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Adherence to Dietary Guidelines Positively Affects Quality of Life and Functional Status of Older Adults
RESEARCH
Original Research
Adherence to Dietary Guidelines Positively Affects Quality of Life and Functional Status of Older Adults Bamini Gopinath, PhD; Joanna Russell, MPH; Victoria M. Flood, PhD, MPH; George Burlutsky, MApplStat; Paul Mitchell, MD, PhD ARTICLE INFORMATION Article history: Accepted 28 August 2013 Available online 14 November 2013
Keywords: Diet quality Quality of life Blue Mountains Eye Study Activities of daily living Older adults Copyright ª 2014 by the Academy of Nutrition and Dietetics. 2212-2672/$36.00 http://dx.doi.org/10.1016/j.jand.2013.09.001
ABSTRACT Background Nutritional parameters could influence self-perceived health and functional status of older adults. Objective We prospectively determined the association between diet quality and quality of life and activities of daily living. Design This was an observational cohort study in which total diet scores, reflecting adherence to dietary guidelines, were determined. Dietary intakes were assessed using a food frequency questionnaire at baseline. Total diet scores were allocated for intake of selected food groups and nutrients for each participant as described in the Australian Guide to Healthy Eating. Higher scores indicated closer adherence to dietary guidelines. Participants/setting In Sydney, Australia, 1,305 and 895 participants (aged 55 years) with complete data were examined over 5 and 10 years, respectively. Main outcome variables The 36-Item Short-Form Survey assesses quality of life and has eight subscales representing dimensions of health and well-being; higher scores reflect better quality of life. Functional status was determined once at the 10-year follow-up by the Older Americans Resources and Services activities of daily living scale. This scale has 14 items: seven items assess basic activities of daily living (eg, eating and walking) and seven items assess instrumental activities of daily living (eg, shopping or housework). Statistical analyses performed Normalized 36-Item Short-Form Survey component scores were used in analysis of covariance to calculate multivariable adjusted mean scores. Logistic regression analysis was used to calculate adjusted odds ratios and 95% CIs to demonstrate the association between total diet score with the 5-year incidence of impaired activities of daily living. Results Participants in the highest vs lowest quartile of baseline total diet scores had adjusted mean scores 5.6, 4.0, 5.3, and 2.6 units higher in these 36-Item Short-Form Survey domains 5 years later: physical function (Ptrend¼0.003), general health (Ptrend¼0.02), vitality (Ptrend¼0.001), and physical composite score (Ptrend¼0.003), respectively. Participants in the highest vs lowest quartile of baseline total diet scores had 50% reduced risk of impaired instrumental activites of daily living at follow-up (multivariable-adjusted Ptrend¼0.03). Conclusions Higher diet quality was prospectively associated with better quality of life and functional ability. J Acad Nutr Diet. 2014;114:220-229.
D
IETARY PATTERN ANALYSIS EXAMINES THE OVERall diet and could have potential advantages.1 For instance, dietary pattern analysis can capture the complexity of the diet because it accounts for the high correlation among intakes of specific foods or nutrients, which are often interdependent in their bioavailability.1 Dietary indexes assess diet quality by
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grouping foods a priori into a single measure that is representative of current nutrition knowledge in the form of dietary guidelines or other dietary recommendations.2 This may be a useful tool in public health practice to assess a population’s adherence to current dietary guidelines based on empirical evidence.3 Older adults with poor overall diet quality are likely to have suboptimal levels of nutrition biomarkers; this could negatively affect quality of life and functional independence. For example, individuals with lower diet quality are likely to have an inadequate antioxidant status and greater inflammation, which can lead to an increased risk of a range of chronic conditions 4 and, in turn, negatively affect health-related quality of life and functional ability. ª 2014 by the Academy of Nutrition and Dietetics.
RESEARCH Health-related quality of life is an important aspect of physical, social, and mental well-being.5 Research regarding the influence of adherence to dietary guidelines on quality of life, however, is limited. A cross-sectional study of adults aged 35 to 74 years showed that greater adherence to a Mediterranean-style dietary pattern was associated with higher self-perceived mental and physical health scores.6 Recently, self-perceived mental and physical quality of life was shown to be positively associated with adherence to a Mediterranean-style diet during a 4-year follow-up among 11,015 Spanish adults (aged 18 to 101 years).7 Inability to perform instrumental activities of daily living (IADL) (ie, activities required to function in the community such as shopping) and basic activities of daily living (BADL) (eg, eating and washing) often precedes dependency.8 Only a handful of population-based studies have assessed the relationship between diet quality and activities of daily living disability. Among middle-aged adults, lower dairy, fruit, and vegetable consumption was associated with poorer IADL scores over 9 years.9 A US cross-sectional study of a representative sample adults aged 60 years showed that participants with higher diet quality were less likely to experience IADL disability and lower extremity immobility.10 Given that there is a paucity of prospective data from population-based studies of older adults on the relationship between overall diet quality and quality of life and activities of daily living functioning, the purpose of this study was to answer the following key questions using a large cohort of adults aged 55 years and older: Does adherence to dietary guidelines have a beneficial effect on quality of life scores in the long term? and, Is overall diet quality prospectively associated with functional status as assessed by an activities of daily living scale?
METHODS Study Population The Blue Mountains Eye Study (BMES) is a population-based cohort study of common eye diseases and other health outcomes in a suburban Australian population living west of Sydney. Study methods and procedures have been described elsewhere.11 Participants were noninstitutionalized residents aged 49 years or older invited to attend a detailed baseline eye examination after a door-to-door census of the study area. Selection bias at baseline was minimized after multiple callback visits, including door-knocking, telephone reminders, and letters at recruitment. Baseline examinations of 3,654 residents aged >49 years were conducted during 1992-1994 (BMES-1, 82.4% participation rate). Surviving baseline participants were invited to attend examinations after 5 (1997-1999, BMES-2), 10 (2002-2004, BMES-3), and 15 years (2007-2009, BMES-4), at which time 2,334 (75.1% of survivors),1,952 (75.6% of survivors), and 1,149 (55.4% of survivors) participants were re-examined, respectively, with complete data. The University of Sydney and the Western Sydney Area Human Ethics Committees approved the study, and written informed consent was obtained from all participants at each examination.
Nutrition Assessment Dietary data were collected using a semiquantitative, 145-item self-administered food frequency questionnaire (FFQ).12 At all four BMES examinations, participants used a 9-category February 2014 Volume 114 Number 2
frequency scale to indicate the usual frequency of consuming individual food items during the past year. For our study, FFQ data collected at BMES-2 and BMES-3 were used in the analyses. The FFQ was validated by comparing nutrients from the FFQ to 34-day weighed food records collected over 1 year to allow for seasonal variation. Most nutrient correlations were between 0.50 and 0.60 for energy-adjusted intakes.13 A dietitian coded data from the FFQ into a customized database that incorporated the Australian Tables of Food Composition 1990 (baseline FFQ data) and follow-up FFQ data used NUTTAB95.14,15 A modified version of the Australian diet quality index,16 based on the Dietary Guidelines for Australian Adults (DGAA)17 and the Australian Guide to Healthy Eating (AGHE),18 was used to establish the total diet score (TDS), assessing adherence to the DGAA. The methodology used to develop total diet scores has been previously reported.3 Briefly, a total diet score was allocated for intake of selected food groups and nutrients for each participant as described in the AGHE18 (see Table 1). The total diet score is divided into 10 components, and each component has a possible score ranging from 0 to 2. A maximum score of 2 was given to subjects who met the recommendations with prorated scores for lower intakes. These were then summed, providing a final score ranging between 0 and 20 with higher scores indicating closer adherence to the dietary guidelines. The total diet score accounts for both food intake and optimal choice with scores allocated to reflect intake characteristics from both sources. Food intake scores were based on total intakes of vegetables, fruit, cereals and breads, meat, fish, poultry, and dairy as well as sodium, alcohol, sugar, and extra foods intakes. Optimal choices scores determined intakes of foods with greater dietary benefits, including servings of whole-grain cereals, lean red meat, low-fat or reduced-fat milk vs full-fat milk, low saturated fat intake, and fish consumption. Cut-points for scores were determined from the recommended number of serves given in the AGHE, with some exceptions.18 We replaced the AGHE’s recommended two servings per day of fruit with three servings per day and the number of vegetables consumed per day from five servings to seven servings to allow for self-reported FFQ overestimation as determined by the validity study.13 Moderate intakes of sugar were determined from the DGAA, which found no evidence that consuming a diet with up to 15% to 20% of energy from sugar was detrimental to a healthy diet.17 Extra foods were defined as foods that were energy dense containing higher levels of sugar, fat, or salt, with one serving equivalent to 600 kJ.18 Examples described in the AGHE include cookies, cakes, soft drinks, ice cream, pies, hot chips (ie, french fries) and high-fat takeaway items. The alcohol cut points reflect guidelines about alcohol consumption in Australia, in which it is recommended that men consume a maximum two standard drinks per day and women consume a maximum one standard drink per day.17 The nondietary component of the AGHE, preventing weight gain, was included in the total diet score. Half the score component was assigned to energy balance, calculated as the ratio of energy intake to energy expenditure with a maximum score given for ratios falling between 0.76 and 1.24 (Table 1), defined as the 95% confidence levels of agreement between energy intake and expenditure.19 The other half of the score was assigned to leisure time physical activity. Details of walking exercise and the performance of moderate or JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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RESEARCH Table 1. Scoring system for total diet score, based on Australian Dietary Guidelines and the Australian Guide to Healthy Eating Dietary guideline component
Component
Subscore
7 servingsb
0.5
5.6 servings
0.4
4.2 servings
0.3
2.8 servings
0.2
1.4 servings
0.2
Eat plenty of vegetables, legumes and fruit Total vegetable intake/da
Vegetable variety score/d
Total fruit intake/dc
2
1 serving green
0.1
1 serving orange
0.1
1 serving cruciferous
0.1
1 serving tuber or bulb
0.1
0.5 servings legumes
0.1
3 servings
1
2 servings
0.5
Eat plenty of cereals, preferably wholegrain/meal Total cereal intake/d, women
Total cereal intake/d, men
Whole grain cereal intake/d, women
Whole grain cereal intake/d, men
Total score
2 4 servings
1
3 servings
0.75
2 servings
0.5
1 serving
0.25
6 servings
1
5 servings
0.83
4 servings
0.66
3 servings
0.5
2 servings
0.33
1 serving
0.16
4 servings
1
3 servings
0.75
2 servings
0.5
1 serving
0.25
6 servings
1
5 servings
0.83
4 servings
0.66
3 servings
0.5
2 servings
0.33
1 serving
0.16
Include lean meats, fish, poultry, and/ or alternatives
2
Meat/alternative intake/d
1 serving
1.5
Lean red mean/wk (ie, >0.428 servings/d)
3 servings
0.5
2-3 servings
1.5
>3-4 servings
1.0
1-<2 servings
1.0 (continued on next page)
Include milk, yogurts, cheese, and/or alternatives Total dairy intake/d
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RESEARCH Table 1. Scoring system for total diet score, based on Australian Dietary Guidelines and the Australian Guide to Healthy Eating (continued) Dietary guideline component
Ratio of skim/low-fat intake to whole-milk intake
Component
Subscore
>4 servings
0.5
0-<1 servings
0
Skim/low-fat>whole milk
0.5
Skim/low-fat¼whole milk
0.25
Whole milk>Skim/low-fat milk
0
Limit saturated fat and moderate total fat intake Percentage of energy from saturate fat
Fish intake/wk
2 <10
1
10-12
0.5
>12
0
2 servings
1
1-<2 servings
0.5
<1 serving
0
40 mmol (920 mg)
2
Choose foods low in salt Sodium intake/d
2 >40-100 mmol (920-2,300 mg)
1
>100 mmol (2,300 mg)
0
0 g-<10 g
2
>10 g-<20 g
1
>20 g
0
Limit alcohol intake if you choose to drink Alcohol intake/d, women
Alcohol intake/d, men
2
0 g-<20 g
2
>20 g
0
Consume only moderate amounts of sugars and foods with added sugars Percent of energy from all sugars
2 <15
2
15-<20
1
20
0
Extra foods, not essential to provide nutrients and may be high in salt, fat, or sugard Extra foods intake/d, women
Extra foods intake/d, men
2 <2.5 servings
2
2.5-<4 servings
1
4 servings
0
<3 servings
2
3-<5 servings
1
5 servings
0
Prevent weight gain: Be physically active and eat according to energy needs Ratio of energy intake to energy expenditure e
Physical activity (METs )
Total score
2 0.76-1. 24
1
<0.76 or >1. 24
0
Lowest tertile
0
Middle tertile
0.5
Highest tertile
1 (continued on next page)
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RESEARCH Table 1. Scoring system for total diet score, based on Australian Dietary Guidelines and the Australian Guide to Healthy Eating (continued) Dietary guideline component
Component
Drink plenty of water
Not scored
Care for food
Not scored
Subscore
Total score
Total score
20
a
Vegetables: 7 servings, as indicated by weighed food records (food frequency questionnaire overestimates) (replacing 5 servings). Servings sizes:18
b
Vegetables: 1 /2 c (2.65 oz) cooked vegetables, cooked dried beans, peas, or lentils 1 c salad vegetable 1 small potato Fruit: 5.3 oz fresh fruit (eg. 1 medium apple, banana, orange pear; 2 small pieces of fruit like apricots, kiwifruit, and plums) 1 c (5.3 oz) diced fruit or canned fruit 11/2 tablespoon sultanas 4.2 fl oz fruit juice Cereals: 2 slices bread (2.1 oz) 1 c cooked rice, pasta, noodles (6.3 oz) 1 c cooked porridge (8.1 oz) 1 1/3 c ready to eat cereal 1 /2 c untoasted muesli 1/3 c flour Meat and meat alternatives: 2.2-3.4 oz cooked meat or chicken 1 /2 c cooked beans, lentils, chickpeas, split peas, and canned beans 2.7-4.1 oz cooked fish fillet 1/3 c peanuts or almonds 1 /4 c sunflower seeds Dairy:
1 c milk or soy milk 1 /2 c evaporated milk 1.4 oz cheese 1 (7.1 oz) small carton yogurt 1 c custard Extra food: 1 serving of extra foods supplies 600 kj (143.4 kcal). Fruit: 3 servings, as indicated by weighed food records (food frequency questionnaire overestimates) (replacing 2 servings). d Examples of extra foods are biscuits, cakes, doughnuts, soft drinks, ice cream, pies, hot chips (ie, french fries), and high-fat takeaway items. e METs¼metabolic equivalents. c
vigorous activities were used to calculate metabolic equivalents (METs).20 Tertiles were created based on the following MET cut points: 600, >600 to 1,500, and >1,500. These cut points were based on the International Physical Activity Questionnaire scoring protocol20; 600 METs is equivalent to moderate physical activity; that is, 530 minutes of moderate activity per week, which is the minimum recommendation in Australia. Subjects in the highest METs tertile scored 1 point; that was reduced to a 0-point score for subjects in the lowest METs tertile.
Assessment of Quality of Life The 36-Item Short-Form Survey (SF-36) contains 36 items, and produces eight subscale scores representing dimensions of health and well-being.21 and was examined at BMES-2 and then 5 years later at BMES-3; that is, during 1997-1999 and 224
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2002-2004. There are eight SF-36 subscales, which are listed in order from those most strongly related to physical health to those most strongly related to mental health. The eight subscales are “physical functioning,” “role limitations due to physical problems,” “bodily pain,” “general health perceptions,” “vitality,” “social functioning,” “role limitations due to emotional problems,” and “mental health.” The subscales were then summarized as a physical and mental composite score, calculated by assigning relative weights to each subscale as described by the developers of this instrument.22,23 The domain and composite scores are rated so that higher values indicate better health (range 0 to 100).
Activities of Daily Living Scale The Older American Resources and Services activities of daily living scale24 includes 14 items: seven items assess BADL (eg, February 2014 Volume 114 Number 2
RESEARCH eating, dressing and undressing, grooming, walking), and seven items assess IADL (eg, using the telephone, get to places out of walking distance, shopping, meal preparation, housework, taking own medications and handling money). The Older American Resources and Services scale was administered at the 10- and 15-year BMES examinations. Each item was rated on a 3-point scale: 2¼performs the activity without help, 1¼performs the activity with some help, and 0¼completely unable to perform the activity. Three summary scales were computed by summing the scores of items: 1¼a total score, the sum of all 14 items (range 0 to 28), 2¼a BADL score, the sum of the seven BADL items (range 0 to 14), and 3¼an IADL score, the sum of the seven IADL items (range 0 to 14). Participants reporting that they needed help with any of the activities or were completely unable to perform any of the activities were considered to have impaired ability to perform activities of daily living; that is, those who did not score 28 points on this scale.
Assessment of Health Covariates A face-to-face interview with trained interviewers was conducted, and comprehensive data, including information about demographic factors and lifestyle behaviors such as exercise and smoking were obtained from all participants. Participants were also asked whether they received a pension and the type they received (eg, age or invalid). Self-rated health was assessed by asking, “For somebody your age, would you say your health is excellent, very good, good, fair, or poor?” Low self-rated health was defined as fair/poor. Walking difficulty or use of a cane, walker, or wheelchair was observed by a trained examiner and categorized as walking disability. Cognitive decline was assessed using the mini mental state examination questionnaire.25 The mini mental state examination has test components covering concentration, language, and memory. Mini mental state examination scores range from zero to 30; scores <24 were considered cognitively impaired. Visual acuity was measured wearing current glasses, using a LogMar chart and was followed by subjective refraction.10 Visual impairment was defined as visual acuity of fewer than 39 letters (<6 out of 12) in the better eye after subjective refraction. Diabetes was defined either by history or from fasting blood glucose 7.0 mmol/L (126.13 mg/dL). Hypertension was defined as systolic blood pressure >140 mm Hg or diastolic blood pressure >90 mm Hg or taking antihypertensive medications.
Statistical Analyses SAS statistical software (version 9.2, 2009, SAS Institute) was used for analyses. The c2 test was used to compare categorical characteristics between participants (eg, sex, house ownership, and visual impairment). We also used F criteria test for heterogeneity from analysis of variance for unbalanced data for testing the hypothesis that the group means for a continuous variable are equal. Total diet score was analyzed either as a continuous variable (each 1-standard deviation increase) or as a categorical variable (quartiles). SF-36 physical and mental component scores were calculated according to previous factor analysis results and were normalized using Australian population scores.22,23 Normalized component scores were used in analysis of covariance to calculate mean scores first adjusted for age and sex and then February 2014 Volume 114 Number 2
for other significant covariates in the model. Potential confounders that were assessed included various sociodemographic variables (eg, education level, receipt of pension payment, and home ownership), as well as admission to a hospital during the past 12 months, body mass index, smoking, serum triglyceride and total cholesterol levels, and chronic conditions (eg, visual impairment, cognitive impairment, and several conditions such as arthritis and diabetes). Only those potential confounders that significantly modified the effect of total diet score in age- and sex-adjusted models were included in subsequent multivariable analyses. Those factors that were not significant covariates included education level, body mass index, smoking, serum triglyceride level, and total cholesterol level. Thus, final models were adjusted for age, sex, receipt of pension payment, home ownership, admission to a hospital during the past 12 months, walking disability, presence of five or more chronic conditions (eg, arthritis, diabetes, and kidney disease), cognitive impairment, visual impairment, and living alone. Multivariable logistic regression analysis was used to calculate adjusted odds ratios and 95% CIs to demonstrate the association between total diet score with the 5-year incidence of impaired IADL and BADL. For activities of daily living analyses potential confounders assessed were similar to those described in the above for quality of life/SF-36 analyses. Final, multivariable models included covariates that significantly modified the effect of total diet score in age- and sexadjusted model or predicted incident activities of daily living/ IADL/BADL disability. Those factors that were not significant covariates included education level, home ownership, receipt of pension payment, body mass index, serum triglyceride level, total cholesterol level, arthritis, and visual impairment. Hence, logistic regression analyses adjusted for age, sex, living alone, self-rated poor health, current smoking, hypertension, diabetes, hospital admissions during the past year, walking disability, and cognitive impairment.
RESULTS Of the 1,952 participants examined at BMES-3 (2002-2004), 1,305 had complete dietary and SF-36 data at both baseline (BMES-2) and 5 years later at BMES-3, and so were included for analyses. Table 2 shows the baseline characteristics of participants included for analyses of the longitudinal relationship between total diet score and quality of life; those in the lowest vs highest quartile of total diet scores were significantly more likely to be men. Of 1,149 participants examined at BMES-3 and BMES-4, 895 participants had complete dietary and activities of daily living data at both these examinations and so were included for analyses. We compared the study characteristics of those included (n¼895) and excluded (n¼254) from analyses. Those excluded (because they had incomplete dietary and activities of daily living data) were more likely to self-report poorer health (P¼0.01) and be admitted to a hospital during the past year (P¼0.003) than included participants. Baseline characteristics of the 895 participants are shown in Table 3; those in the lowest vs highest quartile of total diet scores were more likely to be men and more likely to be smokers. After adjusting for age, sex, receipt of pension payment, home ownership, admission to hospitals, walking disability, JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
225
RESEARCH Table 2. Baseline characteristics of participants included for analyses of total diet score with quality of life/Short Form-36 scores in the Blue Mountains Eye Study between 1997-1999 and 2002-2004 (N¼1,305) Total Diet Score Quartile Characteristics
1 (£8.13) (n[325)
Age (y)
67.17.4
Male sex
162 (49.9)
2 (8.15-9.75) (n[333)
3 (9.76-11.10) (n[320)
4 (‡11.13) (n[327)
P valuea
meanstandard deviation! 67.77.4
67.76.8
67.37.5
0.66
n (%)! Lives alone
145 (43.4)
121(37.8)
105 (32.1)
<0.0001
70 (21.5)
86 (25.8)
66 (20.6)
88 (26.9)
0.16
Receipt of pension payment
209 (64.7)
211 (63.6)
197 (62.2)
202 (62.4)
0.90
Owns a house
304 (93.5)
318 (95.8)
305 (95.3)
305 (93.3)
0.40
Admission to hospital during the past 12 mo
62 (19.1)
78 (23.5)
82 (25.6)
69 (21.1)
0.21
Walking disability
14 (4.3)
16 (4.8)
23 (7.2)
14 (4.3)
0.29
Has 5 chronic conditions
4 (1.2)
4 (1.2)
7 (2.2)
3 (0.9)
0.53
Cognitively impairedb
3 (0.9)
5 (1.5)
3 (0.9)
9 (2.8)
0.19
Visually impairedc
3 (0.9)
4 (1.2)
3 (0.9)
2 (0.6)
0.89
c test and F criteria tests were used to determine significant differences between categorical and continuous variables between the 4 groups, respectively.
a 2
Has mini mental state examination score <24. Visual impairment was defined as visual acuity of less than 39 letters (<6/12) in the better eye after subjective refraction.
b c
Table 3. Baseline characteristics of participants included for analyses of total diet score with activities of daily living scores in the Blue Mountains Eye Study between 2002-2004 to 2007-2009 (N¼895) Total Diet Score Quartile Characteristics
1 (£8.77) (n[223)
Age (y)
71.57.0
2 (8.80-10.23) (n[224)
3 (10.25-11.70) (n[225)
4 (‡11.73) (n[223)
P valuea
meanstandard deviation ! 71.46.7
72.16.7
71.26.7
0.53
n (%)! Male sex Lives alone
117 (52.5)
107 (47.8)
77 (34.2)
73 (32.7)
<0.0001
44 (20.0)
39 (17.4)
47 (20.9)
41 (18.4)
0.79
Poor self-rated health
36 (16.3)
46 (20.5)
28 (12.5)
33 (14.9)
0.13
Current smoker
20 (9.1)
17 (7.6)
9 (4.0)
7 (3.1)
0.02
8 (3.9)
12 (5.9)
19 (9.1)
16 (7.8)
0.16
37 (16.7)
53 (23.7)
41 (18.3)
41 (18.6)
0.28
Walking disability Admission to hospital during the past 12 mo Cognitively impairedb History of type 2 diabetes Hypertension
2 (0.9)
2 (0.9)
1 (0.5)
3 (1.4)
0.81
21 (9.4)
26 (11.6)
22 (9.8)
33 (14.8)
0.26
114 (52.3)
118 (54.4)
119 (54.6)
126 (58.1)
0.68
Test for heterogeneity in 4 groups using F criteria. Has mini-mental state examination score <24.
a
b
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RESEARCH Table 4. Longitudinal association between baseline total diet scores and mean quality of life/Short Form-36 (SF-36) domain 5 years later in the Blue Mountains Eye Study from 1997-1999 to 2002-2004 (n¼1,305) Total Diet Score Quality of life/SF-36 domain
1st Quartile (£8.13) n[325
2nd Quartile (8.15-9.75) n[333
3rd Quartile (9.76-11.10) n[320
4th Quartile (‡11.13) n[327
P for trend
adjusteda meanstandard error! Physical function
66.01.3
70.71.3
70.11.3
71.61.3
0.003
Role limitation due to physical problems
57.62.3
60.72.2
62.92.3
63.72.3
0.05
Bodily pain
69.11.4
66.71.3
67.61.4
72.51.4
0.10
General health
65.21.1
66.21.1
66.71.2
69.21.1
0.02
Vitality
57.01.1
60.41.1
61.31.1
62.31.1
0.001
Social function
83.31.2
85.21.2
85.21.2
85.01.2
0.32
Role limitation due to emotional problems
77.22.0
80.92.0
82.02.0
80.02.0
0.24
Mental health
77.20.9
78.90.9
79.60.9
78.60.9
0.21
Physical composite score
42.40.6
42.90.6
43.20.6
45.00.6
0.003
Mental composite score
51.70.6
52.50.6
53.00.6
52.10.6
0.45
Analysis of covariance calculated mean scores adjusted for age, sex, receipt of pension payment, home ownership, admission to hospitals, walking disability, having five or more chronic conditions (eg, arthritis or diabetes), cognitive impairment, visual impairment, and living alone. a
having five or more chronic conditions (eg, arthritis or diabetes), cognitive impairment, visual impairment, and living alone, participants in the highest compared with the lowest quartile of total diet scores at baseline had adjusted mean scores 5.6, 4.0, 5.3, and 2.6 units higher in the following quality of life/SF-36 domains: vitality (Ptrend¼0.001), physical function (Ptrend¼0.003), physical composite score (Ptrend¼0.003), and general health (Ptrend¼0.02), respectively, 5 years later (Table 4). We examined the 5-year change (from BMES-2 to BMES-3) in SF-36 scores in relation to baseline total diet score (at BMES-2), and this was not significant (data not shown).
After adjusting for age, sex, living alone, self-rated poor health, current smoking, hypertension, diabetes, hospital admissions during the past year, walking disability, and cognitive impairment, participants in the highest compared with those in the lowest quartile of total diet score at baseline had a 50% reduced risk of incident IADL disability after 5 years (Ptrend¼0.03) (Table 5). Each 1-standard deviation increase in total diet score at baseline was associated with 23% reduced risk of IADL disability 5 years later, multivariable-adjusted odds ratio 0.77 (95% CI: 0.63 to 0.95). Significant temporal associations were not observed with BADL.
Table 5. Longitudinal association between baseline total diet score and 5-year incidence of impaired instrumental activities of daily living (IADL) and basic activities of daily living (BADL) in the Blue Mountains Eye Study from 2002 to 2004 to 2007 to 2009 (N¼895) Impaired IADL (n[173) Odds Ratio (95% CI) No. of cases/at risk
Multivariable-adjusteda
Impaired BADL (n[101) Odds Ratio (95% CI) No. of cases/at risk
Multivariable-adjusteda
Total diet score First quartile (8.77)
53/151
1.0 (reference)
22/185
1.0 (reference)
Second quartile (8.80-10.23)
42/164
0.64 (0.37-1.11)
26/197
1.02 (0.53-1.95)
Third quartile (10.25-11.70)
34/152
0.55 (0.31-0.98)
23/187
1.03 (0.53-2.00)
Fourth quartile (11.73)
44/170
0.50 (0.28-0.87)
30/197
1.33 (0.70-2.51)
P value for trend
0.03
0.41
a Multivariable logistic regression analyses was used to calculated odds ratio (95% CI) adjusted for age, sex, living alone, self-rated poor health, current smoker, hypertension, diabetes, hospital admissions during the past year, walking disability, and cognitive impairment.
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RESEARCH DISCUSSION In the BMES, adherence to dietary guidelines at baseline was associated with significantly better quality of life in four domains 5 years later, after adjusting for several confounders such as walking disability and cognitive impairment. Higher total diet score was independently associated with a 50% reduced risk of IADL disability 5 years later. The observed magnitude of difference in quality of life/SF36 scores between the lowest and highest quartile of total diet scores was w3 to 6 units over 5 years. This is within the range of 3 to 10 points, which was previously defined as a meaningful difference in SF-36 scores in a clinical setting.7,26 The positive association between total diet score and quality of life concurs with prior studies showing that adherence to a Mediterranean-style diet beneficially affects quality of life among Spanish adults.6,7 Specifically, higher Mediterraneanstyle diet scores were associated with better scores in physical functioning, general health, and vitality domains 4 years later, and confirms associations observed in the BMES.7 A likely possibility is that subjects may be aware of the link between diet and health and perceive their own quality of life to be higher if they eat well.4,27 This is the first longitudinal study to document an inverse association between diet quality and incidence of IADL difficulty among older adults. These findings concur with a US cross-sectional study that showed an association between Health Eating Index scores and IADL.10 We hypothesize that this observed relationship could be mediated through an inflammatory pathway. Specifically, higher diet quality, typified by high intake of fruits, vegetables, whole grains, and fish, is associated with lower concentrations of inflammatory markers such as C-reactive protein.28 Inflammatory markers in turn are shown to be associated with late-life disability as reflected in difficulties in performing IADL.29 Moreover, fruits and vegetables are high in antioxidants such as vitamin C and carotenoids, which may reduce oxidative damage and therefore slow the decline in physical performance and the onset of functional limitations and disability.9,30 Understanding the health benefits of following dietary guidelines is essential for setting up effective behavioral interventions.10 Given that self-reported quality of life and functional limitations are significant predictors of mortality in the long term,31 our findings could have important public health implications. Specifically, this study suggests that quality of life and functional ability (as indicated by activities of daily living ratings) could be improved by geriatricians and dietetics practitioners targeting the overall diet of older adults. For example, dietary counseling to maintain an optimal diet could lead to appreciable improvements in all domains of quality of life and in the ongoing ability to perform IADL tasks. Strengths of our study include its population-based sample, use of a validated FFQ to collect dietary data, and use of validated quality of life and activities of daily living instruments. Limitations of this study include using FFQs for self-reported dietary intake because these can underestimate energy intake and overestimate fruit, vegetable, and dairy intakes.32 Nevertheless, a comprehensive assessment of the whole diet is less subject to measurement error than is the assessment of energy intake alone33 and several total diet score components used in this study were designed to
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account for misreporting.3 We also cannot exclude the possibility of residual confounding from unmeasured confounders, particularly because diet could be a proxy marker for other healthful lifestyle parameters that could influence quality of life and activities of daily living. Finally, differences in study characteristics were observed between participants included and excluded for analyses; hence, selection bias could have influenced our findings.
CONCLUSIONS A beneficial influence of adherence to recommended dietary guidelines on both quality of life and functional ability (as assessed by an activities of daily living scale) of older adults was observed over 5 years, independent of several potential confounders. These findings could stimulate targeted intervention strategies that modify dietary practices of the aging population, thereby potentially preserving, or delaying further deterioration in general well-being and physical functioning.
References 1.
Anderson AL, Harris TB, Tylavsky FA, et al. Dietary patterns and survival of older adults. J Am Diet Assoc. 2011;111(1):84-91.
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Waijers PM, Feskens EJ, Ocke MC. A critical review of predefined diet quality scores. Br J Nutr. 2007;97(2):219-231.
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Russell J, Flood V, Rochtchina E, et al. Adherence to dietary guidelines and 15-year risk of all-cause mortality. Br J Nutr. 2013;109(3):547555.
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Ravaglia G, Forti P, Maioli F, et al. Homocysteine and cognitive function in healthy elderly community dwellers in Italy. Am J Clin Nutr. 2003;77(3):668-673.
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Young DR, Coughlin J, Jerome GJ, et al. Effects of the PREMIER interventions on health-related quality of life. Ann Behav Med. 2010;40(3):302-312.
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Munoz MA, Fito M, Marrugat J, et al. Adherence to the Mediterranean diet is associated with better mental and physical health. Br J Nutr. 2009;101(12):1821-1827.
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Henriquez SP, Ruano C, de Irala J, et al. Adherence to the Mediterranean diet and quality of life in the SUN Project. Eur J Clin Nutr. 2012;66(3):360-368.
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Bowling CB, Sawyer P, Campbell RC, et al. Impact of chronic kidney disease on activities of daily living in community-dwelling older adults. J Gerontol A Biol Sci Med Sci. 2011;66(6):689-694.
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Houston DK, Stevens J, Cai J, Haines PS. Dairy, fruit, and vegetable intakes and functional limitations and disability in a biracial cohort: The Atherosclerosis Risk in Communities Study. Am J Clin Nutr. 2005;81(2):515-522.
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Xu B, Houston D, Locher JL, Zizza C. The association between Healthy Eating Index-2005 scores and disability among older Americans. Age Ageing. 2012;41(3):365-371.
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Attebo K, Mitchell P, Smith W. Visual acuity and the causes of visual loss in Australia. The Blue Mountains Eye Study. Ophthalmology. 1996;103(3):357-364.
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Willett WC, Sampson L, Browne ML, et al. The use of a selfadministered questionnaire to assess diet four years in the past. Am J Epidemiol. 1988;127(1):188-199.
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Smith W, Mitchell P, Em Reay, Webb K, Harvey PW. Validity and reproducibility of a self-administered food frequency questionnaire in older people. Aust N Z J Public Health. 1998;22(4):456-463.
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Department of Community Services and Health. NUTTAB90 Nutrient Data Table for Use in Australia. Canberra, Australia: Australian Government Publishing Service; 1990.
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Department of Community Services and Health. NUTTAB95 Nutrient Data Table for Use in Australia. Canberra, Australia: Australian Government Publishing Service; 1995.
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Fung TT, McCullough ML, Newby PK, et al. Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr. 2005;82(1):163-173.
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Black AE. The sensitivity and specificity of the Goldberg cut-off for EI: BMR for identifying diet reports of poor validity. Eur J Clin Nutr. 2000;54(5):395-404.
Kuo HK, Al Snih S, Kuo YF, Raji MA. Chronic inflammation, albuminuria, and functional disability in older adults with cardiovascular disease: The National Health and Nutrition Examination Survey, 1999-2008. Atherosclerosis. 2012;222(2):502-508.
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Craig CL, Marshall AL, Sjostrom M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381-1395.
Snowdon DA, Gross MD, Butler SM. Antioxidants and reduced functional capacity in the elderly: Findings from the Nun Study. J Gerontol A Biol Sci Med Sci. 1996;51(1):M10-M16.
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Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473-483.
Rumsfeld JS, MaWhinney S, McCarthy M Jr, et al. Health-related quality of life as a predictor of mortality following coronary artery bypass graft surgery. Participants of the Department of Veterans Affairs Cooperative Study Group on Processes, Structures, and Outcomes of Care in Cardiac Surgery. JAMA. 1999;281(14):1298-1303.
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Ware JE, Kosinski M, Keller SD. SF-36 Physical and Mental Health Summary Scales. Boston, MA: The Health Institute, New England Medical Center; 1994.
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Ibiebele TI, Parekh S, Mallitt KA, et al. Reproducibility of food and nutrient intake estimates using a semi-quantitative FFQ in Australian adults. Public Health Nutr. 2009;12(12):2359-2365.
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National Health Survey. SF-36 Population Norms. Australia. [4399.0]. Canberra, Australia: Australian Bureau of Statistics; 1997.
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Fillenbaum GG. Multidimensional Functional Assessment of Older Adults: The Duke Older Americans Resources and Services Procedures. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.
Zamora D, Gordon-Larsen P, Jacobs DR Jr, Popkin BM. Diet quality and weight gain among black and white young adults: The Coronary Artery Risk Development in Young Adults (CARDIA) study (19852005). Am J Clin Nutr. 2010;92(4):784-793.
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Folstein MF, Folstein SE, McHugh PR. “Mini-Mental State”: A practical method for grading the cognitive state of patients for the clinician. J Psychiat Res. 1975;12:189-198.
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Samsa G, Edelman D, Rothman ML, et al. Determining clinically important differences in health status measures: A general approach with illustration to the Health Utilities Index Mark II. Pharmacoeconomics. 1999;15(2):141-155.
27.
Wayne SJ, Baumgartner K, Baumgartner RN, et al. Diet quality is directly associated with quality of life in breast cancer survivors. Breast Cancer Res Treat. 2006;96(3):227-232.
16.
Australian Diet Quality Index Project. Canberra, Australia: Australian Institute of Health and Welfare; 2007.
17.
Dietary Guidelines for Australian Adults. Canberra, Australia: National Health and Medical Research Council; 2003.
18.
Smith A, Kellet E, Schmerlaib Y. The Australian Guide to Healthy Eating. Canberra, Australia: Commonwealth of Australia; 1998.
19.
AUTHOR INFORMATION B. Gopinath is a senior research fellow, G. Burlutsky is a statistician, and P. Mitchell is a professor of ophthalmology, Centre for Vision Research, Department of Ophthalmology, and Westmead Millennium Institute, University of Sydney, New South Wales, Australia. J. Russell is a PhD student and V. M. Flood is an associate professor in public health, Faculty of Health and Behavioural Sciences, University of Wollongong, Sydney, New South Wales, Australia. Address correspondence to: Bamini Gopinath, PhD, Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Westmead Hospital, Hawkesbury Rd, Westmead, New South Wales, 2145, Australia. E-mail: [email protected]
STATEMENT OF POTENTIAL CONFLICT OF INTEREST No potential conflict was reported by the authors.
FUNDING/SUPPORT The Blue Mountains Eye Study was supported by the Australian National Health and Medical Research Council (grant nos. 974159, 991407, 211069, and 262120).
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Original Research
Adherence to Dietary Guidelines Positively Affects Quality of Life and Functional Status of Older Adults Bamini Gopinath, PhD; Joanna Russell, MPH; Victoria M. Flood, PhD, MPH; George Burlutsky, MApplStat; Paul Mitchell, MD, PhD ARTICLE INFORMATION Article history: Accepted 28 August 2013 Available online 14 November 2013
Keywords: Diet quality Quality of life Blue Mountains Eye Study Activities of daily living Older adults Copyright ª 2014 by the Academy of Nutrition and Dietetics. 2212-2672/$36.00 http://dx.doi.org/10.1016/j.jand.2013.09.001
ABSTRACT Background Nutritional parameters could influence self-perceived health and functional status of older adults. Objective We prospectively determined the association between diet quality and quality of life and activities of daily living. Design This was an observational cohort study in which total diet scores, reflecting adherence to dietary guidelines, were determined. Dietary intakes were assessed using a food frequency questionnaire at baseline. Total diet scores were allocated for intake of selected food groups and nutrients for each participant as described in the Australian Guide to Healthy Eating. Higher scores indicated closer adherence to dietary guidelines. Participants/setting In Sydney, Australia, 1,305 and 895 participants (aged 55 years) with complete data were examined over 5 and 10 years, respectively. Main outcome variables The 36-Item Short-Form Survey assesses quality of life and has eight subscales representing dimensions of health and well-being; higher scores reflect better quality of life. Functional status was determined once at the 10-year follow-up by the Older Americans Resources and Services activities of daily living scale. This scale has 14 items: seven items assess basic activities of daily living (eg, eating and walking) and seven items assess instrumental activities of daily living (eg, shopping or housework). Statistical analyses performed Normalized 36-Item Short-Form Survey component scores were used in analysis of covariance to calculate multivariable adjusted mean scores. Logistic regression analysis was used to calculate adjusted odds ratios and 95% CIs to demonstrate the association between total diet score with the 5-year incidence of impaired activities of daily living. Results Participants in the highest vs lowest quartile of baseline total diet scores had adjusted mean scores 5.6, 4.0, 5.3, and 2.6 units higher in these 36-Item Short-Form Survey domains 5 years later: physical function (Ptrend¼0.003), general health (Ptrend¼0.02), vitality (Ptrend¼0.001), and physical composite score (Ptrend¼0.003), respectively. Participants in the highest vs lowest quartile of baseline total diet scores had 50% reduced risk of impaired instrumental activites of daily living at follow-up (multivariable-adjusted Ptrend¼0.03). Conclusions Higher diet quality was prospectively associated with better quality of life and functional ability. J Acad Nutr Diet. 2014;114:220-229.
D
IETARY PATTERN ANALYSIS EXAMINES THE OVERall diet and could have potential advantages.1 For instance, dietary pattern analysis can capture the complexity of the diet because it accounts for the high correlation among intakes of specific foods or nutrients, which are often interdependent in their bioavailability.1 Dietary indexes assess diet quality by
To take the Continuing Professional Education quiz for this article, log in to www.eatright.org, click the “myAcademy” link under your name at the top of the homepage, select “Journal Quiz” from the menu on your myAcademy page, click “Journal Article Quiz” on the next page, and then click the “Additional Journal CPE Articles” button to view a list of available quizzes, from which you may select the quiz for this article.
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grouping foods a priori into a single measure that is representative of current nutrition knowledge in the form of dietary guidelines or other dietary recommendations.2 This may be a useful tool in public health practice to assess a population’s adherence to current dietary guidelines based on empirical evidence.3 Older adults with poor overall diet quality are likely to have suboptimal levels of nutrition biomarkers; this could negatively affect quality of life and functional independence. For example, individuals with lower diet quality are likely to have an inadequate antioxidant status and greater inflammation, which can lead to an increased risk of a range of chronic conditions 4 and, in turn, negatively affect health-related quality of life and functional ability. ª 2014 by the Academy of Nutrition and Dietetics.
RESEARCH Health-related quality of life is an important aspect of physical, social, and mental well-being.5 Research regarding the influence of adherence to dietary guidelines on quality of life, however, is limited. A cross-sectional study of adults aged 35 to 74 years showed that greater adherence to a Mediterranean-style dietary pattern was associated with higher self-perceived mental and physical health scores.6 Recently, self-perceived mental and physical quality of life was shown to be positively associated with adherence to a Mediterranean-style diet during a 4-year follow-up among 11,015 Spanish adults (aged 18 to 101 years).7 Inability to perform instrumental activities of daily living (IADL) (ie, activities required to function in the community such as shopping) and basic activities of daily living (BADL) (eg, eating and washing) often precedes dependency.8 Only a handful of population-based studies have assessed the relationship between diet quality and activities of daily living disability. Among middle-aged adults, lower dairy, fruit, and vegetable consumption was associated with poorer IADL scores over 9 years.9 A US cross-sectional study of a representative sample adults aged 60 years showed that participants with higher diet quality were less likely to experience IADL disability and lower extremity immobility.10 Given that there is a paucity of prospective data from population-based studies of older adults on the relationship between overall diet quality and quality of life and activities of daily living functioning, the purpose of this study was to answer the following key questions using a large cohort of adults aged 55 years and older: Does adherence to dietary guidelines have a beneficial effect on quality of life scores in the long term? and, Is overall diet quality prospectively associated with functional status as assessed by an activities of daily living scale?
METHODS Study Population The Blue Mountains Eye Study (BMES) is a population-based cohort study of common eye diseases and other health outcomes in a suburban Australian population living west of Sydney. Study methods and procedures have been described elsewhere.11 Participants were noninstitutionalized residents aged 49 years or older invited to attend a detailed baseline eye examination after a door-to-door census of the study area. Selection bias at baseline was minimized after multiple callback visits, including door-knocking, telephone reminders, and letters at recruitment. Baseline examinations of 3,654 residents aged >49 years were conducted during 1992-1994 (BMES-1, 82.4% participation rate). Surviving baseline participants were invited to attend examinations after 5 (1997-1999, BMES-2), 10 (2002-2004, BMES-3), and 15 years (2007-2009, BMES-4), at which time 2,334 (75.1% of survivors),1,952 (75.6% of survivors), and 1,149 (55.4% of survivors) participants were re-examined, respectively, with complete data. The University of Sydney and the Western Sydney Area Human Ethics Committees approved the study, and written informed consent was obtained from all participants at each examination.
Nutrition Assessment Dietary data were collected using a semiquantitative, 145-item self-administered food frequency questionnaire (FFQ).12 At all four BMES examinations, participants used a 9-category February 2014 Volume 114 Number 2
frequency scale to indicate the usual frequency of consuming individual food items during the past year. For our study, FFQ data collected at BMES-2 and BMES-3 were used in the analyses. The FFQ was validated by comparing nutrients from the FFQ to 34-day weighed food records collected over 1 year to allow for seasonal variation. Most nutrient correlations were between 0.50 and 0.60 for energy-adjusted intakes.13 A dietitian coded data from the FFQ into a customized database that incorporated the Australian Tables of Food Composition 1990 (baseline FFQ data) and follow-up FFQ data used NUTTAB95.14,15 A modified version of the Australian diet quality index,16 based on the Dietary Guidelines for Australian Adults (DGAA)17 and the Australian Guide to Healthy Eating (AGHE),18 was used to establish the total diet score (TDS), assessing adherence to the DGAA. The methodology used to develop total diet scores has been previously reported.3 Briefly, a total diet score was allocated for intake of selected food groups and nutrients for each participant as described in the AGHE18 (see Table 1). The total diet score is divided into 10 components, and each component has a possible score ranging from 0 to 2. A maximum score of 2 was given to subjects who met the recommendations with prorated scores for lower intakes. These were then summed, providing a final score ranging between 0 and 20 with higher scores indicating closer adherence to the dietary guidelines. The total diet score accounts for both food intake and optimal choice with scores allocated to reflect intake characteristics from both sources. Food intake scores were based on total intakes of vegetables, fruit, cereals and breads, meat, fish, poultry, and dairy as well as sodium, alcohol, sugar, and extra foods intakes. Optimal choices scores determined intakes of foods with greater dietary benefits, including servings of whole-grain cereals, lean red meat, low-fat or reduced-fat milk vs full-fat milk, low saturated fat intake, and fish consumption. Cut-points for scores were determined from the recommended number of serves given in the AGHE, with some exceptions.18 We replaced the AGHE’s recommended two servings per day of fruit with three servings per day and the number of vegetables consumed per day from five servings to seven servings to allow for self-reported FFQ overestimation as determined by the validity study.13 Moderate intakes of sugar were determined from the DGAA, which found no evidence that consuming a diet with up to 15% to 20% of energy from sugar was detrimental to a healthy diet.17 Extra foods were defined as foods that were energy dense containing higher levels of sugar, fat, or salt, with one serving equivalent to 600 kJ.18 Examples described in the AGHE include cookies, cakes, soft drinks, ice cream, pies, hot chips (ie, french fries) and high-fat takeaway items. The alcohol cut points reflect guidelines about alcohol consumption in Australia, in which it is recommended that men consume a maximum two standard drinks per day and women consume a maximum one standard drink per day.17 The nondietary component of the AGHE, preventing weight gain, was included in the total diet score. Half the score component was assigned to energy balance, calculated as the ratio of energy intake to energy expenditure with a maximum score given for ratios falling between 0.76 and 1.24 (Table 1), defined as the 95% confidence levels of agreement between energy intake and expenditure.19 The other half of the score was assigned to leisure time physical activity. Details of walking exercise and the performance of moderate or JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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RESEARCH Table 1. Scoring system for total diet score, based on Australian Dietary Guidelines and the Australian Guide to Healthy Eating Dietary guideline component
Component
Subscore
7 servingsb
0.5
5.6 servings
0.4
4.2 servings
0.3
2.8 servings
0.2
1.4 servings
0.2
Eat plenty of vegetables, legumes and fruit Total vegetable intake/da
Vegetable variety score/d
Total fruit intake/dc
2
1 serving green
0.1
1 serving orange
0.1
1 serving cruciferous
0.1
1 serving tuber or bulb
0.1
0.5 servings legumes
0.1
3 servings
1
2 servings
0.5
Eat plenty of cereals, preferably wholegrain/meal Total cereal intake/d, women
Total cereal intake/d, men
Whole grain cereal intake/d, women
Whole grain cereal intake/d, men
Total score
2 4 servings
1
3 servings
0.75
2 servings
0.5
1 serving
0.25
6 servings
1
5 servings
0.83
4 servings
0.66
3 servings
0.5
2 servings
0.33
1 serving
0.16
4 servings
1
3 servings
0.75
2 servings
0.5
1 serving
0.25
6 servings
1
5 servings
0.83
4 servings
0.66
3 servings
0.5
2 servings
0.33
1 serving
0.16
Include lean meats, fish, poultry, and/ or alternatives
2
Meat/alternative intake/d
1 serving
1.5
Lean red mean/wk (ie, >0.428 servings/d)
3 servings
0.5
2-3 servings
1.5
>3-4 servings
1.0
1-<2 servings
1.0 (continued on next page)
Include milk, yogurts, cheese, and/or alternatives Total dairy intake/d
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RESEARCH Table 1. Scoring system for total diet score, based on Australian Dietary Guidelines and the Australian Guide to Healthy Eating (continued) Dietary guideline component
Ratio of skim/low-fat intake to whole-milk intake
Component
Subscore
>4 servings
0.5
0-<1 servings
0
Skim/low-fat>whole milk
0.5
Skim/low-fat¼whole milk
0.25
Whole milk>Skim/low-fat milk
0
Limit saturated fat and moderate total fat intake Percentage of energy from saturate fat
Fish intake/wk
2 <10
1
10-12
0.5
>12
0
2 servings
1
1-<2 servings
0.5
<1 serving
0
40 mmol (920 mg)
2
Choose foods low in salt Sodium intake/d
2 >40-100 mmol (920-2,300 mg)
1
>100 mmol (2,300 mg)
0
0 g-<10 g
2
>10 g-<20 g
1
>20 g
0
Limit alcohol intake if you choose to drink Alcohol intake/d, women
Alcohol intake/d, men
2
0 g-<20 g
2
>20 g
0
Consume only moderate amounts of sugars and foods with added sugars Percent of energy from all sugars
2 <15
2
15-<20
1
20
0
Extra foods, not essential to provide nutrients and may be high in salt, fat, or sugard Extra foods intake/d, women
Extra foods intake/d, men
2 <2.5 servings
2
2.5-<4 servings
1
4 servings
0
<3 servings
2
3-<5 servings
1
5 servings
0
Prevent weight gain: Be physically active and eat according to energy needs Ratio of energy intake to energy expenditure e
Physical activity (METs )
Total score
2 0.76-1. 24
1
<0.76 or >1. 24
0
Lowest tertile
0
Middle tertile
0.5
Highest tertile
1 (continued on next page)
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RESEARCH Table 1. Scoring system for total diet score, based on Australian Dietary Guidelines and the Australian Guide to Healthy Eating (continued) Dietary guideline component
Component
Drink plenty of water
Not scored
Care for food
Not scored
Subscore
Total score
Total score
20
a
Vegetables: 7 servings, as indicated by weighed food records (food frequency questionnaire overestimates) (replacing 5 servings). Servings sizes:18
b
Vegetables: 1 /2 c (2.65 oz) cooked vegetables, cooked dried beans, peas, or lentils 1 c salad vegetable 1 small potato Fruit: 5.3 oz fresh fruit (eg. 1 medium apple, banana, orange pear; 2 small pieces of fruit like apricots, kiwifruit, and plums) 1 c (5.3 oz) diced fruit or canned fruit 11/2 tablespoon sultanas 4.2 fl oz fruit juice Cereals: 2 slices bread (2.1 oz) 1 c cooked rice, pasta, noodles (6.3 oz) 1 c cooked porridge (8.1 oz) 1 1/3 c ready to eat cereal 1 /2 c untoasted muesli 1/3 c flour Meat and meat alternatives: 2.2-3.4 oz cooked meat or chicken 1 /2 c cooked beans, lentils, chickpeas, split peas, and canned beans 2.7-4.1 oz cooked fish fillet 1/3 c peanuts or almonds 1 /4 c sunflower seeds Dairy:
1 c milk or soy milk 1 /2 c evaporated milk 1.4 oz cheese 1 (7.1 oz) small carton yogurt 1 c custard Extra food: 1 serving of extra foods supplies 600 kj (143.4 kcal). Fruit: 3 servings, as indicated by weighed food records (food frequency questionnaire overestimates) (replacing 2 servings). d Examples of extra foods are biscuits, cakes, doughnuts, soft drinks, ice cream, pies, hot chips (ie, french fries), and high-fat takeaway items. e METs¼metabolic equivalents. c
vigorous activities were used to calculate metabolic equivalents (METs).20 Tertiles were created based on the following MET cut points: 600, >600 to 1,500, and >1,500. These cut points were based on the International Physical Activity Questionnaire scoring protocol20; 600 METs is equivalent to moderate physical activity; that is, 530 minutes of moderate activity per week, which is the minimum recommendation in Australia. Subjects in the highest METs tertile scored 1 point; that was reduced to a 0-point score for subjects in the lowest METs tertile.
Assessment of Quality of Life The 36-Item Short-Form Survey (SF-36) contains 36 items, and produces eight subscale scores representing dimensions of health and well-being.21 and was examined at BMES-2 and then 5 years later at BMES-3; that is, during 1997-1999 and 224
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2002-2004. There are eight SF-36 subscales, which are listed in order from those most strongly related to physical health to those most strongly related to mental health. The eight subscales are “physical functioning,” “role limitations due to physical problems,” “bodily pain,” “general health perceptions,” “vitality,” “social functioning,” “role limitations due to emotional problems,” and “mental health.” The subscales were then summarized as a physical and mental composite score, calculated by assigning relative weights to each subscale as described by the developers of this instrument.22,23 The domain and composite scores are rated so that higher values indicate better health (range 0 to 100).
Activities of Daily Living Scale The Older American Resources and Services activities of daily living scale24 includes 14 items: seven items assess BADL (eg, February 2014 Volume 114 Number 2
RESEARCH eating, dressing and undressing, grooming, walking), and seven items assess IADL (eg, using the telephone, get to places out of walking distance, shopping, meal preparation, housework, taking own medications and handling money). The Older American Resources and Services scale was administered at the 10- and 15-year BMES examinations. Each item was rated on a 3-point scale: 2¼performs the activity without help, 1¼performs the activity with some help, and 0¼completely unable to perform the activity. Three summary scales were computed by summing the scores of items: 1¼a total score, the sum of all 14 items (range 0 to 28), 2¼a BADL score, the sum of the seven BADL items (range 0 to 14), and 3¼an IADL score, the sum of the seven IADL items (range 0 to 14). Participants reporting that they needed help with any of the activities or were completely unable to perform any of the activities were considered to have impaired ability to perform activities of daily living; that is, those who did not score 28 points on this scale.
Assessment of Health Covariates A face-to-face interview with trained interviewers was conducted, and comprehensive data, including information about demographic factors and lifestyle behaviors such as exercise and smoking were obtained from all participants. Participants were also asked whether they received a pension and the type they received (eg, age or invalid). Self-rated health was assessed by asking, “For somebody your age, would you say your health is excellent, very good, good, fair, or poor?” Low self-rated health was defined as fair/poor. Walking difficulty or use of a cane, walker, or wheelchair was observed by a trained examiner and categorized as walking disability. Cognitive decline was assessed using the mini mental state examination questionnaire.25 The mini mental state examination has test components covering concentration, language, and memory. Mini mental state examination scores range from zero to 30; scores <24 were considered cognitively impaired. Visual acuity was measured wearing current glasses, using a LogMar chart and was followed by subjective refraction.10 Visual impairment was defined as visual acuity of fewer than 39 letters (<6 out of 12) in the better eye after subjective refraction. Diabetes was defined either by history or from fasting blood glucose 7.0 mmol/L (126.13 mg/dL). Hypertension was defined as systolic blood pressure >140 mm Hg or diastolic blood pressure >90 mm Hg or taking antihypertensive medications.
Statistical Analyses SAS statistical software (version 9.2, 2009, SAS Institute) was used for analyses. The c2 test was used to compare categorical characteristics between participants (eg, sex, house ownership, and visual impairment). We also used F criteria test for heterogeneity from analysis of variance for unbalanced data for testing the hypothesis that the group means for a continuous variable are equal. Total diet score was analyzed either as a continuous variable (each 1-standard deviation increase) or as a categorical variable (quartiles). SF-36 physical and mental component scores were calculated according to previous factor analysis results and were normalized using Australian population scores.22,23 Normalized component scores were used in analysis of covariance to calculate mean scores first adjusted for age and sex and then February 2014 Volume 114 Number 2
for other significant covariates in the model. Potential confounders that were assessed included various sociodemographic variables (eg, education level, receipt of pension payment, and home ownership), as well as admission to a hospital during the past 12 months, body mass index, smoking, serum triglyceride and total cholesterol levels, and chronic conditions (eg, visual impairment, cognitive impairment, and several conditions such as arthritis and diabetes). Only those potential confounders that significantly modified the effect of total diet score in age- and sex-adjusted models were included in subsequent multivariable analyses. Those factors that were not significant covariates included education level, body mass index, smoking, serum triglyceride level, and total cholesterol level. Thus, final models were adjusted for age, sex, receipt of pension payment, home ownership, admission to a hospital during the past 12 months, walking disability, presence of five or more chronic conditions (eg, arthritis, diabetes, and kidney disease), cognitive impairment, visual impairment, and living alone. Multivariable logistic regression analysis was used to calculate adjusted odds ratios and 95% CIs to demonstrate the association between total diet score with the 5-year incidence of impaired IADL and BADL. For activities of daily living analyses potential confounders assessed were similar to those described in the above for quality of life/SF-36 analyses. Final, multivariable models included covariates that significantly modified the effect of total diet score in age- and sexadjusted model or predicted incident activities of daily living/ IADL/BADL disability. Those factors that were not significant covariates included education level, home ownership, receipt of pension payment, body mass index, serum triglyceride level, total cholesterol level, arthritis, and visual impairment. Hence, logistic regression analyses adjusted for age, sex, living alone, self-rated poor health, current smoking, hypertension, diabetes, hospital admissions during the past year, walking disability, and cognitive impairment.
RESULTS Of the 1,952 participants examined at BMES-3 (2002-2004), 1,305 had complete dietary and SF-36 data at both baseline (BMES-2) and 5 years later at BMES-3, and so were included for analyses. Table 2 shows the baseline characteristics of participants included for analyses of the longitudinal relationship between total diet score and quality of life; those in the lowest vs highest quartile of total diet scores were significantly more likely to be men. Of 1,149 participants examined at BMES-3 and BMES-4, 895 participants had complete dietary and activities of daily living data at both these examinations and so were included for analyses. We compared the study characteristics of those included (n¼895) and excluded (n¼254) from analyses. Those excluded (because they had incomplete dietary and activities of daily living data) were more likely to self-report poorer health (P¼0.01) and be admitted to a hospital during the past year (P¼0.003) than included participants. Baseline characteristics of the 895 participants are shown in Table 3; those in the lowest vs highest quartile of total diet scores were more likely to be men and more likely to be smokers. After adjusting for age, sex, receipt of pension payment, home ownership, admission to hospitals, walking disability, JOURNAL OF THE ACADEMY OF NUTRITION AND DIETETICS
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RESEARCH Table 2. Baseline characteristics of participants included for analyses of total diet score with quality of life/Short Form-36 scores in the Blue Mountains Eye Study between 1997-1999 and 2002-2004 (N¼1,305) Total Diet Score Quartile Characteristics
1 (£8.13) (n[325)
Age (y)
67.17.4
Male sex
162 (49.9)
2 (8.15-9.75) (n[333)
3 (9.76-11.10) (n[320)
4 (‡11.13) (n[327)
P valuea
meanstandard deviation! 67.77.4
67.76.8
67.37.5
0.66
n (%)! Lives alone
145 (43.4)
121(37.8)
105 (32.1)
<0.0001
70 (21.5)
86 (25.8)
66 (20.6)
88 (26.9)
0.16
Receipt of pension payment
209 (64.7)
211 (63.6)
197 (62.2)
202 (62.4)
0.90
Owns a house
304 (93.5)
318 (95.8)
305 (95.3)
305 (93.3)
0.40
Admission to hospital during the past 12 mo
62 (19.1)
78 (23.5)
82 (25.6)
69 (21.1)
0.21
Walking disability
14 (4.3)
16 (4.8)
23 (7.2)
14 (4.3)
0.29
Has 5 chronic conditions
4 (1.2)
4 (1.2)
7 (2.2)
3 (0.9)
0.53
Cognitively impairedb
3 (0.9)
5 (1.5)
3 (0.9)
9 (2.8)
0.19
Visually impairedc
3 (0.9)
4 (1.2)
3 (0.9)
2 (0.6)
0.89
c test and F criteria tests were used to determine significant differences between categorical and continuous variables between the 4 groups, respectively.
a 2
Has mini mental state examination score <24. Visual impairment was defined as visual acuity of less than 39 letters (<6/12) in the better eye after subjective refraction.
b c
Table 3. Baseline characteristics of participants included for analyses of total diet score with activities of daily living scores in the Blue Mountains Eye Study between 2002-2004 to 2007-2009 (N¼895) Total Diet Score Quartile Characteristics
1 (£8.77) (n[223)
Age (y)
71.57.0
2 (8.80-10.23) (n[224)
3 (10.25-11.70) (n[225)
4 (‡11.73) (n[223)
P valuea
meanstandard deviation ! 71.46.7
72.16.7
71.26.7
0.53
n (%)! Male sex Lives alone
117 (52.5)
107 (47.8)
77 (34.2)
73 (32.7)
<0.0001
44 (20.0)
39 (17.4)
47 (20.9)
41 (18.4)
0.79
Poor self-rated health
36 (16.3)
46 (20.5)
28 (12.5)
33 (14.9)
0.13
Current smoker
20 (9.1)
17 (7.6)
9 (4.0)
7 (3.1)
0.02
8 (3.9)
12 (5.9)
19 (9.1)
16 (7.8)
0.16
37 (16.7)
53 (23.7)
41 (18.3)
41 (18.6)
0.28
Walking disability Admission to hospital during the past 12 mo Cognitively impairedb History of type 2 diabetes Hypertension
2 (0.9)
2 (0.9)
1 (0.5)
3 (1.4)
0.81
21 (9.4)
26 (11.6)
22 (9.8)
33 (14.8)
0.26
114 (52.3)
118 (54.4)
119 (54.6)
126 (58.1)
0.68
Test for heterogeneity in 4 groups using F criteria. Has mini-mental state examination score <24.
a
b
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RESEARCH Table 4. Longitudinal association between baseline total diet scores and mean quality of life/Short Form-36 (SF-36) domain 5 years later in the Blue Mountains Eye Study from 1997-1999 to 2002-2004 (n¼1,305) Total Diet Score Quality of life/SF-36 domain
1st Quartile (£8.13) n[325
2nd Quartile (8.15-9.75) n[333
3rd Quartile (9.76-11.10) n[320
4th Quartile (‡11.13) n[327
P for trend
adjusteda meanstandard error! Physical function
66.01.3
70.71.3
70.11.3
71.61.3
0.003
Role limitation due to physical problems
57.62.3
60.72.2
62.92.3
63.72.3
0.05
Bodily pain
69.11.4
66.71.3
67.61.4
72.51.4
0.10
General health
65.21.1
66.21.1
66.71.2
69.21.1
0.02
Vitality
57.01.1
60.41.1
61.31.1
62.31.1
0.001
Social function
83.31.2
85.21.2
85.21.2
85.01.2
0.32
Role limitation due to emotional problems
77.22.0
80.92.0
82.02.0
80.02.0
0.24
Mental health
77.20.9
78.90.9
79.60.9
78.60.9
0.21
Physical composite score
42.40.6
42.90.6
43.20.6
45.00.6
0.003
Mental composite score
51.70.6
52.50.6
53.00.6
52.10.6
0.45
Analysis of covariance calculated mean scores adjusted for age, sex, receipt of pension payment, home ownership, admission to hospitals, walking disability, having five or more chronic conditions (eg, arthritis or diabetes), cognitive impairment, visual impairment, and living alone. a
having five or more chronic conditions (eg, arthritis or diabetes), cognitive impairment, visual impairment, and living alone, participants in the highest compared with the lowest quartile of total diet scores at baseline had adjusted mean scores 5.6, 4.0, 5.3, and 2.6 units higher in the following quality of life/SF-36 domains: vitality (Ptrend¼0.001), physical function (Ptrend¼0.003), physical composite score (Ptrend¼0.003), and general health (Ptrend¼0.02), respectively, 5 years later (Table 4). We examined the 5-year change (from BMES-2 to BMES-3) in SF-36 scores in relation to baseline total diet score (at BMES-2), and this was not significant (data not shown).
After adjusting for age, sex, living alone, self-rated poor health, current smoking, hypertension, diabetes, hospital admissions during the past year, walking disability, and cognitive impairment, participants in the highest compared with those in the lowest quartile of total diet score at baseline had a 50% reduced risk of incident IADL disability after 5 years (Ptrend¼0.03) (Table 5). Each 1-standard deviation increase in total diet score at baseline was associated with 23% reduced risk of IADL disability 5 years later, multivariable-adjusted odds ratio 0.77 (95% CI: 0.63 to 0.95). Significant temporal associations were not observed with BADL.
Table 5. Longitudinal association between baseline total diet score and 5-year incidence of impaired instrumental activities of daily living (IADL) and basic activities of daily living (BADL) in the Blue Mountains Eye Study from 2002 to 2004 to 2007 to 2009 (N¼895) Impaired IADL (n[173) Odds Ratio (95% CI) No. of cases/at risk
Multivariable-adjusteda
Impaired BADL (n[101) Odds Ratio (95% CI) No. of cases/at risk
Multivariable-adjusteda
Total diet score First quartile (8.77)
53/151
1.0 (reference)
22/185
1.0 (reference)
Second quartile (8.80-10.23)
42/164
0.64 (0.37-1.11)
26/197
1.02 (0.53-1.95)
Third quartile (10.25-11.70)
34/152
0.55 (0.31-0.98)
23/187
1.03 (0.53-2.00)
Fourth quartile (11.73)
44/170
0.50 (0.28-0.87)
30/197
1.33 (0.70-2.51)
P value for trend
0.03
0.41
a Multivariable logistic regression analyses was used to calculated odds ratio (95% CI) adjusted for age, sex, living alone, self-rated poor health, current smoker, hypertension, diabetes, hospital admissions during the past year, walking disability, and cognitive impairment.
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RESEARCH DISCUSSION In the BMES, adherence to dietary guidelines at baseline was associated with significantly better quality of life in four domains 5 years later, after adjusting for several confounders such as walking disability and cognitive impairment. Higher total diet score was independently associated with a 50% reduced risk of IADL disability 5 years later. The observed magnitude of difference in quality of life/SF36 scores between the lowest and highest quartile of total diet scores was w3 to 6 units over 5 years. This is within the range of 3 to 10 points, which was previously defined as a meaningful difference in SF-36 scores in a clinical setting.7,26 The positive association between total diet score and quality of life concurs with prior studies showing that adherence to a Mediterranean-style diet beneficially affects quality of life among Spanish adults.6,7 Specifically, higher Mediterraneanstyle diet scores were associated with better scores in physical functioning, general health, and vitality domains 4 years later, and confirms associations observed in the BMES.7 A likely possibility is that subjects may be aware of the link between diet and health and perceive their own quality of life to be higher if they eat well.4,27 This is the first longitudinal study to document an inverse association between diet quality and incidence of IADL difficulty among older adults. These findings concur with a US cross-sectional study that showed an association between Health Eating Index scores and IADL.10 We hypothesize that this observed relationship could be mediated through an inflammatory pathway. Specifically, higher diet quality, typified by high intake of fruits, vegetables, whole grains, and fish, is associated with lower concentrations of inflammatory markers such as C-reactive protein.28 Inflammatory markers in turn are shown to be associated with late-life disability as reflected in difficulties in performing IADL.29 Moreover, fruits and vegetables are high in antioxidants such as vitamin C and carotenoids, which may reduce oxidative damage and therefore slow the decline in physical performance and the onset of functional limitations and disability.9,30 Understanding the health benefits of following dietary guidelines is essential for setting up effective behavioral interventions.10 Given that self-reported quality of life and functional limitations are significant predictors of mortality in the long term,31 our findings could have important public health implications. Specifically, this study suggests that quality of life and functional ability (as indicated by activities of daily living ratings) could be improved by geriatricians and dietetics practitioners targeting the overall diet of older adults. For example, dietary counseling to maintain an optimal diet could lead to appreciable improvements in all domains of quality of life and in the ongoing ability to perform IADL tasks. Strengths of our study include its population-based sample, use of a validated FFQ to collect dietary data, and use of validated quality of life and activities of daily living instruments. Limitations of this study include using FFQs for self-reported dietary intake because these can underestimate energy intake and overestimate fruit, vegetable, and dairy intakes.32 Nevertheless, a comprehensive assessment of the whole diet is less subject to measurement error than is the assessment of energy intake alone33 and several total diet score components used in this study were designed to
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account for misreporting.3 We also cannot exclude the possibility of residual confounding from unmeasured confounders, particularly because diet could be a proxy marker for other healthful lifestyle parameters that could influence quality of life and activities of daily living. Finally, differences in study characteristics were observed between participants included and excluded for analyses; hence, selection bias could have influenced our findings.
CONCLUSIONS A beneficial influence of adherence to recommended dietary guidelines on both quality of life and functional ability (as assessed by an activities of daily living scale) of older adults was observed over 5 years, independent of several potential confounders. These findings could stimulate targeted intervention strategies that modify dietary practices of the aging population, thereby potentially preserving, or delaying further deterioration in general well-being and physical functioning.
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AUTHOR INFORMATION B. Gopinath is a senior research fellow, G. Burlutsky is a statistician, and P. Mitchell is a professor of ophthalmology, Centre for Vision Research, Department of Ophthalmology, and Westmead Millennium Institute, University of Sydney, New South Wales, Australia. J. Russell is a PhD student and V. M. Flood is an associate professor in public health, Faculty of Health and Behavioural Sciences, University of Wollongong, Sydney, New South Wales, Australia. Address correspondence to: Bamini Gopinath, PhD, Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Westmead Hospital, Hawkesbury Rd, Westmead, New South Wales, 2145, Australia. E-mail: [email protected]
STATEMENT OF POTENTIAL CONFLICT OF INTEREST No potential conflict was reported by the authors.
FUNDING/SUPPORT The Blue Mountains Eye Study was supported by the Australian National Health and Medical Research Council (grant nos. 974159, 991407, 211069, and 262120).
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