories were defined based on individual’s BMI: not overweight (BMI < 25 kg/m2), overweight (25 kg/m2 ≤ BMI < 30 kg/m2), and obesity (BMI ≥ 30 kg/m2) [23]. Depression was assessed through the validated QD2A questionnaire, include in the RECORD questionnaire [24,25,26,27]. The QD2A is composed of 14 items related to possible depressive symptoms (e.g., “I am disappointed and disgusted with myself,” “I’m sad these days,” “I feel hopeless about the future”). Individuals with scores above 7 are considered to have depressive symptoms (no depression as the reference category). Other variables included in the analysis were age (as a continuous variable); gender (male as the reference category); and cohabitation status as a binary variable living alone (living with a partner as the reference category).

Statistical analysis

A descriptive analysis of the study population was performed, including a description by psycho-social profile. Differences between the adverse and the favorable psycho-social profiles were assessed by Kruskal–Wallis test for continuous variables and by Fisher’s exact test for categorical variables.

Since the outcome variable ‘psycho-social profile’ was a binary variable (favorable profile vs adverse profile), to assess its relationship with socioeconomic characteristics (current socioeconomic status, economic status in childhood and neighborhood socioeconomic level), we used binomial logistic regression adjusted by gender, age, cohabitation status, presence of depressive symptoms and weight status. Interactions terms between socioeconomic variables (current socioeconomic status, economic status in childhood and neighborhood socioeconomic level) as well as interaction term between gender and depression were analyzed. The predictive value of the final logistic model was tested with three different methods: Hosmer–Lemeshow test, sensitivity and specificity analysis, and ROC curves. Regression analyses were performed with Stata version 12.0 (StatCorp LP, College Station, TX, USA).

Results

The characteristics of the final study population (n = 4519) are summarized in Table 1. Mean age was 53.3 years and 69% of the participants were men. Around 45% of individuals had a tertiary education and only 4.7% reported regular financial strain. The mean household income per consumption unit in the household was 1899 euros per month and the percentage of participants who were employed was 67.1%. Half of the final population had overweight and around 21% of them had obesity.

Table 1 Descriptive information on the study population, overall and stratified by psycho-social profiles
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Factor analysis: psycho-social factors

Results of the exploratory factor analysis are shown in Table 2. Five psycho-social factors were identified, explaining 69% of the variance. The first factor was labeled weight-related self-efficacy, and included items related to the perceived ability to perform actions to control or to lose weight. The second factor was labeled weight locus of control and included items measuring whether participants felt that individuals are responsible for their own weight. The third factor was underrate impact of weight on QoL (Quality of Life), including items related to the differences between participants’ self-reported weight and the weight above which they felt less fit or the weight they believed would have negative consequences for their health. Two factors associated with body image were identified. The factor-labeled weight dissatisfaction included items measuring the difference between reported weight and perceived ideal or acceptable weight. The factor-labeled negative perceptions of body included items measuring the individual’s evaluation of his/her weight status or his/her waist circumference. The internal consistency of each factor was assessed with the Cronbach Alpha coefficient (α ≥ 0.7). Confirmatory factor analysis was then run to obtain a five factor model (see Table 3). In this model weight-related self-efficacy was negatively correlated with underrate impact of weight on QoL, weight dissatisfaction, and negative perceptions of body. A strong positive correlation, higher than 0.7, was observed between weight dissatisfaction and negative perceptions of body.

Table 2 Results of exploratory factor analysisa on items from RECORDb questionnaire related to psycho-social factors
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Table 3 Results of confirmatory factor analysisa: five psycho-social factors modelb, variances and correlation matrix
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Cluster analysis: psycho-social profiles

Based on the factor scores obtained through the confirmatory factor analysis model, two psycho-social profiles were observed in the hierarchical cluster analysis: an adverse (n = 818) and a favorable (n = 4037) psycho-social profile. In the assessment of the dendogram, these two profiles were clearly differentiated (Fig. 1). The favorable psycho-social profile had a higher mean for weight-related self-efficacy (mean 0.15) than the adverse psycho-social profile (mean − 0.70) but a lower mean for the other psycho-social factors: weight dissatisfaction (− 0.24 vs 1.17), negative perceptions of body (− 0.17 vs 0.82), underrate impact of weight on QoL (− 0.19 vs 0.93), and weight-related locus of control (− 0.01 vs 0.04) (see Table 1).

The current socioeconomic status and the economic status in childhood also differed between the favorable and the adverse psycho-social profiles. There were higher percentages of people with a high current socioeconomic status and with a high economic status in childhood among people with a favorable psycho-social profile than among those with an adverse profile, while the opposite was true for the percentages of people with a low current and childhood socioeconomic statuses. Almost 10% of people with adverse psycho-social profile presented depressive symptoms, more than double of the percentage in the favorable psycho-social profile group. The percentage of individuals with obesity in the adverse psycho-social profile was 38.5%, whereas this percentage was only of 5.5% in the favorable profile.

Binomial logistic regression: socioeconomic variables and psycho-social profiles

When controlling for the other covariates (and especially for the very strong association between BMI and psycho-social profile), current socioeconomic status, economic status in childhood, and educational level in the neighborhood were not associated with the odds of an adverse psycho-social profile (see Table 4). The odds of having an adverse psycho-social profile were more than three times higher for women compared to men (95% CI: 2.70, 4.04). The odds of having an adverse psycho-social profile were two times higher among individuals with depressive symptoms than among non-depressed individuals (95% CI: 1.51, 3.24). As expected, participants with overweight had eight times higher odds and participants with obesity 40 times higher odds of having an adverse psycho-social profile.

Table 4 Associations between three socioeconomic dimensions and having an adverse psycho-social profilea
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Discussion

Psycho-social factors related to obesity could help to understand the relationship between socioeconomic deprivation and obesity. Through factor analysis, five main psycho-social factors were identified among a study population of adults living in Paris. Deriving two clusters of individuals based on their psycho-social factors allowed us to examine the predictors of having an adverse obesity-related psycho-social profile as a unified construct. This study was not able to confirm that three different socioeconomic dimensions (current socioeconomic status, economic status in childhood, and neighborhood education) were associated with psycho-social profiles. However, associations were found between depression and psycho-social profiles.

Psycho-social factors and psycho-social profiles

The five psycho-social factors identified through factor analysis were coherent with the psycho-social factors related to obesity previously described in other studies on obesity. The first factor, self-efficacy, has been described as a key factor in maintaining adequate physical activity and eating behaviors over a long period and a strong predictor of sustained weight loss after weight control treatments [12]. The second factor found in factor analysis was weight-related locus of control. It has been described that individuals with internal weight-related locus of control, or those who relate body weight to people’s own actions and not to external influences, have a lower risk of being overweight [28]. The third factor, underrating the impact of weight on QoL has been described as one of the most important psycho-social factors related to obesity since it is an important predictor of success in weight loss and weight maintenance programs [10]. The factor negative body perceptions has been observed as a relevant factor on weight management failure and treatment discontinuation in weight loss programs [10]. Finally, weight dissatisfaction is a typical factor present on studies regarding eating disorders [29,30,31,32]. Other psycho-social factors previously described in the literature, such as self-regulation [33] and social support [11] were not observed in our analysis.

We describe a strong correlation between negative body perceptions and weight dissatisfaction. This correlation has been previously observed, since these factors are components of a supra-factor, “body image” [34]. In our study, these two factors had also inverse correlations with self-efficacy, which have been described before in studies based on other populations, including young adults or individuals enrolled in weight-loss programs [35].

Socioeconomic characteristics and psycho-social profiles

In the theoretical framework proposed by Matthews et al., the psycho-social factors play a mediating role in the relation between socioeconomic deprivation and negative health outcomes [8]. Different studies have described the association of socioeconomic characteristics with the psycho-social factors observed in our study. A large body of literature has examined the negative impact of socioeconomic deprivation on self-efficacy [15, 16]. Regarding the relationship between socioeconomic deprivation and other psycho-social factors, a cross-sectional study conducted in Germany found the percentage of reported weight dissatisfaction to be higher among individuals with high socioeconomic status [17]. Another study based on data from the Health Survey for England (HSE) observed that underrating the impact of weight on QoL was more common among overweight individuals with a low socioeconomic status than among overweight individuals with a higher socioeconomic status [36]. Antenatal and childhood economic deprivation was an important predictor of external locus of control at 18 years of age in a British cohort study [37].

On the opposite, this study was not able to confirm the relationship between socioeconomic dimensions and psycho-social profiles as a unified construct. These results can be explained because previous studies only assessed one socioeconomic dimension at time, while this study assessed three different socioeconomic dimensions: current socioeconomic status, economic status in childhood, and education status in the residential neighborhood. Another possible explanation is the divergent directions of the relations between the factors composing the psycho-social construct and the socioeconomic characteristics, some factors are more common in low socioeconomic status while others are more common in high socioeconomic status.

Gender, depression, and psycho-social profiles

We documented an association between gender and psycho-social profiles. This gender disparity has been described before; women are more likely to report low self-efficacy and higher body dissatisfaction than men [26, 38]. The observed relationship between having depressive symptoms and showing an adverse psycho-social profile is consistent with previous studies concluding that depressive disorders are related to poor body image [39], low self-efficacy [40], and to an external locus of control [37]. Some authors have suggested that psycho-social factors could play a mediating role in the association between obesity and depression [41]. Three potential pathways are described for this association: behavioral (functional impairment or unhealthy dieting), cognitive (poor body image and low self-efficacy), and social (weight-based stigma). This framework offers an opportunity to develop interventions based on psycho-social factors in individuals with obesity and overweight not only to improve physical health but also to prevent the development of mental disorders [42].

Strengths and limitations

A limitation of the study was the large proportion of observations with missing values (32%). Excluded individuals were more socioeconomically deprived than those included in the study population. This could have led to underestimate the relationship between socioeconomic characteristics and psycho-social profiles. Moreover, certain deprived groups, such as immigrants or unemployed, may have been underepresented due to the recruitment strategy of the study. Other limitation is the risk of “recall bias”, especially regarding the economic status in childhood. The high percentage of men in our study population could affect to the external validity but it should be emphasized that our sample is more heterogeneous than other studies related to psycho-social factors where study populations were composed only by women or by individuals engaged in weight-loss programs [10]. Lastly, the psycho-social profiles as a construct need to be validated by further studies.

This study has important strengths. To understand socioeconomic inequalities in health, it is necessary to rely on an eco-social appraisal by combining individual and ecosystem levels with a life-course perspective [43]. While most of the studies only focused on present individual socioeconomic dimensions (such as current household income) [44], three different socioeconomic dimensions were evaluated in this study: current socioeconomic status, economic status in childhood, and education status in the residential neighborhood. Another strength of our study is the large sample size that was available, compared to most of the studies on psycho-social factors related to obesity were small samples were used [12]. Many studies on obesity used self-reported weight status, which has many limitations, such as overestimation of weight for younger adults or underestimation for people with overweight [45]. In this study, BMI was calculated based on height and weight measures assessed by healthcare professionals. Regardless of the measurement method, however, BMI and its cut-offs for overweight and obesity have been criticized as tools to evaluate the weight status in certain populations such as Asiatic or older adults [46, 47]. Finally, a clear strength of the present study is its use of factor analysis and cluster analysis to derive obesity-related psycho-social profiles from a rich questionnaire.

Conclusion

In this study, a comprehensive methodology was applied to assess the relationship between socioeconomic characteristics and psycho-social factors in a population of adults living in the Paris metropolitan region. To our knowledge this is the first time that a combination of exploratory factor analysis, confirmatory factor analysis and cluster analysis is applied to develop a psycho-social construct based on psycho-social factors related to obesity. Another contribution of this study is to assess three different dimensions of socioeconomic characteristics: current socioeconomic status, economic status in childhood, and education status in the residential neighborhood. In future research on psycho-social factors associated with obesity and socioeconomic characteristics, it is necessary to develop validated and shared instruments to measure psycho-social factors. While a separate focus on each psycho-social factor could be interesting, the study of integrated psycho-social profiles introduced in the present study should not be neglected. This approach can help to understand the multifactorial nature of the determinants of obesity.