Article Field Methods 2014, Vol. 26(4) 343-361 ª The Author(s) 2014 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1525822X14526543 fmx.sagepub.com
‘‘Weighing’’ Two Qualitative Methods: Self-report Interviews and Direct Observations of Participant Food Choices Heather L. Stuckey1, Jennifer L. Kraschnewski1, Michelle Miller-Day2, Kimberly Palm3, Caroline Larosa3, and Christopher Sciamanna1
Abstract Two primary forms of qualitative data collection in the health and social sciences include self-report interviews and direct observations. This study compared these two methods in the context of weight management for people who had varying degrees of success with weight loss (n ¼ 20).
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Penn State Hershey Medical Center, Division of General Internal Medicine, College of Medicine, Hershey, PA, USA Chapman University, Wilkinson College of Humanities and Social Sciences, Orange, CA, USA Penn State Hershey Medical Center, College of Medicine, Hershey, PA, USA
Corresponding Author: Heather L. Stuckey, Penn State Hershey Medical Center, College of Medicine, Mail Code H034, Division of General Internal Medicine, 500 University Drive, P.O. Box 850, Hershey, PA 17033, USA. Email:
[email protected]
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We asked general habits of eating as well as barriers to weight loss and maintaining a healthy diet. Video-recorded observations (20 minutes) followed audio-recorded interviews (45 minutes). Data were organized into four primary sections: (1) confirmatory data, where the interviews and observations held similar information; (2) discrepancies between what was reported in the interview and what was observed in the home; (3) new information that was unique to the observation and was not mentioned during the interview; and (4) clarification of data collected in the interview and observation. In general, the observations contained more confirmatory data for participants who had been successful at weight control than those who had not. The majority of observational data were emergent, which led to the discovery of new data of which we were unaware prior to the observations. Keywords qualitative research, observations, weight loss, weight management, obesity
Introduction The percentage of overweight and obese Americans has grown over the past 20 years (Cowie et al. 2006). Diet is a crucial component of weight loss efforts, and this study evaluates two qualitative methods for assessing food choices. Primary methods to assess dietary fat quality include food frequency questionnaires, 24-hour dietary recalls, and dietary records (Tapsell et al. 2002; Willett 1998). Food frequency questionnaires are validated instruments that report the usual frequency of certain foods through use of an itemized questionnaire (Block et al. 1990). With a 24-hour dietary recall, participants self-report dietary consumption over the past day through an interview using a standardized instrument (Buzzard et al. 1996). Dietary records allow participants to measure or estimate the amount of food and beverages consumed in either an open- or closed-ended method (Hammond et al. 1993; Johnson et al. 1982). One of the criticisms of dietary assessment measures is reliance on selfreport. Informant inaccuracy has long been a concern in research (Bernard et al. 1979, 1984). As memory is known to decay with time, and self-report is largely inaccurate for most behaviors, it is challenging to identify accurate ways of obtaining information about behaviors that are otherwise costly, in terms of participant burden and/or expense (Bernard et al. 1984). Individuals may not accurately report dietary intake for many reasons, including limitations in knowledge, ability to recall, and/or the
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interview situation (Thompson and Subar 2008). Participants with lower calorie intake who use the 24-hour dietary recall method tend to overreport, while those with higher observed intakes tend to underreport intakes (Madden et al. 1976). Beaton et al. (1979) report that the high intraindividual variance component observed in their dietary recall study can be improved by obtaining multiple dietary recalls, leading to consideration of this approach as the current ‘‘gold standard’’ for dietary intake (and see Cade et al. 2002). More recently, researchers have collected dietary and nutritional information through qualitative methods (Plow and Finlayson 2012). Two primary forms of qualitative data collection in the health and social sciences include self-report interviews and direct observations (Farahani et al. 2011; Henwood et al. 2012; Parchman et al. 2009; Quinones et al. 2011). Direct observation can confirm or challenge self-reported interview data (French et al. 2008; Green and Thorogood 2009) and can elucidate behavior changes (Sterling and Peterson 2003; Turris and Finamore 2008). There has been substantial agreement between dietary data collected by telephone, in face-to-face interviews (Brustad et al. 2003; Leighton et al. 1988; Lyu et al. 1998), and through observations (Krantzler et al. 1982). Questionnaires have also been compared to observations for multiple behaviors, including dietary patterns and smoking (Pabayo et al. 2012). And recent studies have compared the validity of self-report with observations for both mental and physical health (Cundiff et al. 2012; Henry and Eggly 2012). However, few studies have compared qualitative methods using self-report interviews to direct observation for dietary intake. Only a few studies with observation, self-report, and health as key words (in the Social Sciences Citation Index and PubMed) had an explicit purpose of examining the relationship between self-report and observation-based measures, where weak-to-moderate correlations were observed (Amris et al. 2011). While newer methods are now being used for behavior observation (e.g., the remote food photography method), there are few comparisons between two qualitative methods, such as self-report interviews and direct observation of food choices, in the literature. The purpose of this research is to compare the data obtained using these two qualitative forms of data collection.
Methods Recruitment and Inclusion Criteria We recruited a purposive sample (n ¼ 20), half of whom were able to maintain a significant weight loss and half of whom were not (Morse 1991). We
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aimed at reaching data saturation at 18 (the point at which no new information is seen or heard), based on a previous, similar qualitative study with weight loss (Kraschnewski et al. 2011). For clarity of their weight loss condition, we call the former participants ‘‘successful’’ (SPs) and the latter participants ‘‘unsuccessful’’ (USPs). Participants were recruited from a single integrated medical center in Pennsylvania using their electronic health record (EHR). Inclusion for SPs age 21–60 were (1) at least one weight recorded each year for four years in the EHR; (2) body mass index was 25.0 at some point in the past year; (3) no history of weight loss surgery, cardiovascular, lung, end-stage liver or kidney disease, cancer, or severe mental health issues; and (4) most recent weight was at least 10% less than the maximum weight recorded in the EHR the past year. The USPs met the same criteria except (4) because they were not successful at losing weight. We mailed eligible participants a letter, which indicated that the study consisted of one visit to the participant’s home, with instructions to contact our study line. We scheduled the participants for a study visit to be conducted at their home with a research coordinator and the lead author. For safety reasons, the participants’ homes were visited in a group, where video recordings were made. From 217 potential participants, 54 met eligibility and 20 received a study visit (Figure 1). The Institutional Review Board at the Penn State College of Medicine approved the ethics of the study (IRB#34192EP).
Study Design After informed consent, we conducted an in-depth, semistructured interview about general habits of eating as well as barriers to weight loss and maintaining a healthy diet (Table 1). Interviews (45 minutes) were audiorecorded, followed by a direct observation (20 minutes) which was videorecorded. The participants led us through a tour of their kitchen and eating areas, the inside of cupboards and other locations where they stored food such as a pantry or basement (Table 2). We used the interview notes to inform the questions asked during observations. If discrepant or new foods were observed but not discussed during the interview, we asked the participant whether that food was something he or she ate, if it was for others in the household, or if it was kept on hand but used rarely.
Qualitative Methods We analyzed the audio interview and video observation transcriptions sentence by sentence using qualitative software to manage the data (NVivo 9,
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2,469 Recruitment Letters Sent
217 Screening Calls
54 Eligible Participants
70 Excluded 38 Unable to reach 32 Not interested 38 Ineligible 28 Did not meet weight criteria 4 Prior or planned weight loss surgery 2 Not trying to lose weight 2 Mental health conditions 2 Unintentional weight loss 55 Not screened - study capacity met
4 Declined home visit 1 Unable to schedule home visit 1 No show for home visit 3 Withdrew prior to home visit 25 Not enrolled, study capacity met
20 Enrolled Participants
10 Successful
10 Unsuccessful
Figure 1. Participant flow diagram.
QSR). Initial codes corresponded to the topics discussed in the interview (e.g., barriers to weight control, foods in a typical meal, preparation of foods, and snack items) and the observation (e.g., foods found in cabinets and refrigerators, preparation items, and seasonings). Subcodes were created under each of the categories to describe the contents of the code. For example, under ‘‘foods in a typical meal’’ as the primary code, subcodes of breakfast, lunch, and dinner were created, with additional codes of protein, carbohydrates, drink, and so on, under each subcode. To initiate the coding analysis, we compared data in the subcodes for the interview that appeared to contradict the observation, resulting in a discrepancy or dissonance (Farmer et al. 2006). Although an interrater reliability analysis using the k statistic was not performed to determine consistency
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Tell us something you want us to Tell me how you began your know about you weight loss journey
What are some of your motivations for wanting to control your weight? Topic 2: What kinds of things do Walk me through a typical week. Describe what you are thinking When was the last time you did this? Is it a routine? Tell or remind yourself of to help Describe the steps you you do that have helped you me what you did you be successful typically try to take toward successfully LOSE (or your weight goal MAINTAIN if applicable) weight? How do you prepare your What do you put on your Topic 3: Specifics to compare What types of foods does a foods? salads? with observations typical meal contain for you (breakfast, lunch, dinner, snacks, and drinks)? When do you eat? Who cooks in the family, and What is one of your favorite who does the shopping? snack food items? Describe what you are thinking or feeling when you encounter Topic 4: What have been some In general, what are some a barrier barriers that you have or have barriers to your weight LOSS had to achieve your weight or MAINTENANCE? goals? What other traits (both in your personality and skills) and resources do you have that you think have helped you be successful or unsuccessful? In a perfect world, what do you think you would need to be more successful?
Topic 1: Personal story
Table 1. Semistructured Interview Question Guide.
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Table 2. Subset of Interview Questions Used to Compare with Observations. Observation Interview Question Guide
Observations for the Researcher to Make
Please give me a tour of your kitchen Notice where the fruit and vegetables are and eating areas placed in the home (especially in relationship to snack food items) Please give me a tour of the insides of Note whether the dressings, mayonnaise, your cupboards and refrigerators and other foods are low fat or reduced fat Please show me your favorite snack Note how the food is prepared and stored item (processed foods) Please show me other areas where Note discrepancies and similarities between you might store food or drinks what was discussed in the interview and what you see and what the participant shows (i.e., if the participant said he or she drinks only water, but there is soda in the refrigerator, ask the participant if he or she drinks soda on occasion) If there is something new that was not discussed in the interview, ask if it is the participant’s, or someone else’s in the family
among raters, consensual coding procedures were used (Hill 2012). The data were reviewed by two additional coders, and the three reached consensus on the data categorization. We discussed the results with internal medicine physicians with research interests in weight loss.
Findings There were no statistically significant differences between USPs and SPs for the demographic characteristics outlined in Table 3 ( p values determined from two-sample t-test or from w2 test; exact test used when needed).
Data Comparison between Observations and Interviews Data were organized into four primary sections: (1) confirmatory data, where the interviews and observations held similar information; (2) discrepancies between what was reported in the interview and what was observed in the home; (3) new information that was unique to the observation and was not mentioned during the interview; and (4) clarification of data collected in the interview and observation.
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Table 3. Demographics of Participants. Characteristic (n ¼ 20) Age, mean + SD Female White Married, % Education: college 4þ years Household income: >US$50,000 Live with children, % BMI, mean + SD
SP (n ¼ 10) 47.6 (+ 8.02) 80% 100% 60% 50% 70% 50% 29.66 (+4.86)
USP (N ¼ 10) 50.0 (+ 7.26) 70% 100% 90% 20% 50% 30% 33.80 (+8.02)
Note: SD ¼ standard deviation; SP ¼ successful participant; USP ¼ unsuccessful participant.
Confirmatory Data between Interviews and Observations Confirmatory data means that the food items and practices recalled during the interview were consistent with the direct observation (Table 4). Thirteen instances of consistent data were noted for SPs, compared to four for USPs, concluding that three times more confirmatory data were found for SPs. An example of a confirmatory finding in an SP was the participant’s recall that she ate canned vegetables from the garden, which was then confirmed during observation when we noticed home-canned vegetables in the basement. One SP said he ate whole grain, organic, and ‘‘fresh-from-the-ground’’ foods, which we confirmed in the observation of raw hemp seeds, organic yogurt, wheat pasta, and seven varieties of brown and wild rice. The items in his cabinets were unlabeled and unprocessed, which affirmed his recall of eating fresh foods. Another SP said (interview) that he liked red velvet cake, and observation revealed a half-eaten red velvet cake on the counter near the kitchen stove. When asked if this was something he enjoyed, he replied, ‘‘I eat what I want.’’ One SP said she liked to cook her main meals ‘‘from scratch,’’ and when we looked into her freezer, we noticed no prepackaged foods. She said, I love greens, whether they are in a can or fresh . . . lots of tomatoes, kalamata olives, pineapple. I like to make teriyaki out of pineapple. Lots of tuna, mushrooms, clams. I love to make linguine with clam sauce. White clam sauce, just with wine, nothing creamy.
In the interview, she said she had a few cookbooks and she enjoyed cooking. The extent to which she cooked was confirmed in the self-packaged soup, chili, and containers with linguine that lined her freezer.
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Table 4. Confirmatory Data between Interviews and Observations. Interview Successful Snacks at night and has peanut participants butter and jelly on graham crackers Drinks ‘‘a lot’’ of water Uses a picture from her doctor to show her what a balanced meal should look like
Observation Found peanut butter, jelly, and graham crackers in cupboard Was drinking water at visit Saw a picture of what each plate should look like, with ½ vegetables, ¼ starch, and ¼ protein, as displayed on refrigerator Found both items in cupboards
Drinks Carnation for breakfast and eats Fiber One bars Eats crackers with jelly instead of Found crackers and jelly in sweets to lose weight cupboard Eats two egg whites and one yolk Found quite a number of eggs in for breakfast refrigerator, and participant restated she likes to make two egg whites and a yolk for breakfast during observation Saw fruit on the counter; said her Keeps fruit on the counter so husband now grabs an apple her family will eat more and so on his way to work and that that it doesn’t go bad they all eat more fruit because they can see it Eats in cycles and typically eats Found all the foods she the same foods everyday mentioned, (e.g., including pickles and apples) but not much else in cabinets Eats more fiber as her Found wheat bread, fiber bars, nutritionist advised and cereal (recommended by nutritionist) in cupboards Unsuccessful Cannot afford fish No fresh fish, but did find bacon, participants turkey, chicken, hamburger, and sirloin in freezer Says she ate prepackaged foods Showed us macaroni and cheese, from the food bank scalloped potatoes, Hamburger Helper. Starting Nutrisystem Found boxes of Nutrisystem foods Eats white bread toast with Found white bread, peanut peanut butter and ‘‘fluff’’ butter, and marshmallow fluff in cupboards
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Table 5. Discrepancies in Semistructured Interviews versus Observations. Interview
Observation
Successful Says she drinks only water participants
Found Sierra Mist (not diet) and Pepsi Max in the refrigerator, which she drinks (she lives alone) Doesn’t eat processed foods Found a variety of processed foods, including Kraft Macaroni and Cheese, frozen breaded chicken nuggets, frozen pizza, microwave-prepared rice Eats yogurt with granola, or Found frozen french toast that she eggs and toast for breakfast puts in the toaster and has for breakfast Found Little Debbie’s cakes in Unsuccessful Keeps snacks and unhealthy cupboard, then said she eats participants food for her daughter ‘‘who them as well if she is running late is very skinny’’ Snacks on apples, grapes, Found baby M&Ms and mini York crackers, and pretzels Peppermint patties, which she said she has as snacks as well States multiple times that she Showed us Crystal Light, tea, hot drinks water only chocolate, and milk at breakfast (occasional chocolate milk)
Discrepancies between Interviews and Observations Discrepancies, or conflicting data, existed between behaviors mentioned in the interview versus the observation for both groups (Table 5). A common example of discrepant data was that participants (both SPs and USPs) said they ‘‘only drank water’’ in the interview, but we observed other drinks (such as regular ginger ale or wine). In an interview, one SP said she drank water. After asking her to show us what she drinks, she said: Water and not as much as I should but I also drink Diet Pepsi but I have cut down a lot. I was drinking too much of that. There would be days that I wouldn’t even touch water . . . but lemonade, I make a lot of lemonade. . . . It’s Country Time.
Another SP said she drank only water. When we noticed the wine, she said, ‘‘Now these wines, I like those . . . not always, but sometimes. Depends on how bad the day was.’’
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We asked one USP about the pop tarts located in her cabinet. She said, ‘‘The kids like the icing ones, and I like the non-icing ones for a snack.’’ In the interview, she recalled that she eats cheese sticks and yogurt for a snack with no mention of pop tarts, icing or no icing.
New Data Obtained through Observations The majority of the qualitative findings were based on new data gained from the observation, rather than through interview alone (Table 6). These data were omitted, purposefully or not, from the interview, but not contradictory to the interview. For instance, one USP did not mention the 48-oz bag of mountain trail mix that was on the dining room table until we observed the bag. When asked, the participant responded, ‘‘That will be gone by the end of the weekend.’’ Another USP said she ate canned vegetables, but they were found in a corner of the basement behind cans of processed foods, rather than in close proximity to the kitchen. This suggests that she ate canned vegetables less often than the other foods. One SP had a practice of keeping a sign in her cupboard, which we observed when she opened the cabinet. The sign showed a silhouette of an overweight woman with a knife, a reminder that the SP would need to have bariatric surgery if she did not lose weight. In addition, we noted that she kept her latest lab results on the refrigerator as a reminder for her to stay healthy. One SP said that he drinks iced tea or lemonade. When we noticed the iced tea in his refrigerator, we asked how much sugar was in the tea. The participant showed us two scoops of sugar, equal to one-half cup. He said, ‘‘Iced tea we always make every day with the Mr. Coffee maker . . . we put sugar in. I don’t know how much, but we put it in. We use these small sugar scoops, but I don’t know how much they measure.’’ We noticed a wine bottle in one USP’s home. When asked how often and how much wine he drank, he said he had two glasses at a time on about two out of 7 days. In another USP’s cupboard, we noticed Fruity Pebbles, which we assumed were for her grandchildren. When asked, she said she ate them, and when we asked whether the iced animal crackers were for her or her grandchildren, she said, ‘‘No, they are definitely for me.’’ When we asked an SP what was next to her cereal, she said, ‘‘Oh, that. It’s [Duncan Hines] brownies. Yeah, I love them.’’ That opened up a conversation about other sweets that she enjoys.
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Table 6. New/Emergent Data Gained through Observation Alone. Successful Participants
Unsuccessful Participants
Hid Little Debbie Oatmeal Cakes in the cupboard; participant’s wife hid food so he wouldn’t eat it Saw chips, banana nut muffins, and sugary cereal placed on the counter by participant’s husband when he got home from the grocery store during her interview Noticed few processed foods in kitchen No place for the whole family to sit and eat a meal together; chairs were cabinets and pantry (natural items with present around the television few labels) Found broccoli. Participant said that she Found beer in the refrigerator. puts broccoli with everything and also Participant said that it was her likes brussels sprouts husband’s, but added that she drinks strawberry daiquiris and makes ‘‘slushies’’ with rum at home Found ‘‘stash’’ drawer where participant Found iced tea in the refrigerator. keeps her treats and chocolate Participant sweetens with 2 scoops of sugar and said he drinks about a quart a day Found a box of Betty Crocker cake mix. Separated food so that unhealthier Said she bakes occasionally ‘‘because it snacks for her son are on one side of is on sale’’ the pantry and healthier options on the other. She avoids seeing the food that tempts her Had both rosemary and thyme, in her Found almost all items to be low fat garden, that she used for cooking (dressings, frozen items, mayonnaise, etc.) Had light canned peaches, apples, and Found striped green and red tomatoes on bananas on the counter the counter that looked ready to eat. Saw large amount of American cheese (size of a loaf of bread) was within reach in the refrigerator Found large containers of homemade vegetable soup with vegetables from participant’s garden
Clarification of Data Collected in the Interview through Observation In several cases, the observation was not discrepant, confirmatory, or new data but served as a clarification of the interview. One USP stated in the interview that he eats prepackaged foods. When we observed his cabinets, the food items were processed, such as eight Hamburger Helper boxes, au gratin potatoes, cheesy rice, Italian white bread, and Jell-O. Another USP said she ate chocolate, but we observed only chocolate graham crackers that she ate to satisfy chocolate cravings. A USP told us that he ate whatever he
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could grab. We noticed that he ate sausage and pepperoni. We found that the sausage was 40% lean Morningstar sausage and 50% low-fat pepperoni, which is not what we assumed when we were interviewing him. One USP mentioned that he liked meat, but it was not until our observation that we realized the extent to which participant ate meat, because there were approximately 50 qt-sized clear, zippered bags of hamburger, and sirloin in the freezer. One SP said she eats ‘‘a lot of yogurt,’’ which could be interpreted as low-fat, regular, Greek, organic, or other types. The yogurt that we found was a 6-oz Yo-Crunch with granola in the lid, which has different nutritional values than yogurt alone.
Discussion This study compared dietary data obtained from interviews and observations, constructive methods for collecting information on food eating habits in the home. Instances of confirmatory data were higher for SPs than USPs. Likewise, more clarification was required for USPs than with SPs. This might be explained by the SPs being more forthcoming about the food items they eat due to social desirability. Studies demonstrate that food frequency questionnaires, which are quantitative assessments of dietary intake, result in underreporting of total energy intake for participants who are overweight and obese (Bothwell et al. 2009; Braam et al. 1998; Johansson et al. 2001). It appears that a similar bias exists in these qualitative interviews. There were a similar number of discrepancies between SPs and USPs (Table 5). Without the observations, important aspects of dietary intake would have been lost. One example of this was the presence of sugared drinks and alcohol in the refrigerator or the snack cakes and candy throughout the house that the participant did not recall in the interview. Further, new and emergent data found on the observations would have been missed, which compared similarly between SPs and USPs (Table 6). These findings included participants’ gardens and the extent to which garden foods were used in home meals. Moving beyond what was self-reported in the interview, the observations revealed data on dietary intake and practices previously not considered by the participant. For example, using environmental cues to stimulate discussion (such as the box of Duncan Hines brownies in the cabinet), participants relayed new information during the observations. The observational data were not retrospective but were current assessments of the environment (Dewalt et al. 1998). Another finding was that SPs required less questioning for clarification during observations than USPs. This suggests that the SPs were more accurate in reporting (i.e., were more likely to confirm in interview)
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than the USPs, a finding that has also been shown in food frequencies as well as height and weight reporting (Dauphinot et al. 2009; Shiely et al. 2010). Beyond the kitchen cupboards and pantries, the observations led to insight into the physical environment in which the participant lived, which could not have been done through interview alone. For example, during some observations, the television was turned on during the interview, prompting discussion of whether or not the TV was on during dinner. These observations allowed for assessment of the social environment, beyond the kitchen, which plays an important role in the context surrounding a participant’s weight loss efforts (Browne 2010; Wenrich et al. 2010).
Limitations One limitation of this study is selection bias as it only included participants who were willing to invite researchers into their homes, which may have reduced the occurrence of discrepant data in this data set. Further, participants might have answered more truthfully in a home-based interview rather than an interview conducted in a research office where no observation could have corroborated self-reports. Some noted they participated in the study to help other people with weight management; thus, the study included motivated individuals. They were white, which is consistent with the demographics of the region. Nearly half of the participants finished college, and the majority had a household income of >$50,000, which limits study generalizability. Future research is required with diverse groups of participants to determine if similar conclusions can be reached. The observation was done in a single visit, which limits the accuracy of assessment as compared to multiple visits (Sisk et al. 2010). However, the purpose of the study was to provide methodological insight into qualitative research, not a nutritional study nor a complete home food inventory. A longitudinal study or additional time points could be conducted. This study did not address dietary intake outside of the home, as the purpose was not to recall all foods eaten but to provide a qualitative cross-sectional analysis of foods by comparing interviews and direct home observations. In both interviews and observations, participant reactivity could have been a factor, but this can be a research bias in any study (Lindahl 2001).
Conclusion New data gained through observations, when compared to interviews, may be crucial to the understanding of a participant’s dietary behaviors. Data
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from observations were confirmatory, discrepant, new, or clarifying. In general, the observations contained more confirmatory data for participants who had been successful at weight control than those who had not. The majority of observational data were emergent, which led to the discovery of new data of which we were unaware prior to the observations. Authors’ Note The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Diabetes and Digestive and Kidney Diseases, or the National Institutes of Health.
Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Preparation of this manuscript was funded, in part, by the National Institute of Diabetes and Digestive and Kidney Diseases, NIH, awarded to Dr. Stuckey, through Grant K01-DK090403-04. The study was also funded under a grant with the Pennsylvania Department of Health using Tobacco CURE Funds (TSF #4100042746) awarded to Dr. Christopher Sciamanna.
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