Anthropometric characteristics and body composition of Taiwanese older adults: Age and gender differences

doi:10.21203/rs.3.rs-1716149/v1

A previous study provided reference data on the age and gender distribution of anthropometric parameters in Taiwanese adults. However, there are few large-scale analyses of anthropometric data of older adults in Taiwan. Therefore, the main purpose of this study was to describe gender- and age-specific distributions of anthropometric measurements and body composition of Taiwanese older adults. This was a cross-sectional study conducted on 22,389 older adults who participated in Taiwan’s National Physical Fitness Survey 2014–2015.The anthropometric measurements and body composition included in the present study were assessment of height, weight, body mass index (BMI), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR). The results showed statistically significant differences in weight, height, WC, HC, and WHR among Taiwanese individuals aged 65 years and older. However, there was no significant difference in BMI between men and women in our sample. Anthropometric status provides an initial assessment of the overall health of the population. This study uses a representative population of Taiwanese older adults over the age of 65 for analysis and provides detailed information on anthropometric data distribution.

Anthropometry

body composition

obesity

older adults

Taiwan

Taiwan's population is aging very rapidly. According to the National Development Council of the Republic of China (2020), it was estimated that the population over the age of 85 would account for 10.5% of the older adult population in 2021, and the ratio will increase to 27.4% in 2070. The World Health Organization (WHO) and the United Nations define an "aging society" as one in which the population over 65 years old accounts for 7% of the total population; up to 14% is called an "aged society"; and up to 20% is called a "superaged society". Taiwan became an aging society in 1993 (1.49 million, 7.1%) and turned into an aged society in 2018 (3.43 million, 14.6%). It is estimated that it will become a superaged society in 2025 (4.7 million, 20.1%) [1]. Compared to other countries, Taiwan's population is aging very rapidly [2]. Moreover, since January 2018, the demographic structure in Taiwan has begun to experience negative population growth, which means that the birth rate is lower than the mortality rate [1].

Rapid aging is accompanied by social and financial challenges in most developed countries [3]. Even countries that have established long-term care financing mechanisms still require their citizens to pay substantial costs out of their own pockets [4, 5]. The cost of long-term care creates catastrophic financial risk for most families. In addition, rapid growth in government fiscal spending on the social welfare system and older adults’ health care casts doubt on the sustainability of national long-term care systems [6]. Therefore, developing a long-term care insurance plan appears to be a more practical alternative for a rapidly aging society. In developing long-term care insurance plans, the assessment of the physical condition of older adults is very important, as it affects the development of disease [7] and the risk of disease and death [8, 9].

An individual's physical fitness [10] and general health [8] can be evaluated through anthropometric parameters. Body measures have also been shown to be significantly correlated with an individual's health status [9]. In addition, anthropometric measurements are an essential component of a complete assessment of older adults [11], with the advantages of being portable, noninvasive, low-cost, and easy to use [12].

The WHO Expert Committee recommended that older adults regularly monitor their health using anthropometric methods to know instantly if their health status has changed [13]. Furthermore, they also suggested that due to the great variability in the older adult population, the age group of at least 10 years of age should have validation data displayed by gender. Ideally, each anthropometric index and age group should also include data from the overall group [12, 14]. A previous study provided reference data on the age and gender distribution of anthropometric parameters in Taiwanese adults [15]. However, there are few large-scale analyses of anthropometric data of older adults in Taiwan [15, 16]. Therefore, the main purpose of this study was to describe gender- and age-specific distributions of anthropometric measurements and body composition of Taiwanese older adults.

Study design and subjects

This cross-sectional study used data from the Taiwan’s National Physical Fitness Survey (TNPFS) [17–19]. The aim of this study was to examine the health-related physical fitness status of Taiwanese individuals from October 2014 to March 2015. This study was conducted by the Sports Administration, Ministry of Education in Taiwan. The details of the design, sampling protocols, and data validation of the series of annual surveys have been described previously [12]. Briefly, all participants were recruited using stratified convenience sampling from 46 physical fitness test stations in 22 cities or counties in Taiwan. The study protocols were as follows: participants completed a standardized, structured questionnaire, followed by a pretest health screening, anthropometric measures, and health-related physical fitness tests. The questionnaire included questions on demographic characteristics, socioeconomic status, lifestyle behaviors, current health status, and associated factors related to happiness. A pretest health screening included measures of resting heart rate and blood pressure (systolic and diastolic), anthropometric assessments, health-related physical fitness tests (screening for health limitations affecting eligibility for participation in health-related physical fitness tests), and a modified Physical Activity Readiness Questionnaire [13]. Finally, a representative sample of Taiwanese older adults 65 years and older was included in the survey (n = 22,389). These data contain deidentified secondary data that have been released to the public for research purposes [17–19]. This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of Fu Jen Catholic University in Taiwan (FJU-IRB C110113).

Data collection

The data were collected by a standardized, structured questionnaire that included sociodemographic variables, self-reported health status, and lifestyle behaviors. All participants finished the questionnaire via face-to-face interviews; questions included those regarding gender (male, female); age (65–69, 70–74, 75–79, 80–84, ≥ 85 years); education (elementary school or lower, junior or senior high school, college or higher); monthly income (≤ 20,000 New Taiwan dollar [NTD], 20,001–40,000 NTD, ≥ 40,001 NTD); marital status (never married, married, divorced/separated/widowed); self-reported health status (excellent or good, fair, very bad or poor); smoking (never, current, former); chewing betel nut (never, current, former).

Anthropometric variable measurements

The body weight, height, waist circumference (WC), and hip circumference (HC) of each survey participant were measured as anthropometric variables, and the body mass index (BMI, in kilograms per square meter) and waist-to-hip ratio (WHR) were calculated after measuring body weight, height, WC, and HC. The examiners who had attended a regional training seminar and passed a certification test of which detailed standardized procedures have been reported in previous studies conducted these anthropometric measurements [17–19]. To measure the anthropometric variables, the participants had to remove their shoes and heavy clothes. Body weight and height were recorded in kilograms and meters to the nearest 0.1 kg and 0.1 cm with an electronic height–weight scale. Participants were in a standing position and exhaled while the examiners measured WC, which was measured to the nearest 0.1 cm with a flexible steel tape measure placed midway between the lowest rib and the iliac crest. HC was measured to the nearest 0.1 cm at the widest part of the hip region in the standing position.

Body composition assessment

BMI and WHR were calculated as body composition in this study. BMI and WHR were calculated with the following formulas (BMI = body weight kg/height squared m²; WHR = WC cm/HC cm) after measuring anthropometric variables, including body weight, height, WC, and HC. The Health Promotion Administration, Ministry of Health and Welfare in Taiwan, suggested the cutoff BMI points for general obesity, which classified BMI into four categories: underweight (BMI < 18.5 kg/m²), normal weight (18.5 ≤ BMI < 24 kg/m²), overweight (24 ≤ BMI < 27 kg/m²) and obese (BMI ≥ 27 kg/m²) [12]. In addition, the WC points were divided for two categories to evaluate whether participants had abdominal obesity, and WC was calculated using the following cutoff points: for men, a WC ≥ 90 cm, and for women, a WC ≥ 80 cm [20].

Statistical analyses

This study used SAS 9.4 (SAS Institute, Cary, NC, USA) to analyze the data. The anthropometric measurements were calculated for gender- and age group-specific (65–69, 70–74, 75–79, 80–84 and ≥ 85 years) means, standard deviations and percentiles (5th, 10th, 15th, 25th, 50th, 75th, 85th, 90th, 95th). Continuous variables were analyzed by using Student’s t test or analysis of variance (ANOVA), and the chi-square test was used to analyze categorical variables. If the F value was significant (P < 0.05), Tukey’s post hoc test was performed to determine the differences between the pairs of means. Statistical results were considered to be significant at P < 0.05.

Table 1 shows the demographic and anthropometric characteristics of the study population. The final sample consisted of 22,389 adults aged 65 years and older, of which 64.19% were women (N = 14,372). Their ages ranged from 65 to 103 years, with a mean of 73.29 (SD = 6.43) years. For women, the mean age was 72.69 (SD = 6.11) years, and for men, it was 74.36 (SD = 6.83) years. In this sample, only 14.22% had completed college or higher education, 28.91% completed junior or senior high school and more than half (56.87%) had completed elementary school or lower education; 64.29% women reported having elementary school or lower education, 26.68% completed junior or senior high school and 9.03% reported having college or higher education. For men, the reported values were 43.69%, 32.86% and 23.45%, respectively, and these differences in educational attainment were statistically significant (P < 0.001). Moreover, 74.64% of women in the sample were single (including those who were divorced, separated or widowed) at the time of the survey, whereas the proportion of those who were married was 25.36%. Similarly, 69.96% of the men were single (including those who were divorced, separated or widowed), and 30.04% reported being married. These differences were statistically significant (P < 0.001). Regarding self-reported health status, most participants felt excellent or good (women 63.65%; men 68.27%), 27.08% of women and 24.87% of men felt fair, and 8.55% of women and 6.86% of men felt very bad or poor. These differences in self-reported health status were statistically significant (P < 0.001). Regarding smoking status, 8.42% of men and 0.81% of women used to smoke, and 11.13% of men and 1.76% of women currently smoked. In contrast, 80.45% of men and 97.44% of women never smoked, and these differences in smoking status were statistically significant (P < 0.001). Regarding betel nut chewing, 3.29% of men and 0.37% of women used to chew betel nut, and 1.83% of men and 0.72% of women currently chewed betel nut. In contrast, 94.89% of men and 98.92% of women had never chewed betel nut, and these differences in betel nut chewing status were statistically significant (P < 0.001). ANOVA showed a statistically significant difference by age group for body weight, height, and BMI (Tables 2 and 4), as well as for WC, HC, and WHR (P < 0.05) (Tables 3 and 5). In addition, statistically significant differences were found between men and women in body weight, height, BMI, WC, and WHR (P < 0.05) (Table 1). However, there was no significant difference in HC between men and women (P = 0.747).

Table 1

Demographic and anthropometric characterization of study population
Variables	Total (n = 22389)	Men (n = 8017)	Women (n = 14372)	p-value
Age groups (%)				< 0.0001^*
65–69	34.39	29.77	36.96
70–74	26.92	25.13	27.91
75–79	20.68	21.62	20.16
80–84	11.66	13.86	10.43
≧ 85	6.36	9.62	4.54
Body weight (kg)	60.56 ± 9.55	65.65 ± 8.93	57.76 ± 8.68	< 0.0001^*
Height (cm)	156.50 ± 7.77	163.38 ± 6.01	152.68 ± 5.75	< 0.0001^*
BMI (kg/m²)	24.72 ± 3.33	24.59 ± 3.03	24.78 ± 3.49	< 0.0001^*
WC (cm)	86.25 ± 9.61	89.05 ± 8.96	84.69 ± 9.61	< 0.0001^*
HC (cm)	96.36 ± 6.89	96.38 ± 6.30	96.34 ± 7.19	0.747
WHR	0.89 ± 0.07	0.92 ± 0.06	0.88 ± 0.07	< 0.0001^*
Education level (%)				< 0.0001^*
Elementary school or lower	56.87	43.69	64.29
Junior or senior school	28.91	32.86	26.68
College or higher	14.22	23.45	9.03
Income level (%)				< 0.0001^*
≦ 20,000 NTD	86.27	78.96	90.37
20,001–40,000 NTD	7.99	11.32	6.12
≧ 40,001 NTD	5.74	9.72	3.51
Marital status (%)				< 0.0001^*
Never married	53.53	58.78	49.02
Married	27.04	30.04	25.36
Divorced/separation/widowed	20.43	11.18	25.62
Self-reported health status (%)				< 0.0001^*
Excellent or good	65.31	68.27	63.65
Fair	26.75	24.87	27.08
Very bad or poor	7.94	6.86	8.55
Smoking status (%)				< 0.0001^*
Never	91.34	80.45	97.44
Current	5.12	11.13	1.76
Former	3.54	8.42	0.81
Chewing betel nut (%)				< 0.0001^*
Never	97.47	94.89	98.92
Current	1.11	1.83	0.72
Former	1.41	3.29	0.37

Table 2

Body weight, height and BMI in men aged 65 years and older (Numbers; mean values and SD)
Variables	n	Mean	SD	%CV	p5	p10	p15	p25	p50	p75	p85	p90	p95
Body weight (kg)^*†
65–69	2333	67.2^a	8.8	13.0	53.0	56.0	58.0	61.0	67.0	73.0	77.0	79.0	82.0
70–74	1978	66.0^b	8.6	13.0	52.0	55.0	57.0	60.0	66.0	72.0	75.0	77.0	80.0
75–79	1698	65.5^b	9.0	13.7	50.3	54.0	56.0	59.0	65.5	72.0	75.0	77.0	80.0
80–84	1106	64.2^c	9.0	13.9	50.0	52.9	55.0	58.0	64.0	70.0	73.2	76.0	79.1
≧ 85	764	62.4^d	9.0	14.4	47.3	51.0	53.0	56.0	63.0	69.0	72.0	74.0	77.0
Total	7879	65.7	8.9	13.6	51.0	54.0	56.0	59.5	66.0	72.0	75.0	77.6	80.0
Height (cm)^*†
65–69	2368	164.3^a	5.9	3.6	154.0	157.0	158.0	161.0	164.7	168.3	170.1	172.0	174.0
70–74	2007	163.6^b	6.0	3.7	153.0	156.0	157.5	160.0	164.0	168.0	170.0	171.0	173.0
75–79	1725	163.0^c	6.0	3.7	153.0	156.0	157.0	159.0	163.0	167.0	169.0	171.0	173.0
80–84	1107	162.7^c	5.9	3.6	153.0	155.0	157.0	159.0	163.0	167.0	169.0	170.0	172.1
≧ 85	770	161.7^d	6.2	3.8	152.0	154.0	156.0	158.0	162.0	166.0	168.0	170.0	172.0
Total	7977	163.4	6.0	3.7	153.0	156.0	157.0	160.0	164.0	167.2	170.0	171.0	173.0
BMI (kg/m²) ^†
65–69	2314	24.9^a	3.0	12.0	20.1	21.1	21.8	22.9	24.8	26.9	28.0	28.8	30.0
70–74	1970	24.7^a	2.9	11.9	19.9	20.9	21.7	22.7	24.6	26.7	27.8	28.6	29.6
75–79	1690	24.7^a	3.1	12.5	19.5	20.7	21.5	22.7	24.5	26.8	27.9	28.9	30.0
80–84	1102	24.2^b	3.0	12.5	19.0	20.4	21.1	22.3	24.2	26.2	27.3	28.1	29.4
≧ 85	764	23.9^b	3.1	12.8	18.7	19.7	20.4	21.8	24.0	26.0	27.2	27.8	28.7
Total	7840	24.6	3.0	12.3	19.6	20.7	21.5	22.6	24.5	26.6	27.7	28.6	29.7

Table 3

WC, HC and WHR in men aged 65 years and older (Numbers; mean values and SD)
Variables	n	Mean	SD	%CV	p5	p10	p15	p25	p50	p75	p85	p90	p95
WC (cm)^*†
65–69	2387	88.6^a	8.8	9.9	74.5	77.0	79.5	83.0	88.0	94.0	98.0	100.0	104.0
70–74	2015	89.0^a	8.8	9.9	75.0	78.0	80.0	83.0	89.0	95.0	98.0	100.0	104.0
75–79	1733	89.9^b	9.2	10.3	74.0	78.0	80.5	84.0	90.0	96.0	99.0	101.0	105.0
80–84	1111	89.0^a	9.1	10.2	73.0	77.0	79.5	83.0	89.0	96.0	98.0	100.0	104.0
≧ 85	771	88.7^a	9.1	10.2	74.0	77.5	79.0	82.0	89.0	95.0	98.0	100.0	103.0
Total	8017	89.0	9.0	10.1	74.0	77.5	80.0	83.0	89.0	95.0	98.0	100.0	104.0
HC (cm)^*
65–69	2387	96.4	6.2	6.5	87.0	89.0	90.0	92.0	96.0	100.0	102.5	104.0	107.0
70–74	2015	96.1	6.2	6.5	86.0	89.0	90.0	92.0	96.0	100.0	102.0	104.0	107.0
75–79	1733	96.7	6.4	6.6	86.0	89.0	90.0	93.0	96.5	101.0	103.0	105.0	107.0
80–84	1111	96.5	6.4	6.6	87.0	88.5	90.0	92.0	96.0	101.0	103.0	104.5	108.0
≧ 85	771	96.3	6.5	6.8	86.0	88.0	90.0	92.0	96.0	100.5	103.0	104.0	107.5
Total	8017	96.4	6.3	6.5	86.5	89.0	90.0	92.0	96.0	100.0	103.0	104.0	107.0
WHR^*†
65–69	2387	0.92^a	0.06	6.4	0.83	0.85	0.86	0.88	0.92	0.96	0.98	0.99	1.01
70–74	2015	0.93^b	0.06	6.8	0.82	0.85	0.86	0.89	0.92	0.96	0.98	1.00	1.03
75–79	1733	0.93^b	0.07	7.2	0.83	0.85	0.87	0.89	0.93	0.97	0.99	1.01	104
80–84	1111	0.92^a	0.07	7.2	0.82	0.84	0.86	0.88	0.92	0.97	0.99	1.00	1.03
≧ 85	771	0.92^a	0.06	7.0	0.82	0.84	0.85	0.88	0.92	0.96	0.99	1.00	1.03
Total	8017	0.92	0.06	6.9	0.82	0.85	0.86	0.88	0.92	0.96	0.98	1.00	1.03

Table 4

Body weight, height and BMI in women aged 65 years and older (Numbers; mean values and SD)
Variables	n	Mean	SD	%CV	p5	p10	p15	p25	p50	p75	p85	p90	p95
Body weight (kg) ^*†
65–69	5297	58.8^a	8.7	14.8	45.7	48.0	50.0	53.0	58.0	65.0	68.0	70.0	74.0
70–74	3995	58.3^b	8.4	14.4	45.0	48.0	50.0	52.3	58.0	64.0	67.0	70.0	73.0
75–79	2894	57.3^c	8.7	15.2	44.0	46.0	48.0	51.0	57.0	63.0	67.0	69.0	72.0
80–84	1496	55.4^d	8.4	15.2	42.0	45.0	47.0	50.0	55.0	60.6	64.0	67.0	78.0
≧ 85	647	53.3^e	8.3	15.6	41.0	43.0	45.0	47.1	52.0	59.0	62.0	65.0	68.0
Total	14329	57.8	8.7	15.0	44.0	47.0	49.0	52.0	57.0	63.0	67.0	69.3	73.0
Height (cm) ^*†
65–69	5310	154.1^a	5.6	3.6	145.0	147.0	148.0	150.0	154.0	158.0	160.0	161.0	163.0
70–74	4008	152.9^b	5.5	3.6	144.0	146.0	147.0	149.0	153.0	156.0	158.1	160.0	162.0
75–79	2895	151.7^c	5.7	3.8	142.0	144.0	146.0	148.0	152.0	155.0	157.0	159.0	161.0
80–84	1497	150.7^d	5.5	3.7	142.0	144.0	145.0	147.0	151.0	154.0	156.0	157.2	160.0
≧ 85	649	149.5^e	6.1	4.1	140.0	142.0	143.0	145.0	149.5	154.0	156.0	157.0	159.0
Total	14359	152.7	5.8	3.8	143.0	145.0	147.0	149.0	152.4	156.0	158.8	160.0	162.0
BMI (kg/m²) ^†
65–69	5295	24.8^a	3.5	14.1	19.6	20.5	21.3	22.4	24.5	26.9	28.5	29.4	31.1
70–74	3992	25.0^a	3.4	13.7	19.8	20.8	21.5	22.6	24.7	27.1	28.5	29.5	31.1
75–79	2891	24.9^a	3.6	14.3	19.3	20.5	21.2	22.4	24.7	27.2	28.5	29.6	31.2
80–84	1494	24.4^b	3.5	14.4	18.9	20.1	20.8	21.9	24.2	26.6	28.2	29.1	30.5
≧ 85	645	23.9^c	3.3	14.0	18.7	19.8	20.6	21.6	23.5	25.9	27.2	28.7	30.2
Total	14317	24.8	3.5	14.1	19.5	20.5	21.2	22.4	24.5	27.0	28.4	29.4	31.0

Table 5

WC, HC and WHR in women aged 65 years and older (Numbers; mean values and SD)
Variables	n	Mean	SD	%CV	p5	p10	p15	p25	p50	p75	p85	p90	p95
WC (cm) ^*†
65–69	5312	83.4^a	9.2	11.0	69.0	72.0	74.0	77.0	83.0	89.3	93.0	95.5	100.0
70–74	4011	84.6^b	9.5	11.2	70.0	73.0	75.0	78.0	84.0	91.0	95.0	97.0	101.0
75–79	2898	85.9^c	9.9	11.5	70.0	73.5	76.0	79.0	86.0	92.0	96.0	98.5	102.5
80–84	1499	86.2^c	10.0	11.6	70.5	74.0	76.0	79.0	86.0	93.0	97.0	100.0	103.0
≧ 85	652	86.6^c	9.7	11.2	71.0	75.0	77.0	80.0	86.0	93.0	97.0	100.0	104.0
Total	14372	84.7	9.6	11.3	70.0	73.0	75.0	78.0	84.0	91.0	95.0	97.5	101.5
HC (cm) ^*†
65–69	5312	96.3^a	7.0	7.3	86.0	88.0	89.0	91.5	96.0	101.0	104.0	105.0	109.0
70–74	4011	96.6^a	7.2	7.4	86.0	88.0	89.5	92.0	96.0	101.0	104.0	106.0	109.5
75–79	2898	96.6^a	7.3	7.6	85.0	88.0	89.0	92.0	96.0	101.0	104.0	106.5	110.0
80–84	1499	96.1^a	7.4	7.7	85.0	87.0	88.5	91.0	96.0	101.0	104.0	106.0	109.0
≧ 85	652	95.0^b	7.3	7.7	83.5	86.0	87.5	90.0	94.0	99.7	102.5	104.0	108.0
Total	14372	96.3	7.2	7.5	85.1	88.0	89.0	91.0	96.0	101.0	104.0	106.0	109.0
WHR^*†
65–69	5312	0.87^a	0.07	7.7	0.76	0.79	0.80	0.82	0.86	0.90	0.93	0.95	0.98
70–74	4011	0.88^b	0.07	8.4	0.77	0.79	0.80	0.83	0.87	0.92	0.95	0.97	0.99
75–79	2898	0.89^c	0.07	8.2	0.78	0.80	0.82	0.84	0.89	0.93	0.96	0.98	1.01
80–84	1499	0.90^d	0.07	8.1	0.79	0.81	0.82	0.85	0.89	0.94	0.98	0.99	1.02
≧ 85	652	0.91^e	0.08	8.4	0.80	0.82	0.84	0.86	0.91	0.96	0.99	1.02	1.05
Total	14372	0.88	0.07	8.3	0.77	0.79	0.81	0.83	0.87	0.92	0.95	0.97	1.00

Tables 2 and 4 show the results for weight, height and BMI (mean, standard deviation, CV and percentiles) for men and women, respectively, and distributed by age group. For both women and men, mean weight and height were lower in the oldest age groups compared with the youngest (P < 0.05), and the weight in women was lower. A multiple comparison procedure using Tukey’s test showed that for men, differences were significant among all groups, with the exception of the 70- to 74-year-old and 75- to 79-year-old groups (Table 2), whereas for women, differences were significant among all groups for body weight (Table 4). For height, the differences were significant among all groups in men except for the 75–79 and 80–84 age groups (Table 2), whereas for women, the differences were significant among all groups for body weight (Table 4).

Mean BMI was lower with increasing age (P < 0.05) in men. Multiple comparisons showed that both men and women showed no significant difference among the three groups (65–69, 70–74, 75–79 age groups) (Tables 2 and 4). Women had significant differences in all the other groups (80–84, ≥ 85 age groups) for BMI (Table 4). However, there was no significant difference in men between the 80–84 and the ≥ 85 age groups (Table 2).

Distributions of body circumference (means, standard deviations, percentiles and CV) are shown in Tables 3 and 5. The mean WC values for men were higher than those for women in all age groups. For men, the results showed a significant difference between the two groups (65–69 and 75–79 age groups), but similar to the HC results, there were significant differences between the 65–69 and 75–79 age groups. For women, there were no significant differences among the three groups (75–79, 80–84 and ≥ 85 years) but significant differences in the other groups (65–69 and 70–74 years) for WC. On the other hand, HC was significantly different in the ≥ 85 years age group compared to all the other groups (65–69, 70–74, 75–79, 80–84 age groups). The mean WHR values for men were higher than those for women. For men, the 70–74 and 75–79 age groups were significantly different from the 65–69, 80–84 and ≥ 85 age groups in terms of HC. In contrast, HC in women was significantly different in all the age groups.

Table 6 presents the prevalence of BMI categories according to the World Health Organization cutoff points [12]. The results showed that both men and women with normal weight had the highest prevalence (men 39.18%, women 40.89%). However, the ratio decreased with increasing age in normal weight, overweight and obese individuals. Table 7 presents the prevalence of WC categories according to the World Health Organization cutoff points (men 90 cm and women 80 cm) [20]. The results show that men have a higher ratio of the WC < 90 (cm) cutoff point (52.53%), but women have a higher ratio of the WC ≥ 80 (cm) cutoff point (69.10%). Table 8 shows the prevalence of WHR categories according to the suggested cutoff point (men 0.9 and women 0.85) of the Health Promotion Administration in Taiwan. The results show that both men and women have a higher prevalence than the suggested cutoff point (men 64.72%, women 64.17%).

Table 6

Categories of BMI in men and women aged 65 years and older (Numbers and percentages)
Variables	Age groups
	65–69		70–74		75–79		80–84		≧ 85		Total
	n	%	n	%	n	%	n	%	n	%	N	%
Men^*
Normal 18.5–23.9 (kg/m²)	865	36.24	782	38.81	678	39.12	469	42.21	347	45.01	3141	39.18
Underweight < 18.5 (kg/m²)	111	4.65	82	4.07	85	4.90	47	4.23	40	5.19	365	4.55
Overweight 24.0–26.9 (kg/m²)	868	36.36	720	35.73	583	33.64	409	36.81	260	33.72	2840	35.42
Obesity ≥ 27 (kg/m²)	543	22.75	431	21.39	387	22.33	186	16.74	124	16.08	1671	20.84
Total	2387	100.00	2015	100.00	1733	100.00	1111	100.00	771	100.00	8017	100.00
Women^*
Normal 18.5–23.9 (kg/m²)	2189	41.21	1582	39.44	1115	38.74	655	43.70	335	51.38	5876	40.89
Underweight < 18.5 (kg/m²)	135	2.54	89	2.22	80	2.76	56	3.74	34	5.21	394	2.74
Overweight 24.0–26.9 (kg/m²)	1693	31.87	1308	32.61	939	32.40	455	30.35	173	26.53	4568	31.78
Obesity ≥ 27 (kg/m²)	1295	24.38	1032	25.73	764	26.36	333	22.21	110	16.87	3534	24.59
Total	5312	100.00	4011	100.00	2898	100.00	1499	100.00	652	100.00	14372	100.00
Pooled^*
Normal 18.5–23.9 (kg/m²)	3054	39.67	2364	39.23	1793	38.72	1124	43.07	682	47.93	9017	40.27
Underweight < 18.5 (kg/m²)	246	3.20	171	2.84	165	3.56	103	3.95	74	5.20	759	3.39
Overweight 24.0–26.9 (kg/m²)	2561	33.26	2028	33.65	1522	32.87	864	33.10	433	30.43	7408	33.09
Obesity ≥ 27 (kg/m²)	1838	23.87	1463	24.28	1151	24.85	519	19.89	234	16.44	5205	23.25
Total	7699	100.00	6026	100.00	4631	100.00	2610	100.00	1423	100.00	22389	100.00

Table 7

Categories of WC in men and women aged 65 years and older (Numbers and percentages)
Variables	Age groups
	65–69		70–74		75–79		80–84		≧ 85		Total
	n	%	n	%	n	%	n	%	n	%	N	%
Men^*
WC < 90 (cm)	1317	55.17	1077	53.45	830	47.89	570	51.31	403	52.27	4197	52.35
WC ≧ 90 (cm)	1070	44.83	938	46.55	903	52.11	541	48.69	368	47.73	3820	47.65
Total	2387	100.00	2015	100.00	1733	100.00	1111	100.00	771	100.00	8017	100.00
Women^*
WC < 80 (cm)	1873	35.24	1240	30.91	781	26.95	390	26.02	158	24.23	4441	30.90
WC ≧ 80 (cm)	3440	64.76	2771	69.09	2117	73.05	1109	73.98	494	75.77	9931	69.10
Total	5312	100.00	4011	100.00	2898	100.00	1499	100.00	652	100.00	14372	100.00
Pooled^*
WC < 90/80 (cm)	3189	41.42	2317	38.45	1611	34.79	960	36.78	561	39.42	8638	38.58
WC ≧ 90/80 (cm)	4510	58.58	3709	61.55	3020	65.21	1650	63.22	862	60.58	13751	61.42
Total	7699	100.00	6026	100.00	4631	20.68	2610	100.00	1423	100.00	22389	100.00

Table 8

Categories of WHR in men and women aged 65 years and older (Numbers and percentages)
Variables	Age groups
	65–69		70–74		75–79		80–84		≧ 85		Total
	n	%	n	%	n	%	n	%	n	%	N	%
Men^*
WHR < 0.9	892	37.37	674	33.45	562	32.43	408	36.72	292	37.87	2828	35.28
WHR ≧ 0.9	1495	62.63	1341	66.55	1171	67.57	703	63.28	479	62.13	5189	64.72
Total	2387	100.00	2015	100.00	1733	100.00	1111	100.00	771	100.00	8017	100.00
Women^*
WHR < 0.85	2226	41.91	1494	37.25	893	30.81	408	27.22	129	19.79	5150	35.83
WHR ≧ 0.85	3086	58.09	2517	62.75	2005	69.19	1091	72.78	523	80.21	9222	64.17
Total	5312	100.00	4011	100.00	2898	100.00	1499	100.00	652	100.00	14372	100.00
Pooled^*
WHR < 0.9/0.85	3118	40.50	2168	35.98	1455	31.42	816	31.26	421	29.59	7978	35.63
WHR ≧ 0.9/0.85	4581	59.50	3858	64.02	3176	68.58	1794	68.74	1002	70.41	14411	64.37
Total	7699	100.00	6026	100.00	4631	100.00	2610	100.00	1423	100.00	22389	100.00

The aim of this study was to summarize anthropometric measures in a representative sample of older Taiwanese adults aged 65 years and older. The results showed statistically significant differences in weight, height, WC, HC, and WHR among Taiwanese individuals aged 65 years and older. However, there was no significant difference in BMI between men and women in our sample. In addition, men had higher mean WC values than women in all age groups, but the difference was statistically significant in two age groups (65–69 and 75–79 years). The HC results were similar in the same two age groups. The average WHR of males in all age groups was higher than that of females. The 75- to 79-year-old group was significantly different from the 65- to 69- and ≥ 85-year-old groups in the male group. In addition, there was also a statistically significant difference between the 65–69 and 70–74 age groups in the male group. In contrast, there were significant differences among women across all age groups.

According to the World Health Organization's BMI classification criteria, a BMI greater than 24 is considered overweight, and a BMI greater than 27 is considered obese [12]. Although the results show that both men and women with normal weight had the highest prevalence (men 39.87%, women 41.66%), 57.88% of men and 56.17% of women were in the overweight and obese categories. This means that overweight and obesity are obvious problems in Taiwan's older adult population. A previous study showed that women presented a higher prevalence of obesity among Mexican older adults [21]; the same results were found in American adults [22, 23]. However, our results show that the percentages of males and females in these two categories are not very different. We conclude that this may be related to the imbalance of the male to female ratio in our sample (approximately 1: 2 male to female ratio). In addition, this may also be caused by cultural [24] and lifestyle differences [25] between Eastern and Western countries.

Although BMI is widely used as an indicator of obesity [26], some individuals with a BMI in the normal category may still be in the obese category for other body measurements [27]. As a result, body shape or body composition may be more of an indicator of obesity [28]. Individuals with a higher WHR may have a higher risk of abdominal obesity [29]. Abdominal obesity is significantly associated with many chronic diseases [30]. In a previous study on Taiwanese adults, WHR increased with age [14]. Moreover, according to the recommendation of the Health Promotion Administration of the Republic of China [31], men with a WC greater than or equal to 90 cm and women with a WC greater than 80 cm are considered to have abdominal obesity. According to our data, 48.02% of males and 30.10% of females had a WC of ≥ 90/80. Moreover, men with a WHR greater than or equal to 0.9 and women with a WHR greater than 0.85 cm are considered to have abdominal obesity as well. According to our data, 64.72% of males and 64.17% of females had a WHR of ≥ 0.9/0.85. However, in the classification of BMI, 21.74% of men and 23.91% of women classified as obese were over 65 years old. There are significant differences in such ratios. Overall, our data provide WC, HC, and WHR data as a reference. This can reveal possible health risks to Taiwanese older adults. In addition, helping the population promote a healthier lifestyle is also essential for national development [14]. We believe that cross-referencing several indicators can lead to more accurate population characteristics.

We noticed that 65% of older adults in our sample had a WHR greater than the cutoff point. We believe this may be the result of loss of muscle and reduced activity in the older population. Loss of muscle can lead to changes in body shape. In future studies, we suggest that body composition analysis be added to further understand body composition ratios, which refer to the percentage of fat, bone, and muscle in the body of older adults in Taiwan, and to compare whether the body composition ratios of older adults are different from those of adults. In addition, the anthropometric characteristics described above can also be analyzed with disease to establish better criteria and cutoff points for the Taiwanese population.

Despite the above outcomes, there were still some limitations in this study. First, although this study collected age and gender data, it used a cross-sectional design, so it was difficult to establish a causal relationship between gender- and age-specific anthropometrics. Therefore, it is recommended that future studies adopt an experimental design to confirm the causal relationship. Second, this study recruited people over 65 years old, which cannot effectively estimate the anthropometric measurements of the Taiwanese population. For that reason, future studies can comprehensively present population groups of different ages, such as young adults, children and infants, to establish a wider range of anthropometric data, which will be necessary to increase the amount of information on the entire Taiwanese population in the future. Third, anthropometric measurements include many different factors; to have complete information on the entire population, it will be necessary to collect more anthropometric data. Last, these data do not provide older individuals’ medical history or current medical condition. Older adults are a relatively vulnerable group, and the rate of disease among older adults is much higher than that among adults [3]. Studies have also demonstrated that whether older adults have a disease affects an individual's physical activity and anthropometric status [32]. It is recommended to add an individual's disease history and current disease status to the database to facilitate future analysis and research.

In summary, this study recruited anthropometric and body composition data of adults over 65 years of age to evaluate gender- and age-specific distributions. However, a number of studies have investigated the relationship between anthropometric measurements and products/workplaces [33, 34, 35]. Based on the current study, anthropometric measurements of older adults can be used to bridge the gap at the first stage of assistive device or product design to improve comfort and practicality. Furthermore, the development of assistive technology involves the expansion of functional abilities and the restoration of deficient functions or help in carrying out activities [36], which are important to improve the quality of life and well-being of older people who live alone. In a rapidly aging population, this will be an essential issue for anthropometric measurement collection and related industry development in Taiwan.

In conclusion, anthropometric status provides an initial assessment of the overall health of the population. This study uses a representative population of Taiwanese older adults over the age of 65 for analysis and provides detailed information on anthropometric data distribution. The results of these data reveal the profile of Taiwan's older adult population and provide a demographic basis for future research. Additionally, these data may provide authorities with an adequate overview for clinical and theoretical purposes.

ANOVA	analysis of variance
BMI	body mass index
CV	coefficient of variance
HC	hip circumference
NTD	New Taiwan Dollar
SAS	Statistical Analysis System
SD	standard deviation
TNPFS	Taiwan’s National Physical Fitness Survey
WC	waist circumference
WHO	World Health Organization
WHR	waist-to-hip ratio

Ethical Approval and consent to participate

This study was approved by the Ethical Committee of Fu Jen Catholic University (FJU-IRB C110113). Written informed consent was obtained from all the participants. All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and material

The data that support the findings of this study are available from [the Sports Cloud: Information and Application Research Center of Sports for All, Sport Administration, Ministry of Education in Taiwan] but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of [the Sports Cloud: Information and Application Research Center of Sports for All, Sport Administration, Ministry of Education in Taiwan].

Funding

The authors received no financial support for the design and analysis of the study or the writing of the manuscript.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

CCH participated in the design, conducted the statistical analyses, interpreted the data, and drafted the manuscript. YJS and PFL supervised the study, assisted in data interpretation, and critically reviewed the manuscript. CFL and CTH helped in conducting the study and revising the manuscript. YTC, KYL, and HLC. helped to manage and analyze the data. All authors read and approved the final manuscript. YJS and PFL contributed equally as first author.

Author Details

¹Department of Gerontology, University of Massachusetts Boston, Boston, MA 02125, USA. ²Department of Leisure Industry and Health Promotion, National Ilan University, Yilan County 260, Taiwan. ³College of Humanities and Management, National Ilan University, Yilan County 260, Taiwan. ⁴Department of Physical Education and Sport Science, National Taiwan Normal University, Taipei City 106, Taiwan. ⁵Department of Health and Leisure Management, Yuanpei University of Medical Technology, Hsinchu City 306, Taiwan. ⁶Sai Wan College, National Sun Yat-sen University, Kaohsiung City 804, Taiwan. ⁷Graduate Institute of Educational Leadership and Development, Fu Jen Catholic University, New Taipei City 242, Taiwan. ⁸Department of Physical Education, Fu Jen Catholic University, New Taipei City 242, Taiwan. ⁹Research and Development Center for Physical Education, Health and Information Technology, College of Education, Fu Jen Catholic University, New Taipei City 242, Taiwan. ¹⁰Department of Kinesiology, National Tsing Hua University, Hsinchu City300, Taiwan

National Development Council: Population estimates of Republic of China (2020 to 2070). https://pop-proj.ndc.gov.tw/download.aspx?uid=70&pid=70 (2020). Accessed 10 May 2022.
United Nations [UN]: Ageing. https://www.un.org/en/global-issues/ageing (2019). Accessed 10 May 2022.
World Health Organization [WHO]: Global health and Aging. https://www.who.int/ageing/publications/global_health.pdf (2011). Accessed 10 May 2022.
Brown JR, Finkelstein A. The private market for long-term care insurance in the United States. J. Risk Insur. 2009;76(1):5–29.
Costa-Font J, Courbage C, Swartz K. Financing long-term care: ex ante, ex post, or both? Health Econ. 2015;24 Suppl 1:45–57.
Colombo F, Mercier J. Help wanted? Fair and sustainable financing of long-term care services. Appl Econ Perspect Pol. 2012;34(2):316–332.
Munro HN. Nutrition and aging. Br Med Bull. 1981;37:83–88.
Newman AB, Yanez D, Harris T, Duxbury A, Enright PL, Fried LP, et al. Weight change in old age and its association with mortality. Journal of the American Geriatrics Society. 2001; doi:10.1046/j.1532-5415.2001.49258.x.
Miller MD, Crotty M, Giles LC, Bannerman E, Whitehead C, Cobiac L, et al. Corrected arm muscle area: An independent predictor of long-term mortality in community‐dwelling older adults? Journal of the American Geriatrics Society, 2002; doi:10.1046/j.1532-5415.2002.50316.x.
Towlson C, Cobley S, Parkin G, Lovell R. When does the influence of maturation on anthropometric and physical fitness characteristics increase and subside? Scand. J. Med. Sci. Sports. 2018;28:1946–1955.
Perissinotto E, Pisent C, Sergi G, Grigoletto F, ILSA Working Group (Italian Longitudinal Study on Ageing). Anthropometric measurements in the elderly: Age and gender differences. British Journal of Nutrition. 2002; doi:10.1079/bjn2001487.
De Onis M, Habicht JP. Anthropometric reference data for international use: recommendations from a World Health Organization Expert Committee. The American Journal of Clinical Nutrition. 1996;64(4):650–658.
World Health Organization. Physical status: The use and interpretation of anthropometry. Report of a WHO Expert Committee. Technical Report Series, no. 854. Geneva: WHO; 1995.
World Health Organization. Use and Interpretation of Anthropometric Indicators of Nutritional Status. Bull World Health Organ. 1995;64:929–941.
Chen SC, Lin CW, Lee PF, Chen HL, Ho CC. Anthropometric characteristics in Taiwanese adults: Age and gender differences. International Journal of Environmental Research and Public Health. 2021;18(14):7712.
Hou MT, Wu PY, Lin YB, Chang YF, Chen CY, Guan DS, et al. Physical fitness performance and normative score of elderly male rural dwellers. Taiwan Geriatrics & Gerontology. 2011;6:48–60.
Ho CC, Lee PF, Chen HL, Tseng CY, Hsieh XY, Chiu CH. Poor health-related physical fitness performance increases the overweight and obesity risk in older adults from Taiwan. BMC Geriatrics. 2021;21:170.
Chen HH, Chen HL, Lin YT, Lin CW, Ho CC, Lin HY, et al. The associations between functional fitness test performance and abdominal obesity in healthy elderly people: Results from the National Physical Fitness Examination Survey in Taiwan. Int J Environ Res Public Health. 2020;18:264.
Lee PF, Ho CC, Yeh DP, Hung CT, Chang YC, Liu CC, et al. Cross-sectional associations of physical fitness performance level and sleep duration among older adults: Results from the National Physical Fitness Survey in Taiwan. Int J Environ Res Public Health. 2020;17:388.
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–1645.
López-Ortega M, Arroyo P. Anthropometric characteristics and body composition in Mexican older adults: age and sex differences. British Journal of Nutrition. 2016;115(3):490–499.
Cline MG, Meredith KE, Boyer JT, Burrows B. Decline of height with age in adults in a general population sample: estimating maximum height and distinguishing birth cohort effects from actual loss of stature with aging. Hum Biol. 1989;61:415–425.
Sorkin JD, Mueller DC, Andres R. Longitudinal change in height of men and women: implications for interpretation of the body mass index. Am J Epidemiol.1999;150:969–977.
Devi Y. Perceptions of body image and obesity among cross culture: A review. The Open Obesity Journal. 2018;5:1–4.
Jansen A, Morgan BL. A cross cultural study of body image and eating habits between genders. UW-L Journal of Undergraduate Research. 2008;XI
Aune D, Sen A, Prasad M, Norat T, Janszky I, Tonstad S, et al. BMI and all cause mortality: Systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants. BMJ. 2016;353:i2156.
Rothman KJ. BMI-related errors in the measurement of obesity. International journal of obesity. 2008; 32 Suppl 3: S56–S59.
Christakoudi S, Tsilidis KK, Muller DC, Freisling H, Weiderpass E, Overvad K, et al. A Body Shape Index (ABSI) achieves better mortality risk stratification than alternative indices of abdominal obesity: Results from a large European cohort. Sci Rep. 2020;10:14541.
World Health Organization. Waist Circumference and Waist-Hip Ratio. In Proceedings of the WHO Expert Consultation; 2008 December 8–11; Geneva, Switzerland.
Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011;378:804–814.
Health Promotion Administration of Republic of China. Methods of Measuring and Interpreting Waist Circumference in Adults" New Edition Promotion. https://www.hpa.gov.tw/Pages/Detail.aspx?nodeid=1125&pid=1697 (2020). Accessed 10 May 2022.
Dunsky A, Zach S, Zeev A, Goldbourt U, Shimony T, Goldsmith R, et al. Level of physical activity and anthropometric characteristics in old age—results from a national health survey. Eur Rev Aging Phys Act. 2014;11:149–157.
Hanson L, Sperling L, Gard G, Ipsen S, Olivares CV. Vergara, Swedish anthropometrics for product and workplace design. Appl Ergon. 2009;40(4):797–806.
Lee YS. Applied Korean anthropometric database for product design: Clothing design. Agency for Technology and Standards, MOCIE, Korea. 2000.
Tavana M, Kazemi MR, Vafadarnikjoo A, Mobin M. An artificial immune algorithm for ergonomic product classification using anthropometric measurements. Measurement. 2016;94:621–629.
Prajapati G, Sharmila K. Role of assistive devices in wellbeing of elderly: A review. Indian Journal of Gerontology. 2020;34(3):394–402.

No competing interests reported.

Anthropometric characteristics and body composition of Taiwanese older adults: Age and gender differences

Status:

Version 1

Abstract

Introduction

Methods

Study design and subjects

Data collection

Anthropometric variable measurements

Body composition assessment

Statistical analyses

Results

Discussion

Conclusion

Abbreviations

Declarations

References

Competing Interests

Status:

Version 1