Longitudinal Relationship between Elapsed
Time in the Action Stages of Change and
Weight Loss
Everett E. Logue,* David G. Jarjoura,† Karen S. Sutton,‡ William D. Smucker,* Kristin R. Baughman,§ and
Cynthia F. Capers¶
Abstract
LOGUE, EVERETT E., DAVID G. JARJOURA, KAREN
S. SUTTON, WILLIAM D. SMUCKER, KRISTIN R.
BAUGHMAN, AND CYNTHIA F. CAPERS. Longitudinal
relationship between elapsed time in the action stages of
change and weight loss. Obes Res. 2004;12:1499-1508.
Objective: The objective of this study was to examine the
longitudinal relationship between the elapsed time in the
action and maintenance stages of change for multiple target
behaviors and weight loss or gain.
Research Methods and Procedures: The research design
was a prospective cohort study of overweight and obese
primary care patients randomized to an obesity management
intervention based on the Transtheoretical Model and a
chronic disease paradigm. The target behaviors included
increased planned exercise and usual physical activity, decreased dietary fat, increased fruit and vegetable consumption, and increased dietary portion control. The participants
were 329 middle-aged men and women with elevated body
mass indices recruited from 15 primary care practices in
Northeastern Ohio; 28% of the participants were African
Americans. The main outcomes were weight loss (5% or
more) or weight gain (5% or more) after 18 or 24 months of
follow-up.
Results: There were significant (p ⬍ 0.05) longitudinal
relationships between the number of periods (0 to 4) in
Received for review October 8, 2003.
Accepted in final form July 8, 2004.
The costs of publication of this article were defrayed, in part, by the payment of page
charges. This article must, therefore, be hereby marked “advertisement” in accordance with
18 U.S.C. Section 1734 solely to indicate this fact.
*Department of Family Practice, Summa Health System, Akron, Ohio; †Office of Biostatistics, Northeastern Ohio Universities College of Medicine, Rootstown, Ohio; ‡Department
of Psychology, Oberlin College, Oberlin, Ohio; §Louis Stokes Veteran Affairs Medical
Center, Brecksville, Ohio; and ¶College of Nursing, University of Akron, Akron, Ohio.
Address correspondence to Everett Logue, Department of Family Practice, Summa Health
System, 525 East Market Street, Suite 290, Akron, OH 44309-2090.
E-mail: [email protected]
Copyright © 2004 NAASO
action or maintenance for each of the five target behaviors,
or a composite score taken across the five target behaviors,
and weight loss. In all cases, there was a significant (p ⬍
0.05) stepped (graded) relationship between the time in
action or maintenance and weight loss (or gain).
Discussion: The data support the concept of applying the
Transtheoretical Model to the problem of managing obesity
in primary care settings. The remaining challenge is to
identify those factors that reliably move patients into the
action and maintenance stages for long periods.
Key words: Transtheoretical Model, weight loss, longitudinal study, primary care
Introduction
Obesity is epidemic in the general population because of
a widespread polygenetic susceptibility and an obesogenic
environment that encourages multiple behaviors that frequently produce a small positive energy balance in many
individuals (1,2– 4). The Transtheoretical Model has previously been applied to weight-loss related behaviors, but
most studies have been cross-sectional (5–23). There are
relatively few longitudinal data describing the empirical
relationship between the elapsed time in the action and
maintenance stages of change for multiple target behaviors
and weight loss or gain (24 –29). Thus, the objective of this
study was to examine the longitudinal relationship between
stages of change (SOC)1 for increased planned exercise,
usual physical activity, portion control, fruit and vegetable
consumption, or decreased dietary fat and weight loss or
gain. We hypothesized that overweight or obese primary
care patients who spend more time in the action or mainte-
1
Nonstandard abbreviations: SOC, stage(s) of change; REACH, Reasonable Eating and
Activity to Change Health; RR, risk ratio.
OBESITY RESEARCH Vol. 12 No. 9 September 2004
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Time in Action and Weight Loss, Logue et al.
nance SOC for weight loss-related behaviors would be more
likely to lose weight, or would be less likely to gain weight,
than patients who spend more time in the preaction SOC.
(18,30) The data for this study were obtained from 329
overweight or obese primary care patients who were assigned to a minimal intervention in a randomized clinical
trial. Staging data were not available for 336 patients assigned to the comparison group.
Research Methods and Procedures
The Reasonable Eating and Activity to Change Health
(REACH) trial was a 24-month randomized, parallel group,
weight management trial conducted at 15 primary care
practices in Northeastern Ohio between July 1998 and December 2002. The principle objective of the trial was to test
a hypothesis concerning the effectiveness of a minimal
cognitive-behavioral intervention based on the chronic disease paradigm (18).
Participants were recruited when they inquired about the
study after either talking to their physician or reading study
brochures, posters, or informational letters. All participants
provided written informed consent according to a procedure
approved by multiple institutional review boards. Primary
care patients, 40 to 69 years old, with elevated BMIs (⬎27)
or elevated waist-to-hip ratios (⬎0.950 for men or 0.800 for
women) were eligible for the study. Exclusion criteria were
no access to a telephone, low literacy, pregnancy, lactation,
less than 6 months postpartum, use of a wheel chair, or
severe heart or lung disease. After the informed consent
process was completed and baseline data were collected,
participants were randomized into permuted blocks of 10
patients.
Participants assigned to the experimental group had the
same basic care as the comparison group (31,32). In addition, the experimental group completed periodic SOC assessments for five target behaviors and received SOC mailings and telephone calls from a weight loss advisor (30).
The target behaviors were increased planned exercise (deliberately walking for exercise), increased usual physical
activity (walking incidental to paid employment or domestic
chores), increased dietary portion control, decreased dietary
fat, and increased fruit and vegetable consumption. SOC for
the five target behaviors was assessed by a set of multi-item
algorithms that included a decision rule based on order
statistics (23). Instead of the mean response to the items, the
response at the 33rd percentile from the lowest stage was
used as the SOC estimate. The rationale for this approach
has been previously described in detail (23). In brief, the
measurement goal was to assess SOC for multiple dietary
(e.g., regularly drinking skim milk instead of whole milk)
and exercise-related (e.g., regularly using the stairs instead
of elevators) behaviors that are related to weight loss. We
chose the 33rd percentile to highlight problematic target
areas in which intervention could be made (see Appendix A,
1500
OBESITY RESEARCH Vol. 12 No. 9 September 2004
available at www.obesityresearch.org). The entire 24-month
follow-up period was first divided into four subperiods: 0 to
6 months, ⬎6 to 12 months, ⬎12 to 18 months, and ⬎18 to
24 months. Then, the 6-, 12-, 18-, and 24-month SOC data
for each behavior and patient were examined to ascertain
whether the patient was largely in action or maintenance for
a given target behavior for the preceding 6-month period.
General physical and mental health, self-efficacy, social
desirability, and psychopathology data were also collected
(33,34).
Anthropometric assessments were scheduled at 0 (baseline), 6, 12, 18, and 24 months. Longitudinal weight change
was ascertained by comparing baseline and 24-month
weights, or baseline and 18-month weights, if the 24-month
weight was not available. Weight was measured with portable electronic scales that were periodically checked
against a standard balance beam scale. Patients were
weighed in their street clothes but without heavy outer
garments. Measured weights were adjusted downward by
1.0 lb when the patients did not take off their shoes as
requested. Weights from primary care records were substituted for some measured weights when the latter were
missing. Seventy percent of the participants had a measured
weight at 18 or 24 months of follow-up, but an additional
20% of participants had a chart weight at 18 or 24 months
of follow-up, resulting in only 10% of our patients with
missing weights at 18 or 24 months. Pearson correlations
between measured weights and chart weights, when both
were available, averaged 0.99 across the four postbaseline
measurement points.
Statistical Analysis
The empirical distribution of relative weight change was
divided into three groups: weight loss (5% or more), no
weight change (⬍5% loss or gain), or weight gain (5% or
more) for analysis purposes. We used these cut-off points
because weight change of ⬍5% over 24 months is not likely
to affect clinically important risk factors such as blood
pressure, blood lipids, or blood glucose (35). In addition, a
5% or greater change cannot be explained away by natural
variation or instability of weight, whereas a change of ⬍5%
over 24 months may not be meaningful. We derived a time
in action or maintenance SOC variable by counting the
number of periods a subject was in action or maintenance,
thus yielding scores that ranged from 0 to 4 for each target
behavior. We also derived a composite score by summing
the number of periods in action or maintenance across target
behaviors. The composite score ranged from 0 (zero periods ⫻ five behaviors) to 20 (four periods ⫻ five behaviors).
The relationship between time in action or maintenance for
each target behavior and weight change was summarized in
five ⫻ three contingency tables (Proc Freq, SAS version
8.2; SAS Institute Inc., Cary, NC). Ordinal logistic regres-
Time in Action and Weight Loss, Logue et al.
sion analysis was used to model the relation between the
composite score and the weight change triplet (Proc Logistic with an extra intercept term) while adjusting for baseline
covariables. Statistical significance was set at ␣ ⫽ 0.05.
Results
The SOC and weight change recodes yielded nonmissing
data for 294 (89%) of 329 patients. Missing baseline data
were due to incomplete questionnaires. The majority of the
missing follow-up weight data were due to patient refusals
to attend the scheduled assessment and missing chart
weights. However, there were no important demographic or
medical history differences between the 294 patients with
nonmissing data and the 329 randomized patients.
Participant Characteristics
Table 1 summarizes the characteristics of the REACH
trial participants who were randomized to the experimental
intervention. Eighty-four percent of the participants were
between the ages of 40 and 59 years, 30% were men, 28%
identified themselves as African American, and 45% had
BMIs over 34.9 kg/m2. Mean SF12 physical health and
mental health scores appeared to be slightly lower than the
norms for similarly aged individuals from the general population (36). Table 2 shows the baseline SOC distribution
for the five target behaviors. Preparation was the modal
stage for all behaviors, which is consistent with the status of
the participants as volunteers for a weight management trial.
The 24-month weight change distribution was symmetric;
23% experienced a weight loss of 5% or more of their
baseline weight, and 18% experienced a weight gain of 5%
or more of their baseline. The fact that 60% of the patients
were within 5% of their baseline weight at the end of
follow-up is presumably a function of the obesogenic environment interacting with participant characteristics and the
nature of the experimental REACH trial intervention (1,2).
The regaining of initial weight losses over time is a wellrecognized problem (37). Moreover, as noted earlier, the
experimental intervention was minimal by design (31).
SOC for Dietary Targets and Weight Change
Table 3 describes the longitudinal relationship between
SOC for increased portion control, less dietary fat, or more
fruits and vegetables and weight change. In particular, the
top of the table shows the significant [␹2 (df ⫽ 8) ⫽ 35.0;
p ⬍ 0.0001) nominal relationship when the ordering across
periods is ignored. The linear trend in weight loss incidence,
which considers the ordering across periods in action for
portion control, explains one-half of this, and was also
significant (␹2 ⫽ 18.3; p ⫽ 0.00002). When the number of
periods in the action or maintenance stage increased from 0
to 4, the incidence of weight loss (5% or more of baseline
Table 1. Characteristics of patients randomized to the
stage of change intervention
N ⴝ 329
Age, gender, ethnicity
40 to 49 years
50 to 59 years
60 to 69 years
Men
African Americans
Baseline BMI Group
25 to 29.9 kg/m2
30 to 34.9
35 to 39.0
40.0 or more
Medical History
Hypertension
Elevated blood cholesterol
(Osteo) arthritis
Stomach problems
Diabetes mellitus
Prior/current psychotropic meds
Depression (PRIME-MD)
Anxiety
Eating disorder
Prior attempts to lose weight
MD said to lose weight
Prior commercial program
Physical health score (SF12)
Mental health score (SF12)
Self-efficacy for healthy eating
Self-efficacy for exercise
Social desirability
Frequency
(%)
139
138
52
97
88
(42.3)
(42.0)
(15.8)
(29.5)
(27.8)
59
119
69
79
(18.1)
(36.5)
(21.2)
(24.2)
138
107
106
73
41
85
12
26
64
306
246
147
Mean
45.3
47.9
136.9
28.9
20.0
(44.1)
(35.6)
(35.3)
(24.7)
(13.9)
(25.9)
(3.7)
(8.0)
(19.6)
(96.8)
(78.9)
(47.0)
(SD)
(9.8)
(10.9)
(35.2)
(11.0)
(5.8)
PRIME-MD, Primary Care Evaluation of Mental Disorders.
weight) increased from 9.9% to 37.9 [risk ratio (RR) ⫽
3.84; 95% confidence interval ⫽ (1.9, 7.9)]. Conversely, the
incidence of weight gain (5% or more of baseline) decreased from 33.3% to 9.1% [RR ⫽ 0.27; 95% confidence
interval ⫽ (0.1, 0.6)] in a similar comparison. Figure 1
shows the incidence of weight loss or gain by the number of
periods in action or maintenance for portion control.
The middle of Table 3 shows that the nominal test for
weight change and the dietary fat variable just missed being
significant [␹2 (df ⫽ 8) ⫽ 13.2; p ⫽ 0.10], but the linear
trend explained one-half of this and was significant (␹2 ⫽
6.4; p ⫽ 0.01). The average incidence of weight loss inOBESITY RESEARCH Vol. 12 No. 9 September 2004
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Time in Action and Weight Loss, Logue et al.
Table 2. Baseline stage of change for the five target behaviors
Baseline stage of change frequency (%)
Target behavior
Precontemplation
Contemplation
Preparation
Action*
Total
Portion control
Dietary fat
Fruits and vegetables
Planned exercise
Usual physical activity†
41 (12.5)
45 (13.8)
17 (5.2)
6 (1.8)
75 (22.9)
70 (21.4)
81 (24.9)
59 (18.1)
30 (9.2)
68 (20.8)
174 (53.2)
148 (45.4)
159 (48.8)
226 (69.1)
104 (31.8)
42 (12.8)
52 (15.9)
91 (27.9)
65 (19.9)
80 (24.5)
327 (100.0)
326 (100.0)
326 (100.0)
327 (100.0)
327 (100.0)
* Patients indicating that they were in maintenance were counseled as if were in action because they were starting a weight loss program.
† Walking and stair climbing at home, at work, or while shopping.
creased from 17.9% to 35.5% [RR ⫽ 1.98; 95% confidence
interval ⫽ (1.3, 3.0)] as the average periods in action or
maintenance for dietary fat increased from ⬃1.5 to 4,
whereas the average incidence of weight gain decreased
from 20.2% to 10.5% (p ⫽ 0.06).
The bottom of Table 3 shows that weight change was also
associated with the number of periods in action or mainte-
nance for increased servings of fruits and vegetables. Both
the test statistic for the contingency table [␹2 (df ⫽ 8) ⫽
18.2; p ⫽ 0.02] and the linear trend that explains more than
one-half of the nominal result were significant (␹2 ⫽ 10.8;
p ⫽ 0.001). The incidence of weight loss increased from
9.7% to 28.7% as the number of periods in action or
maintenance for fruits and vegetables increased from 0 to 4
Table 3. Action or maintenance stages for dietary targets and weight change at 24 months
Periods in action or
maintenance
For increased portion control
0
1
2
3
4
For less dietary fat
0
1
2
3
4
For more fruits and vegetables
0
1
2
3
4
Weight gain
(%)
No change
(%)
Weight loss
(%)
Total
[%(n)]
33.3*
8.9
18.2
12.8
9.1
56.8
71.4
65.9
55.3
53.0
9.9
19.6
15.9
31.9
37.9
100.0 (81)
100.0 (56)
100.0 (44)
100.0 (47)
100.0 (66)
22.3†
21.3
12.9
19.6
10.5
59.6
61.7
71.0
60.9
54.0
18.1
17.0
16.1
19.6
35.5
100.0 (94)
100.0 (47)
100.0 (31)
100.0 (46)
100.0 (76)
25.0‡
25.7
18.9
10.2
12.9
65.3
54.3
64.9
55.1
58.4
9.7
20.0
16.2
34.7
28.7
100.0 (72)
100.0 (35)
100.0 (37)
100.0 (49)
100.0 (101)
* ␹2 (df ⫽ 8) ⫽ 35.0; p ⬍ 0.0001.
† ␹2 (df ⫽ 8) ⫽ 13.2; p ⫽ 0.10.
‡ ␹2 (df ⫽ 8) ⫽ 18.2; p ⫽ 0.02.
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OBESITY RESEARCH Vol. 12 No. 9 September 2004
Time in Action and Weight Loss, Logue et al.
Figure 1: Time in action stages for portion control and weight
change. N ⫽ 294; Chi-square (df ⫽ 8) ⫽ 35.0; P value ⬍ .0001.
[RR ⫽ 2.95; 95% confidence interval ⫽ (1.4, 6.4)]. Similarly, the incidence of weight gain decreased from 25.0% to
12.9% as the number of periods increased [RR ⫽ 0.51; 95%
confidence interval ⫽ (0.3, 0.98)].
SOC for Exercise and Activity Targets and Weight
Change
Figure 2 and the top of Table 4 show the incidence of
weight loss or gain by the number of periods in action or
maintenance for planned physical exercise. The nominal
test statistic from the contingency table was significant [␹2
(df ⫽ 8) ⫽ 18.5; p ⫽ 0.02], as was the linear trend test (␹2
⫽ 6.0; p ⫽ 0.01). The average incidence of weight loss
increased from 15.8% to 31.0% as the average number of
periods in action or maintenance for exercise increased from
⬃0.5 to ⬃3.0 [RR ⫽ 1.97; 95% confidence interval ⫽ (1.3,
3.0)]. Conversely, the average incidence of weight gain
decreased from 21.8% to 12.4% in a similar comparison
[RR ⫽ 0.57; 95% confidence interval ⫽ (0.3, 0.98)].
Finally, the second panel of Table 4 shows the incidence
of weight loss (or gain) associated with the number of
periods in action or maintenance for usual physical activity.
Both the contingency table test [␹2 (df ⫽ 8) ⫽ 23.9; p ⫽
0.002] and the linear trend test were significant (␹2 ⫽ 8.7;
p ⫽ 0.003). The incidence of weight loss increased from
12.9% to 30.8% as the number of periods in action or
maintenance for usual activity increased from 0 to 4 [RR ⫽
2.4; 95% confidence interval ⫽ (1.2, 4.6)]. The incidence of
weight gain decreased from 27.1% to 9.2% as the number of
periods increased [RR ⫽ 0.34; 95% confidence interval ⫽
(0.2, 0.8)].
Multiple Target Behaviors
Figure 3 and the bottom of Table 4 show the incidence of
weight loss or gain by successive composite score groups
(0, 1 to 5, 6 to 11, 12 to 16, 17 to 20). Patients with
composite scores of 0 were not in action or maintenance for
any target behavior during follow-up, whereas patients with
Figure 2: Time in action stages for exercise and weight change.
Chi-square (df ⫽ 8) ⫽ 18.5; P value ⫽ .02.
composite scores of 17 to 20 were in action or maintenance
for most behaviors during most periods. The contingency
table test was significant [␹2 (df ⫽ 8) ⫽ 20.6; p ⫽ 0.008],
and the linear trend explains almost one-half of this and was
significant (␹2 ⫽ 9.9;p ⫽ 0.002). The incidence of weight
loss increased from 7.5% to 32.7 [RR ⫽ 4.36; 95% confidence interval ⫽ (1.4, 13.8)], and the incidence of weight
gain decreased from 35.0% to 10.9% [RR ⫽ 0.31; 95%
confidence interval ⫽ (0.1, 0.7)] when the number of periods and behaviors in action or maintenance increased from
0 to ⬃18.5.
Regression Analyses
Ordinal logistic regression analysis (of weight loss, no
change, and weight gain) was undertaken to address the
possibility of confounding by different distributions of selected baseline characteristics and to allow for the possibility of interactions between these baseline characteristics and
the composite score variable. Table 5 shows the results from
a logistic model with the main effects of all of the Table 1
variables included in the model. According to this analysis,
the estimated odds ratio for a four-unit increase (15th percentile to the 90th percentile) in the grouped composite
SOC score [odds ratio ⫽ 5.7; 95% confidence interval ⫽
(2.4, 13.5)] was largely unaffected by adjustments for the
main effects of 21 extraneous variables. The extraneous
variables were age group, gender, ethnicity, BMI group,
hypertension, high cholesterol, arthritis, stomach problems,
diabetes, psychotropic medicines, depression, anxiety, eating disorder, prior weight loss attempts, physician advice,
prior experience with commercial programs, physical or
mental health scores, healthy eating or exercise self-efficacy, or social desirability. Anxiety (according to the
PRIME-MD instrument) was the only variable that was
significantly associated with weight loss (p ⫽ 0.02) other
than an increase in the composite SOC score (p ⬍ 0.0001).
OBESITY RESEARCH Vol. 12 No. 9 September 2004
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Time in Action and Weight Loss, Logue et al.
Table 4. Action or maintenance stages for exercise, activity, or a dietary and exercise composite score, and weight
change at 24 months
Periods in action or maintenance
For more planned exercise
0
1
2
3
4
For more usual activity
0
1
2
3
4
Dietary and exercise composite score
0
1 to 5
6 to 11
12 to 16
17 to 20
Weight gain
(%)
No change
(%)
Weight loss
(%)
Total
[%(n)]
25.5*
14.6
8.2
21.9
10.4
60.0
67.3
55.1
50.0
62.5
14.6
18.2
36.7
28.1
27.1
100.0 (110)
100.0 (55)
100.0 (49)
100.0 (32)
100.0 (48)
27.1†
14.3
26.3
10.5
9.2
60.0
71.4
39.5
63.2
60.0
12.9
14.3
34.2
26.3
30.8
100.0 (85)
100.0 (49)
100.0 (38)
100.0 (57)
100.0 (65)
35.0‡
23.8
10.6
14.3
10.9
57.5
60.3
63.6
60.0
56.4
7.5
15.9
25.8
25.7
32.7
100.0 (40)
100.0 (63)
100.0 (66)
100.0 (70)
100.0 (55)
* ␹2 (df ⫽ 8) ⫽ 18.5; p ⫽ 0.02.
† ␹2 (df ⫽ 8) ⫽ 23.9; p ⫽ 0.002.
‡ ␹2 (df ⫽ 8) ⫽ 20.6; p ⫽ 0.008.
The presence of anxiety appeared to increase, rather than
decrease, weight loss. Given the number of tests, this may
be a chance finding.
A model with main effects and cross-product terms between the grouped composite score and gender, ethnicity,
BMI group, and age group did not yield any strong evidence
of interactions for these variables. However, there was some
evidence of interaction between the grouped composite
score variable and the dichotomized at the median (SF12)
mental health variable (p ⫽ 0.004). The model included
variables for the grouped composite score, mental health,
age group, gender, ethnicity, BMI group, depression, anxiety, eating disorder, and (SF12) physical health, and crossproduct terms between the grouped composite scores and
ethnicity, gender, and the two SF12 variables. The main
effect of the dichotomized mental health variable was negative with respect to weight loss, whereas the cross-product
term with the score variable was positive. This suggests that
the trend between the SOC composite score and weight loss
was stronger in those patients with better mental health.
This finding emerged from an exploratory analysis, so its
significance must be interpreted cautiously.
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OBESITY RESEARCH Vol. 12 No. 9 September 2004
In a sensitivity analysis, we modeled the continuous
outcome of percentage weight change from baseline, instead of the trichotomized outcome with the ⫾5% cut-off
points. The results from the linear regression analyses were
almost identical to those from the logistic regression analyses. The composite SOC score was inversely related to
continuous percentage weight change (p ⫽ 0.0009). Specifically, patients in the highest quintile of the composite SOC
score showed a 4.6% greater negative weight change than
the patients in the lowest quintile. The interaction between
the SOC composite and the dichotomized (SF12) mental
health scores was also significant (p ⫽ 0.01) and had the
same general interpretation in the linear regression model;
i.e., patients with mental health scores above the median
showed a stronger negative relationship between the SOC
composite score and percentage weight change.
Discussion
This is the first report describing the longitudinal relationship between the number of periods in action or maintenance for multiple weight-related behaviors and weight
Time in Action and Weight Loss, Logue et al.
Figure 3: Composite score and weight change. Composite score
includes five target behaviors (usual activity, exercise, dietary fat,
fruits and vegetables, and portion control) and the four measurement periods (0 – 6, 6.1–12, 12.1–18, 18.1–24 months). Chi-square
(df ⫽ 8) ⫽ 20.6; P value ⫽ .008.
change over 2 years in a primary care sample. Bivariate
analyses of elapsed time in action or maintenance for five
target behaviors and weight loss incidence were significant.
In addition, bivariate analysis describing the relationship
between a composite SOC variable and weight loss was
supported by ordinal logistic regression analysis. Because
longitudinal data are not compromised by the antecedentconsequent problem that limits inferences from cross-sectional data, longitudinal data constitute a higher level of
evidence. Thus, the data from this longitudinal study provide strong support for the a priori hypothesis that there is
a positive relationship between the elapsed time in action or
maintenance stages for weight-related behaviors and weight
loss. These data also seem to validate our application of the
Transtheoretical Model to the problem of weight management in primary care.
Increased portion control had the largest RR estimate
(RR ⫽ 3.84) for weight loss among the five target behaviors. This result is consistent with prior research indicating
Table 5. Ordinal logistic regression models of weight change (loss, no change, gain)
Independent variables
Model coefficient (SE)
Composite score only*
Composite score*
Age group
Gender
Ethnicity
BMI group
Hypertension
High cholesterol
Arthritis
Stomach problems
Diabetes
Psychotropic medicines
Depression
Anxiety
Eating disorder
Prior weight loss attempts
MD advice
Commercial program
Physical health
Mental health
Healthy eating self-efficacy
Exercise self-efficacy
Social desirability
0.37 (.091)
0.43 (0.11)
⫺0.09 (0.19)
0.057 (0.17)
⫺0.32 (0.35)
⫺0.072 (0.15)
0.26 (0.29)
⫺0.46 (0.28)
⫺0.029 (0.32)
0.13 (0.34)
0.10 (0.41)
⫺0.19 (0.28)
⫺0.19 (0.36)
0.67 (0.28)
0.010 (0.17)
⫺0.33 (0.72)
0.41 (0.37)
⫺0.24 (0.30)
⫺0.0096 (0.016)
0.00062 (0.015)
0.0047 (0.0048)
⫺0.019 (0.016)
0.019 (0.024)
Odds ratio
(95% confidence interval)
4.3 (2.1, 8.9)†
5.7 (2.4, 13.5)†
3.80 (1.3, 11.3)
p
⬍0.0001
⬍0.0001
0.65
0.73
0.36
0.63
0.38
0.10
0.93
0.71
0.80
0.51
0.59
0.02
0.95
0.65
0.26
0.43
0.55
0.97
0.32
0.22
0.43
* Grouped raw score (approximate quintiles) treated as a continuous variable with values 0, 1, 2, 3, and 4.
† Four “quintile” change in grouped composite score.
OBESITY RESEARCH Vol. 12 No. 9 September 2004
1505
Time in Action and Weight Loss, Logue et al.
that dietary energy restriction is the most direct means of
inducing weight loss (38). Other research indicates that
increased exercise is important for weight loss and maintenance (39). In our data, fewer patients were in action for
planned exercise during all four periods (n ⫽ 48) than for
increased activity (n ⫽ 65), portion control (n ⫽ 66),
decreased dietary fat (n ⫽ 76), and increased fruits and
vegetables (n ⫽ 101). Portion control (decreased portion
sizes) may be a behaviorally easier target than increased
planned exercise in the current environment but not as easy
as increased fruit and vegetable consumption. However,
increased fruit and vegetable consumption had a weaker
relationship (RR ⫽ 2.95) with weight loss than portion
control.
The internal validity of any prospective study can be
compromised by a combination of selection, information, or
confounding bias (40). Selection bias can occur when participant follow-up is ⬍100%, and there is a relationship
between the study factor and participant retention (41). In
this study, 35 of 329 (11%) patients were excluded from the
longitudinal bivariate analyses (Figures 1 to 3) because of
missing weight change data. Because (we believe that) these
patients are more likely to have failed to lose weight early
in follow-up and had low composite SOC scores, our RR
estimates may be (conservatively) biased toward the null
valve. This interpretation is consistent with Ware’s recommendation to make the conservative assumption that early
dropouts have not lost weight (42).
It is possible that the criteria for the action stage were
insufficiently stringent to correctly classify patients who
were actually in preparation, or in an earlier stage. Because
our staging algorithm was specifically designed to minimize
misclassification bias from insufficiently stringent action
criteria for complex domains, this source of bias is probably
small relative to other methods (23). However, more research in this area would be useful. Retrospective data from
the National Weight Loss Registry suggest that successful
long-term loss of 15 kg may require an hour of moderate to
vigorous exercise each day and strict adherence to a portioncontrolled lower calorie (fat) diet (43). Patient cycling from
action to earlier stages between assessments may partially
account for the observation that ⬍40% of patients who
ostensibly spent four periods in action (Tables 3 and 4) also
loss 5% or more of their baseline weight.
Weight measurement errors should be small because of
the high correlation between measured and chart weights
and the use of a standardized weight measurement protocol
on calibrated scales. The ordinal logistic regression (and the
linear regression) analyses suggest that confounding bias
from multiple relevant extraneous factors is minimal, but
unmeasured confounders are always an issue in observational studies.
The external validity of our results is limited by the
characteristics of the participants in the REACH trial
1506
OBESITY RESEARCH Vol. 12 No. 9 September 2004
(31,32,33). Because participants were recruited in a primary
care setting, they are more likely to have diagnosed hypertension and other conditions associated with obesity than
members of the general population. Participants may not be
a representative sample of middle-aged primary care patients with weight problems seen in Northeastern Ohio or
elsewhere. In terms of the baseline prevalence of (binge)
eating disorder, the participants seem to be similar to participants who volunteer for other obesity treatment programs (44).
Most observational studies that have applied the Transtheoretical Model to weight loss-related behaviors have had
cross-sectional designs and have used different staging algorithms (5–23). However, the majority of these studies
offer qualified support for the construct validity and potential clinical utility of the staging concept for multiple weight
loss-related dietary and exercise behaviors (5,6,9,23). Skeptics have either challenged the merits of the underlying
concept or highlighted the measurement or classification
issues raised by the necessity of staging patients on multiple
complex behaviors (45,46).
The few studies with longitudinal data have had qualified
or mixed results or have not assessed weight change (24 –
29). Anderson and Apovian reported that patients prescribed low-energy diets and in action stage for weight loss
according to the University of Rhode Island Change Assessment Scale lost significantly more weight over 4 weeks
than patients in the contemplation or maintenance stages
(24). However, the University of Rhode Island Change
Assessment Scale instruments were not completed at baseline; thus, the short follow-up interval was indeterminate; in
addition, weight loss was an indicator of a negative energy
balance, not a target behavior. Greene and Rossi (25) reported, “Between 12 and 18 months, participants progressing at least 1 stage reduced their fat intake to a greater extent
that participants who did not progress”; however, the theory
suggested that all stage progressions should not be equally
predictive of decreased dietary fat consumption. Jeffery et
al. found no relationship between baseline SOC and weight
loss over a 3-year period (26). However, they used a staging
algorithm that defined the action stage in terms of dieting
only; the authors did not consider portion control along with
other important target behaviors such as planned physical
exercise or usual activity in their definition of the action
stage(s). Macqueen et al. found no association between
baseline SOC for weight loss and the percentage of weight
loss over a 4- to 6-week period (27). Marcus et al. found a
significant relationship between baseline SOC for physical
activity and physical activity 6 months later in a sample of
employees (28). Plotnikoff et al. reported that only 45% of
their longitudinal predictions based on the Transtheoretical
Model were supported (29); however, the authors did not
have an independent measure of exercise, which was the
target behavior.
Time in Action and Weight Loss, Logue et al.
This study is one of the first to document that the elapsed
time in action or maintenance for multiple weight lossrelated target behaviors is longitudinally related to weight
loss (or weight gain) over a 2-year period. Future studies
could focus on identifying those environmental, psychosocial, and programmatic factors that increase (or decrease)
the amount of time in action or maintenance (for relevant
target behaviors) over prolonged follow-up periods. However, no investigator has yet tested whether an intervention
based on the consistent application of stage-matched processes of change will produce better weight management
outcomes than an intervention based on the consistent application of stage-mismatched processes of change (47).
Plotnikoff et al.’s work challenges the standard application
of the processes of change to exercise behavior by suggesting that behavioral processes of change are useful in the
preaction stages for exercise, which is contrary to SOC
theory (29).
Acknowledgments
We thank Yen-Pin Chiang, Agency for Healthcare Research and Quality Project Officer; Keding Hua, statistical
programming; Margaret Reitenbach, administrative support; and physicians and patients from the study practices
for their time and efforts. This study was supported by
Grants 1 R01 HS08803-01A2 through 5 R01 HS08803-04
and 3 R01 HS08803-02S1 through 3 R01 HS08803-04S1
from the Agency for Healthcare Research and Quality and
the National Institute of Diabetes, Digestive, and Kidney
Diseases and by consecutive Nutrition and Exercise Studies
grants (1998 to 2002) from the Summa Health System
Foundation. Author contributions were as follows: study
concept and design, E.E.L., D.G.J., W.D.S., and K.S.S.;
acquisition of data, K.R.B., W.D.S., C.F.C., and K.F.S.;
analysis and interpretation of data, E.E.L. and D.G.J.; drafting of the manuscript, E.E.L., D.G.J., W.D.S., K.S.S.,
K.R.B., and C.F.C.; critical revision of the manuscript for
important intellectual content, E.E.L., D.G.J., W.D.S., and
K.S.S.; statistical expertise, D.G.J. and E.E.L.; obtained
funding, E.E.L., W.D.S., and D.G.J.; administrative, technical, or material support, K.R.B. and E.E.L.; and study
supervision, K.R.B. and E.E.L. REACH trial results were
presented at the American Academy of Family Physicians,
National Research Network’s Convocation of Practices, Improving Patient Care through Healthy Behaviors Research
(Arlington, VA, March 20 to 23, 2003).
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