Starting The Conversation
Performance of a Brief Dietary Assessment and
Intervention Tool for Health Professionals
Amy E. Paxton, MPH, Lisa A. Strycker, MA, Deborah J. Toobert, PhD,
Alice S. Ammerman, DrPH, RD, Russell E. Glasgow, PhD
This activity is available for CME credit. See page A4 for information
Introduction: For chronic disease prevention and management, brief but valid dietary assessment
tools are needed to determine risk, guide counseling, and monitor progress in a variety of settings.
use in primary care and health-promotion settings.
Purpose: This report investigates the feasibility, validity, and sensitivity to change of the STC tool,
a simplifıed screener instrument for assessment and counseling.
tion (N?463) were used to document STC validity, robustness, stability, and sensitivity to change from
Results: The eight STC items and summary score performed well. STC items and the summary
icantly correlated with the NCI fat screener at baseline (r ?0.39, p?0.05), and change in the STC
summary score correlated with reduction in percentage of calories from fat (r ?0.22, p?0.05) from
baseline to 4 months. The STC was sensitive to the intervention, with intervention participants
improving signifıcantly more than controls on the summary score (M?1.16 vs 0.46, p?0.05).
intervention in the clinical setting. It is available in English and Spanish and is in the public domain.
Researchers and practitioners are encouraged to assess its utility in other settings and with other
(Am J Prev Med 2011;40(1):67–71) © 2011 American Journal of Preventive Medicine
ment tools tend to be costly and inappropriate for time-
limited primary care or public health settings. Food diaries
are burdensome and can influence behavior, diet histories
settings as a means of chronic disease prevention
sive.5Existing brief dietary assessment tools tend to be
nutrient- or food-group–specifıc, or take longer than 10
tools for primary care settings are nearly absent. Because
dietary patterns rather than single nutrients influence the
development of chronic diseases, real-world dietary assess-
ment tools must be brief and actionable, and focus on di-
Starting The Conversation (STC) is a simplifıed
screening instrument designed for nondietitians in
clinical practices for assessment and counseling. It
identifıes dietary patterns and was derived from a val-
idated 54-item instrument.19Using data collected in a
diabetes self-management intervention study con-
ducted in the primary care setting, the present brief
report further investigates the STC’s feasibility, valid-
ity, and sensitivity to change.
Carolina; Oregon Research Institute (Strycker, Toobert), Eugene, Oregon;
Address correspondence to: Amy E. Paxton, MPH, University of North
Carolina at Chapel Hill, Center for Health Promotion and Disease Preven-
tion, 1700 Martin Luther King Jr. Boulevard, Chapel Hill NC 27599-7426.
© 2011 American Journal of Preventive Medicine • Published by Elsevier Inc.Am J Prev Med 2011;40(1)67–71
at baseline and 4 months as part of a patient-randomized practical
effectiveness trial20to evaluate the impact of interactive diabetes
self-management intervention relative to “enhanced” usual care.
Recruitment is detailed elsewhere.20Adults with type 2 diabetes
were recruited from primary care medical offıces within Kaiser
Colorado (KPCO). Potential participants were mailed letters, in-
no postcard was received, a project recruiter telephoned to explain
the study and determine eligibility. Inclusion criteria included
1 year; (3) BMI of ?25 kg/m2and at least one other heart disease
to read in English or Spanish; and (6) ability to perform physical
activity as assessed by the Brief Physical Activity Readiness Ques-
gether, the criteria were
selected to target at-risk
adult type 2 diabetes pa-
tients able to complete
the requirements of the
were approved by the
Of 2603 recruitment
letters sent, 229 decliner
postcards and 15 letters
were returned. Of 2359
tempted, 544 patients
were eligible and agreed
to participate; 463 pa-
tients were randomized.
The participation level
17]?number of partici-
divided by number con-
fırmed eligible) or 37%
(number of participants
completing baseline di-
we attempted to contact;
tails). Participant charac-
teristics are presented in
The eight-item STC is
shown in Figure 1. Re-
sponse options for the
survey items are orga-
nized into three columns:
the left column indicates
the most healthful dietary practices (scored 0); the center column
indicates less healthful practices (scored 1); and the right column
indicates the least healthful practices (scored 2). Item scores are
added to create a summary score (range 0–16), with lower sum-
mary scores reflecting a more healthful diet and higher scores
and used without charge or permission.
race; education; smoking status; health literacy (items recommended
Dietary measures were the STC and the NCI Percent Energy from
from electronic medical records and height and weight measure-
behavior and were not expected to intercorrelate signifıcantly;
Table 1. Baseline characteristics of participants randomized across three conditions
(N?463), % unless otherwise indicated
Age (years, M?SD)
58.4?9.258.7?9.158.7?9.3 57.8?9.3 0.618
34.8?6.534.8?6.534.4?6.2 35.3?6.8 0.388
American Indian/Alaskan6.7 11.1 4.94.8—
Asian1.6 1.61.9 1.4—
Black/African-American 15.412.7 17.818.4—
White 72.070.6 74.170.7—
21.816.8 25.325.3 0.178
50,000–89,999 35.236.6 33.535.7—
?90,000 17.513.0 20.6 18.2—
High school or less
Low–moderate health literacy
5.97.6 6.04.3 0.495
Computer use (hours/week)
0–2.516.3 15.1 16.616.6—
3–6.5 17.721.2 20.212.4—
7–8.5 6.1 4.55.4 8.0—
?9 60.0 59.157.7 63.0—
10.8 9.1 10.113.00.531
aOne-way ANOVA or ?2test, as appropriate
CASM, computer-assisted self-management intervention; UC, usual care control condition
Paxton et al / Am J Prev Med 2011;40(1):67–71
thus, measures of scale reliability were not calculated. Pearson
product–moment correlation coeffıcients were computed at base-
line to explore relationships among the STC items and summary
score. To determine stability, Pearson product–moment baseline
and 4-month correlation coeffıcients were calculated for the STC
summary scale using usual care data only (n?114). Chi-square
tests and t-tests were conducted, as appropriate, to test the STC
items and summary score for robustness across a range of partici-
ANCOVAs were conducted, associating treatment condition
with change in items and the summary scale from baseline to 4
months, to test for sensitivity to treatment. Baseline participant
characteristics relating to the STC in univariate analyses were co-
varied. Data were collected from 2008 to 2010, and analyzed in
A relatively diverse, heterogeneous sample of 463 adult
outpatients with type 2 diabetes was recruited (Table 1).
The sample was fairly representative of type 2 diabetes
outpatients in the local area, based on distributions of age,
registry data. Most participants were older (mean age?58
range of income and
education levels was
the sample were His-
panic and African-
KPCO population of
type 2 diabetes pa-
tients (22% Hispanic
KPCO records and
in the sample vs 11%
in KPCO records).
completed surveys in
Spanish. There were
differences on any
of the measures in
All of the STC items
performed well. The
measure was robust
tion level, smoking
status, health literacy, and computer experience. Excep-
tions were that (1) older participants generally reported
consuming less fast food (?2?10.2, p?0.01); soda
(?2?6.5, p?0.05); and chips (?2?12.1, p?0.01)
and more vegetables (?2?9.4, p?0.01), and (2) non-
Hispanic participants generally reported consuming less
soda (?2?17.8, p?0.001). Because of these differ-
ences, baseline age and Hispanic ethnicity were covaried
in further analyses.
STC items were moderately intercorrelated, as ex-
pected because items assess different aspects of healthful
eating. Individual items correlated signifıcantly with the
summary score (r ?0.39–0.59, p?0.05). The fruit and
vegetable items correlated most highly (r ?0.41), sug-
gesting a distinct subset.
Temporal Stability and Validity
baseline STC summary score and fat intake as measured
by the NCI fat screener was r ?0.39, p?0.05. Change in
the STC summary score correlated signifıcantly with re-
duction in fat intake, r ?0.22, p?0.05.
Figure 1. Items and scoring instructions for the Starting The Conversation: Diet instrument
Paxton et al / Am J Prev Med 2011;40(1):67–71
mary score correla-
tions for participants
dition ranged from
only r ?0.40 to 0.62
for individual items
and r ?0.66 for the
summary score (all
that the assessment
was stable over time
to the intervention
(Table 2). Random-
ized intervention participants improved signifıcantly
more than controls on two of the eight STC items (fast
vs 0.46, p?0.05).
and unhealthful dietary behaviors in a diverse sample,
indicating the measure’s feasibility for use in public
to performance of the longer Food Habits Questionnaire
and Rate Your Plate.24,25The STC was robust across a
in the absence of treatment, was sensitive to treatment,
and was a reasonably valid measure of dietary intake
NCI screener.23The current study used the STC for as-
sessment, but previous studies have employed it as an
The STC offers an attractive option for dietary as-
sessment and intervention by nondietitians in busy
clinical settings. To our knowledge, it is the shortest
instrument available designed specifıcally to help
clinic staff identify atherogenic dietary patterns and
Although the STC compared favorably to serum
carotenoid levels in a previous sample,19the tool has
not been validated against a criterion standard of di-
etary intake (e.g., 3-day dietary recalls) in a large-scale
trial. Other limitations include data from a single site
(although relatively heterogeneous) and the absence of
criterion standard bioassays. Further work is recom-
mended to validate the STC in other populations and
across multiple interventions focused on improving
This research was supported by R01 DK035524-21 from the
National Institute of Diabetes and Digestive and Kidney
No fınancial disclosures were reported by the authors of this
1. CDC. The guide to community preventive services. www.
Washington DC: Government Printing Offıce, 2000.
3. Whitlock EP, Orleans CT, Pender N, Allan J. Evaluating primary care
behavioral counseling interventions: an evidence-based approach.
Am J Prev Med 2002;22:267–84.
4. Glasgow RE, Ory MG, Klesges LM, Cifuentes M, Fernald DH, Green
iors for primary care research. Ann Fam Med 2005;3:73–81.
5. Willet W. Nutritional epidemiology. 2nd ed. New York NY: Oxford
University Press, 1998.
6. Coates RJ, Serdula MK, Byers T, et al. A brief, telephone administered
food frequency questionnaire can be useful for surveillance of dietary
fat intakes. J Nutr 1995;125:1473–83.
7. Govig B, deSouza R, Levitan WB, et al. The Eating Assessment
Table—an evidence-based nutrition tool for clinicians. Crit Pathw
8. Block G, Gillespie C, Rosenbaum EH, Jenson C. A rapid food screener
to assess fat and fruit and vegetable intake. Am J Prev Med
9. Kris-Etherton P, Eissenstat B, Jaax S, et al. Validation for MEDFICTS,
a dietary assessment instrument for evaluating adherence to total and
saturated fat recommendations of the National Cholesterol Education
program step 1 and step 2 diets. J Am Diet Assoc 2001;101:81–6.
attending colorectal cancer screening: the effıcacy of a brief tailored
intervention. Cancer Epidemiol Biomarkers Prev 2002;11:203–6.
Table 2. Change score in STC items and summary score, all cases and split by treatment
Fast food 0.28?0.790.06?0.73 0.38?0.80
Fruit0.17?0.61 0.13?0.47 0.18?0.66 0.397
Sodas 0.07?0.58 0.09?0.670.07?0.54 0.760
Beans–0.03?0.68 0.00?0.64–0.05?0.70 0.495
Chips 0.15?0.67 0.07?0.65 0.18?0.670.144
Eight-item summary score0.94?2.080.46?1.93 1.16?2.11 0.002
STC, Starting The Conversation; Tx, treatment; UC, usual care control condition
Paxton et al / Am J Prev Med 2011;40(1):67–71
11. Thompson FE, Kipnis V, Subar AF, et al. Evaluation of 2 brief instru-
servings of fruit and vegetables. Am J Clin Nutr 2000;71:1503–10.
12. McCullough ML, Feskanich D, Stampfer MJ, et al. Diet quality and
major chronic disease risk in men and women: moving toward im-
proved dietary guidance. Am J Clin Nutr 2002;76:1261–71.
13. Hu FB. Dietary pattern analysis: a new direction in nutritional epide-
miology. Curr Opin Lipidol 2002;13:3–9.
14. Trichopoulos D, Lagiou P. Dietary patterns and mortality. Br J Nutr
15. Kant AK. Dietary patterns and health outcomes. J Am Diet Assoc
16. Jacques PF, Tucker KL. Are dietary patterns useful for understanding
the role of diet in chronic disease? Am J Clin Nutr 2001;73:1–2.
17. Wonderling D, Langham S, Buxton M, et al. What can be concluded
from the OXCHECK and British Family Heart Study: commentary on
cost-effectiveness analysis. BMJ 1996;312:1274–8.
treatment of conditions managed in general practice. Br J Gen Pract
Ammerman AS. Validation of a brief dietary assessment to guide
population. J Am Diet Assoc 2007;107:246–55.
20. Glasgow RE, Strycker LA, Kurz D, et al. Recruitment for an Internet-
cations. Ann Behav Med 2010;40:40–8.
21. Quinn E. PAR-Q: The Physical Activity Readiness Questionnaire, take
the PAR-Q before you start an exercise program. sportsmedicine.
22. Chew LD, Bradley KA, Boyko EJ. Brief questions to identify patients
with inadequate health literacy. Fam Med 2004;36:588–94.
23. Thompson FE, Kipnis V, Subar AF, et al. Performance of a short
Eur J Clin Nutr 1998;52(2S):S63.
24. Glasgow RE, Perry JD, Toobert DJ, Hollis JF. Brief assessments of
dietary behavior in fıeld settings. Addict Behav 1996;21:239–47.
25. Gans KM, Hixson ML, Eaton CB, Lasater TM. Rate Your Plate: a
dietary assessment and educational tool for blood cholesterol control.
Nutr Clin Care 2000;3:163–9.
26. Keyserling TC, Samuel-Hodge C, Jilcott SB, et al. Randomized trial
of a clinic-based, community-supported lifestyle intervention to
improve physical activity and diet: the North Carolina enhanced
WISEWOMAN project. Prev Med 2008;46:499–510.
27. Mochari H, Gao Q, Mosca L. Validation of the MEFICTS dietary
assessment questionnaire in a diverse population. J Am Diet Assoc
28. Gans KM, Risica PM, Wylie-Rosett J, et al. Development and evalua-
tion of the nutrition component of the Rapid Eating and Activity
J Nutr Educ Behav 2006;38:286–92.
ity of brief dietary assessment tools for Hispanics. Prev Chronic Dis
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Paxton et al / Am J Prev Med 2011;40(1):67–71