The WELL diet score correlates with the alternative healthy eating index‐2010

Abstract The quality of one's overall diet has proven to be of great importance to health and well‐being. Unfortunately, diet quality is time‐consuming to assess. The Stanford Wellness Living Laboratory (WELL) administered an online survey that included the WELL Diet Score (a novel diet quality assessment calculated from 12 diet‐related items). Subsequently, WELL participants were asked to complete the 127‐item Block Food Frequency Questionnaire (FFQ) online. The present study's primary objective was to compare the WELL Diet Score with the established FFQ‐based Alternative Healthy Eating Index‐2010 (AHEI‐2010), in a subset of WELL participants (n = 248) who completed both dietary measures through WELL’s online platform. The two scores were significantly correlated (r = .69; p < .0001). Regression analyses demonstrated that the WELL Diet Score was positively significantly associated with sociodemographic determinants of diet quality and protective health factors, including older age, higher education, lower BMI, and higher physical activity. In summary, the WELL Diet Score, derived from 12 small diet‐related items that can be completed in 5 min, was significantly positively correlated with the AHEI‐2010 derived from the lengthy 127‐item FFQ, suggesting the potential utility of the WELL Diet Score in future large‐scale studies, including future WELL studies.


| INTRODUC TI ON
While the quality of one's overall diet is linked to chronic disease prevention and well-being, it is time-consuming to assess.
Nutritional epidemiologic studies tend to quantify hypothesis-driven diet quality using a priori-derived indices that measure adherence to established, evidence-based dietary patterns for chronic disease prevention (Alkerwi, 2014;Chiuve et al., 2012;Hu, 2002;McCullough et al., 2002;Reedy et al., 2014;Schwingshackl & Hoffmann, 2015). In recent years, several of these diet quality indices have been created and associated with lower risk of chronic disease and all-cause mortality (McCullough et al., 2002). For example, the Alternative Healthy Eating Index-2010 (AHEI-2010) is recognized as a leading method and widely used for predicting diet-related chronic disease outcomes (Chiuve et al., 2012;McCullough et al., 2002). Nevertheless, there is not | 2711 SPRINGFIELD Et aL. a "gold-standard" index or consensus as to the definition of diet quality (Alkerwi, 2014). Diet assessments used in observational studies typically include 24-hr diet recalls and food frequency questionnaires (FFQ).
Diet quality index scores can be derived from 24-hr diet recall and FFQ data. However, both methods tend to be time-consuming and costly, especially when research staff are involved in data collection (Shaghaghi, Bhopal, & Sheikh, 2011). For example, the 24-hr diet recall requires reporting an entire day of self-reported dietary intake (Young & Nestle, 1995). While collections of multiple 24-hr diet recalls are superior to a single day of diet data, each additional recall adds burden to participants. Evidence suggests that online, self-administered 24-hr dietary recalls are useful in large studies, albeit they may not be well received among older participants or certain population groups (Ettienne-Gittens et al., 2013;Frankenfeld et al., 2012). Alternatively, a single self-administered food frequency questionnaire (FFQ) can be relatively more efficient than multiple 24-hr diet recalls, because it probes participants for estimates of typical dietary intake over specified time ranges (in windows of six months to one year), using a list of ~110-150 food items and food groups, and does not require an interviewer (Steinemann et al., 2017). However, FFQs typically require 30-60 min to complete, which can be a burden for participants, especially when it is administered as part of larger set of questionnaires (Steinemann et al., 2017). Given these considerations, it is desirable to have tools that can assess diet quality with reasonable accuracy in a short amount of time (≤5 min). Such an index could be used in future observational research but may also have potential application in the clinical setting.
To this end, our research team developed a short self-administered online survey that aims to measure diet quality. The primary objective of the present study was to compare the WELL Diet Score (calculated from 12 short diet-related items embedded in the WELL survey) with the established Alternative Healthy Eating Index-2010 (AHEI-2010), derived from the 127-item Block Food Frequency Questionnaire (FFQ). It is important to note that the AHEI-2010 is not considered the gold-standard diet quality assessment tool; thus, the present study is not seeking to use it to validate the WELL Diet Score. Instead, we seek to test the rigor of our original WELL Diet Score against the established AHEI-2010 to support its use in the WELL study and potentially in future studies.

| Study Design
As an initiative developed in the Stanford Prevention Research Center (SPRC), investigators are using WELL to generate comprehensive scientific data to help define, understand, and improve well-being among people from diverse backgrounds. Based on emergent themes from one hundred semi-structured qualitative interviews, the WELL survey is a 76-item instrument, focused on 10 domains of well-being "paper under review." As of June 2019, 4,248 women and men, 18 years or older, have completed the survey. Details about the WELL study design, protocol, informed consent measures, and recruitment are available elsewhere "paper under review." The present study is a cross-sectional analysis on 248 WELL study participants who completed both a WELL online survey that included 12 diet-related questions and a Block FFQ up to one year apart. Completion of the FFQ was optional. Up to four email reminders were sent to encouraged participants to fill out the Block FFQ (Guy et al., 2012;Houston et al., 2010;McLean et al., 2014).

| WELL diet survey
The WELL diet survey elicited information about the frequency of dietary intake and meal preparation behaviors. Participants were asked how frequently they consume the following diet-related items: (a) vegetables, (b) fruits, (c) whole grains, (d) beans or lentils, (e) sugarsweetened beverages (including 100% fruit juice), (f) red/processed meats, (g) nuts and seeds, (h) high-sodium processed foods, (i) sugarsweetened baked goods or candy, and (j) fish. They were also asked how frequently they engaged in the following behaviors: (h) preparing meals at home and (i) eating fast food (e.g., McDonald's). These items were included based on the expert opinion of SPRC nutrition professionals, and evidence suggesting preparing and consuming foods at home is positively associated with diet quality (Hartmann, Dohle, & Siegrist, 2013;Todd et al., 2010;Wolfson & Bleich, 2015;Mancino, Todd, & Lin, 2009).
To minimize measurement error and to reduce participant cognitive burden, responses to these questions regarding frequency of consumption used a branching technique (Malhotra, Krosnick, & Thomas, 2009). First, participants reported if they consumed the food (or engaged in the behavior) less than once a week, every week but not every day, or every day. Depending on their response, participants were then offered a set of more specific responses. For example, participants who reported "less than once a week" were then provided the following choices: never, 1 time in the past month, and 2-3 times in the past month. Participants who first reported "every week but not every day" were provided the more specific choices of 1-2 times a week, 3-4 times a week, and 5-6 times a week. Lastly, those participants who initially reported "every day" were asked how often in a day: 1 time a day, 2-3 times a day, 4-5 times a day, or 6 or more times a day. This strategy created 10 mutually exclusive ordinal responses with participants only being presented with 3 or 4 choices at a time.
For the 12 diet-related items, the team of nutrition professionals working on the project agreed, by consensus, how to distribute points across the different frequencies of consumption. There were 10 possible frequency levels: Never, 1/month, 2-3/months, 1-2/weeks, 3-4/ weeks, 5-6/weeks, 1/day, 2-3/days, 4-5/days, 5-6/days. Notably, points were not distributed as simply one additional point for each incremental frequency, and points were not similarly distributed for each food category. For example, for "vegetables," 0 points were assigned for both the categories of "Never" and "1/month," and 10 points were assigned for both the categories of "4-5/days" and "5-6/days" suggesting the opinion of the group of nutrition professionals that a frequency of "1/month" was no better than "Never," and that no additional health benefit was likely from going beyond 4-5/day. In contrast, for "nuts, seeds, and nut butters," 0 points were assigned for "Never," 1 point was assigned for 1/month, 10 points were assigned for "1/day" and "2-3/days," and then decreasing points were assigned for frequencies greater than 3/days due to the opinion that intakes higher than "2-3/days" could be problematic in terms of excessive energy intake.
The detailed scoring approach is contained in the supplemental information, see Appendix S1.
Scores were then combined to generate a total WELL diet quality score, ranging from 0 to 120. The estimated time to complete the 12 diet-related items is approximately 4 min based on our survey analytics.

| Block food frequency questionnaire
The Block FFQ is regarded as a leading instrument for diet assessment (Subar et al., 2001). It was derived from a food list gathered during two waves of National Health and Nutrition Examination Survey (NHANES) dietary recall data, 2007-2008 and 2009-2010. The reliability and validity of the 127-item FFQ were established across a wide range of age, gender, income, and groups (Boucher  , 2006;Norris et al., 1997;Steinemann et al., 2017;Subar et al., 2001

| Alternative healthy eating index
The AHEI-2010 measures adherence to the Harvard Healthy Eating Plate through 11 dietary components that total 110 points.
These include 6 adequacy-focused components, such as servings of vegetables, fruits, whole grains, nuts and legumes, intake of fatty acids from fish, and intake of polyunsaturated fatty acids.
There are also four avoidance components-including red meats, trans-fats, sugary beverages, and sodium. Finally, there is one moderation component for alcohol consumption. For each component, scores range from 0 to 10 points (Chiuve et al., 2012).
Similar to the WELL Diet Score index, a higher score means a better diet quality.
The WELL Diet Score (maximum total score 120) and AHEI-2010 (maximum total score 110) consist of 12 and 11 individual diet components, each scored from 0 to 10, respectively. As illustrated in

| Other measurements
For their potential associations with diet quality, the following sociodemographic characteristics were included in the univariate linear regression analyses: age, race/ethnicity, years of education, marital status, and work status. Other potential diet-related health factors included self-reported height and weight (used to calculate BMI), being a current smoker (i.e., current smoker versus nonsmoker), and physical activity (engaged in moderate physical activity (i.e., brisk walking) for 30 min or more at least 5 times per week versus not).

| Statistical analysis
In addition to the WELL Diet Score and AHEI-2010 total and component scores, standard descriptive statistics, including medians and inter-quartile ranges, were used to describe participant characteristics and differences in the sociodemographic and health factors between FFQ completers and noncompleters. The total WELL

| RE SULTS
On average, WELL participants were predominantly female, white, young, middle-aged (aged < 50 yr old), college educated, married, employed, had a normal (or slightly overweight) BMI, and were nonsmokers, see Table 2. Most achieved the recommended amount of physical activity but had room for improvement in diet quality, see Table 2. Compared with FFQ noncompleters, those who completed the FFQ were more likely to be older white women and more highly educated (e.g., postgrad and professional degrees), married, more physically active, and had moderately better diets as measured by our WELL Diet Score.  Table 3   sugar-sweetened beverages, red/processed meat, and sodium were correlated significantly, as expected since they were intended to measure the same behaviors/food choices. With the exception of sodium, all correlations were above 0.45 and thus considered satisfactory (Willett & Lenart, 2013). Additionally, univariate linear regression analyses revealed that a higher WELL Diet Score was significantly associated with older age, higher education, lower BMI, and higher physical activity, see Appendix S3.

| D ISCUSS I ON
Dietary assessment measures that are efficient, user friendly, and streamlined are essential to gather nutritional information and develop strategies to improve diets for chronic disease prevention. In this study, the WELL Diet Score derived from 12 diet-related questions was positively and significantly correlated with the AHEI-2010.
Similar individual subcomponents in the two diet scores (e.g., vegetables, red/processed meats) were also significantly correlated.
Furthermore, the WELL Diet Score demonstrated a significant association with established sociodemographic and health determinants of diet quality.
Consistent with our findings, other observational studies have highlighted the effectiveness of shortened versions of diet quality assessments in predicting diet-related health outcomes (Funtikova et al., 2012;Schröder et al., 2011;Whitton et al., 2018). Overall, these findings provide further evidence that a diet quality score from a shortened dietary assessment tool can generate similar rankings of diet quality within a study population compared with those derived from a longer FFQ. Lower participant burden for diet assessment tools can allow for broader implementation and thus may be useful for understanding diet-disease relationships in populations of women and men from diverse sociodemographic backgrounds.
The study design and implementation involved several strengths.
The WELL diet questions (12) were relatively easy to answer, as they are based solely on frequency-not portion sizes, which can be difficult to recall (Ervin & Smiciklas-Wright, 2001;Harnack et al., 2004). Our study has the further advantage of enabling researchers to independently obtain a rapid assessment of diet quality online, without having to rely on a third party to calculate the diet quality index score, as can be the case with the AHEI.
Another strength involved the context in which the diet data were collected. Serving as both an observational and intervention study, WELL's design can be used as an example for future studies.  (10) 8.0 (6.0, 10.0) Sugar-sweetened beverages and fruit juice, servings/day, (10) 9.0 (5.8, 9.7) 0.47*** Red meat or processed meat, 6.0 (4.0, 8.5) Red/processed meat, servings/day, Finally, we recognize the small proportion of participants who opted to complete the FFQ (~300 out of ~4,000) as a limitation.
Our findings add to the accumulating evidence base that suggests brief diet quality assessments may play a strategically important role in the methodological advancement of diet-related studies.
This bears, particularly, on those assessments where diet quality is only one of many variables being assessed and where respondent burden and cost are of concern. Further studies are necessary to support the generalizability of the WELL Diet Score in assessing diet quality in other populations and research settings, and its usefulness in assessing changes in dietary behaviors related to changes in wellness outcomes. Overall, this study serves as a foundational step toward applying more practical and useful nutrition assessment methods in the WELL study.

ACK N OWLED G M ENTS
We want to acknowledge the participants of the WELL study as well as the staff members. WELL community partners should also be acknowledged for their support with the recruitment of study

CO N FLI C T O F I NTE R E S T S
The authors declare that they do not have any conflict of interest.

E TH I C A L S TATEM ENTS
This study conforms to the Declaration of Helsinki and U.S.
Medicines Agency Guidelines for human subjects. This study's protocols and procedures were ethically reviewed and approved by the Institutional Review Board of Stanford University. Informed consent was obtained and documented for all of WELL participants, and a statement confirming informed consent was obtained. Human and animal testing was unnecessary in this study.