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Time trend of prevalence of self-reported cataract and its association with prolonged sitting in Taiwan from 2001 and 2013

  • Ya-Hui Shih1,
  • Hsing-Yi Chang1Email author,
  • Ming-Ing Lu2 and
  • Baai-Shyun Hurng2
BMC Ophthalmology201414:128

https://doi.org/10.1186/1471-2415-14-128

Received: 7 April 2014

Accepted: 22 October 2014

Published: 5 November 2014

Abstract

Background

Prolong sitting has been found associated with metabolic disorders. Little is known about the self-reported cataract status in general population of Taiwan, not to mention its relation to prolong sitting. We aimed to examine the prevalence of cataract between 2001 and 2013 in Taiwan and to the association between prolonged sitting and cataract.

Methods

We used three data sets with those aged 40 years and older from the National Health Interview Survey (NHIS) from 2001 (n = 8334), 2009 (n = 11207), and 2013 (n = 10940). Subsequent statistical analyses involved chi-square test, t test, and logistic regression modeling. SUDAAN was used to account for sampling scheme.

Results

The prevalence of cataract ranged from 10.7% in 2001, 13.13% in 2009, to 11.84% in 2013. Participants who sat for more than 7 hours per day had a significantly higher risk of cataract (OR = 1.20, CI = (1.04-1.39)) compared with those who sat for fewer than 3 hours per day after controlling for age and other risk factors like being older or female, lower education level, not being currently employed, living in a highly urbanized area, having hypertension, diabetes, myopia, and being an former smoker (compared to a never smoker).

Conclusion

Increased daily sitting time was associated with cataract, especially for people who sat more than 7 hours per day.

Keywords

Prevalence of cataract Prolonged sitting National Health Interview Survey Taiwan

Background

According to 2010 World Health Organization (WHO) data, there are 39 million persons who are blind worldwide, with the three leading causes of blindness being cataract (51%), glaucoma (8%), and age-related macular degeneration (5%) [1]. In addition to being an important cause of blindness, cataract also leads to high health-care costs. According to US research published in 2004, cataracts represent 19.21% of total expenditure on eye health care in those aged 40 years and older and an even greater proportion of expenditure (68.6%) in those aged 65 years and older [2]. The prevalence of cataract has a very strong positive relationship with age [3]. Taiwan officially became an ageing society in 1993, and in 2012 the proportion of the population aged 65 years and older reached 11.2%. This proportion is expected to reach 14% in 2018, making Taiwan an aged society, and then surpass 20% in 2025, making Taiwan a super-aged society. Therefore, diseases that have a strong relationship with older age, such as cataract, require increased attention and disease control efforts.

A cataract is a clouding of the lens inside the eye which leads to a decrease in vision. It is mostly due to biological aging [4]. Other factors including trauma, radiation [5], skin diseases, smoking [6] and use of corticosteroids [7] are known to cause cataract. In addition, factors include hypertension [8], diabetes [9, 10], and myopia [11] are also found to be associated with cataract. Previous research found that individuals living in rural areas were more likely to have certain types of cataracts [11]; but, another study reported that urban dwellers were more likely to have cataracts [12].

Nowadays, people spend a lot of time staring at computer screen, watching TV, or using smart phones or tablets. These electronic devices might produce photo-toxicity [13]. Long time exposure to photo-toxicity could damage lens protein, thus induce early onset of cataract [14]. The length of time spent sitting could possibly have an impact on eye health. A small number of studies have found an association between a sedentary lifestyle and visual impairment in older persons [15, 16] and age-related macular degeneration [17]. Sedentary behavior is also associated with visual function in people with diabetes [18, 19]. Prolonged sitting and lack of physical activity are both distinct risk factors for cataract, and even those who frequently exercise can spend prolonged time sitting in front of a TV [2022]. It is very likely to induce cataract.

There has been less research conducted in Taiwan about the prevalence of cataract and its associated factors. Most of these researches have focused on small populations older than 50 years in specific localities [23, 24]. Little is known about the cataract status in general population of Taiwan, not to mention its relation to prolong sitting. In this study, we used the National Health Interview survey to examine the time trend of cataract and its association with prolong sitting time.

Methods

Data for this study were from the 2001, 2009 and 2013 National Health Interview Surveys (NHIS). These surveys used a multistage stratified systematic sampling design. In 2001, it stratified the whole Taiwan area into seven strata according to the degree of urbanization, geographic location, and administrative boundaries sampled. Individuals were sampled with probability proportional to size [25]. The target population for the survey was individuals whose households were registered in any one of the 23 counties or cities in Taiwan at the end of the year prior to the survey. In the 2001 survey, households were the basic sampling unit, whereas in the 2009 and 2013 survey, individuals were the basic sampling unit. Written informed consent for participation in the study was obtained from participants (≥19 years), where participants were children (<19 years), a parent or guardian. Comparison was carried out between the sample and the target population, and none of the chi-square tests demonstrated a statistically significant difference.

In the present study we used the Taiwanese sample weights provided by the NHIS working group to carry out weighting for 2009 and 2013. The response rates for the 2001, 2009 and 2013 surveys were 93.8% [25], 83.96% and 75.2%, respectively. Further details regarding questionnaire content and design and sampling design are provided on the NHIS website (http://nhis.nhri.org.tw/) [25]. On the same website, researchers and government workers can apply for the survey data. The NHIS was designed to be carried out every 4 years, but the 2005 survey did not include a measure of time spent sitting for participants those aged 65 years and over. Therefore, we were only able to use data from the 2001, 2009 and 2013 surveys.

Participants aged 40 years and older were included in the current analysis. Outcome variable was the self-reported current status of cataract (told by medical professionals) in one or two eyes. Explanatory variables included age, sex, education level, marital status, employment status, monthly household income, degree of urbanization, health status (hypertension, diabetes, and myopia), smoking status, and daily sitting time. Disease of hypertension, diabetes, and myopia were self-reported and told by a medical professional. This set of questions has been used since 2001. We have validated the question on diabetes in 2002, when we had measurements and blood samples from half of the original sample aged 15 and above. Among the 2002 survey, 86% of females and 77% of males were aware of their disease status [26]. That indicated the agreement was reasonable. Age was divided into three groups: 40–54 years, 55–64 years, and 65 years and above. Education level was categorized as none (without any education), junior high school and below, senior high school, college or Bachelor’s degree, and Master’s degree and above. Participants who had studied at an online university or professional college were categorized as having a college or Bachelor’s degree. Marital status was categorized as never married, married, and other (including living together, separated, divorced, and widowed). Employment status referred to the response to the question “Are you currently working?” Participants who had previously worked were categorized as “no” if they were not currently working. Monthly income was categorized as < NT$30,000, NT$30,000 to < NT$70,000, ≥NT$70,000 (1NT ≈ 0.033USD), and “not stated” (including unwilling to report, unknown, and unclear). Degrees of urbanization were categorized as high urbanization, moderate urbanization, developing towns, general towns, and other towns (including aged towns, agricultural towns, and villages). Smoking status was categorized as never, former smokers, or current. Daily sitting time was assessed by the question “How many hours do you spend sitting on an average day, including time spent at work, at school, driving, reading books, reading the newspapers and using the computer?” Responses were categorized as <3 hours, 3–4 hours, 5–6 hours, and 7 hours or more, which were approximate quartiles of the data. These cut-points were also used in other studies [2729]. We performed imputations for missing value of daily sitting time (1.7%) by using age, gender, education, employed status and marital status as imputation data.

Statistical analyses were carried out using SAS 9.3 software and the SUrvey DAta ANalysis (SUDAAN) to account for sampling schemes. Descriptive statistics were used to examine the distribution of basic characteristics. Trend test was used to assess time trend between socio-demographical factors, health status, and other variables and three surveys. Chi-square test and t test were applied to compare risk factors between cataract and no cataract. Logistic regression was used to determine the association between daily sitting time and the presence of cataract controlling for demographic variables and other risk factors.

Results

In the 2001, 2009 and 2013 NHIS data, there were 8,334, 11,207 and 10,940 respondents aged 40 years and older respectively (Table 1). The prevalence of cataract ranged from 10.7% in 2001 to 11.84% in 2013. Daily sitting time and the mean age was higher in 2013 than in 2001 (p < 0.001). Around 25% of people sat more than 7 hours per day in 2001 and 2009, and increased to more than 30% in 2013. The proportion of elderly (≥65 years old) reached 23.19% in 2013, where it was 24.68% in 2001. People in 2013 had higher education levels than those in 2001. A higher proportion of people were married in 2001 than in 2013. People living in highly urbanized areas were higher in 2013 than in 2001 but those livings in other towns areas were higher in 2001 than in 2013. More people in 2013 had hypertension, diabetes, and myopia than those in 2001. Table 2 compared the characteristics of those with cataract to those without in all surveys. Almost all variables like age, sex, education level, marital status, employment status, monthly income, health status, and smoking were associated with cataract, except sitting time in 2001 and 2009 and degrees of urbanization in 2001.
Table 1

Characteristics of the study sample aged 40 years and older in three surveys

Variable

Year 2001

Year 2009

Year 2013

P value*

n

(%)

N

(%)

N

(%)

Total

8334

11207

10940

 

Mean sitting time (SD)

5.17 ± 0.09

5.38 ± 0.06

5.69 ± 0.05

<0.001

Sitting time (grouped), hours

      

<0.001

  <3

1892

(22.70)

2100

(18.74)

1624

(14.84)

 

  3–4

2421

(29.05)

3198

(28.54)

2993

(27.36)

 

  5–6

1803

(21.63)

2820

(25.17)

2852

(26.07)

 

  ≥7

2218

(26.61)

3088

(24.55)

3471

(31.73)

 

Mean age (SD)

56.11 ± 0.26

56.31 ± 0.16

57.04 ± 0.17

<0.001

Age (grouped), years

      

0.055

  40–54

4586

(55.03)

6104

(54.46)

5507

(50.34)

 

  55–64

1691

(20.29)

2474

(22.08)

2896

(26.47)

 

  ≥ 65

2057

(24.68)

2629

(23.46)

2538

(23.19)

 

Sex

      

0.697

  Male

4086

(49.39)

5535

(49.39)

5336

(48.77)

 

  Female

4248

(50.97)

5671

(50.61)

5605

(50.23)

 

Education level†

      

0.006

  None

1458

(17.49)

1018

(9.08)

839

(7.67)

 

  Junior high school and below

4127

(49.52)

4883

(43.57)

4265

(38.98)

 

  Senior high school

1572

(18.86)

2995

(26.72)

3051

(27.89)

 

  College or Bachelor’s degree

1090

(13.08)

1993

(17.78)

2267

(20.73)

 

  Master’s degree and above

87

(1.04)

319

(2.84)

518

(4.73)

 

Marital status

      

<0.001

  Never married

262

(3.14)

596

(5.31)

787

(7.19)

 

  Married

6737

(80.84)

8566

(76.43)

8129

(74.30)

 

  Other

1335

(16.02)

2045

(18.25)

2025

(18.51)

 

Employed status

4084

(49.00)

6318

(56.38)

6373

(58.25)

<0.001

Monthly household income ($NT)

      

<0.001

  <30,000

2101

(25.21)

2849

(25.42)

2266

(20.71)

 

  ≥ 30,000 to <70,000

3444

(41.32)

3775

(33.68)

3751

(34.28)

 

  ≥70,000

2687

(32.24)

2650

(23.65)

3170

(28.98)

 

  Not stated

102

(1.22)

1932

(17.24)

1753

(16.03)

 

Degrees of urbanization§

      

<0.001

  High urbanization

1769

(21.23)

2530

(22.58)

2948

(26.95)

 

  Moderate urbanization

2249

(26.99)

2558

(22.82)

5369

(49.07)

 

  Developing towns

1827

(21.92)

3598

(32.11)

1353

(12.37)

 

  General towns

1226

(14.71)

1802

(16.08)

884

(8.08)

 

  Other towns

1263

(15.15)

719

(6.41)

387

(3.53)

 

Health status

       

  Cataract

882

(10.70)

1471

(13.13)

1296

(11.84)

0.122

  Hypertension

1825

(21.90)

2934

(26.18)

3141

(28.71)

<0.001

  Diabetes

759

(9.11)

1173

(10.47)

1302

(11.90)

<0.001

  Myopia

1641

(19.69)

3384

(30.19)

3839

(35.09)

<0.001

Smoking status

      

<0.001

  Never smokers

5872

(70.46)

7077

(63.15)

7157

(65.42)

 

  Former smokers

477

(5.72)

1695

(15.13)

1569

(14.34)

 

  Current smokers

1985

(23.82)

2434

(21.72)

2214

(20.24)

 

*P value based on test of trend.

†Education level: College and above includes online universities and online professional colleges.

‡Marital status: other includes living together, separated, divorced, and widowed.

§Degrees of urbanization: other towns include aged towns, agricultural towns, and villages.

Table 2

Factors associated with cataract in those aged 40 years and older in three surveys

Variable

Year 2001

P value*

Year 2009

P value*

Year 2013

P value*

N

(%)

N

(%)

N

(%)

Total

892

(10.70)

 

1471

(13.13)

 

1296

(11.84)

 

Mean sitting time (SD)

5.44 ± 0.14

0.040

5.39 ± 0.12

0.725

5.66 ± 0.12

0.665

Sitting time (grouped), hours

  

0.088

  

0.487

  

0.037

  <3

195

(10.31)

 

303

(14.44)

 

218

(13.40)

 

  3–4

236

(9.75)

 

414

(12.94)

 

313

(10.45)

 

  5–6

187

(10.37)

 

366

(12.97)

 

369

(12.94)

 

  ≥7

274

(12.35)

 

388

(12.57)

 

396

(11.42)

 

Mean age (SD)

70.12 ± 0.33

<0.001

71.88 ± 0.32

<0.001

70.87 ± 0.36

<0.001

Age (grouped), years

  

<0.001

  

<0.001

  

<0.001

  40–54

61

(1.33)

 

80

(1.31)

 

71

(1.30)

 

  55–64

179

(10.59)

 

276

(11.15)

 

297

(10.27)

 

  ≥ 65

652

(31.70)

 

1115

(42.42)

 

927

(36.53)

 

Sex

  

<0.001

  

<0.001

  

<0.001

  Male

364

(8.91)

 

610

(11.02)

 

531

(9.95)

 

  Female

528

(12.43)

 

861

(15.18)

 

765

(13.64)

 

Education level†

  

<0.001

  

<0.001

  

<0.001

  None

349

(23.94)

 

398

(39.08)

 

301

(35.92)

 

  Junior high school and below

400

(9.69)

 

773

(15.83)

 

614

(14.40)

 

  Senior high school

81

(5.15)

 

152

(5.09)

 

182

(5.96)

 

  College or Bachelor’s degree

55

(5.05)

 

128

(6.26)

 

157

(6.94)

 

  Master’s degree and above

7

(8.05)

 

23

(7.23)

 

41

(7.90)

 

Marital status‡

  

<0.001

  

<0.001

  

<0.001

  Never married

11

(4.20)

 

28

(4.63)

 

32

(4.03)

 

  Married

592

(8.79)

 

921

(10.75)

 

861

(10.59)

 

  Other

289

(21.65)

 

523

(25.55)

 

403

(19.92)

 

Employment status

  

<0.001

  

<0.001

  

<0.001

  Employed

125

(3.06)

 

230

(3.64)

 

283

(4.44)

 

  Not employed

767

(18.05)

 

1241

(25.38)

 

1013

(22.18)

 

Monthly household income ($NT)

  

<0.001

  

<0.001

  

<0.001

  <30,000

359

(17.09)

 

562

(19.72)

 

395

(17.42)

 

  ≥ 30,000 to <70,000

304

(8.83)

 

317

(8.39)

 

360

(9.60)

 

  ≥70,000

219

(8.15)

 

163

(6.16)

 

247

(7.78)

 

  Not stated

10

(9.80)

 

429

(22.21)

 

294

(16.79)

 

Degrees of urbanization§

  

0.324

  

<0.001

  

0.002

  High urbanization

188

(10.63)

 

388

(15.34)

 

396

(13.42)

 

  Moderate urbanization

235

(10.45)

 

346

(13.52)

 

560

(10.43)

 

  Developing towns

170

(9.30)

 

363

(10.10)

 

155

(11.43)

 

  General towns

127

(10.36)

 

268

(14.88)

 

127

(14.35)

 

  Other towns

172

(13.62)

 

105

(14.64)

 

59

(15.20)

 

Health status

         

Hypertension

  

<0.001

  

<0.001

  

<0.001

  Yes

357

(19.56)

 

766

(26.12)

 

724

(23.4)

 

  No

535

(8.22)

 

705

(8.52)

 

572

(7.33)

 

Diabetes

  

<0.001

  

<0.001

  

<0.001

  Yes

187

(24.64)

 

345

(29.38)

 

328

(25.22)

 

  No

705

(9.31)

 

1126

(11.22)

 

967

(10.04)

 

Myopia

  

<0.001

  

<0.001

  

<0.001

  Yes

84

(5.12)

 

210

(6.20)

 

251

(6.54)

 

  No

808

(12.07)

 

1261

(16.12)

 

1045

(14.71)

 

Smoking status

  

<0.001

  

<0.001

  

<0.001

  Never smokers

673

(11.46)

 

1019

(14.40)

 

979

(13.67)

 

  Former smokers

83

(17.40)

 

286

(16.88)

 

211

(13.43)

 

  Current smokers

136

(6.85)

 

166

(6.81)

 

106

(4.80)

 

*P value based on chi-square test for categorical variables and t-test for continuous variables (cataract versus no cataract).

†Education level: College and above includes online universities and online professional colleges.

‡Marital status: other includes living together, separated, divorced, and widowed.

§Degrees of urbanization: other towns include aged towns, agricultural towns, and villages.

Table 3 shows logistic regression results for potential factors associated with the presence of cataract. After controlling for all potential associated factors, the odds ratio for the presence of cataract was 1.20 (p = 0.016) for those sitting for 7 or more hours per day compared to those sitting for less than 3 hours per day. The probability of having cataract in 2009 was significantly higher compared with 2001 (OR = 1.42, p < 0.001). The probability of having cataract increased with increasing age (p < 0.001). Women had a greater risk of cataract than men (OR = 1.33, p < 0.001). Those with a junior high school and below were less likely to have cataract than those without education (OR = 0.85, p = 0.020). Similar pattern was found in those with a senior high school (OR = 0.71, p = 0.001) and college or Bachelor’s degree (OR = 0.77, p = 0.028). Participants who were currently employed were less likely to have cataract (OR = 0.61, p < 0.001). Participants living in highly urbanized areas were more likely to have cataract than those living in other levels of urbanization (p < 0.01). Participants with hypertension (OR = 1.39, p < 0.001), diabetes (OR = 1.58, p < 0.001), or myopia (OR = 1.26, p = 0.008) were more likely to have cataract. In addition, those who were former smokers were more likely to have cataract (OR = 1.20, p = 0.046) and those who were current smokers were less likely to have cataract than those who were never smokers (OR = 0.83, p = 0.045).
Table 3

Logistic regression analysis of factors associated with cataract in Taiwanese persons aged 40 years and older

Variable

Category

OR

95% CI

P value

Survey year

    
 

2001

1.00

  
 

2009

1.42

(1.23 - 1.63)

<0.001

 

2013

1.07

(0.93 - 1.23)

0.335

Sitting time (grouped), hours

    
 

<3

1.00

  
 

3–4

1.09

(0.95 - 1.25)

0.226

 

5–6

1.02

(0.88 - 1.18)

0.783

 

≥7

1.20

(1.04 - 1.39)

0.016

Age (group), years

    
 

40–54

0.04

(0.03 - 0.05)

<0.001

 

55–64

0.25

(0.22 - 0.29)

<0.001

 

≥ 65

1.00

  

Sex

    
 

Male

1.00

  
 

Female

1.33

(1.16 - 1.53)

<0.001

Education level†

    
 

None

1.00

  
 

Junior high school and below

0.85

(0.74 - 0.97)

0.020

 

Senior high school

0.71

(0.59 - 0.87)

0.001

 

College or Bachelor’s degree

0.77

(0.61 - 0.97)

0.028

 

Master’s degree and above

1.03

(0.64 - 1.66)

0.899

Marital status‡

    
 

Never married

0.83

(0.58 - 1.18)

0.282

 

Married

1.00

  
 

Other

1.09

(0.98 - 1.23)

0.121

Employed status

 

0.61

(0.53 - 0.70)

<0.001

Monthly household income ($NT)

    
 

<30,000

1.00

  
 

≥ 30,000 to <70,000

0.89

(0.78 - 1.02)

0.098

 

≥70,000

0.87

(0.74 - 1.03)

0.102

 

Not stated

0.82

(0.69 - 0.96)

0.018

Degrees of urbanization§

    
 

High urbanization

1.00

  
 

Moderate urbanization

0.78

(0.68 - 0.90)

0.001

 

Developing towns

0.64

(0.54 - 0.76)

<0.001

 

General towns

0.71

(0.59 - 0.85)

<0.001

 

Other towns

0.68

(0.53 - 0.87)

0.003

Health status

    
 

Hypertension

1.39

(1.25 - 1.54)

<0.001

 

Diabetes

1.58

(1.38 - 1.80)

<0.001

 

Myopia

1.26

(1.06 - 1.48)

0.008

Smoking status

    
 

Never smokers

1.00

  
 

Former smokers

1.20

(1.00 - 1.56)

0.046

 

Current smokers

0.83

(0.70 - 1.00)

0.045

†Education level: College and above includes online universities and online professional colleges.

‡Marital status: other includes living together, separated, divorced, and widowed.

§Degrees of urbanization: other towns include aged towns, agricultural towns, and villages.

Discussion

In this study we used data from three large-scale, nationally representative samples collected 12 years apart and examined daily sitting time in an analysis of risk factors for cataract. Results showed that risk of cataract significantly (p < 0.05) increased with longer daily sitting time controlling for other risk factors. Our results confirmed our two study hypotheses that prolonged sitting would increase the risk of cataract.

The total number of persons with cataract increased 31.27% in 2013 compared to 2001. The overall prevalence increased by 2.43% in 2009 and 1.14% in 2013, which demonstrates the growth of cataract disease in Taiwan during this period. However, this growth may be due to several other factors. People in 2013 had higher mean age, higher education levels and there were more people living in high urbanization area than those in 2001. This change of characteristics of study sample may affect self-report of cataracts in recent surveys.

The prevalence of cataract in Taiwan is low compared with other countries. If we take the prevalence at age 40 years and older, for example, the prevalence in the United States is 22% [30]; in Asian regions such as Tibet [31], South Korea [32], India [33], Malaysia [34], Singapore [3], Sri Lanka [35], and Myanmar [36], the prevalence is at least 20% or more, with the highest over 50% [3]. The prevalence rates found in the present study are also lower than those found in previous surveys in the Peitou and Shihpai regions of Taiwan (prevalence rates of more than 50% were found) [23, 24]. These observed differences could be due to the method of ascertainment. We used a self-reported disease status. We emphasized “currently been told by a medical professional” during the survey interview in order to obtain diagnosed cases of cataract. However, this did not include individuals who were at early stage of cataract and had not been examined by a doctor. Omitting these persons could result in underestimate the prevalence.

Nevertheless, we observed the existence of self-reported cataract was associated with pro-long sitting time. This is possibly related to screen time. Nowadays, people sit for long time are most likely exposed to higher-luminance displays, which emanate short wavelength blue-violet light or ultraviolet [13]. This light would have photochemical reaction with lenses and produces reactive oxygen species (ROS), which induces oxidative stress to the protein of lenses and become oxidative damage. That is oxidative stress-induced cataract [14]. However, a 2003 study [37] investigated the cumulative incidence of cataract over 10 years and found no statistically significant association between prolonged sitting and cataract in either eye or any type of cataract. Limited research has been conducted on prolonged sitting and the development of cataract. Some studies have found that a sedentary lifestyle is associated with reduced visual function in individuals with diabetes [18, 19]. In addition, older persons with a sedentary lifestyle are more likely to have visual impairment [15] or age-related macular degeneration [17]. We controlled the age and diabetes, prolong sitting time was still associated with cataract significantly.

In addition to the previously mentioned factors, being female and having low socioeconomic status (including low education, unemployment, and low monthly household income), hypertension, diabetes, and myopia were also associated with an increased risk of cataract in our study. These findings are similar to those of previous studies [8, 11, 30, 35, 38].

In terms of smoking, we found that past smokers had an increased likelihood of cataract and current smokers had a decreased likelihood of cataract. It is possible that past smokers had a strong addiction or have quit smoking due to illness. In contrast, current smokers could be maintaining a low level smoking habit (such as one or two cigarettes now and then) or may have only recently started smoking. Therefore, further analysis by quantity of tobacco consumed is needed for a clearer comparison. We found that the risk of cataract was higher among residents in highly urbanized areas, which is similar to findings reported by Xu et al. [12].

Our study has several limitations. First, we used the self-reported current status of cataract told by a medical professional. This may underestimate the prevalence of cataract in the population, since those at early stage might not be diagnosed. Second, we didn’t record the type of cataract (nuclear, cortical or posterior subcapsular cataract (PSC)). It was difficult to identify association between risk factors and specific pattern of cataract. Third, we did not measure the time spent watching television, using the computer, or reading, so we were unable to examine whether the association between sitting time and cataract depends on the activity involved. Finally, several variables shown to be associated with cataract in international research were not included: UV radiation, type of occupation (outdoors or office-based), and medications. Living area was used as a proxy for the sunlight exposure, the relationship between prolong sitting with cataract was still apparent after control for living area.

Conclusion

Prolonged sitting is a risk factor for cataract disease, particularly in those who sit for more than 7 hours per day. Attention should be given to the activities while sitting.

Ethics approval

This study was approved by the Institutional Review Board of the National Health Research Institutes.

Declarations

Acknowledgements

We would like to express our gratitude to our institute (the Division of Preventive Medicine and Health Services Research, Institute of Population Health Sciences, National Health Research Institutes), Health Promotion Administration, Department of Ministry of Health and Welfare, and the Food and Drug Administration, Department of Ministry of Health and Welfare for providing access to data from the 2001 National Health Interview Survey and the 2009 National Health and Drug Abuse Interview Survey. The interpretations and conclusions expressed in this paper are those of the authors and do not represent those of our institution, the Bureau of Health Promotion or the Food and Drug Administration.

Funding

Part of this study was funded by the National Health Research Institutes.

Authors’ Affiliations

(1)
Division of Preventive Medicine and Health Services Research, Institute of Population Health Sciences, National Health Research Institutes
(2)
Health Promotion Administration, Ministry of Health and Welfare

References

  1. Pascolini D, Mariotti SP: Global estimates of visual impairment: 2010. Br J Ophthalmol. 2012, 96 (5): 614-618. 10.1136/bjophthalmol-2011-300539.View ArticlePubMedGoogle Scholar
  2. Rein DB, Zhang P, Wirth KE, Lee PP, Hoerger TJ, McCall N, Klein R, Tielsch JM Vijan S, Saaddine J: The economic burden of major adult visual disorders in the United States. Arch Ophthalmol. 2006, 124 (12): 1754-1760. 10.1001/archopht.124.12.1754.View ArticlePubMedGoogle Scholar
  3. Foster P, Wong T, Machin D, Johnson G, Seah S: Risk factors for nuclear, cortical and posterior subcapsular cataracts in the Chinese population of Singapore: the Tanjong Pagar survey. Br J Ophthalmol. 2003, 87 (9): 1112-1120. 10.1136/bjo.87.9.1112.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Bergman B, Nilsson-Ehle H, Sjostrand J: Ocular changes, risk markers for eye disorders and effects of cataract surgery in elderly people: a study of an urban Swedish population followed from 70 to 97 years of age. Acta Ophthalmol Scand. 2004, 82 (2): 166-174. 10.1111/j.1600-0420.2004.00182.x.View ArticlePubMedGoogle Scholar
  5. Roberts JE: Ultraviolet radiation as a risk factor for cataract and macular degeneration. Eye Contact Lens. 2011, 37 (4): 246-249. 10.1097/ICL.0b013e31821cbcc9.View ArticlePubMedGoogle Scholar
  6. DeBlack SS: Cigarette smoking as a risk factor for cataract and age-related macular degeneration: a review of the literature. Optometry. 2003, 74 (2): 99-110.PubMedGoogle Scholar
  7. Panda A, Sood NN, Agarwal LP: Corticosteroid induced glaucoma and cataract. Indian J Ophthalmol. 1981, 29 (4): 377-379.PubMedGoogle Scholar
  8. Tarwadi KV, Agte VV: Interrelationships between nutritional status, socioeconomic factors, and lifestyle in Indian cataract patients. Nutrition. 2011, 27 (1): 40-45. 10.1016/j.nut.2009.11.015.View ArticlePubMedGoogle Scholar
  9. Prokofyeva E, Wegener A, Zrenner E: Cataract prevalence and prevention in Europe: a literature review. Acta Ophthalmol (Copenh). 2012, 91 (5): 395-405.View ArticleGoogle Scholar
  10. West S: Epidemiology of cataract: accomplishments over 25 years and future directions. Ophthalmic Epidemiol. 2007, 14 (4): 173-178. 10.1080/09286580701423151.View ArticlePubMedGoogle Scholar
  11. McCarty CA, Nanjan MB, Taylor HR: Attributable risk estimates for cataract to prioritize medical and public health action. Invest Ophthalmol Vis Sci. 2000, 41 (12): 3720-3725.PubMedGoogle Scholar
  12. Xu L, Cui T, Zhang S, Sun B, Zheng Y, Hu A, Li J, Ma K, Jonas JB: Prevalence and risk factors of lens opacities in urban and rural Chinese in Beijing. Ophthalmology. 2006, 113 (5): 747-755. 10.1016/j.ophtha.2006.01.026.View ArticlePubMedGoogle Scholar
  13. Kirk Smick O, Villette T: Blue Light Hazard: New Knowledge, New approaches to Maintaining Ocular Health In. 2013, Essior of America: New York CityGoogle Scholar
  14. Varma SD, Kovtun S, Hegde KR: Role of UV irradiation and oxidative stress in cataract formation: medical prevention by nutritional antioxidants and metabolic agonists. Eye Contact Lens. 2011, 37 (4): 233-10.1097/ICL.0b013e31821ec4f2.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Bharati DR, Pal R, Rekha R, Yamuna TV, Kar S, Radjou AN: Ageing in Puducherry, South India: an overview of morbidity profile. J Pharm Bioallied Sci. 2011, 3 (4): 537-542. 10.4103/0975-7406.90111.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Klein R, Lee KE, Gangnon RE, Klein BE: Relation of smoking, drinking, and physical activity to changes in vision over a 20-year period: the beaver dam eye study. Ophthalmology. 2014, 121 (6): 1220-1228. 10.1016/j.ophtha.2014.01.003.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Gautam P, Shrestha JK, Joshi SN: The factors associated with age related macular degeneration and quality of life of the patients in a tertiary-level ophthalmic center in Kathmandu. Nepal J Ophthalmol. 2009, 1 (2): 114-117.PubMedGoogle Scholar
  18. Klein R, Moss SE, Klein BE, Gutierrez P, Mangione CM: The NEI-VFQ-25 in people with long-term type 1 diabetes mellitus: the Wisconsin epidemiologic study of diabetic retinopathy. Arch Ophthalmol. 2001, 119 (5): 733-740. 10.1001/archopht.119.5.733.View ArticlePubMedGoogle Scholar
  19. de Fine Olivarius N, Siersma V, Almind GJ, Nielsen NV: Prevalence and progression of visual impairment in patients newly diagnosed with clinical type 2 diabetes: a 6-year follow up study. BMC Public Health. 2011, 11: 80-10.1186/1471-2458-11-80.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Owen N, Healy GN, Matthews CE, Dunstan DW: Too much sitting: the population health science of sedentary behavior. Exerc Sport Sci Rev. 2010, 38 (3): 105-113. 10.1097/JES.0b013e3181e373a2.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Owen N, Sugiyama T, Eakin EE, Gardiner PA, Tremblay MS, Sallis JF: Adults' sedentary behavior determinants and interventions. Am J Prev Med. 2011, 41 (2): 189-196. 10.1016/j.amepre.2011.05.013.View ArticlePubMedGoogle Scholar
  22. Vallance JK, Eurich D, Marshall AL, Lavallee CM, Johnson ST: Associations between sitting time and health-related quality of life among older men. Mental Health Phys Act. 2013, 6 (1): 46-54.View ArticleGoogle Scholar
  23. Kuang TM, Tsai SY, Hsu WM, Cheng CY, Liu JH, Chou P: Body mass index and age-related cataract: the Shihpai eye study. Arch Ophthalmol. 2005, 123 (8): 1109-1114. 10.1001/archopht.123.8.1109.View ArticlePubMedGoogle Scholar
  24. Cheng CY, Liu JH, Chen SJ, Lee FL: Population-based study on prevalence and risk factors of age-related cataracts in Peitou, Taiwan. Zhonghua Yi Xue Za Zhi (Taipei). 2000, 63 (8): 641-648.Google Scholar
  25. Shih YT, Hung YT, Chang HY, Liu JP, Lin HS, Chang MC, Chang FC, Hsiung AC, Wu SL: The design, contents, operation and the characteristics of the respondents of the 2001 national health interview survey in Taiwan. Taiwan J Public Health. 2003, 22 (6): 419-430.Google Scholar
  26. Chang HY, Chiou CJ, Lin MC, Lin SH, Tai TY: A population study of the self-care behaviors and their associated factors of diabetes in Taiwan: results from the 2001 national health interview survey in Taiwan. Prev Med. 2005, 40 (3): 344-348. 10.1016/j.ypmed.2004.06.012.View ArticlePubMedGoogle Scholar
  27. Beunza JJ, Martinez-Gonzalez MA, Ebrahim S, Bes-Rastrollo M, Nunez J, Martinez JA, Alonso A: Sedentary behaviors and the risk of incident hypertension: the SUN cohort. Am J Hypertens. 2007, 20 (11): 1156-1162.PubMedGoogle Scholar
  28. Gierach GL, Chang SC, Brinton LA, Lacey JV, Hollenbeck AR, Schatzkin A, Leitzmann MF: Physical activity, sedentary behavior, and endometrial cancer risk in the NIH-AARP diet and health study. Int J Cancer J Int Cancer. 2009, 124 (9): 2139-2147. 10.1002/ijc.24059.View ArticleGoogle Scholar
  29. Xiao Q, Yang HP, Wentzensen N, Hollenbeck A, Matthews CE: Physical activity in different periods of life, sedentary behavior, and the risk of ovarian cancer in the NIH-AARP diet and health study. Cancer Epidemiol Biomarkers Prev Publ Am Assoc Cancer Res Am Soc Prev Oncol. 2013, 22 (11): 2000-2008. 10.1158/1055-9965.EPI-13-0154.View ArticleGoogle Scholar
  30. Richter GM, Torres M, Choudhury F, Azen SP, Varma R, Los Angeles Latino Eye Study G: Risk factors for cortical, nuclear, posterior subcapsular, and mixed lens opacities: the Los Angeles Latino eye study. Ophthalmology. 2012, 119 (3): 547-554. 10.1016/j.ophtha.2011.09.005.View ArticlePubMedGoogle Scholar
  31. Wang GQ, Bai ZX, Shi J, Luo S, Chang HF, Sai XY: Prevalence and risk factors for eye diseases, blindness, and low vision in Lhasa, Tibet. Int J Ophthalmol. 2013, 6 (2): 237-241.PubMedPubMed CentralGoogle Scholar
  32. Yoon KC, Mun GH, Kim SD, Kim SH, Kim CY, Park KH, Park YJ, Baek SH, Song SJ, Shin JP, Yang SW, Yu SY, Lee JS, Lim KH, Park HJ, Pyo EY, Yang JE, Kim YT, Oh KW, Kang SW: Prevalence of eye diseases in South Korea: data from the Korea National Health and Nutrition Examination Survey 2008–2009. Korean J Ophthalmol. 2011, 25 (6): 421-433. 10.3341/kjo.2011.25.6.421.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Nirmalan PK, Robin AL, Katz J, Tielsch JM, Thulasiraj RD, Krishnadas R, Ramakrishnan R: Risk factors for age related cataract in a rural population of southern India: the Aravind comprehensive eye study. Br J Ophthalmol. 2004, 88 (8): 989-994. 10.1136/bjo.2003.038380.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Wu R, Wang JJ, Mitchell P, Lamoureux EL, Zheng Y, Rochtchina E, Tan AG, Wong TY: Smoking, socioeconomic factors, and age-related cataract: the Singapore Malay eye study. Arch Ophthalmol. 2010, 128 (8): 1029-1035. 10.1001/archophthalmol.2010.147.View ArticlePubMedGoogle Scholar
  35. Athanasiov PA, Edussuriya K, Senaratne T, Sennanayake S, Sullivan T, Selva D, Casson RJ: Cataract in central Sri Lanka: prevalence and risk factors from the Kandy eye study. Ophthalmic Epidemiol. 2010, 17 (1): 34-40. 10.3109/09286580903324900.View ArticlePubMedGoogle Scholar
  36. Athanasiov PA, Casson RJ, Sullivan T, Newland HS, Shein WK, Muecke JS, Selva D, Aung T: Cataract in rural Myanmar: prevalence and risk factors from the Meiktila eye study. Br J Ophthalmol. 2008, 92 (9): 1169-1174. 10.1136/bjo.2008.139725.View ArticlePubMedGoogle Scholar
  37. Klein BE, Klein R, Lee KE, Meuer SM: Socioeconomic and lifestyle factors and the 10-year incidence of age-related cataracts. Am J Ophthalmol. 2003, 136 (3): 506-512. 10.1016/S0002-9394(03)00290-3.View ArticlePubMedGoogle Scholar
  38. Duan XR, Liang YB, Wang NL, Wong TY, Sun LP, Yang XH, Tao QS, Yuan RZ, Friedman DS: Prevalence and associations of cataract in a rural Chinese adult population: the Handan eye study. Graefes Arch Clin Exp Ophthalmol. 2013, 251 (1): 203-212. 10.1007/s00417-012-2012-x.View ArticlePubMedGoogle Scholar
  39. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2415/14/128/prepub

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© Shih et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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