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Anthropometric and dietary differences among Mexican older adults with and without adequate body image perception
Journal of Psychosomatic Research 131 (2020) 109967
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Anthropometric and dietary differences among Mexican older adults with and without adequate body image perception
T
J.A. Bricio-Barriosa, M. Ríos-Silvab,c, R. García-Rodríguezd, M. Huertab, M. Del Toro-Equihuaa, ⁎ M. Ortiz-Mesinae, Z. Urzúa-Garcíaf, X. Trujillob, a
Faculty of Medicine, Nutrition School, Universidad de Colima, Colima, Mexico University Centre for Biomedical Research, Universidad de Colima, Colima, Mexico c University Centre for Biomedical Research, Universidad de Colima-Cátedras-CONACyT, Colima, Mexico d Psychology Faculty, Universidad de Colima, Colima, Mexico e Family Unit Medicine #1, Instituto Mexicano del Seguro Social, Colima, Mexico f Family Unit Medicine #17, Instituto Mexicano del Seguro Social, Colima, Mexico b
A R T I C LE I N FO
A B S T R A C T
Keywords: Body image perception Nutritional status Older adults
Objective: We compared anthropometric and dietary indicators between groups of older Mexican adults with accurate or inaccurate body image perception (BIP). Methods: A cross-sectional study was carried out with 201 older adults (age ≥ 60 years) of both sexes who completed the Stunkard scale for BIP, which consists of nine silhouettes with an equivalent of body mass index (BMI) status, then, the accuracy with their real BMI was calculated and reported energy and macronutrient intake through a 24-h dietary recall directed by different geriatric centers in Colima, Mexico. Basic anthropometry and bioelectrical impedance analyses were performed. Results: We found that 71.1% of the older adults had inaccurate BIP; 66.6% underestimated their body mass and 4.5% overestimated their body mass, the other 28.9% hat accurate BIP. The overall concordance between the real nutritional status and BIP was poor (kappa coefficient = 0.03). The inaccurate BIP group had a significantly higher mean body mass index, body fat percentage, muscle mass, and arm and calf circumference compared to the accurate BIP group (p < .001); only 4.3% of the older adults who were overweight and 6.2% who were obese had an accurate BIP. Regarding dietary consumption, we found significant differences only in energy and carbohydrate intake between the two groups. Finally, excess body fat was associated with an inaccurate BIP (OR: 2.8, 95% CI: 1.5–5.5). Conclusion: In older adults, an inaccurate BIP is generally associated with high anthropometric values and less than adequate dietary intake.
1. Introduction The older adult population is increasing worldwide, with estimations that the proportion of people older than 60 years of age will double from 11% in 2000 to 22% in 2050 [1]. Several physical body changes are inherent to age, including weight gain, reduced muscle mass and tone, and mass fat gain, as well as a reduced basal metabolic rate [2]. Excess body weight and the presence of obesity results in unhealthy nutritional transition, such as an increase in nutrient-poor, energy-dense foods, which can lead to stunted growth and weight gain. This condition results in higher body mass index (BMI) and worse health outcomes throughout life [3]. The BMI increases at the same rate
or faster in rural areas than cities, particularly in low- and middle-income countries [4]. Increasing rural BMI is the greatest contributor to the increasing BMI in low- and middle-income regions and the world over the last 33 years, which challenges the current paradigm of urban living and urbanization as the key driver of the global epidemic of obesity. Accordingly, a significant increase has occurred in the risk of chronic diseases, such as cardiovascular disease [5], which is the leading cause of death in Mexicans > 65 years of age [6]. Body image perception (BIP) is a cognitive, subjective representation of self-body appearance and the related sensations experienced [7]. At an old age, BIP may become distorted due to social association of the advanced age with negative characteristics, such as disability and
⁎ Corresponding author at: Universidad de Colima, Centro Universitario de Investigaciones Biomédicas, Av. 25 de Julio #965, Col. Villas San Sebastián, 28045 Colima, México. E-mail address: [email protected] (X. Trujillo).
https://doi.org/10.1016/j.jpsychores.2020.109967 Received 19 November 2019; Received in revised form 13 February 2020; Accepted 13 February 2020 0022-3999/ © 2020 Elsevier Inc. All rights reserved.
Journal of Psychosomatic Research 131 (2020) 109967
J.A. Bricio-Barrios, et al.
An additional anthropometric assessment based on bioelectrical impedance analysis, estimated body fat percentage, muscle mass, and body water percentage was performed using the body composition monitor Tanita BC-568 (Tanita, Arlington, USA) according to the manufacturer's instructions with four recording electrodes: two placed on both feet and two in hands. The monitor was programmed previously with the data for each participant (sex, age, and height). In addition, to determine the arm and calf circumferences, a measuring tape (Lukfin W606P) was used in accordance to ISAK methodology [18]. The body fat percentage was based on NIH/WHO guidelines [21], which classifies the range of normal body fat percentage for individuals between 60 and 79 years of age as 24 to 36% for women and 13 to 25% for men. We conducted all anthropometric assessments from 9:00 to 13:00 h. Dietary analysis was carried out using the 24-h food recall technique; the participants listed all the food and drink consumed during the 24 h prior to the survey. The energy intake and consumption of carbohydrates, lipids, proteins, sugar, and fiber (in grams) was determined using the software nutre.in™ based on the Mexican Equivalent System [22] used widely in Mexico to analyze and design meal plans. The diet adequacy (%) was calculated for each participant using the following equation: (quantity ingested/quantity required) × 100. The energy requirement was calculated using the Harris-Benedict equation. An adequate macronutrient distribution was defined as 55% carbohydrates, with sugar comprising 10% of the total carbohydrate allowance; 30% lipids; and 15% proteins. Adequate dietary fiber was defined as daily consumption of at least 25 g/d [23]. Any estimates between 90 and 110% were considered adequate [24]. In accordance with the Helsinki Declaration Ethical and Good Clinical Practice Guidelines, all volunteers gave informed consent, and the work was approved by the ethical committee (CEICANCL131216-BIOALZR-11). In addition, an individual report was given to each participant including his results, interpretations, and general recommendations.
incompetence [8]. A distorted BIP could lead to dissatisfaction, especially when BIP is associated with social ideals [8,9]. During the aging process, a failure to adapt to age-related changes occurs, which could contribute to dissatisfaction with BIP and life in general [10]. An inaccurate BIP could be related to uncontrolled eating [8]. Conversely, an accurate BIP reflects an awareness of body weight, which leads to controlled eating habits and accurate physical activity. Therefore, it is important to have an accurate BIP to preserve health and prevent or control diseases related to inappropriate eating habits and physical activity [11]. The main aim of this study was to assess anthropometric and dietary indicators in older Mexican adults with accurate and inaccurate BIP. 2. Method 2.1. Study design and setting A total of 201 older adults (≥60 years) of both sexes from different geriatric centers (nursing homes and recreational groups) located in the metropolitan area of Colima, Mexico, were included in this cross-sectional study. We excluded individuals with severe cognitive impairment, blindness, or amputations. To ensure an accurate comparison of the variables, each participant underwent all evaluations on the same day. The Stunkard scale was used to evaluate BIP [12] stratified by sex [13–16]. Each scale consisted of nine silhouettes in a line, gradually increasing in girth. The first silhouette on the left portrayed a person with a BMI of 17 kg/m2, and each successive silhouette portrayed a BMI increased by two units until the last one corresponded to a BMI of 33 kg/m2 [17]. Each participant was shown the adequate version of the test according to sex and asked the following question: “From the following silhouettes, which is most like you?” The evaluator was careful to avoid influencing the responses of the participants. The height was determined by a Seca 213 portable stadiometer high rod (Seca, Hamburg, Germany) with the subject erect, with both heels together, their head on the Frankfort plane, in deep inspiration. Weight was measured using a Tanita BC-568 inner scan segmental body composition monitor (Tanita, Arlington, USA) in accordance with the International Society for the Advancement of Kinanthropometry (ISAK) methodology [18] with the participant standing on the center of the scale, distributing equally on both feet, with minimum clothing. The actual BMI was determined for each patient in kg/m2, and the concordance between the perceived and calculated BMIs was evaluated. The participant was considered part of the “accurate body perception” group when the BMI classification coincided with the BIP classification, otherwise there were part of the “inaccurate body perception” group [19]. For the perceived and measured nutritional status, the scale was adapted based on the criteria for BMI classifications in older adults using the following cut-off points [20]: low weight, < 23 kg/m2; normal weight, 23.1 to 27.9 kg/m2; overweight, 28 to 31.9 kg/m2; and obesity, ≥32 kg/m2. Accordingly, the BIP classifications were as follows: low weight, silhouettes 1 to 3; normal weight, silhouettes 4 to 6; overweight, silhouettes 7 to 8; and obesity, silhouette 9 [16]. When the participant had a BMI > 33 kg/m2, they were classified in silhouette 9.
2.2. Statistical analysis The Student's t-test for independent groups and one-way ANOVA were used to compare groups. A two-way ANOVA was used to analyze interactions between independent variables and the anthropometric and dietary variables. The chi-squared test was used to test associations between variables, and the odds ratios (ORs) and 95% confidence intervals (CIs) are reported. As a concordance index between nutritional status and BIP, the kappa coefficient was calculated and interpreted as follows: < 0.2, poor; 0.21 to 0.40, fair; 0.41 to 0.60, moderate; 0.61 to 0.80, good; and 0.81 to 1.00, very good. P < .05 was considered significant. The association between BIP and BMI was considered null when p > .05. Analyses were performed using IBM SPSS V.22 (IBM Corp, Armonk, NY, USA) and Epi-Info™ V. 7 statistical software packages. The results are expressed as frequencies and percentages or means and standard deviations. 3. Results We found discordances between BIP and BMI in 71.1% of
Table 1 Demographic and clinical characteristics of participants. Characteristic
Total (n = 201)
Inaccurate body perception (n = 143)
Accurate body perception (n = 58)
p⁎
Age (years)a Women Illiterate Participants in nursing homes History of diabetes mellitus History of hypertension
70.5 ± 9.3 77.6% (n = 156) 15.4% (n = 31) 5.5% (n = 11) 28.4% (n = 57) 51.7% (n = 104)
70.0 ± 9.8 75.5% (n = 108) 16.8% (n = 24) 4.9% (n = 7) 30.1% (n = 43) 54.5% (n = 78)
71.3 ± 7.6 82.8% (n = 48) 12.1% (n = 7) 6.9% (n = 4) 24.1% (n = 14) 44.8% (n = 26)
0.37 0.26 0.40 0.57 0.39 0.21
a ⁎
Values represent the mean ± standard deviation. Student t-test or chi squared test, as appropriate. 2
Journal of Psychosomatic Research 131 (2020) 109967
J.A. Bricio-Barrios, et al.
Table 2 Comparison of anthropometric and dietary variables according to body perception. Characteristic
Inaccurate body perception Total (n = 143)
Anthropometric variables Height (cm) Weight (kg) BMI (kg/m2) Body fat (%) Muscle mass (%) Body water (%) Arm circumference (cm) Calf circumference (cm) Dietary variables (percentage Energy (%) Carbohydrates (%) Sugar (%) Lipids (%) Proteins (%) Fiber (%)
Females (n = 108)
154.3 ± 10.8 150.6 ± 9.4 a 74.4 ± 15.3 71.1 ± 14.7a 30.7 ± 5.1 30.6 ± 5.1 36.9 ± 6.5 38.9 ± 5.7 a 59.6 ± 7.0 57.9 ± 6.2 a 45.6 ± 4.6 44.1 ± 3.9 a 32.9 ± 4.2 32.8 ± 4.6 35.8 ± 3.7 35.3 ± 3.8 b of adequacy of macronutrients) 83.9 ± 29.2 86.8 ± 30.6 b 81.6 ± 29.3 84.2 ± 30.9 b 149.2 ± 11.3 155.6 ± 11.6 90.5 ± 44.5 95.1 ± 45.7 94.9 ± 31.7 97.6 ± 31.9 107.3 ± 56.0 108.7 ± 58.5
Accurate body perception
p (total)
Males (n = 35)
Total (n = 58)
Females (n = 48)
Males (n = 10)
165.7 ± 5.4 84.5 ± 12.4 31.0 ± 4.9 30.6 ± 4.7 64.7 ± 7.1 50.1 ± 3.4 33.0 ± 2.8 37.1 ± 2.9
153.4 ± 8.5 61.9 ± 10.8 26.2 ± 3.7 34.0 ± 6.1 64.0 ± 11.3 47.2 ± 4.8 29.5 ± 3.9 33.6 ± 2.9
151.1 ± 7.0 60.1 ± 10.6** a 26.2 ± 4.0** b 35.5 ± 5.3** a 62.9 ± 12.1** a 46.2 ± 4.2** a 29.4 ± 4.2** 33.4 ± 3.1**
164.3 ± 6.9 70.8 ± 7.4** 26.1 ± 1.5** 26.9 ± 4.5* 69.3 ± 4.3 52.4 ± 3.3 29.8 ± 1.2** 34.5 ± 1.9*
0.5 < 0.001 < 0.001 0.005 0.001 0.02 < 0.001 < 0.001
75.1 ± 22.6 73.8 ± 22.2 129.7 ± 10.4 76.6 ± 37.7 86.8 ± 30.1 102.8 ± 47.9
96.5 ± 29.7 94.8 ± 27.0 154.9 ± 9.6 99.1 ± 49.2 107.8 ± 35.8 104.2 ± 54.0
97.4 ± 29.5* 95.7 ± 28.2* 158.7 ± 10.0 98.2 ± 45.8 110.5 ± 35.8* 102.5 ± 54.2
91.9 ± 31.4 90.1 ± 20.9* 137.1 ± 8.1 103.4 ± 66.0 95.3 ± 35.2 112.2 ± 54.8
0.007 0.004 0.7 0.2 0.01 0.7
Values represent the mean ± standard deviation. P-values were calculated using the Student's t-test for independent groups to compare the total (females plus males) inaccurate and accurate body perception groups. * p < .05; ** p < .001 comparing the sex-stratified groups using the Student's T-test for independent groups. ap < .05; bp < .001 comparison between sex (females vs males) using the Student's t-test for independent groups within each body perception group.
Fig. 1. Body image perceptions of participants in each category according to BMI classification. The colors inside the bars indicate the percentages of individuals in the indicated groups. Bold numbers represent the percentages of individuals with adequate body image perception (i.e., the perceived BMI matches the actual BMI).
mass, body water, arm circumference, calf circumference; p > .05 for all) and dietary variables (energy intake: carbohydrates, sugar, lipids, proteins, and fiber; p > .05 for all). Next, we considered caloric consumption. In the inaccurate BIP group, only 18.2% had adequate caloric consumption; 64.3% had deficient consumption and 17.5% had excess consumption. In the accurate BIP group, 20.7% had adequate caloric consumption: 48.3% had deficient consumption and 31.0% had excess consumption. However, no difference was found between these groups (p = .06). In addition, the inaccurate and accurate BIP groups had similar proportions, that considered adequate consumption of carbohydrates (15.4% vs. 24.1%), lipids (18.9% vs. 13.8%), proteins (22.4% vs. 17.2%), and fiber (55.2% vs. 53.4%). The percentage of participants adhering to an adequate diet was higher in the accurate BIP group than the inaccurate BIP group. The adequate BIP group had a higher mean energy, carbohydrate and protein intake than the inaccurate BIP group (p < .05 for all). Moreover, significant differences were observed between the BIP groups in the adequacy percentage of carbohydrate and protein consumption (Table 2), but when data were analyzed by sex, no significant
participants (n = 143); of these, 66.6% underestimated and 4.5% overestimated their body mass. We found no significant differences in basic demographic variables between the accurate vs. inaccurate BIP groups (Table 1). The inaccurate BIP group had a greater mean body mass than the accurate BIP group. In addition, the mean body weight, BMI, body fat content, muscle mass, and arm and calf circumferences were higher in the inaccurate BIP group than in the accurate BIP group (Table 2). When we analyzed the data by sex, significant differences were observed among women, with higher BMI, body fat, and arm and calf circumferences in the inaccurate BIP group compared to the accurate BIP group (p < .05 for all). In contrast, among men, significantly higher body weight, BMI, arm and calf circumference were observed in the inaccurate BIP group compared to the accurate BIP group (p < .05 for all). In both BIP groups, the men had greater weight, height, muscle mass, and body water, but lower body fat than women (Table 2). We also evaluated the interaction between sex and BIP concordance on the nutritional variables using two-way ANOVA but failed to show a significant difference in anthropometric measures (BMI, body fat, muscle 3
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percentage of dietary adequacy was higher among older adults with accurate BIP than among those with inaccurate BIP. A few previous studies investigated BIP in older adults and compared anthropometric and dietary variables between groups with different BIP. They reported higher percentages of accurate BIP for older adult populations similar in age to the population studied here. For example, in Portugal, Pinto et al. [8] evaluated BIP concordance in 72 older adults with a mean age of 71.6 ± 6.4 years; 29% had an accurate BIP, 40% underestimated their body weight, and 31% overestimated their body weight. In contrast, we found that 4.5% of individuals overestimated and 66.6% underestimated their body weight. In the Netherlands [25], a study that included participants aged 60 to 96 years reported a low proportion of individuals with an accurate BIP (kappa coefficient < 0.2). However, the percentage of participants who were overweight but considered themselves to have a normal weight was lower in that study (59%) than in the present study (84.3%; see Fig. 1). In addition, they found that women with high BMI tended to underestimate their body weight to a greater extent than those with normal BMIs, and older women tended to underestimate their body composition to a greater extent than younger women. Knight et al. [26] described another way to assess BIP. They acquired whole-body photographs of men and women over 65 years old living in Australia, allowing the participants to digitally manipulate the image to create thinner and larger sizes until they achieved the size matching their self-image; 60.5% of participants matched their actual size. A similar technique was applied by Pruis and Janowsky [27] to evaluate current BIP using the Stunkard scale with younger and older women, comparing BIP with body photographic images. A similar mean BIP was found among older women using both methods (mean Stunkard scale 4.67 ± 1.10 vs. 4.89 ± 1.03). In the present study, we found an association between high body adiposity and inaccurate BIP (OR = 2.8, 95% CI: 1.5–5.5), but no significant differences were found between BIP groups in the consumption of macronutrients. Diet is a basic element in avoiding the double burden of malnutrition in older individuals [2]. An optimal state of health depends on the optimal dietary pattern in older individuals. This goal should influence the selection, preparation, and consumption of food. Here, we showed that nutritional status also affected intrapersonal elements, such as BIP. A semiquantitative dietary analysis of adult Mexican women found that consumption of carbohydrates, sugary drinks, and refined foods was significantly associated with a larger BIP and high BMI. In addition, the consumption of fruits and vegetables was associated with a low risk of high BMI [26]. Our results indicate that older adults with inaccurate BIP reported dietary intake below their energy requirements, but it is possible that dietary patterns varied between individuals; that is, they may have had important differences in the amount and type of food consumed day by day. Monteagudo et al. [25] hypothesized that older adults underestimate their body weight due to a strong societal influence. However, a gradual and constant increase in body mass frequently occurs with age; thus, the development of an overweight status and obesity could be considered a normal process, a misinterpretation that negatively influences healthy body weight perception in this age group. In another study that included women and men over 50 years old from England, people with obesity reported a lower quality of life, lower life satisfaction, and higher depressive symptoms than peers without obesity; these parameters worsened as the degree of obesity increased [28]. Differences in BIP have been found according to age and gender. In Italy, individual expectations of weight loss were compared between young people (18 to 38 years old) and older adults (60 to 78 years old). They found no difference in body dissatisfaction between these groups, but among overweight and obese women, older women had lower expectations of body weight loss than younger women [29]. These findings agree with results from Lipowska et al. [30], who found that older men and women express lower satisfaction with their physical appearance compared to younger men and women. In addition, they
differences were found in these dietary variables. The concordance between nutritional status and BIP had a kappa coefficient of 0.03 (women: 0.02 men: 0.05), which was considered poor. However, BIP was in concordance with the actual BMI in 57.1% of the participants with low body weight and 63.8% of the participants with normal body weight. In contrast, only 4.3% of the overweight group and 6.2% of the obese group had concordance between BIP and actual BMI (Fig. 1). Among men, 100% of older individuals with low body weight (n = 1) had an accurate BIP. In contrast, among men with a normal body weight (n = 14), 64.3% had an accurate BIP and 35.7% considered themselves to have a low body weight. Among men who were overweight (n = 21), none had an accurate BIP and 81.0% considered themselves to have a normal body weight. Finally, among men who were obese (n = 9), 100% had an inaccurate BIP: 55.6% considered themselves to have a normal body weight, and 44.4% considered themselves to be overweight. Among women with low body weight (n = 13), 53.8% had an accurate BIP, 38.5% considered themselves to have a normal body weight, and the remainder considered themselves to be overweight. Among women with normal body weight (n = 55), 63.6% had an accurate BIP, 30.9% considered themselves underweight, and 3.6% considered themselves to be overweight. Among overweight women (n = 49), only 6.1% had an accurate BIP and 85.7% considered themselves to have a normal body weight. Finally, among women who were obese (n = 39), only 7.7% had an accurate BIP, 56.4% considered themselves to have a normal body weight, 30.8% considered themselves to be overweight, and 5.1% considered themselves to be underweight. Subsequently, we analyzed the mean values of body composition variables according to the nutritional status classified by the perceived silhouette, in which the greater percentage of body fat and lower body water and muscle mass are appreciated (see Fig. 3). The same results were obtained when we analyzed the perceived silhouette in women, who only exhibited significance in body fat and water (Supplementary Table 1). Older adults who were overweight and obese tended to underestimate their body classification (Fig. 1). Older adults with inaccurate BIP had a 2.8–fold greater risk of high adiposity compared to those with accurate BIP (95% CI: 1.5–5.5, p = .001; Fig. 2). When we stratified this association by sex, women had an OR of 2.4 (95% CI: 1.2–5.0, p = .01) and men an OR of 5.1 (95% CI: 1.0–26.5, p = .03). 4. Discussion One of the main results of our study was that older adult participants with inaccurate BIP had higher BMI, body fat percentage, and calf and arm circumference than those with accurate BIP. In addition, the
Fig. 2. Distribution of body image concordance with the real BMI in relation to adiposity. Normal adiposity = fat percentage between 24 and 36% for women or 13 and 25% for men. **Chi-squared test. 4
Journal of Psychosomatic Research 131 (2020) 109967
J.A. Bricio-Barrios, et al.
Fig. 3. Mean percentage of body fat (A), muscle (B), and water (C) according to the perceived silhouette for low weight (silhouettes 1 to 3); normal weight (silhouettes 4 to 6); overweight (silhouettes 7 to 8); and obesity (silhouette 9). asignificant difference (p < .001) (vs. normal weight group, bsignificant difference (p < .001) vs. overweight group, csignificant difference (p < .001) vs. obesity group, one-way ANOVA.
5
Journal of Psychosomatic Research 131 (2020) 109967
J.A. Bricio-Barrios, et al.
demonstrated that older women considered their bodies as objects evaluated according to appearance; in contrast, men considered their bodies in terms of a process, and they focused on efficient functioning. In the present study, the concordance between nutritional status and BIP was weak among both men and women, but the risk of having high adiposity was higher among women with inaccurate BIP. One limitation of this study is the small number of males in the sample. The group with inadequate BIP presented differences in the anthropometric profile, but they were not significant among men. Regarding the scales used for BIP measurements, each silhouette on the Stunkard scale was equivalent with a BMI status [17]; therefore, new studies are required to analyze this relationship between BIP and its equivalent body adiposity. Finally, the quantification of diet adequacy is strongly determined by how the energy requirement is calculated based on the use of predictive equations, we expected those equations to be validated for this age group [31]. The 24-h diet reminder is usually one of the methods commonly used to assess the dietary pattern, but it fails to detect distortions in the dietary pattern [32], such as binge eating disorder [33], so the diet pattern could be studied in more detail. Other important aspects related to BIP need to be investigated. For example, satisfaction with current BIP should be used to ask participants to indicate the silhouette they consider the ideal body form. In addition, it would be interesting to record the verbal BIP and compare it to BMI classification and daily food consumption in order to obtain more detail about daily dietary intake. In summary, this study shows that inaccurate BIP among older adults is related to high anthropometric values (fat percentage, BMI, muscle mass, calf and arm circumference) and low dietary adequacy (carbohydrate and protein intake). The concordance between BIP and actual BMI was poor among both men and women. In addition, high body adiposity was associated with inaccurate BIP. Finally, the overweight and obese older men and women more greatly underestimated BIP than normal or low body weight men and women. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jpsychores.2020.109967.
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15] [16]
[17]
[18]
[19]
[20] [21]
[22]
Declaration of Competing Interest
[23]
The authors have no competing interests to report. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
[24]
Acknowledgements
[25]
The authors want to thank the study participants for their contribution to the research and to the geriatric centers for the facilities to perform this research.
[26] [27] [28]
References [29] [1] World Health Organization, Ageing and Life-Course. Facts About Ageing, http:// www.who.int/ageing/about/facts/en/, (2014) accessed 08 April 2019. [2] I. Elmadfa, A.L. Meyer, Body composition, changing physiological functions and nutrient requirements of the elderly, Ann. Nutr. Metab. 52 (1) (2008) 2–5. [3] L. Abarca-Gómez, Z.A. Abdeen, Z.A. Hamid, N.M. Abu-Rmeileh, B. Acosta-Cazares, C. Acuin, C. Agyemang, Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 populationbased measurement studies in 128· 9 million children, adolescents, and adults, Lancet 390 (10113) (2017) 2627–2642. [4] H. Bixby, J. Bentham, B. Zhou, M. Di Cesare, C.J. Paciorek, NCD risk factor collaboration, rising rural body-mass index is the main driver of the global obesity epidemic, Nature 569 (7755) (2019) 260–264. [5] World Health Organization, Global status report on noncommunicable diseases, https://apps.who.int/iris/bitstream/handle/10665/148114/9789241564854_eng. pdf;jsessionid=20C008CA5AF1682DA42A89BB962A9B7D?sequence=1 (2014) accessed 11 September 2019. [6] National Institute for Geography and Statistics, Characteristics of Defunctions
[30]
[31]
[32]
[33]
6
Registered in Mexico During 2017, https://www.inegi.org.mx/contenidos/ saladeprensa/boletines/2018/EstSociodemo/DEFUNCIONES2017.pdf, (2018) accessed 13 April 2019. K.A. Bailey, K.L. Gammage, C. van Ingen, How do you define body image? Exploring conceptual gaps in understandings of body image at an exercise facility, Body Image. 23 (2017) 69–79. E. Pinto, D. Carção, D. Tomé, J. Faustino, L. Campaniço, M. Soares, N. Braz, Body mass index and body image perception in older adults, Geriatr. Med. Care. 1 (2) (2017) 1–3. C.B. Becker, C.L. Verzijl, L.S. Kilpela, S.A. Wilfred, T. Stewart, Body image in adult women: associations with health behaviors, quality of life, and functional impairment, J. Health Psychol. 4 (11) (2017) 1536–1547. A.D. Watt, C.A. Konnert, C.E.C. Speirs, The mediating roles of primary and secondary control in the relationship between body satisfaction and subjective wellbeing among middle-aged and older women, J. Gerontol. 72 (4) (2015) 603–612. S.C. Lemon, M.C. Rosal, J. Zapka, A. Borg, V. Andersen, Contributions of weight perceptions to weight loss attempts: differences by body mass index and gender, Body Image. 6 (2) (2009) 90–96. A.J. Stunkard, T. Sørensen, F. Schulsinger, Use of the Danish adoption register for the study of obesity and thinness, Res. Publ. Assoc. Res. Nerv. Ment. Dis. 60 (1983) 115–120. M. Kaufer-Horwitz, J. Martínez, L.M. Goti-Rodríguez, H. Ávila-Rosas, Association between measured BMI and self-perceived body size in Mexican adults, Ann. Hum. Biol. 33 (5–6) (2006) 536–545. J.M. Kearney, M.J. Kearney, S. McElhone, M.J. Gibney, Methods used to conduct the pan-European Union survey on consumer attitudes to physical activity, body weight and health, Public Health Nutr. 2 (1a) (1999) 79–86. D.M. Lewis, F.M. Cachelin, Body image, body dissatisfaction, and eating attitudes in midlife and elderly women, Eat. Disord. 9 (1) (2001) 29–39. S. McElhone, J.M. Kearney, I. Giachetti, H.J.F. Zunft, J.A. Martínez, Body image perception in relation to recent weight changes and strategies for weight loss in a nationally representative sample in the European Union, Public Health Nutr. 2 (1a) (1999) 143–151. P. Acevedo, N. López-Ejeda, I. Alférez-García, J.R. Martínez-Álvarez, A.M.D. Villarino, M.D. Cabañas, Marrodán, Body mass index through self-reported data and body image perception in Spanish adults attending dietary consultation, Nutrition 30 (6) (2014) 679–684. S. Ganhão-Arranhado, C. Paúl, R. Ramalho, P. Pereira, Food insecurity, weight and nutritional status among older adults attending senior centres in Lisbon, Arch. Gerontol. Geriatr. 78 (2018) 81–88. D.A. Naigaga, D. Jahanlu, H.M. Claudius, A.K. Gjerlaug, I. Barikmo, S. Henjum, Body size perceptions and preferences favor overweight in adult Saharawi refugees, Nutr. J. 17 (1) (2018) 17. G. Conroy-Ferreccio, Mistakes on measurement of body mass index in elder population, Nutr. Hosp. 34 (1) (2017) 251. D. Gallagher, S.B. Heymsfield, M. Heo, S.A. Jebb, R.P. Murgatroyd, Y. Sakamoto, Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index, Am. J. Clin. Nutr. 72 (3) (2000) 694–701. A.B. Pérez-Lizaur, B. Palacios-González, A.L. Castro-Becerra, I. Flores-Galicia, Sistema mexicano de alimentos equivalentes, fourth ed., Fomento de Nutrición y Salud, Mexico, 2014. US Food and Drug Administration, Nutrition facts label, dietary fiber, https://www. accessdata.fda.gov/scripts/interactivenutritionfactslabel/factsheets/Dietary_Fiber. pdf (2009) accessed 08 April 2019. E. Winzer, M. Luger, K. Schindler, Using digital photography in a clinical setting: a valid, accurate, and applicable method to assess food intake, Eur. J. Clin. Nutr. 72 (6) (2018) 879–887. C. Monteagudo-Sanchez, S.C. Dijkstra, M. Visser, Self-perception of body weight status in older Dutch adults, J. Nutr. Health Aging 19 (6) (2015) 612–618. T. Knight, K. Illingworth, L. Ricciardelli, Health implications of body size perception and weight tolerance in older adults, J. Health Psychol. 14 (3) (2009) 425–434. T.A. Pruis, J.S. Janowsky, Assessment of body image in younger and older women, J. Gen. Psychol. 137 (3) (2010) 225–238. S.E. Jackson, R.J. Beeken, J. Wardle, Obesity, perceived weight discrimination, and psychological well-being in older adults in England, Obesity 23 (5) (2015) 1105–1111. E.H. Evans, L.G. Boothroyd, E. Muscariello, B.C.M. Stephan, G. Nasti, A. Colantuoni, M. Siervo, Lower weight loss expectations and healthier eating attitudes in older overweight and obese women attempting weight loss, Clin. Obesity. 5 (3) (2015) 136–144. M. Lipowska, M. Lipowski, H. Olszewski, D. Dykalska-Bieck, Gender differences in body-esteem among seniors: beauty and health considerations, Arch. Gerontol. Geriatr. 67 (2016) 160–170. D.P. Reidlinger, J.M. Willis, K. Whelan, Resting metabolic rate and anthropometry in older people: a comparison of measured and calculated values, J. Hum. Nutr. Diet. 28 (1) (2015) 72–84. Y. Sun, D.L. Roth, C.S. Ritchie, K.L. Burgio, J.L. Locher, Reliability and predictive validity of energy intake measures from the 24-hour dietary recalls of homebound older adults, J. Am. Diet. Assoc. 110 (5) (2010) 773–778. J. Thompson, Body image, eating disorders, and obesity: an integrative guide for assessment and treatment, Am. Psychol. Associ. (2001), https://doi.org/10.1037/ 10502-000.
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Journal of Psychosomatic Research journal homepage: www.elsevier.com/locate/jpsychores
Anthropometric and dietary differences among Mexican older adults with and without adequate body image perception
T
J.A. Bricio-Barriosa, M. Ríos-Silvab,c, R. García-Rodríguezd, M. Huertab, M. Del Toro-Equihuaa, ⁎ M. Ortiz-Mesinae, Z. Urzúa-Garcíaf, X. Trujillob, a
Faculty of Medicine, Nutrition School, Universidad de Colima, Colima, Mexico University Centre for Biomedical Research, Universidad de Colima, Colima, Mexico c University Centre for Biomedical Research, Universidad de Colima-Cátedras-CONACyT, Colima, Mexico d Psychology Faculty, Universidad de Colima, Colima, Mexico e Family Unit Medicine #1, Instituto Mexicano del Seguro Social, Colima, Mexico f Family Unit Medicine #17, Instituto Mexicano del Seguro Social, Colima, Mexico b
A R T I C LE I N FO
A B S T R A C T
Keywords: Body image perception Nutritional status Older adults
Objective: We compared anthropometric and dietary indicators between groups of older Mexican adults with accurate or inaccurate body image perception (BIP). Methods: A cross-sectional study was carried out with 201 older adults (age ≥ 60 years) of both sexes who completed the Stunkard scale for BIP, which consists of nine silhouettes with an equivalent of body mass index (BMI) status, then, the accuracy with their real BMI was calculated and reported energy and macronutrient intake through a 24-h dietary recall directed by different geriatric centers in Colima, Mexico. Basic anthropometry and bioelectrical impedance analyses were performed. Results: We found that 71.1% of the older adults had inaccurate BIP; 66.6% underestimated their body mass and 4.5% overestimated their body mass, the other 28.9% hat accurate BIP. The overall concordance between the real nutritional status and BIP was poor (kappa coefficient = 0.03). The inaccurate BIP group had a significantly higher mean body mass index, body fat percentage, muscle mass, and arm and calf circumference compared to the accurate BIP group (p < .001); only 4.3% of the older adults who were overweight and 6.2% who were obese had an accurate BIP. Regarding dietary consumption, we found significant differences only in energy and carbohydrate intake between the two groups. Finally, excess body fat was associated with an inaccurate BIP (OR: 2.8, 95% CI: 1.5–5.5). Conclusion: In older adults, an inaccurate BIP is generally associated with high anthropometric values and less than adequate dietary intake.
1. Introduction The older adult population is increasing worldwide, with estimations that the proportion of people older than 60 years of age will double from 11% in 2000 to 22% in 2050 [1]. Several physical body changes are inherent to age, including weight gain, reduced muscle mass and tone, and mass fat gain, as well as a reduced basal metabolic rate [2]. Excess body weight and the presence of obesity results in unhealthy nutritional transition, such as an increase in nutrient-poor, energy-dense foods, which can lead to stunted growth and weight gain. This condition results in higher body mass index (BMI) and worse health outcomes throughout life [3]. The BMI increases at the same rate
or faster in rural areas than cities, particularly in low- and middle-income countries [4]. Increasing rural BMI is the greatest contributor to the increasing BMI in low- and middle-income regions and the world over the last 33 years, which challenges the current paradigm of urban living and urbanization as the key driver of the global epidemic of obesity. Accordingly, a significant increase has occurred in the risk of chronic diseases, such as cardiovascular disease [5], which is the leading cause of death in Mexicans > 65 years of age [6]. Body image perception (BIP) is a cognitive, subjective representation of self-body appearance and the related sensations experienced [7]. At an old age, BIP may become distorted due to social association of the advanced age with negative characteristics, such as disability and
⁎ Corresponding author at: Universidad de Colima, Centro Universitario de Investigaciones Biomédicas, Av. 25 de Julio #965, Col. Villas San Sebastián, 28045 Colima, México. E-mail address: [email protected] (X. Trujillo).
https://doi.org/10.1016/j.jpsychores.2020.109967 Received 19 November 2019; Received in revised form 13 February 2020; Accepted 13 February 2020 0022-3999/ © 2020 Elsevier Inc. All rights reserved.
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An additional anthropometric assessment based on bioelectrical impedance analysis, estimated body fat percentage, muscle mass, and body water percentage was performed using the body composition monitor Tanita BC-568 (Tanita, Arlington, USA) according to the manufacturer's instructions with four recording electrodes: two placed on both feet and two in hands. The monitor was programmed previously with the data for each participant (sex, age, and height). In addition, to determine the arm and calf circumferences, a measuring tape (Lukfin W606P) was used in accordance to ISAK methodology [18]. The body fat percentage was based on NIH/WHO guidelines [21], which classifies the range of normal body fat percentage for individuals between 60 and 79 years of age as 24 to 36% for women and 13 to 25% for men. We conducted all anthropometric assessments from 9:00 to 13:00 h. Dietary analysis was carried out using the 24-h food recall technique; the participants listed all the food and drink consumed during the 24 h prior to the survey. The energy intake and consumption of carbohydrates, lipids, proteins, sugar, and fiber (in grams) was determined using the software nutre.in™ based on the Mexican Equivalent System [22] used widely in Mexico to analyze and design meal plans. The diet adequacy (%) was calculated for each participant using the following equation: (quantity ingested/quantity required) × 100. The energy requirement was calculated using the Harris-Benedict equation. An adequate macronutrient distribution was defined as 55% carbohydrates, with sugar comprising 10% of the total carbohydrate allowance; 30% lipids; and 15% proteins. Adequate dietary fiber was defined as daily consumption of at least 25 g/d [23]. Any estimates between 90 and 110% were considered adequate [24]. In accordance with the Helsinki Declaration Ethical and Good Clinical Practice Guidelines, all volunteers gave informed consent, and the work was approved by the ethical committee (CEICANCL131216-BIOALZR-11). In addition, an individual report was given to each participant including his results, interpretations, and general recommendations.
incompetence [8]. A distorted BIP could lead to dissatisfaction, especially when BIP is associated with social ideals [8,9]. During the aging process, a failure to adapt to age-related changes occurs, which could contribute to dissatisfaction with BIP and life in general [10]. An inaccurate BIP could be related to uncontrolled eating [8]. Conversely, an accurate BIP reflects an awareness of body weight, which leads to controlled eating habits and accurate physical activity. Therefore, it is important to have an accurate BIP to preserve health and prevent or control diseases related to inappropriate eating habits and physical activity [11]. The main aim of this study was to assess anthropometric and dietary indicators in older Mexican adults with accurate and inaccurate BIP. 2. Method 2.1. Study design and setting A total of 201 older adults (≥60 years) of both sexes from different geriatric centers (nursing homes and recreational groups) located in the metropolitan area of Colima, Mexico, were included in this cross-sectional study. We excluded individuals with severe cognitive impairment, blindness, or amputations. To ensure an accurate comparison of the variables, each participant underwent all evaluations on the same day. The Stunkard scale was used to evaluate BIP [12] stratified by sex [13–16]. Each scale consisted of nine silhouettes in a line, gradually increasing in girth. The first silhouette on the left portrayed a person with a BMI of 17 kg/m2, and each successive silhouette portrayed a BMI increased by two units until the last one corresponded to a BMI of 33 kg/m2 [17]. Each participant was shown the adequate version of the test according to sex and asked the following question: “From the following silhouettes, which is most like you?” The evaluator was careful to avoid influencing the responses of the participants. The height was determined by a Seca 213 portable stadiometer high rod (Seca, Hamburg, Germany) with the subject erect, with both heels together, their head on the Frankfort plane, in deep inspiration. Weight was measured using a Tanita BC-568 inner scan segmental body composition monitor (Tanita, Arlington, USA) in accordance with the International Society for the Advancement of Kinanthropometry (ISAK) methodology [18] with the participant standing on the center of the scale, distributing equally on both feet, with minimum clothing. The actual BMI was determined for each patient in kg/m2, and the concordance between the perceived and calculated BMIs was evaluated. The participant was considered part of the “accurate body perception” group when the BMI classification coincided with the BIP classification, otherwise there were part of the “inaccurate body perception” group [19]. For the perceived and measured nutritional status, the scale was adapted based on the criteria for BMI classifications in older adults using the following cut-off points [20]: low weight, < 23 kg/m2; normal weight, 23.1 to 27.9 kg/m2; overweight, 28 to 31.9 kg/m2; and obesity, ≥32 kg/m2. Accordingly, the BIP classifications were as follows: low weight, silhouettes 1 to 3; normal weight, silhouettes 4 to 6; overweight, silhouettes 7 to 8; and obesity, silhouette 9 [16]. When the participant had a BMI > 33 kg/m2, they were classified in silhouette 9.
2.2. Statistical analysis The Student's t-test for independent groups and one-way ANOVA were used to compare groups. A two-way ANOVA was used to analyze interactions between independent variables and the anthropometric and dietary variables. The chi-squared test was used to test associations between variables, and the odds ratios (ORs) and 95% confidence intervals (CIs) are reported. As a concordance index between nutritional status and BIP, the kappa coefficient was calculated and interpreted as follows: < 0.2, poor; 0.21 to 0.40, fair; 0.41 to 0.60, moderate; 0.61 to 0.80, good; and 0.81 to 1.00, very good. P < .05 was considered significant. The association between BIP and BMI was considered null when p > .05. Analyses were performed using IBM SPSS V.22 (IBM Corp, Armonk, NY, USA) and Epi-Info™ V. 7 statistical software packages. The results are expressed as frequencies and percentages or means and standard deviations. 3. Results We found discordances between BIP and BMI in 71.1% of
Table 1 Demographic and clinical characteristics of participants. Characteristic
Total (n = 201)
Inaccurate body perception (n = 143)
Accurate body perception (n = 58)
p⁎
Age (years)a Women Illiterate Participants in nursing homes History of diabetes mellitus History of hypertension
70.5 ± 9.3 77.6% (n = 156) 15.4% (n = 31) 5.5% (n = 11) 28.4% (n = 57) 51.7% (n = 104)
70.0 ± 9.8 75.5% (n = 108) 16.8% (n = 24) 4.9% (n = 7) 30.1% (n = 43) 54.5% (n = 78)
71.3 ± 7.6 82.8% (n = 48) 12.1% (n = 7) 6.9% (n = 4) 24.1% (n = 14) 44.8% (n = 26)
0.37 0.26 0.40 0.57 0.39 0.21
a ⁎
Values represent the mean ± standard deviation. Student t-test or chi squared test, as appropriate. 2
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Table 2 Comparison of anthropometric and dietary variables according to body perception. Characteristic
Inaccurate body perception Total (n = 143)
Anthropometric variables Height (cm) Weight (kg) BMI (kg/m2) Body fat (%) Muscle mass (%) Body water (%) Arm circumference (cm) Calf circumference (cm) Dietary variables (percentage Energy (%) Carbohydrates (%) Sugar (%) Lipids (%) Proteins (%) Fiber (%)
Females (n = 108)
154.3 ± 10.8 150.6 ± 9.4 a 74.4 ± 15.3 71.1 ± 14.7a 30.7 ± 5.1 30.6 ± 5.1 36.9 ± 6.5 38.9 ± 5.7 a 59.6 ± 7.0 57.9 ± 6.2 a 45.6 ± 4.6 44.1 ± 3.9 a 32.9 ± 4.2 32.8 ± 4.6 35.8 ± 3.7 35.3 ± 3.8 b of adequacy of macronutrients) 83.9 ± 29.2 86.8 ± 30.6 b 81.6 ± 29.3 84.2 ± 30.9 b 149.2 ± 11.3 155.6 ± 11.6 90.5 ± 44.5 95.1 ± 45.7 94.9 ± 31.7 97.6 ± 31.9 107.3 ± 56.0 108.7 ± 58.5
Accurate body perception
p (total)
Males (n = 35)
Total (n = 58)
Females (n = 48)
Males (n = 10)
165.7 ± 5.4 84.5 ± 12.4 31.0 ± 4.9 30.6 ± 4.7 64.7 ± 7.1 50.1 ± 3.4 33.0 ± 2.8 37.1 ± 2.9
153.4 ± 8.5 61.9 ± 10.8 26.2 ± 3.7 34.0 ± 6.1 64.0 ± 11.3 47.2 ± 4.8 29.5 ± 3.9 33.6 ± 2.9
151.1 ± 7.0 60.1 ± 10.6** a 26.2 ± 4.0** b 35.5 ± 5.3** a 62.9 ± 12.1** a 46.2 ± 4.2** a 29.4 ± 4.2** 33.4 ± 3.1**
164.3 ± 6.9 70.8 ± 7.4** 26.1 ± 1.5** 26.9 ± 4.5* 69.3 ± 4.3 52.4 ± 3.3 29.8 ± 1.2** 34.5 ± 1.9*
0.5 < 0.001 < 0.001 0.005 0.001 0.02 < 0.001 < 0.001
75.1 ± 22.6 73.8 ± 22.2 129.7 ± 10.4 76.6 ± 37.7 86.8 ± 30.1 102.8 ± 47.9
96.5 ± 29.7 94.8 ± 27.0 154.9 ± 9.6 99.1 ± 49.2 107.8 ± 35.8 104.2 ± 54.0
97.4 ± 29.5* 95.7 ± 28.2* 158.7 ± 10.0 98.2 ± 45.8 110.5 ± 35.8* 102.5 ± 54.2
91.9 ± 31.4 90.1 ± 20.9* 137.1 ± 8.1 103.4 ± 66.0 95.3 ± 35.2 112.2 ± 54.8
0.007 0.004 0.7 0.2 0.01 0.7
Values represent the mean ± standard deviation. P-values were calculated using the Student's t-test for independent groups to compare the total (females plus males) inaccurate and accurate body perception groups. * p < .05; ** p < .001 comparing the sex-stratified groups using the Student's T-test for independent groups. ap < .05; bp < .001 comparison between sex (females vs males) using the Student's t-test for independent groups within each body perception group.
Fig. 1. Body image perceptions of participants in each category according to BMI classification. The colors inside the bars indicate the percentages of individuals in the indicated groups. Bold numbers represent the percentages of individuals with adequate body image perception (i.e., the perceived BMI matches the actual BMI).
mass, body water, arm circumference, calf circumference; p > .05 for all) and dietary variables (energy intake: carbohydrates, sugar, lipids, proteins, and fiber; p > .05 for all). Next, we considered caloric consumption. In the inaccurate BIP group, only 18.2% had adequate caloric consumption; 64.3% had deficient consumption and 17.5% had excess consumption. In the accurate BIP group, 20.7% had adequate caloric consumption: 48.3% had deficient consumption and 31.0% had excess consumption. However, no difference was found between these groups (p = .06). In addition, the inaccurate and accurate BIP groups had similar proportions, that considered adequate consumption of carbohydrates (15.4% vs. 24.1%), lipids (18.9% vs. 13.8%), proteins (22.4% vs. 17.2%), and fiber (55.2% vs. 53.4%). The percentage of participants adhering to an adequate diet was higher in the accurate BIP group than the inaccurate BIP group. The adequate BIP group had a higher mean energy, carbohydrate and protein intake than the inaccurate BIP group (p < .05 for all). Moreover, significant differences were observed between the BIP groups in the adequacy percentage of carbohydrate and protein consumption (Table 2), but when data were analyzed by sex, no significant
participants (n = 143); of these, 66.6% underestimated and 4.5% overestimated their body mass. We found no significant differences in basic demographic variables between the accurate vs. inaccurate BIP groups (Table 1). The inaccurate BIP group had a greater mean body mass than the accurate BIP group. In addition, the mean body weight, BMI, body fat content, muscle mass, and arm and calf circumferences were higher in the inaccurate BIP group than in the accurate BIP group (Table 2). When we analyzed the data by sex, significant differences were observed among women, with higher BMI, body fat, and arm and calf circumferences in the inaccurate BIP group compared to the accurate BIP group (p < .05 for all). In contrast, among men, significantly higher body weight, BMI, arm and calf circumference were observed in the inaccurate BIP group compared to the accurate BIP group (p < .05 for all). In both BIP groups, the men had greater weight, height, muscle mass, and body water, but lower body fat than women (Table 2). We also evaluated the interaction between sex and BIP concordance on the nutritional variables using two-way ANOVA but failed to show a significant difference in anthropometric measures (BMI, body fat, muscle 3
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percentage of dietary adequacy was higher among older adults with accurate BIP than among those with inaccurate BIP. A few previous studies investigated BIP in older adults and compared anthropometric and dietary variables between groups with different BIP. They reported higher percentages of accurate BIP for older adult populations similar in age to the population studied here. For example, in Portugal, Pinto et al. [8] evaluated BIP concordance in 72 older adults with a mean age of 71.6 ± 6.4 years; 29% had an accurate BIP, 40% underestimated their body weight, and 31% overestimated their body weight. In contrast, we found that 4.5% of individuals overestimated and 66.6% underestimated their body weight. In the Netherlands [25], a study that included participants aged 60 to 96 years reported a low proportion of individuals with an accurate BIP (kappa coefficient < 0.2). However, the percentage of participants who were overweight but considered themselves to have a normal weight was lower in that study (59%) than in the present study (84.3%; see Fig. 1). In addition, they found that women with high BMI tended to underestimate their body weight to a greater extent than those with normal BMIs, and older women tended to underestimate their body composition to a greater extent than younger women. Knight et al. [26] described another way to assess BIP. They acquired whole-body photographs of men and women over 65 years old living in Australia, allowing the participants to digitally manipulate the image to create thinner and larger sizes until they achieved the size matching their self-image; 60.5% of participants matched their actual size. A similar technique was applied by Pruis and Janowsky [27] to evaluate current BIP using the Stunkard scale with younger and older women, comparing BIP with body photographic images. A similar mean BIP was found among older women using both methods (mean Stunkard scale 4.67 ± 1.10 vs. 4.89 ± 1.03). In the present study, we found an association between high body adiposity and inaccurate BIP (OR = 2.8, 95% CI: 1.5–5.5), but no significant differences were found between BIP groups in the consumption of macronutrients. Diet is a basic element in avoiding the double burden of malnutrition in older individuals [2]. An optimal state of health depends on the optimal dietary pattern in older individuals. This goal should influence the selection, preparation, and consumption of food. Here, we showed that nutritional status also affected intrapersonal elements, such as BIP. A semiquantitative dietary analysis of adult Mexican women found that consumption of carbohydrates, sugary drinks, and refined foods was significantly associated with a larger BIP and high BMI. In addition, the consumption of fruits and vegetables was associated with a low risk of high BMI [26]. Our results indicate that older adults with inaccurate BIP reported dietary intake below their energy requirements, but it is possible that dietary patterns varied between individuals; that is, they may have had important differences in the amount and type of food consumed day by day. Monteagudo et al. [25] hypothesized that older adults underestimate their body weight due to a strong societal influence. However, a gradual and constant increase in body mass frequently occurs with age; thus, the development of an overweight status and obesity could be considered a normal process, a misinterpretation that negatively influences healthy body weight perception in this age group. In another study that included women and men over 50 years old from England, people with obesity reported a lower quality of life, lower life satisfaction, and higher depressive symptoms than peers without obesity; these parameters worsened as the degree of obesity increased [28]. Differences in BIP have been found according to age and gender. In Italy, individual expectations of weight loss were compared between young people (18 to 38 years old) and older adults (60 to 78 years old). They found no difference in body dissatisfaction between these groups, but among overweight and obese women, older women had lower expectations of body weight loss than younger women [29]. These findings agree with results from Lipowska et al. [30], who found that older men and women express lower satisfaction with their physical appearance compared to younger men and women. In addition, they
differences were found in these dietary variables. The concordance between nutritional status and BIP had a kappa coefficient of 0.03 (women: 0.02 men: 0.05), which was considered poor. However, BIP was in concordance with the actual BMI in 57.1% of the participants with low body weight and 63.8% of the participants with normal body weight. In contrast, only 4.3% of the overweight group and 6.2% of the obese group had concordance between BIP and actual BMI (Fig. 1). Among men, 100% of older individuals with low body weight (n = 1) had an accurate BIP. In contrast, among men with a normal body weight (n = 14), 64.3% had an accurate BIP and 35.7% considered themselves to have a low body weight. Among men who were overweight (n = 21), none had an accurate BIP and 81.0% considered themselves to have a normal body weight. Finally, among men who were obese (n = 9), 100% had an inaccurate BIP: 55.6% considered themselves to have a normal body weight, and 44.4% considered themselves to be overweight. Among women with low body weight (n = 13), 53.8% had an accurate BIP, 38.5% considered themselves to have a normal body weight, and the remainder considered themselves to be overweight. Among women with normal body weight (n = 55), 63.6% had an accurate BIP, 30.9% considered themselves underweight, and 3.6% considered themselves to be overweight. Among overweight women (n = 49), only 6.1% had an accurate BIP and 85.7% considered themselves to have a normal body weight. Finally, among women who were obese (n = 39), only 7.7% had an accurate BIP, 56.4% considered themselves to have a normal body weight, 30.8% considered themselves to be overweight, and 5.1% considered themselves to be underweight. Subsequently, we analyzed the mean values of body composition variables according to the nutritional status classified by the perceived silhouette, in which the greater percentage of body fat and lower body water and muscle mass are appreciated (see Fig. 3). The same results were obtained when we analyzed the perceived silhouette in women, who only exhibited significance in body fat and water (Supplementary Table 1). Older adults who were overweight and obese tended to underestimate their body classification (Fig. 1). Older adults with inaccurate BIP had a 2.8–fold greater risk of high adiposity compared to those with accurate BIP (95% CI: 1.5–5.5, p = .001; Fig. 2). When we stratified this association by sex, women had an OR of 2.4 (95% CI: 1.2–5.0, p = .01) and men an OR of 5.1 (95% CI: 1.0–26.5, p = .03). 4. Discussion One of the main results of our study was that older adult participants with inaccurate BIP had higher BMI, body fat percentage, and calf and arm circumference than those with accurate BIP. In addition, the
Fig. 2. Distribution of body image concordance with the real BMI in relation to adiposity. Normal adiposity = fat percentage between 24 and 36% for women or 13 and 25% for men. **Chi-squared test. 4
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Fig. 3. Mean percentage of body fat (A), muscle (B), and water (C) according to the perceived silhouette for low weight (silhouettes 1 to 3); normal weight (silhouettes 4 to 6); overweight (silhouettes 7 to 8); and obesity (silhouette 9). asignificant difference (p < .001) (vs. normal weight group, bsignificant difference (p < .001) vs. overweight group, csignificant difference (p < .001) vs. obesity group, one-way ANOVA.
5
Journal of Psychosomatic Research 131 (2020) 109967
J.A. Bricio-Barrios, et al.
demonstrated that older women considered their bodies as objects evaluated according to appearance; in contrast, men considered their bodies in terms of a process, and they focused on efficient functioning. In the present study, the concordance between nutritional status and BIP was weak among both men and women, but the risk of having high adiposity was higher among women with inaccurate BIP. One limitation of this study is the small number of males in the sample. The group with inadequate BIP presented differences in the anthropometric profile, but they were not significant among men. Regarding the scales used for BIP measurements, each silhouette on the Stunkard scale was equivalent with a BMI status [17]; therefore, new studies are required to analyze this relationship between BIP and its equivalent body adiposity. Finally, the quantification of diet adequacy is strongly determined by how the energy requirement is calculated based on the use of predictive equations, we expected those equations to be validated for this age group [31]. The 24-h diet reminder is usually one of the methods commonly used to assess the dietary pattern, but it fails to detect distortions in the dietary pattern [32], such as binge eating disorder [33], so the diet pattern could be studied in more detail. Other important aspects related to BIP need to be investigated. For example, satisfaction with current BIP should be used to ask participants to indicate the silhouette they consider the ideal body form. In addition, it would be interesting to record the verbal BIP and compare it to BMI classification and daily food consumption in order to obtain more detail about daily dietary intake. In summary, this study shows that inaccurate BIP among older adults is related to high anthropometric values (fat percentage, BMI, muscle mass, calf and arm circumference) and low dietary adequacy (carbohydrate and protein intake). The concordance between BIP and actual BMI was poor among both men and women. In addition, high body adiposity was associated with inaccurate BIP. Finally, the overweight and obese older men and women more greatly underestimated BIP than normal or low body weight men and women. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jpsychores.2020.109967.
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15] [16]
[17]
[18]
[19]
[20] [21]
[22]
Declaration of Competing Interest
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The authors have no competing interests to report. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Acknowledgements
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The authors want to thank the study participants for their contribution to the research and to the geriatric centers for the facilities to perform this research.
[26] [27] [28]
References [29] [1] World Health Organization, Ageing and Life-Course. Facts About Ageing, http:// www.who.int/ageing/about/facts/en/, (2014) accessed 08 April 2019. [2] I. Elmadfa, A.L. Meyer, Body composition, changing physiological functions and nutrient requirements of the elderly, Ann. Nutr. Metab. 52 (1) (2008) 2–5. [3] L. Abarca-Gómez, Z.A. Abdeen, Z.A. Hamid, N.M. Abu-Rmeileh, B. Acosta-Cazares, C. Acuin, C. Agyemang, Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 populationbased measurement studies in 128· 9 million children, adolescents, and adults, Lancet 390 (10113) (2017) 2627–2642. [4] H. Bixby, J. Bentham, B. Zhou, M. Di Cesare, C.J. Paciorek, NCD risk factor collaboration, rising rural body-mass index is the main driver of the global obesity epidemic, Nature 569 (7755) (2019) 260–264. [5] World Health Organization, Global status report on noncommunicable diseases, https://apps.who.int/iris/bitstream/handle/10665/148114/9789241564854_eng. pdf;jsessionid=20C008CA5AF1682DA42A89BB962A9B7D?sequence=1 (2014) accessed 11 September 2019. [6] National Institute for Geography and Statistics, Characteristics of Defunctions
[30]
[31]
[32]
[33]
6
Registered in Mexico During 2017, https://www.inegi.org.mx/contenidos/ saladeprensa/boletines/2018/EstSociodemo/DEFUNCIONES2017.pdf, (2018) accessed 13 April 2019. K.A. Bailey, K.L. Gammage, C. van Ingen, How do you define body image? Exploring conceptual gaps in understandings of body image at an exercise facility, Body Image. 23 (2017) 69–79. E. Pinto, D. Carção, D. Tomé, J. Faustino, L. Campaniço, M. Soares, N. Braz, Body mass index and body image perception in older adults, Geriatr. Med. Care. 1 (2) (2017) 1–3. C.B. Becker, C.L. Verzijl, L.S. Kilpela, S.A. Wilfred, T. Stewart, Body image in adult women: associations with health behaviors, quality of life, and functional impairment, J. Health Psychol. 4 (11) (2017) 1536–1547. A.D. Watt, C.A. Konnert, C.E.C. Speirs, The mediating roles of primary and secondary control in the relationship between body satisfaction and subjective wellbeing among middle-aged and older women, J. Gerontol. 72 (4) (2015) 603–612. S.C. Lemon, M.C. Rosal, J. Zapka, A. Borg, V. Andersen, Contributions of weight perceptions to weight loss attempts: differences by body mass index and gender, Body Image. 6 (2) (2009) 90–96. A.J. Stunkard, T. Sørensen, F. Schulsinger, Use of the Danish adoption register for the study of obesity and thinness, Res. Publ. Assoc. Res. Nerv. Ment. Dis. 60 (1983) 115–120. M. Kaufer-Horwitz, J. Martínez, L.M. Goti-Rodríguez, H. Ávila-Rosas, Association between measured BMI and self-perceived body size in Mexican adults, Ann. Hum. Biol. 33 (5–6) (2006) 536–545. J.M. Kearney, M.J. Kearney, S. McElhone, M.J. Gibney, Methods used to conduct the pan-European Union survey on consumer attitudes to physical activity, body weight and health, Public Health Nutr. 2 (1a) (1999) 79–86. D.M. Lewis, F.M. Cachelin, Body image, body dissatisfaction, and eating attitudes in midlife and elderly women, Eat. Disord. 9 (1) (2001) 29–39. S. McElhone, J.M. Kearney, I. Giachetti, H.J.F. Zunft, J.A. Martínez, Body image perception in relation to recent weight changes and strategies for weight loss in a nationally representative sample in the European Union, Public Health Nutr. 2 (1a) (1999) 143–151. P. Acevedo, N. López-Ejeda, I. Alférez-García, J.R. Martínez-Álvarez, A.M.D. Villarino, M.D. Cabañas, Marrodán, Body mass index through self-reported data and body image perception in Spanish adults attending dietary consultation, Nutrition 30 (6) (2014) 679–684. S. Ganhão-Arranhado, C. Paúl, R. Ramalho, P. Pereira, Food insecurity, weight and nutritional status among older adults attending senior centres in Lisbon, Arch. Gerontol. Geriatr. 78 (2018) 81–88. D.A. Naigaga, D. Jahanlu, H.M. Claudius, A.K. Gjerlaug, I. Barikmo, S. Henjum, Body size perceptions and preferences favor overweight in adult Saharawi refugees, Nutr. J. 17 (1) (2018) 17. G. Conroy-Ferreccio, Mistakes on measurement of body mass index in elder population, Nutr. Hosp. 34 (1) (2017) 251. D. Gallagher, S.B. Heymsfield, M. Heo, S.A. Jebb, R.P. Murgatroyd, Y. Sakamoto, Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index, Am. J. Clin. Nutr. 72 (3) (2000) 694–701. A.B. Pérez-Lizaur, B. Palacios-González, A.L. Castro-Becerra, I. Flores-Galicia, Sistema mexicano de alimentos equivalentes, fourth ed., Fomento de Nutrición y Salud, Mexico, 2014. US Food and Drug Administration, Nutrition facts label, dietary fiber, https://www. accessdata.fda.gov/scripts/interactivenutritionfactslabel/factsheets/Dietary_Fiber. pdf (2009) accessed 08 April 2019. E. Winzer, M. Luger, K. Schindler, Using digital photography in a clinical setting: a valid, accurate, and applicable method to assess food intake, Eur. J. Clin. Nutr. 72 (6) (2018) 879–887. C. Monteagudo-Sanchez, S.C. Dijkstra, M. Visser, Self-perception of body weight status in older Dutch adults, J. Nutr. Health Aging 19 (6) (2015) 612–618. T. Knight, K. Illingworth, L. Ricciardelli, Health implications of body size perception and weight tolerance in older adults, J. Health Psychol. 14 (3) (2009) 425–434. T.A. Pruis, J.S. Janowsky, Assessment of body image in younger and older women, J. Gen. Psychol. 137 (3) (2010) 225–238. S.E. Jackson, R.J. Beeken, J. Wardle, Obesity, perceived weight discrimination, and psychological well-being in older adults in England, Obesity 23 (5) (2015) 1105–1111. E.H. Evans, L.G. Boothroyd, E. Muscariello, B.C.M. Stephan, G. Nasti, A. Colantuoni, M. Siervo, Lower weight loss expectations and healthier eating attitudes in older overweight and obese women attempting weight loss, Clin. Obesity. 5 (3) (2015) 136–144. M. Lipowska, M. Lipowski, H. Olszewski, D. Dykalska-Bieck, Gender differences in body-esteem among seniors: beauty and health considerations, Arch. Gerontol. Geriatr. 67 (2016) 160–170. D.P. Reidlinger, J.M. Willis, K. Whelan, Resting metabolic rate and anthropometry in older people: a comparison of measured and calculated values, J. Hum. Nutr. Diet. 28 (1) (2015) 72–84. Y. Sun, D.L. Roth, C.S. Ritchie, K.L. Burgio, J.L. Locher, Reliability and predictive validity of energy intake measures from the 24-hour dietary recalls of homebound older adults, J. Am. Diet. Assoc. 110 (5) (2010) 773–778. J. Thompson, Body image, eating disorders, and obesity: an integrative guide for assessment and treatment, Am. Psychol. Associ. (2001), https://doi.org/10.1037/ 10502-000.