Fruit and vegetables intake among elderly Iranians: a theory-based interventional study using the five-a-day program

Leili Salehi
Kazem Mohammad
Ali Montazeri
Fruit and vegetables intake among elderly Iranians: a theory-based interventional study using the five-a-day program
R E S E A R C H Open Access
Background: The benefit of FV intake in old age is well documented. However, there is evidence that old people do not consume enough FV. The purpose of this study was to evaluate the effectiveness of a tailored nutrition intervention that aimed to increase the FV intake among elderly Iranians aged 60 and over.Methods: This quasi-experimental study was performed among a community-based sample of elderly in Tehran, Iran in year 2008 to 2009. Data were collected at baseline and 4 weeks follow-up. At baseline face-to-face interviews were conducted using a structured questionnaire including items on demographic information, stages of change, self-efficacy, decisional balance, daily servings of FV intake. Follow-up data were collected after implementing the intervention.Results: In all 400 elderly were entered into the study (200 individuals in intervention group and 200 in control group). The mean age of participants was 64.06 ± 4.48 years and overall two-third of participants were female. At baseline total FV intake was not differed between two groups but it was significantly increased in the intervention group at posttest assessment (mean serving/day in intervention group 3.08 ± 1.35 vs. 1.79 ± 1.08 in control group; P = 0.001). Further analysis also indicated that elderly in intervention group had higher FV intake, perceived benefits and self-efficacy, and lower perceived barriers. Compared with control group, greater proportions of elderly in intervention group moved from pre-contemplation to contemplation/preparation and action/ maintenance stages (P < 0.0001), and from contemplation/preparation to action/maintenance stages (P = 0.004) from pretest to posttest assessments. Conclusion: This study suggests that the Transtheoretical Model is a useful model that can be applied to dietary behavior change, more specifically FV consumption among elderly population in Iran and perhaps elsewhere with similar conditions.

Background

Adequate FV intakes could decrease risk of various chronic diseases such as cancers, cardiovascular disease, diabetes, and stroke [1][2][3][4][5][6]. The World Health Organization (WHO) dietary guideline recommends the minimum 5-a-day consumption of FV [7]. Individuals have not, as yet, adopted the minimum recommendations to consume five servings of FV per day in spite of all the benefits of FV on improving health and reducing the economic burdens of chronic disease. Based on data obtained from FV intakes in 21 countries (mainly developing countries) only in three countries FV intake met the minimum WHO recommended consumption [8].

The benefit of FV intake in old age is well documented [9][10][11]. However, there is evidence that old people do not consume enough FV. For instance a study from Canada showed that only about 47% of elderly consume recommend amount of FV [12]. Data from a study of 400 elderly in Iran showed that FV consumption among participations was low. Overall the mean serving of FV consumption eaten per day for the elderly was 1.76 (SD = 1.15) [13]. Another cross sectional study showed that the prevalence of daily FV intake of 5 or more serving was * Correspondence: firstname.lastname@example.com 4 Mental Health Research Group, Mother and Child Health Research Centre, Iranian Institute for Health Sciences Research, ACECR, Tehran, Iran Full list of author information is available at the end of the article 37% and the mean daily FV consumption separately was 1.86 ± 0.68 and 2.74 ± 0.83 respectively [14].

Such observations from Iran and elsewhere indicate that there is urgent need for health promotion programs in order to increase FV consumption among elderly population. But, the debate about effectiveness of these programs still remains [15]. It is argued that these programs, at least, should be theory driven if one expects any appropriate changes in dietary behaviors [16]. The Transtheoretical Model (TTM) of behavior change is one of the most popular models for studying behavioral change in health education/promotion programs. This model assumes that health behavior change involves progress through six stages: pre-contemplation (unaware of a problem and/or not intending to change), contemplation (considering a change and thinking about it), preparation (intending to take action in very near future), action (initiating a new behavior), maintenance (in which people strive to prevent relapse), and termination (in which individuals show complete self-efficacy) [17].

Several studies showed that dietary interventions based on stages of change model are effective in increasing FV intake [18][19][20]. Only a few studies however have been conducted using the TTM for elderly population [21,22]. The purpose of this study was to evaluate the effectiveness of a tailored nutrition intervention that aimed to increase the FV intake among elderly Iranians. According to the report by Iranian Ministry of Health the proportion of elderly (≥ 65) in Iran accounts for 5.4 percent and it is estimated that this rate will be rise to 10.5 percent by 2025 [23]. To our best knowledge this is the first paper from Iran that reports on the topic.

Methods

Design and data collection

This quasi-experimental study was performed among a community-based sample of elderly people aged 60 and over in Tehran, Iran in year 2008 to 2009. Data were collected at baseline and 4 weeks follow-up. At baseline faceto-face interviews were conducted with the whole sample using a structured questionnaire including items on demographic information, stages of change, self-efficacy, decisional balance, daily servings of FV intake. Four weeks follow-up data were collected after implementing the intervention.

Participants

Of 30 existing elderly centers in Tehran, 10 centers were randomly selected through multistage sampling to represent centers from all 5 main areas in Tehran (2 centers from each area: south, north, east, west and city center). Within the 10 selected centers the membership list for each center was asked and relative to density a systematic sampling method was applied. The selected participants contacted and asked if they were willing to participate in the study. Participants were also informed about the study and the number of required meetings during the study. Participants were then randomized to the intervention or the control groups. A health professional not connected to the study carried out the randomization. People in both groups received a four-cession program.

Measures

Several instruments were used to collect data:

Demographic and anthropometrics Questionnaire

This comprised three sections covering demographic and anthropometrics data including information on age, sex, education, income, marital status, health status (having chronic disease or not) and body mass index (BMI). Chronic disease was indicated by asking each individual to respond to the following question: 'Do you have any longstanding disease?' Anyone who responded positively then was asked to name the disease. Weight was measured using the same digital scales [SECA, Calibrated in Iran] while the participants were minimally clothed and not wearing shoes. Height was measured by a tape measure while the respondents were standing and not wearing shoes and the shoulders were in a normal position. BMI was calculated and expressed in kg/m 2 , and economic status was measured using the asset-based approach developed by Ferguson and colleagues [24] and used in previous cross-national studies of economic status and health in developing countries [25]. According to this scale, 0-3 assets were considered low, 4-6 assets were considered intermediate and 8 or more assets were considered high economic status. The items considered as assets were: television, refrigerator, washing mashing, microwave oven, dish-washer, computer, electrical sweeper, automobile and phone.

Stages of change questionnaire regarding FV consumption behavior

This part of questionnaire adapted from the literature [26] and was consisted of five statements by which the participants were categorized into different stages of change: pre-contemplation, contemplation, preparation, action and maintenance. In fact the participants were asked to respond to one question choosing the statement that best described their status. Choices for the response were: (1) I am not currently consuming five servings of FV a day and I am not thinking of doing so in the upcoming six months, (2) I am not currently consuming five servings of FV a day but I plan to do so within the next six months. (3) I am not currently consuming five servings of FV a day but I plan to do so within the next month (4) I am currently consuming five servings of FV a day but I have been doing so for less than six months (5) I am currently consuming five servings of FV a day but I have been doing that for more than six months.

Self-efficacy

Self-efficacy was assessed to measure confidence in one's ability to persist with FV consumption in various situations. It was assessed using a five item questionnaire developed by Ma et al. [26] and participants were asked to respond to these items: 'I can keep fruits and vegetables at hand/readily available'; 'When I have the chance to choose, I can eat the recommended number of servings of fruits and vegetables'; 'I can shop for a variety of fruits and vegetables'; 'I can make time to eat fruits and vegetables'; 'When I eat at home, I can eat more fruits and vegetables'. Each item is rated on a 5-point scale (from not at all confident = 0 to very confident about recommended FV consumption = 5). The total score ranged from 5 to 25 with higher scores indicating a greater degree of self-efficacy.

Perceived benefits and barriers regarding FV consumption

This part was generated from previous studies and focus group discussions with convenient sample of elderly individuals. Participants were asked about their perception regarding amount of FV intake. The perceived benefit consisted of 15 items and each item is rated on a 5-point scale ranging from 'strongly agree' to 'strongly disagree'. The perceived barrier consisted of 11 items and each item is also rated on a 5-point scale ranging from 'strongly agree' to 'strongly disagree'. The total score for the perceived benefits ranged from 15 to 75 and for perceived barriers from 11 to 55 (Table 1).

Daily FV consumption

The section comprised two parts as follows.  (3) one-half cup of fruit cup of fruit juice, not artificially flavored. We calculated the daily serving FV consumption for each individual according to information provided from the above-mentioned measures. This included of calculation of weekly consumption of FV based on frequency and portion of each item in the food frequency questionnaire. Then we compared total score of daily FV consumption between two groups.

Intervention

Both those randomized to the control group and those randomized to the intervention group received four weekly sessions. The control group sessions focused on general health education and did not include content related to the health benefits of fruits and vegetables, while the intervention group sessions were focused on increasing fruit and vegetable intake.

After randomization, those randomized to intervention group were further divided by stages of change and the sessions were then tailored to that stages of change and techniques (processes) associated with the stages of change. The goal of the intervention was to increase participants' consumption of FV to 5 servings per day. The intervention was composed of four consecutive sessions (one meeting per week). Each session was around 90 minutes in length and included a 40-minute power point presentation, 30 minutes discussion, 10 minutes questions and answers, and 10 minutes reception with FV.

(I) The first session was introductory.

(II) During the second session, the stages of change for FV intake was assessed in participants in order to deliver the appropriate intervention. Based on each individual's status at least one tailored technique (according to the Transtheoretical Model) was used:

(II-a) Participants in pre-contemplation stage completed session that incorporated conscious raising (raising awareness about unhealthy dietary behavior); dramatic relief (react emotionally to warnings about unhealthy dietary behavior. The topics included personal recommendation regarding losing a loved one due to a chronic disease and discussion of nutritional habits associated with this chronic disease; emotional arousal (which is a certain technique that produce increased emotional experiences that can encourage people towards an action. In fact, this process of behavioral change was used to help the participants understand the relationship between lower consumption of FV and increased risk for chronic diseases. The topics included the statistics about the prevalence of the previously mentioned chronic diseases in world and in Iran, the scientific studies relating the protective effect of FV against each of these diseases); and environmental reevaluation (assessing the impact of one's dietary behavior on family members and others).

(II-b) Participants in contemplation/preparation stage completed session that incorporated self-evaluation (which is an assessment of one's self-image with and without a particular unhealthy habit. The researcher asked the participants who were now consuming 5 servings of FV per day to compare their lifestyle and diet before and after increasing their intake of FV to five or more servings per day); and self-liberation (which is the belief that one can change and have the commitment to act on that belief. Participants were asked to make a plan and set a goal and be committed to that goal).

(II-c) Participants in the action/maintenance stage completed session that incorporated helping relationship (which is defined as having a caring, trusted, and accepted person who can give the support and the counseling for the healthy behavior change), stimulus control (removing or countering stimuli that elicit problem behavior), reinforcement management (rewarding oneself or being rewarded by others for making dietary change).

(III) During the third session, the content of the second session was reinforced.

(IV) The fourth session was planned to help participants anticipate and overcome barriers, increase selfassurance and self-efficacy in addition to improve skills in obtaining and arranging FV.

Statistical Analysis

The characteristics of participants in two groups were compared using analysis of variance and x 2 tests as appropriate. Responses to the interventions were assessed by calculating changes in fruit and vegetable intake from baseline to 4 weeks, with positive values indicating an increase in consumption at follow-up assessment. Similar analysis was performed for assessment of stages of change (posttest data vs. pre-contemplation, contemplation/preparation, and action/maintenance at baseline). The data were analyzed on an intention to treat basis including all 400 participants. Analysis of covariance (ANCOVA) was used, controlling for variables previously shown to be related to FV consumption, namely, age, education, marital status, income, chronic disease and BMI. All analyses were conducted using SPSS 16.0. Alpha level of .05 was used for all statistical tests.

Outcome measures

Two main outcomes of the current study were changes in FV intake and to examine the stage transitions.

Ethics

Ethics committee of Tehran University of Medical Sciences approved the study. All participants singed a consent form.

Results

The study samples

In all 400 elderly were entered into the study (200 individuals in intervention group and 200 in control group). The two groups did not differ in terms of demographic characteristics. The mean age of participants was 64.0 6 (SD = 4.48) years and overall two-third of participants were female (n = 298, 74.5%). The characteristics of participants in the two groups are shown in Table 2.

Change in FV intake

At baseline total FV intake was not differed between two groups (intervention and control groups) but it was significantly increased in the intervention group at posttest assessment (mean serving/day in intervention group 3.08 ± 1.35 vs. 1.79 ± 1.08 in control group; P = 0.001). The detailed results are shown in Table 3 and Table 4. Pearson correlation also showed significant correlation between stages of change and benefits, barriers, self-efficacy and FV intake ( Table 5). Further analysis of the data performing the analysis of variance adjusting for covariate also indicated that there were significant differences between intervention and control groups Elderly in intervention group had higher FV intake, perceived benefits and self-efficacy, and lower perceived barriers. The results are presented in (Table 6).

Change in stage transition

There was no difference at baseline in distribution of the stages of change between the two groups. Larger number of participants fell into the pre-contemplation stage than the other stages. Compared with control group, greater proportions of elderly in intervention group moved from pre-contemplation to contemplation/preparation and action/maintenance stages (χ 2 = 233.7, P < 0.0001), and from contemplation/preparation to action/maintenance stages (χ 2 = 8.1, P = 0.004) from pretest to posttest measurements (Table 7).

Discussion

This study indicated the efficacy of a TTM-based intervention for increasing fruit and vegetable consumption in elderly. The findings also confirmed that a theory driven program could have effect on stages of change in elderly in order to improve their lifestyle and health behavior.

We found an average increase of 1.29 daily servings of fruits and vegetables in the intervention group. The results from current study were very similar to those reported by other investigators. For instance, an interventional study that applied the TTM to promote FV consumption showed that FV intake in elderly increased from 0.5 to 1.0 serving a day [22]. Another study reported a significant increase in FV consumption; 1.49 serving/day increase when using a theory based intervention [27]. Di Noia et al. conducted the TTM based study with urban African-American adolescents to determine whether the delivery of stage-tailored change process would promote movement through successive stages and effect positive changes in FV consumption, pros, cons and self efficacy and found that the intervention group had greater increase in the perceived pros of eating FV and increase of 0.9 daily serving of FV compared with the control group [28]. Similarly our findings demonstrated that the intervention had positive effect on perceived benefits and barriers, and self-efficacy. We believe the strength of the current study was due to the fact that our intervention not only was drawn from the TTM stages of change, but also it included examination of perceived barriers and self-efficacy.

Overall most interventional studies based on the TTM model showed that the interventions have produced positive influence on FV intakes. However, the program developed by Amanda Park indicated that stage-tailored nutrition education produced positive shift in several indicators and mediators of vegetables but not for fruits intake [29]. Our study assessed both fruits and vegetables together. Hence it is better to assess the two items apart in future studies as a match-mismatch test. A study was conducted to test the transtheoretical model applied to fruit intake and failed to support the superiority of stage-matching compared with stagemismatching [30].   Limitations Given that all our respondents were members of elderly centers, the findings of this study might not be generalized to all elderly Tehran residents. These elderly might differ from others in Tehran in terms of socioeconomic status, family cohesiveness, social support and availability and access to FV. In addition, it should be noted that our findings on FV intake were based on self-reported information and it might be associated with measurement errors.

Conclusion

This study demonstrated that the TTM is a useful model that can be applied to dietary behavior change, more specifically FV consumption among elderly populations.

Author details  Table 7 Chi-square analysis of between groups differences in posttest stages of change by pretest stages of change* Control (n = 7) 0 0 7 PC = Pre-contemplation, C/PR = Contemplation/Preparation, A/M = Action/ maintenance * The format of table was adapted from [28]. ** Note that it is impossible to progress from action/maintenance.

Table 1
1Perceived benefits and barriers questionnaire Eating FV would help me to be less aggressive Eating FV treats constipation Eating FV would help me to maintain my weight FV consumption are recommended by physicians Eating FV cheering my family members Eating FV is common in my culture Eating FV would keep me of sickness Eating FV would help me to live longer I feel that if I eat more FV, I will be more healthy By eating FV, I feel better Perceived Barriers Providing FV is expensive I did not used to eat FV since childhood Eating FV leads to overeating Media advertisements are not about eating FV Eating more FV is not recommended in my culture My family members do not like consumption of FV Eating more FV is difficult for me I have health problems with eating FV (e.g. flatus) I have limitations to provide FV in my meal I do not like taste of FV I do not have time to provide FV Please indicate whether you agree or disagree with the following items when you are deciding on consuming or not to consume FV. Check the best response.5.1. Food frequency questionnaireThis was consisted of two main questions related to fruits and vegetables (38 items in all) available in Tehran's markets. Response categories were: never, 1-2 times per week, 3-4 times per week, 5-6 times per week, and every day. Accordingly the respondents were asked to indicate the amount of intake. 5.2. A 24 hour recall participants were asked to estimate their daily servings of FV at breakfast, lunch, dinner, and between meals as snacks or deserts in accordance with a nutrition guideline cards. The nutrition guideline card categorized one serving of vegetables into one of three following groups: (1) one cup of raw green leafy vegetables such as spinach or salad; (2) one-half cup of other vegetables cooked or chopped raw, such as tomatoes, carrots, pumpkin, corn, Chinese Cabbage, beans, or onions; and (3) one half cup of vegetable juice. The nutrition guideline categorized one serving of fruit into one of three groups: (1) one medium size fruit such as an apple, banana, or orange; (2) one half cup of cooked, chopped, or canned fruit; andStrongly
agree

Agree Neither agree nor
disagree

Disagree Strongly
disagree

Table 2
2The characteristics of the study sampleTotal
Intervention group (n = 200)
Control group (n = 200)

No. (%)
No. (%)
No. (%)
P

Age
0.55

60-64
255 (63.8)
120 (65.0)
125 (62.5)

65-69
87 (21.7)
34 (17.0)
53 (26.5)

70-74
47 (11.7)
30 (15.0)
17 (8.5)

≥ 75
11 (2.8)
6 (3.0)
5 (2.5)

Mean (SD)
64.06 (4.48)
63.93 (5.08)
64.2 (3.8)

Gender

Female
298 (74.5)
144 (72.0)
154 (77.0)
0.81

Male
102 (25.5)
56 (28.0)
46 (23.0)

Education
0.08

Illiterate
165 (41.2)
82 (41.0)
83 (41.5)

Primary
143 (35.8)
82 (41.0)
61 (30.5)

Junior Secondary
64 (17.0)
23 (11.5)
41 (20.5)

Senior Secondary & above
28 (7.0)
13 (6.5)
15 (7.5)

Marital status

Married
230 (55.0)
120 (60.0)
110 (55.0)
0.45

Never married/Divorced/widow
170 (45.0)
80 (40.0)
90 (45.0)

Income
0.03

Low (0-3assets)
306 (76.5)
165 (82.5)
141 (70.5)

Moderate (4-6 asset)
65 (16.2)
22 (11.0)
43 (21.5)

High (8 or more assets)
29 (7.3)
13 (6.5)
16 (8.0)

Employment status
0.45

Employed
54 (13.5)
31 (15.5)
23 (21.5)

Housewife
283 (70.8)
133 (66.5)
150 (70.0)

Retired
63 (15.7)
36 (18.0)
27 (13.5)

BMI
0.79

< 25
106 (26.5)
56 (28.0)
50 (25.0)

25-29
192 (48)
94 (47.0)
98 (49.0)

≥ 30
103 (25.5)
50 (25.0)
52 (26.0)

Table 3
3Comparison of perceived benefits, perceived barriers, self-efficacy and FV consumption between two groups Salehi et al. Nutrition Journal 2011, 10:123 http://www.nutritionj.com/content/10/1/123Before
After

Intervention group
Control group
Intervention group
Control group

Mean (SD)
Mean (SD)
P
Mean (SD)
Mean (SD)
P

Perceived Benefits
54.91 (9.29)
56.62 (8.34)
0.21
64.34 (9.11)
57.01 (8.47)
< 0.001

Perceived Barriers
34.23 (8.04)
35.79 (8.29)
0.50
27.32 (8.09)
35.89 (8.72)
< 0.001

Self-efficacy
13.59 (6.41)
12.72 (6.02)
0.22
19.23 (5.72)
12.79 (6.04)
< 0.001

FV Consumption
1.78 (1.21)
1.75 (1.09)
0.17
3.08 (1.36)
1.79 (1.08)
< 0.001

Table 5
5Correlation between stage of change, benefits, barriers, self-efficacy and FV consumption* * Figures above triangle relate to before intervention and figures below triangle relate to after intervention ** Correlation is significant at the 0.01 levels.Stages of change
Benefits
Barriers
Self-efficacy
FV consumption

Stages of change
1
0.213**
-0.181**
0.181**
0.448**

Benefits
0.371**
1
-0.033
0.096
0.237**

Barriers
-0.403**
-0.217**
1
-0.173**
-0.296**

Self-efficacy
0.437**
0.303**
-0.375**
1
0.337**

FV consumption
0.636**
0.355**
-0.402**
-0.485**
1

Table 6
6Analysis of covariance of perceived benefit, perceived barrier, self-efficacy, and FV consumption Salehi et al. Nutrition Journal 2011, 10:123 http://www.nutritionj.com/content/10/1/123Source of variance
Type III sum of square
df.
Mean square
F statistic
P

Perceived Benefit

Arm
6846.086
1
6846.086
179.074
< 0.0001

Pretest
15388.664
1
15388.664
402.523
< 0.0001

Age
14.127
1
14.127
0.37
0.544

Education
11.286
1
11.286
0.295
0.587

Marital status
77.527
1
77.527
2.028
0.155

Income
65.055
1
65.055
1.702
0.193

Chronic disease
50.452
1
50.452
1.32
0.251

BMI
41.750
1
41.750
1.092
0.297

Error
14948.144
391
38.231

Perceived Barrier

Arm
5423.038
1
5423.038
174.115
< 0.0001

Pretest
13877.096
1
13877.096
455.545
< 0.0001

Age
38.961
1
38.961
1.251
0.264

Education
80.167
1
80.167
2.574
0.109

Marital status
19.014
1
19.014
0.61
0.435

Income
50.268
1
50.268
1.614
0.205

Chronic disease
2.029
1
2.029
0.065
0.799

BMI
0.172
1
0.172
0.006
0.941

Error
12178.223
391
31.146

Self-efficacy

Arm
3252.374
1
3252.374
475.144
< 0.0001

Pretest
9563.134
1
9563.134
1.397
< 0.0001

Age
23.900
1
23.900
3.492
0.062

Education
5.188
1
5.188
0.758
0.385

Marital status
1.006
1
1.006
0.147
0.703

Income
30.368
1
30.368
4.436
0.036

Chronic disease
1.022
1
1.022
0.149
0.699

BMI
0.282
1
0.282
0.041
0.839

Error
2676.407
391
6.845

FV consumption

Arm
156.226
1
156.226
294.929
< 0.0001

Pretest
218.617
1
218.617
412.712
< 0.0001

Age
2.400
1
2.400
4.530
0.034

Education
0.017
1
0.017
0.032
0.858

Marital status
1.301
1
1.301
2.456
0.118

Table 6
6Analysis of covariance of perceived benefit, perceived barrier, self-efficacy, and FV consumption (Continued)Income
0.055
1
0.055
0.105
0.746

Chronic disease
0.198
1
0.198
0.374
0.541

BMI
0.062
1
0.062
0.118
0.732

Error
207.116
391
0.530

Authors' contributions LS was the main investigator, analyzed the data and involved in drafting the manuscript. KM contributed to the study design and statistical analysis, and supervised the study. AM contributed to statistical analysis, edited the paper and provided the final version. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
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Open Access Tilescope: online analysis pipeline for high-density tiling microarray data
2007

Zhengdong D Zhang
Department of Molecular Biophysics and Biochemistry
Yale University
06520New HavenCTUSA

Joel Rozowsky
Department of Molecular Biophysics and Biochemistry
Yale University
06520New HavenCTUSA

Hugo Yk Lam
Interdepartmental Program in Computational Biology and Bioinformatics
Yale University
06520New HavenCTUSA

Jiang Du
Department of Computer Science
Yale University
06520New HavenCTUSA

Michael Snyder
Department of Molecular, Cellular and Developmental Biology
Yale University
06520New HavenCTUSA

Mark Gerstein
Department of Molecular Biophysics and Biochemistry
Yale University
06520New HavenCTUSA

Interdepartmental Program in Computational Biology and Bioinformatics
Yale University
06520New HavenCTUSA

Department of Computer Science
Yale University
06520New HavenCTUSA

Open Access Tilescope: online analysis pipeline for high-density tiling microarray data
8581200710.1186/gb-2007-8-5-r81)Genome Biology 2007, 8:R81 comment reviews reports deposited research refereed research interactions information Software Correspondence: Mark Gerstein. Tiling microarray data analysis tool
Tilescope is a fully integrated and automated new data-processing pipeline for analyzing high-density tiling-array data.

We developed Tilescope, a fully integrated data processing pipeline for analyzing high-density tilingarray data http://tilescope.gersteinlab.org. In a completely automated fashion, Tilescope will normalize signals between channels and across arrays, combine replicate experiments, score each array element, and identify genomic features. The program is designed with a modular, three-tiered architecture, facilitating parallelism, and a graphic user-friendly interface, presenting results in an organized web page, downloadable for further analysis.

Rationale

Microarray technology is now more accessible than ever before. Thanks to its unrivaled capability to carry out a very large number of parallel quantitative measurements, this technology has been widely applied since its emergence in the early 1990s [1,2] to systematic studies of various biological phenomena, ranging from differential gene expression, to DNA copy number polymorphism, and to transcription factor binding.

Traditional microarrays, constructed by mechanically depositing or printing PCR products, typically of approximately 1 Kb in length, in a dense matrix on a glass slide, have been successfully used in numerous studies and have become prevalent in the research field. Many computer programs and software tools, including free software packages, such as ExpressYourself [3] or MIDAS [4], are available to process and analyze the data sets generated in such studies. However, limited by its manufacturing methodology, traditional micro-arrays are not amenable for systematic coverage of large genomes or even some large genomic regions. To fully realize the parallel-measurement potential of microarray technology, the current trend is to present large genomic regions (for example, ENCODE regions or a complete human chromosome) or even an entire genome on one or several microarrays in an unbiased fashion by using oligonucleotides (that is, tiles) uniformly sampled from presented genomic sequences. Recent technology breakthroughs [5,6] made it possible for such oligonucleotides, typically of 25-60 base-pairs (bp) in length, to be chemically synthesized directly on the microarray slides in a very high density (up to 6.6 million elements in less than 2 cm 2 ). Such oligonucleotide tiling microarrays, which give unprecedented genomic coverage and resolution, can be used for genomic studies of gene expression [7][8][9][10], chromatin immuno-precipitation (ChIP-chip) [11], copy number variation [12], histone modification [13], and chromatin DNaseI sensitivity [14].

Like for any other nascent technologies, ready-to-use data analysis software packages for tiling array experiments are hard to find. Existing data processing software for traditional microarrays cannot be used since the considerably larger size and different nature of tiling array data require a new analysis approach [15]. Recently, a model-based method for tiling array ChIP-chip data analysis has been proposed [16]. Two other methods, based on curve fitting [17] and multi-channel combination [18], respectively, have also been developed for tiling array transcription data analysis. The excellent opensource Bioconductor software project [19] provides many sophisticated statistical methods written in R for microarray data analysis. However, as a software toolbox and a programming environment, it is rather difficult for non-programmers to use.

Here we present Tilescope, an automated data processing pipeline for analyzing data sets generated in experiments using high-density tiling microarrays. Suitable microarray data processing methods, either previously published elsewhere or newly developed, were implemented and made available conveniently in a single online software pipeline. It has a user-friendly interface and is freely accessible over the worldwide web. The software performs data normalization, combination of replicate experiments, tile scoring, and feature identification. We demonstrate the modular nature of the pipeline design by showing how different methods can be plugged in -at major data processing steps, such as normalization and feature identification, several methods are available to be chosen from depending on the nature of the data and the user's data-analysis goal. The program can process gene expression and ChIP-chip tiling microarray data. The results, presented in a clear, well organized manner, can be downloaded for further analysis.

System implementation and user interface

Tilescope was entirely developed in Java. Java was chosen as the programming language because of its built-in threading capability and its excellent library support for graphic user interface and networking development. More importantly, it was chosen because of its object-oriented nature: the program code is organized into different coherent classes and, thus, it naturally modularizes the system, which greatly facilitates parallel system development and subsequent system updating, a desideratum for any software engineering project of non-trivial complexity.

As a web-accessible program system, Tilescope is composed of three connected components: an applet, a servlet, and a pipeline program. The applet is the graphical interface through which the user interacts with Tilescope. It is automatically downloaded and launched inside a Java-enabled web browser whenever the pipeline web page is browsed. Through the Tilescope applet, a user can upload array data files to the pipeline server, select appropriate pipeline param-eters and methods, run the data processing program, and view or download analysis results. The applet, however, cannot run the pipeline program directly. Instead, it makes data processing requests to the servlet, a server program that acts as the proxy of the pipeline program on the web and communicates with the applet upon requests. The servlet, the central layer of Tilescope, runs two 'daemon' threads in the background to handle -that is, accept and schedule or reject based on the current system load -file upload or data processing requests, prepare the pipeline running environment, and initiate with user-specified parameters the back-end pipeline program, which carries out the heavy lifting -the actual data processing procedure. This modular design -the separation between the request handling and the data processing itselfenables the usage of a computer farm for parallel computing and multiple concurrent processing.

On the web form of the Tilescope applet ( Figure 1a), a user can either upload a parameter file, if available from a previous use of Tilescope, to have all parameters set accordingly in one easy step, or set parameters one by one manually, which is more likely to happen if an array data set is to be analyzed for the first time. The main body of the form was organized into two panels, one for setting the tile scoring parameters and the other for selecting the feature identification method, reflecting two main stages of data processing in the pipeline. After the pipeline program is started on the server, the users can monitor its progress through pipeline messages, which are constantly updated by the server throughout each pipeline run.

When data processing is done, a web page with analysis results will be presented to the user in a new browser window ( Figure 1b). On the result web page, the parameters and methods that were used to analyze the data are summarized at the top, followed by log-intensity scatter plots for each array and log-intensity histograms for all arrays in the data set before and after normalization. These enlargeable plots enable the user to quickly identify any problematic arrays visually and subsequently exclude them from further consideration. Both tile maps with log-ratio and P value annotations and the feature list in various text formats can be downloaded for further processing and analysis. The feature list in regular tab-delimited text format gives the user the chromosome (or other genomic sequence ID), the genomic start and end coordinates, the log-ratio, the P value, and, if the tiled genome is specified, the upstream and downstream genes of each feature. If it is the human genome that is under investigation, Tilescope will also provide links to display identified features on custom tracks in the UCSC genome browser. Moreover, if the tiling array was designed from a previous human genome build (for example hg16, NCBI 34), Tilescope will also provide an additional feature list with the coordinates lifted over to the current human genome build (for example hg17, NCBI 35).

Data processing in Tilescope

Tilescope processes the data in a sequential fashion using the major steps shown in Figure 2a. These steps can be approximately grouped into three stages: data input, tile scoring, and feature identification. Here, we use the data set from a ChIPchip experiment of the transcription factor STAT1 to demonstrate how high-density tiling microarray data are processed by Tilescope. We compared features of Tilescope and several other programs that are explicitly applicable to high-density tiling microarray data, and the result is tallied in Table 1.

Data input

The data input to Tilescope reside in tab-delimited text files generated by image analysis software. Currently, Tilescope recognizes data files in Affymetrix, Pair [20] and GFF [20,21] formats. Whenever available, GFF format is always recommended since it is a more standardized format and thus less problematic for processing. Although the aforementioned formats are not fully inter-compatible, they all provide the essential data, namely the chromosomes (or other genomic identifiers, such as Contig IDs and ENCODE region IDs), the genomic coordinate, and the fluorescence intensity (Tilescope automatically detects the base-two logarithm of intensity) for each array element.

We implemented Tilescope to support various formats of the input data file -the GFF format, the PAIR format, the Nimble-Gen format (POS + GFF/PAIR), and the Affymetrix format (BPMAP + CEL), and also developed a new algorithm (see Additional data file 1 for details) that can reduce the physical data file size, handle the data set in an organized manner, and enhance the performance of Tilescope.

Data normalization

Unlike printed PCR arrays, the array elements (oligonucleotides) of a tiling microarray are directly synthesized on the array slide. Direct in situ synthesis creates morphologically uniform array elements, which, to a large degree, obviates the need for spot filtering, an imperative procedure for PCR microarray data analysis. Moreover, direct oligonucleotide synthesis makes it possible to have a very large number of spots in a small area (thus high-density). Miniature slide design of the tiling microarrays allows more uniform hybridization and, thus, greatly reduces the spatial heterogeneity in the probing conditions across the slide, a potentially severe problem suffered by PCR microarrays.

For each array in an experimental set, the relative contributions of the test and reference signals are compared. Ideally, if nucleic acid probes have equal concentration in the test and  reference samples, the signals of the two dyes should be approximately equal (that is, the ratio of the two signals should be close to one for probes hybridizing to an equal degree in both fluorescence channels). In practice, the signals can be rather different due to different chemical properties of dyes and nonspecific or incomplete hybridization to the array. Normalization is used to compensate for these effects bydepending on what method is being used -either applying a scale factor to equalize signals from probes with unchanged concentration or imposing the same empirical distribution of signal intensities. We put together and implemented standard statistical methods that were described in various literature sources and made them conveniently available for tiling array data analysis. At present, Tilescope can normalize tiling array data by mean/median, loess, or quantile normalization ( Figure 2b). These methods have also been implemented elsewhere, most notably in Bioconductor R packages. The mean/ median and the loess normalization methods are both available in the 'marray' package. The 'affy' package contains another implementation of the loess and the quantile normalization methods. In addition, other publicly available software, such as TM4 [4] and TAS/GTRANS, provides some similar functionalities for array data normalization. These methods are summarized below with appropriate references.

Screenshots of Tilescope

Mean/median normalization

Normalization by mean or median [3], the so-called 'constant majority' methods, is based on the assumption that the majority of genes do not change their expression level in response to the experimental perturbation [22] or, more precisely, that the average or median gene expression level does not change under experimental perturbation. It is carried out by subtracting the mean or median of the base-two logarithm of the ratio of test to reference signal intensities from the logratio value of each tile on a single array. This procedure transforms the log-ratio distribution by centering it at zero. The mean of a probability distribution is its center of gravity, while the median divides it into two equal parts. In theory, they are different measures of the location of a distribution. In practice, however, because the mean and the median of the log-intensities from the probes on each array are often very close to each other, these two methods usually give very similar results. The advantages of these two methods include the easiness of their implementation and their robustness to the violation of the assumption -they remain applicable even in cases where up to 50% of probes have altered concentrations.

Loess normalization

Loess normalization [3,23,24] normalizes array data between channels and removes the intensity-specific artifacts in the log-ratio measurements simultaneously. Like normalization by mean or median, loess normalization is also performed on an array-by-array basis. For each array, Tilescope first uniformly samples 50,000 log-ratio values from the original data, and then performs the locally weighted regression on the sampled data. The dependency of the log-ratio on the intensity is removed by subtracting predicted log-ratio based on the loess regression from the actual log-ratio, and the new test and reference log-intensities after normalization are recovered from the residuals. The main disadvantage of Loess normalization is that the locally weighted regression is computationally intensive, and thus the necessity of using sam-pled data instead of the original, much larger data set. Since loess normalization is carried out for each array one by one, even after data sampling it remains expensive to use.

Tiling array data processing by Tilescope Figure 2 Tiling array data processing by Tilescope. (a) Flow chart of major data processing steps. Yellow icons represent data in user-accessible files, and blue ones data in the pipeline program memory. See main text for details. (b) Log-intensity scatter plots of a tiling array from the STAT1 experiment set before and after normalization by four different methods. The first panel is the log 2 T verses log 2 R plot before normalization, where T and R are test intensity and reference intensity, respectively. The gray line represents where these two log-intensities are equal. The second panel is log 2 (T/R) verses log 2 (T×R) plot (the MA plot) before normalization. The dependency of the log-ratio on the intensity, evinced by a fitted loess curve, is prominent in the data. The other panels are the MA plots of array data after mean, median, loess, or quantile normalization. They clearly show that the distribution of log-ratios is centered at zero by all normalization methods, but the intensity-specific artifacts in the log-ratio measurements are removed by only loess or quantile normalization and not by the mean-or median-based method. (c) Signal and P value maps of all tiles in the ENCODE ENm002 region. In this region, the tiles near the transcription start site of IRF1, a transcription factor known to be regulated by STAT1, give the strongest signals.

Quantile normalization

Unlike the normalization methods discussed above, quantile normalization [24] not only normalizes data between channels and across arrays simultaneously but also removes the dependency of the log-ratio on the intensity in one step. It imposes the same empirical distribution of intensities to each channel of every array. To achieve this, Tilescope first creates an n×2p (log) intensity matrix M, where n is the number of tiles on an array and p is the number of arrays in an experimental data set, and then sorts each column of M separately to give M s . Afterwards, it takes the mean across rows of M s and creates M s ', a matrix of the same dimension as M, but where all values in each row are equal to the row means of M s . Finally, Tilescope produces the quantile-normalized (log) intensity matrix M n by rearranging each column of M s ' to have the same ordering as the corresponding column of M. Quantile normalization is fast and has been demonstrated to outperform other normalization methods [24]. Thus, it is the default normalization method used by Tilescope.

Tile scoring

Some arrays are designed to tile genomic sequences of both strands and most array experiments are conducted in replicate. To facilitate subsequent data processing, Tilescope pools the normalized log-ratios of all tiles on every array into a matrix and sorts them based on the tiles' genomic locations regardless of which strand they come from. (Thus, to use Tilescope to process strand-specific data, the user needs to parse data from plus-stranded tiles and minus-stranded tiles into two separate files and process them separately. This limitation will be addressed in the next version of Tilescope.) At the tile scoring step, the program identifies tiles that exhibit differential hybridization. Depending on the nature of the experiment, these tiles ultimately correspond to genes whose expression levels have changed or the locations of transcription factor binding sites (TFBSs).

Compared with traditional PCR arrays, tiling arrays accommodate a much larger number of array elements, which are in situ synthesized oligonucleotides, typically dozens of nucleotides long. However, there is a trade-off for better coverage of the genome: as the average length of the array elements gets smaller, the variance of data increases due to the rise of the relative magnitude of random noise and the possibility of cross-hybridization and sequence artifacts. To deal with this problem, Affymetrix used a different method to score (onechannel) tiling arrays [10,25] than the one used for PCR arrays; instead of considering each tile across array replicates separately, they used a sliding window around each tile to incorporate the hybridization intensity of its neighboring tiles. In our implementation of this method in Tilescope, we modified it by adding a nonparametric statistical test to assess the significance of the intensity difference between the test and the control samples at each tile. This extension enables us to score each tile using two different criteria. Moreover, we also adapted the original method to NimbleGen two-channel tiling arrays data, which in effect significantly increased the usability of this method.

For each tile, given its neighboring tiles across replicates, Tilescope calculates the pseudo-median log-ratio value as its signal. The pseudo-median (that is, the Hodges-Lehmann estimator) of the log-ratio is a nonparametric estimator of the difference between the logged intensities of the test sample and those of the reference sample. It is calculated for each tile using a sliding window. The tiles from all arrays in a sliding window are first collected into a tile set, and the pseudomedian is calculated for this window as:
S = median[ (log-ratio i + log-ratio j )/2 ]
from all (i, j) pairs of tiles in the tile set. As a nonparametric estimator, pseudo-median is less susceptible to distributional abnormalities (such as skewness, unusual kurtosis, and outliers).

Due to the small sample size in each sliding window, whether the intensity distribution is normal or not in a given window cannot be reliably assessed. Without making the normality assumption about the intensity distribution, Tilescope uses the nonparametric Wilcoxon signed-rank test [26] to compare the test with the reference signal intensities and quantifies the degree of significance by which the former consistently deviates from the latter across each of the sliding windows. It tests the null hypothesis that the median of the probability distribution of the differences between the logarithm of the intensities from the test sample and those from the reference sample is zero. As a non-parametric test, Wilcoxon signed-rank test has low power when the sample size is small. To increase the test power, the user needs to use larger window sizes.

At the scoring step, Tilescope generates two tile maps, the signal map and the P value map (Figure 2c). Two values are calculated for each tile position: the pseudo-median of logratios, as a measure of the fold enrichment of the hybridization signal in the test sample over the reference at this genomic location and the probability, the P value, that the null hypothesis -the local intensities of the test and the reference samples are the same -is true. In a recent study of transcript mapping with high-density tiling arrays, Huber et al. [17] used a different approach to score tiles. Their method does not assess intensity difference at individual tiles. Instead, it tries to find a step function that best fits the logratio intensities along genomic coordinates.

Feature identification

Given the tile map annotated with pseudo-medians and P values, Tilescope filters away tiles that are below user-specified thresholds. Retained tiles are used to identify either deferentially expressed genes or TFBSs. Currently, Tilescope users can choose one of three methods to identify such features ( Figure 2d). The first method, 'max-gap and min-run', is a well-used method, initially used by Cawley et al. [25] to analyze their ChIP-chip tiling array data. The second method, 'iterative peak identification', is a new method that we developed to find genomic features iteratively. The third method, whose theoretical development is described in full elsewhere [27], effectuates file segmentation by using a 'hidden Markov model' (HMM) explicitly built on validated prior knowledge.

Max-gap and min-run

Based on the observation that a tile is usually too short to constitute a feature alone, the first method, modified from the scoring scheme used in Cawley et al. [25] and Emanuelsson et al. [28], groups together qualified tiles that are close to each other along the genomic sequence into 'proto-features' and then discards any proto-features that are too short. To use this method, a user needs to specify the maximum genomic distance ('max-gap') below which two adjacent qualified tiles can be joined and the minimum length ('min-run') of a protofeature for it to be qualified as a feature.

Iterative peak identification

The second method, which we have recently developed and implemented as part of the pipeline, does not group tiles above thresholds into features. Instead, it identifies local signal 'peaks' in an iterative fashion. This method was developed to generate lists of non-overlapping features of a uniform genomic size.

Taking the signal map that has been generated in the tile scoring step using window-smoothing to integrate the data from multiple replicate arrays, this method first identifies the tile ('point source') that corresponds to the peak in the signal map with the global maximum signal that also meets a predefined P value threshold. A feature is then created centered at the genomic position of the peak with a predefined genomic size. We choose a feature size that is comparable with the average size of the fragmented ChIP DNA (typically about 1 Kb). The feature is assigned a signal measurement from the associated peak.

All tiles within a predefined distance from the located 'peak' are then removed from the signal map data. Typically, the distance is the same size as the selected features, though it can be larger. This is to ensure that apparent 'secondary peaks' in the signal maps that are really part of the same feature are not separately identified. The procedure is then iterated to find the next maximum 'peak' in the remaining signal map data. The iteration generates a list of features ranked by 'peak' signals and terminates when the identified 'peak' signal is below a specified signal enrichment threshold.

Hidden Markov model

The third method uses a supervised scoring framework based on HMMs to predict and score features in the genome tiled on the microarray [27]. Our method, based on a similar motiva-tion as in [29], differs from previous HMM-based studies [30,31] by specifically considering validated biological knowledge (for example, experimental validation, gene annotation, and so on) and systematically incorporating it to score different types of array assays within the same framework.

For identification of transcriptionally active regions (TARs)/ transcribed fragments (transfrags) in transcriptional tiling array data, a four-state (TAR, non-TAR, and two other intermediate transition states) HMM is constructed using the knowledge of gene annotation (information of the probes that fall into annotated gene regions). For ChIP-chip data, a twostate (TFBS and non-TFBS) HMM is constructed by using the knowledge of inner regions in genes to estimate the signal emission distribution g(t) of the non-TFBS state, and by using the subtraction of g(t) from the overall emission distribution h(t) to estimate the emission distribution f(t) of the TFBS state. In a more general ideal scenario, our framework first selects a medium-sized set of sub-regions by using some appropriate analysis methods (for example, the MaxEntropy sampling scheme discussed in [27]), and then utilizes the knowledge in these sub-regions as the training set to build the model for accurate analysis.

Further scoring on the initial analysis results can also be done by computing the posterior probabilities of each probe being active. The scores indicate the confidence in every single probe-level prediction and can be used to refine the previous analysis results by HMM. For instance, the identified active probes can be ranked according to the overall confidence levels in their regions and a threshold confidence level may either be set manually or be learned automatically to refine the original results.

Method comparison

We compared the performance of these three feature identification methods using a well-studied STAT1 ChIP-chip data set. Composed of three technical replicates, this data set was used to identify a list of STAT1 binding sites in the ENCODE regions [27]. These sites were later experimentally tested. We analyzed this data set using Tilescope and generated three STAT1 binding site lists, each by a different feature identification method. Since identical tile scoring and thresholding parameters were used, the difference among these three lists reflects the underlying difference among the three feature identification methods. By using the list of the experimentally tested STAT1 binding sites, we were able to assess the sensitivity and specificity of each method. The receiver operating characteristic (ROC) curves in Figure 3 show that while, in general, the three feature identification methods implemented in Tilescope have similar performance (which can be measured by the area under the curve or other measurements such as the Matthews' correlation coefficient [32,33] and the minimum error rate [34]), the 'iterative peak identification' method is appreciably more sensitive at high (>95%) specificity.

Conclusion Summary

Tilescope is an online software pipeline for processing highdensity tiling microarray data. In a completely automated fashion, it will normalize signals between channels and across arrays, combine replicate experiments, score each array element, and identify genomic features. The program can process data from most gene expression, ChIP-chip, and arry-CGH (comparative genomic hybridization) experiments. Tilescope is designed with a graphical user-friendly interface to facilitate a user's data analysis task, and the results, presented in a clear, well organized manner on a web page, can be downloaded for further analysis.

Future improvements

Tilescope is under active development: it is continually updated as better data processing methods become available.

Availability

Tilescope is freely accessible for use at [35]. The source code of the pipeline is available at [36].

Additional data files

The following additional data are available with the online version of this paper. Additional data file 1 includes the following supplementary material: a 'Data format optimization and standardization' section; a 'Technical details of the optimization algorithm' section; supplementary Table 1, 'The meaning of columns of various tab-delimited input data file formats'; supplementary Table 2, 'Attributes of the element  tag defined in the configuration file'; and supplementary  Table 3, 'File size reduction by Zip and our optimization algorithm'.

Additional data file 1 Supplementary material Supplementary material: a 'Data format optimization and stand-ardization' section; a 'Technical details of the optimization algo-rithm' section; supplementary Table 1, 'The meaning of columns of various tab-delimited input data file formats'; supplementary Table  2, 'Attributes of the element tag defined in the configuration file'; and supplementary Table 3, 'File size reduction by Zip and our opti-mization algorithm'. Click here for file

The ROC curves of the three feature identification methods implemented in Tilescope Figure 3 The ROC curves of the three feature identification methods implemented in Tilescope. The comparison of the performance of these methods was based on a well-studied STAT1 ChIP-chip data set and a list of experimentally tested STAT1 binding sites.

Genome
Biology 2007, 8:R81

Figure 1
1Screenshots of Tilescope. (a) The applet of Tilescope, the graphic user interface of the pipeline. (b) An example of the data analysis result web page.

( d )
dTilescope-identified STAT1 binding sites at the 5'-end of IRF1 are shown on the custom track in the UCSC genome browser.

Table 1
1Feature comparison between tiling microarray data analysis software* *Only programs explicitly applicable to high-density tiling microarray data were considered. The websites of the compared programs are listed as follows: Tilescope at [35]; Bioconductor at[37]; TAS at [38]; MAT at [39]; TileMap at[40]. † Strictly speaking, Bioconductor is not a ready-to-run program. It is a collection of software packages/libraries written in R. As a tool box, the analysis methods that it provides need to be written in an R program to run. ‡ TAS is previously known as GTRANS. § MAT standardizes the probe value through the probe model, which obviates the need for sample normalization. Comparison symbols used in the table: √, available; ×, not available; ~, available but need to be programmed; /, not applicable.Tilescope
Bioconductor  †
TAS  ‡
MAT  §
TileMap

Implementation
Web
R packages
Standalone
Standalone
Standalone

Graphic user interface
√
×
√
×
×

Intended usage

Transcription data
√
√
√
×
√

ChIP-chip data
√
√
×
√
√

Applicable array platform

Affymetrix
√
√
√
√
√

NimbleGen
√
×
×
×
×

Data normalization

Mean/median
√~√
/
×

Loess
√~×
/
×

Quantile
√~×
/
√

Feature identification

Max gap and min run
√~√
/
√

Iterative peak identification
√ (new)
×
×
/
×

Hidden Markov model
√~×
/
√

http://genomebiology.com/2007/8/5/R81 Genome Biology 2007, 8:R81
R81.4 Genome Biology 2007, Volume 8, Issue 5, Article R81 Zhang et al. http://genomebiology.com/2007/8/5/R81 Genome Biology 2007, 8:R81
AcknowledgementsZDZ was funded by an NIH grant (T15 LM07056) from the National Library of Medicine. This work was also supported by other NIH grants: 1U01HG003156-01 to MS and 1 S10 RR19895-01 and 5P30DK072442-02 to MG.
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