Patient Education and Counseling 87 (2012) 81–92

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Patient Education and Counseling

journal homepage: www.elsevier.com/locate/pateducou

Self Management

Twelve-month outcomes of an Internet-based diabetes self-management

support program
Russell E. Glasgow a,e,*, Deanna Kurz a, Diane King a, Jennifer M. Dickman a, Andrew J. Faber a,
Eve Halterman a, Tim Woolley c, Deborah J. Toobert b, Lisa A. Strycker b, Paul A. Estabrooks d,
Diego Osuna a, Debra Ritzwoller a
a
Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, USA
b
Oregon Research Institute, Eugene, OR, USA
c
InterVision Media, Eugene, OR, USA
d
Virginia Polytechnic Institute and State University, Roanoke, VA, USA
e
Implementation Science, Division of Cancer Control and Population Science, National Cancer Institute, Rockville, MD, USA

A R T I C L E I N F O A B S T R A C T

Article history: Objective: Internet-based programs offer potential for practical, cost-effective chronic illness self-
Received 8 February 2011 management programs.
Received in revised form 5 July 2011 Methods: We report 12-month results of an Internet-based diabetes self-management program, with
Accepted 29 July 2011
and without additional support, compared to enhanced usual care in a 3-arm practical randomized trial.
Patients (n = 463) were randomized: 77.3% completed 12-month follow-up. Primary outcomes were
Keywords: changes in health behaviors of healthy eating, physical activity, and medication taking. Secondary
Diabetes
outcomes were hemoglobin A1c, body mass index, lipids, blood pressure, and psychosocial factors.
Self-management
RCT
Results: Internet conditions improved health behaviors significantly vs. usual care over the 12-month
Internet intervention period (d for effect size = .09–.16). All conditions improved moderately on biological and psychosocial
Pragmatic trial outcomes. Latinos, lower literacy, and higher cardiovascular disease risk patients improved as much as
Multiple behavior change other participants.
Conclusions: The Internet intervention meets the reach and feasibility criteria for a potentially broad
public health impact. However, 12-month magnitude of effects was small, suggesting that different or
more intensive approaches are necessary to support long-term outcomes. Research is needed to
understand the linkages between intervention and maintenance processes and downstream outcomes.
Practice implications: Automated self-management interventions should be tailored and integrated into
primary care; maintenance of patient self-management can be enhanced through links to community
resources.
Published by Elsevier Ireland Ltd.

1. Introduction or time to provide diabetes self-management education and

follow-up support [7,8]. Widespread use of the Internet provides
With the increased prevalence of diabetes [1], there is an opportunity to expand the reach of diabetes education
increasing need for diabetes self-management support that has programs, and to provide continuous support and tools for
the ability to reach large numbers of adults [2]. Traditional clinical achieving necessary changes in multiple lifestyle behaviors, such
approaches, such as physician counseling and group-based as healthful eating, regular physical activity, and managing
diabetes education programs [3], have inadequate reach, and have medications [9,10].
not been sufficient to support long-term behavior changes [4–6]. In Despite reviews suggesting that computerized interactive
addition, primary care offices generally do not have the resources behavioral health change interventions can be effective
[4,11,12], questions remain about whether these programs will
prove equitable in terms of access to services, or whether the
‘‘digital divide’’ may increase disparities and about their longer
* Corresponding author at: Dissemination and Implementation Science, Division
term effects and overall public health impact [13]. From an
of Cancer Control and Population Sciences, National Cancer Institute, 6130
Executive Blvd., Room 6144, Rockville, MD 20852, USA. Tel.: +1 301 435 4912;
ecological perspective on health behavior change [14], it is also not
fax: +1 301 594 6787. known whether website use and outcomes are influenced by
E-mail addresses: glasgowre@mail.nih.gov, russg@re-aim.net (R.E. Glasgow). factors such as individual characteristics, especially factors such as

0738-3991/$ – see front matter . Published by Elsevier Ireland Ltd.

doi:10.1016/j.pec.2011.07.024
82 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92

level of computer use and health literacy and numeracy; social Spanish, and ability to perform mild to moderate exercise.
network/social support; and community/environmental influ- Participants were individually randomized via a computer
ences. program developed by our computer programmer and statistician.
Evidence from our previous research indicates that effective Data were collected from April 2008 to August 2010 and analyzed
diabetes self-management interventions (a) incorporate the from September 2010–January 2011. All procedures were ap-
patient as an active participant in setting goals, (b) are based on proved by the KPCO institutional review board.
behavioral and social-ecological theory, (c) emphasize problem
solving and use of supportive resources, and (d) provide proactive 2.1. Interventions
follow-up support [15–17]. Translating these important principles
into interactive components in an electronic or virtual environ- Both interventions included a set of behavior change techniques
ment is complex. Furthermore, integrating such programming which we have listed using the classification system developed by
with primary care activities is important. With the growing Michie and colleagues [24]. These techniques are listed in Table 1 by
emphasis on telemedicine and electronic health records, integrat- intervention and intervention phase. Social cognitive theory [25]
ing Internet-based diabetes self-management activities with and a social-ecological model [26] were the primary intervention
primary care is a logical step. Whether web-based approaches frameworks used. The RE-AIM framework was used for planning and
can eliminate or substantially reduce the need for personal and evaluation. Interventions were available in English and Spanish, and
social support is unclear, and research is needed to understand the based on refinements of interactive self-management programs
right formula of human and computerized support to produce found effective in our prior research [27].
sustained, long-term behavior change [18].
In this paper we follow up on our earlier publications [19,20] to 2.1.1. CASM
report 12-month results of a three-arm pragmatic randomized trial CASM participants were given access to the ‘‘My Path to Healthy
to evaluate an Internet-based, computer-assisted diabetes self- Life’’/‘‘Mi Camino A La Vida Sana’’ website and instructed in log-in,
management (CASM) intervention compared to a CASM plus navigation, and usage procedures by a research staff member.
human support (CASM+) condition. BOTH versions of the Participants were asked to select initial, easily achievable goals in
intervention were offered in English and Spanish, and compared each of three areas: medication adherence, physical activity, and
to enhanced usual care (EUC). Initial results at a 4-month follow-up food choices. They recorded their progress on these three daily
revealed relatively high levels of website use as well as dietary and goals using the tracking section of the website and received
exercise behavior improvements relative to the enhanced usual immediate feedback on success in meeting their goals over the past
care comparison condition, but only modest and non-significant 7 days. The website, described in detail elsewhere [28], included a
improvements in biological outcomes relative to the EUC condition graphic display of the patient’s hemoglobin A1c, blood pressure,
[19,20]. and cholesterol results; a moderated forum; and community
Our primary purposes in this article are to expand upon our resources (e.g., healthful recipes, printable handouts) for diabetes
earlier immediate treatment results to: (a) report longer-term (12- self-management and healthful lifestyles, as well as features to
month) results, including engagement, attrition, behavior change, enhance user engagement, such as rotating quiz questions and
biological impacts, and psychosocial outcomes; (b) using the RE- motivational tips.
AIM model, investigate if the earlier, promising engagement and After 6 weeks, participants created personalized ‘‘action plans’’
initial behavior change results were maintained and translated for medication taking, healthy eating, and physical activity. For
into broader public health outcomes at 12 months; and (c) each of the three areas, users identified barriers to achieving the
investigate potential effects of moderator variables hypothesized goal(s) they had selected, and then chose from a list of problem-
to impact the outcomes of the intervention (e.g., health literacy or solving strategies to overcome those barriers [29]. Each user’s
numeracy, age, racial or ethnic differences, and level of baseline action plan summary was available for easy reference and revision.
computer use). In addition to the website, CASM participants received periodic
motivational calls and prompting using a computer-based
2. Methods telephone system that initiated outbound calls, received inbound
calls, and collected data.
A patient-randomized practical effectiveness trial [21] evaluat-
ed two Internet-based diabetes self-management programs 2.1.2. CASM+
relative to EUC. The interventions were (a) self-administered, CASM+ participants received all aspects of the CASM interven-
computer-assisted self-management (CASM), based on social- tion with the addition of two follow-up calls from an intervention-
ecological theory [22] and the ‘‘5 As’’ self-management model [23] ist, and an invitation to attend three group visits with other
and (b) the CASM program with the addition of enhanced social participants in the same study condition. The two extra follow-up
support (CASM+). EUC provided computer-based health risk calls occurred 2 and 8 weeks after the initial visit to answer any
appraisal feedback and recommended preventive care behaviors intervention-related questions and troubleshoot problems with
using the same contact schedule as the CASM conditions, but did the website or self-management goals, and to discuss the
not include the key intervention procedures. participant’s action plans, respectively. The first call was from a
The study was conducted in five primary care clinics within research project staff member and the second call to coordinate
Kaiser Permanente Colorado (KPCO). Clinics were selected based with the patients more general diabetes management goals was
on variability in size, location and socioeconomic status of from a KPCO diabetes care coordinator.
neighborhood, and to maximize percentage of Latino patients. The 120-min group sessions focused on (1) healthy eating,
Recruitment issues are described in detail in Glasgow et al. [19] interacting with one’s physician and using community resources
and summarized in Fig. 1. Eligibility criteria included: 25–75 years and (2) maintenance enhancement through the use of analyzing
of age, diagnosis of type 2 diabetes, body mass index (BMI) of personal behavior chains related to relapse [30]. The first group
25 kg/m2 or greater, and at least one other risk factor for heart session for CASM+ participants, scheduled after their action plans
disease (e.g., hypertension, smoking, hyperlipidemia). Additional were created, focused on healthful eating, and was led by a
inclusion criteria were access to a telephone and at least biweekly nutritionist. The meeting included information on healthful
access to the Internet, ability to read and write in English or restaurant eating behaviors and grocery shopping tips. The second
 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92 83

Fig. 1. Flow diagram of My Path/Mi Camino participation and retention results.

group visit was designed to supplement the Behavior Chain 2.2. Measures
exercise introduced to enhance maintenance of the CASM+
intervention. The Behavior Chain Activity was designed to help Baseline participant characteristics included age, gender, race,
participants understand that lapses in healthful eating, physical ethnicity, income, education level, and tobacco and computer use.
activity, and medication-taking practices usually result from a Health literacy was assessed during the recruitment call using three
chain of behaviors leading up to the lapse. The Behavior Chain links items from the widely used assessment of health literacy identified as
may be thought of as high-risk situations in which unhealthful most sensitive in prior research [31]. Health numeracy was assessed
behaviors may be substituted for healthful ones. To prevent future by eight items from the subjective health numeracy scale [32].
lapses, the activity was designed to help participants identify their
links, and then develop strategies for each link in their own 2.2.1. Behavioral outcomes
Behavior Chain. The third group meeting was led by a bilingual Eating behaviors were assessed using the Ammerman et al.
family physician to educate participants about community [33,34] ‘‘Starting The Conversation’’ scale, found to be sensitive to
diabetes resources and how to obtain maximum benefit from change for assessing healthy eating patterns [35]. Starting The
their doctor visits. Conversation items were averaged to calculate a total score.
84 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92

Table 1
Specific focus on behavior addressing motivation (CASM and CASM+).

Phase of trial Behavioral technique Description

First in-person/computer Assess health behaviors Assess levels of dietary fat and FV intake, medication taking, and physical activity (PA),
session and use the measurement as a motivational tool.
Assess level of social support Assess the extent to which friends, relatives, and work colleagues, and more distal
sources of support will be supportive of the goal attainment.
Provide feedback on current behavior Give feedback arising from assessment of current self-reported or objectively
monitored behavior.
Provide normative information Give information about how the diet and PA levels compare with national norms
Use assessment results for tailoring Use relevant information from the participant to tailor the behavioral support
goal setting provided.
Emphasize choice Emphasize participant choice within the bounds of evidence-based practice.
Identify reasons for establishing and Help the participant to arrive at a clear understanding of health benefits of eating a
maintaining healthful lifestyle healthful diet, engaging in regular physical activity, and taking medications.
behaviors
Boost motivation and self- efficacy Encourage participants to achieve success by setting appropriate goals in small
achievable units.
Facilitate barrier identification Help participants identify general barriers (e.g., susceptibility to stress) that might
make it harder to eat a healthful diet, engage in regular PA, or take medications.
Facilitate action planning Work with participants to generate a clear action plan (e.g., days and time of week for
engaging in PA).
Prompt commitment to a healthful Encourage participants to affirm or reaffirm a strong commitment to start, continue, or
lifestyle. restart their goal-attainment efforts.
Assess self-efficacy (i.e., confidence Assess confidence in success, and, if low confidence, encourage to reset goals.
in success).
Prompt tracking of lifestyle behaviors. Help participants establish a routine of recording their daily diet, physical activity, and
medication taking to track their own progress toward goals.

Second intervention session Provide feedback on progress Give feedback arising from assessment of current self-reported progress toward goal
and IVR calls attainment.
Provide rewards contingent on successful Give praise or other rewards for achieving goals.
goal attainment

CASM+ in-person support Facilitate relapse prevention and coping Help participants understand how lapses occur and how they lead to relapse, and to
group using behavior chains develop specific strategies for preventing lapses or avoiding lapses turning into
relapse.
Advise on/facilitate use of social support Advise on or facilitate development of social support from friends, relatives,
colleagues, or ‘‘buddies.’’
Adopt appropriate local community Give information about options for additional support for diet and PA (e.g., websites,
resources self-help groups, telephone helpline).

Estimated fat intake was assessed using the NCI Percent Energy feasible to administer lengthy measurement scales [45]. Selected
from Fat Screener [36]. The CHAMPS instrument [37] was used to item subsets and items from these scales were identified based on
estimate total weekly caloric expenditure in PA. Adherence to items that were most strongly associated with the overall scale,
diabetes, blood pressure, and cholesterol medications was and that reflected key subscales (where relevant) a priori thought
assessed through the medication-taking items of the Hill-Bone to be targeted by the intervention, and items that were not
Compliance Scale [38] that determines how often and why considered relevant were deleted (e.g., items on workplace support
respondents missed taking medications (with scale scores were deleted since many patients were not employed).
dichotomized to represent 1 = perfect adherence vs. 0 = other
levels of adherence). 2.2.3. Biological outcomes
Biologic variables included: BMI, hemoglobin A1c, lipids, and
2.2.2. Psychosocial outcomes mean arterial pressure. Hemoglobin A1c was measured on a Bio-
Self-efficacy was measured with Lorig’s eight-item Diabetes Rad Variant II Turbo liquid by high-pressure liquid chromatogra-
Self-Efficacy scale [39], which measures participant confidence phy. Lipids were assayed on a Modular chemistry analyzer from
regarding planning and eating healthful meals, following an eating Roche Diagnostics through a modified version of the Abell Kendall
plan, exercising regularly, and controlling diabetes. Participants method.
rated their confidence on a scale of 1–10, with higher scores
indicating greater self-efficacy. Use of problem-solving skills was 2.3. Analyses
assessed by six items on the dimension of Positive Transfer of Past
Experience from the Diabetes Problem Solving Scale of Hill-Briggs Survey data were entered and verified, and scores were
[40]. Supportive resources were measured using nine of the 22 calculated for multiple-item instruments according to previously
items from the Chronic Illness Resources Survey (CIRS) [41] to established procedures. Descriptive statistics were computed to
assess utilization of social-environmental resources supportive of determine the nature of the data and test for normality
diabetes self-management. General health status was measured assumptions. Chi-square tests and analyses of variance were used
using the visual analog scale from the EuroQol health status to evaluate differences in participant characteristics between the
instrument, on which participants rate ‘‘how good or bad is your treatment conditions, and between dropouts and those who
own health today?’’ from 0 (worst) to 100 (best) [42]. The Diabetes completed the study at 12 months.
Distress Scale (DDS) [43] was used to assess diabetes-related
quality of life. This measure assesses the degree to which common 2.3.1. Moderator analyses
diabetes situations are currently problematic for respondents. This Hierarchical multiple regression models were specified to test
was a pragmatic trial [21,44] in a real-world setting and it was not for potential effects of variables hypothesized to moderate 4- and
 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92 85

12-month treatment effects. In the first step, the baseline value of procedures via the expectation–maximization (EM) algorithm
the outcome variable and demographic variables (age, gender, with NORM software [47].
computer experience, Latino ethnicity, health literacy, numeracy,
education, insulin use, and 10-year coronary heart disease [CHD] 2.3.4. Statistical power
risk) were entered. In the second step, treatment condition was Power analyses in our grant proposal demonstrated that an
entered (1 = EUC; 2 = CASM/CASM+). In the third step, multiplica- initial sample size of 424, allowing for 20% attrition, resulted in a
tive interactions between treatment condition and the demo- power of .90 (alpha = .05, two-tailed) to detect an effect size d of .32
graphic variables were entered. Because of the large number of for comparisons between the combined intervention conditions
moderator analyses, significance was set at p < .01. and the EUC condition, and a power of .80 to detect a d of .28
between the two CASM conditions on the a priori analyses on
2.3.2. Generalized estimating equations primary behavior change outcomes specified in the grant proposal.
Generalized estimating equations (GEE) models [46] were used
to compare long-term treatment effects on outcome measures
from baseline to 12 months. GEE models were specified using a 3. Results
first-order autoregressive correlation structure, and separate
models were conducted to examine treatment group interactions 3.1. Participants and preliminary analyses
with both linear and quadratic trends. Linear-trend results are
presented here, as model results were similar for linear and A total of 463 patients participated. Recruitment and partici-
quadratic trends. Age, gender, Latino ethnicity, and education pant details have been reported elsewhere [19]. We recruited a
status (dichotomized at high school) were covaried in all analyses, diverse sample across age, gender, ethnicity (21% Latino), race (14%
as they were found in bivariate correlational analyses to be African American), and education and income levels (Table 2).
significantly associated with some outcomes at baseline. Separate There were no significant differences among outcomes on baseline
GEE models were performed to compare the combined interven- characteristics. Distributions of all variables were normal with the
tion conditions to EUC, and to compare the two CASM conditions to exceptions of fat intake and physical activity, which were
each other. Statistical analyses were performed using SPSS 12.0 leptokurtotic. To obtain normal distributions for these variables,
(SPSS Inc., Chicago). Effect sizes (Cohen’s d) were calculated cases reporting >50% calories from fat were recoded to 50 and
comparing the two treatment conditions on baseline-to-4-month cases reporting >10,000 calories per week of exercise were
change and on baseline-to-12-month change. recoded to 10,000.
Twelve-month attrition rates differed by condition (chi-
2.3.3. Missing data square = 6.78, p = .034); 18.2% attrition in the EUC condition was
GEE analyses were performed two ways. First, a complete- significantly lower than the 31.4% and 25.3% rates in the CASM and
case approach was used, in which participants with missing CASM+ conditions, respectively. Participant characteristics mea-
follow-up data on the outcome variable of interest were excluded sured at baseline did not differ significantly by 12-month attrition
from the analysis. Second, identical analyses were conducted status across the three treatment conditions. Missingness patterns
after missing data were imputed using multiple imputation were not found to be systematically related to any of the predictor

Table 2
Baseline characteristics of participants randomized across three conditions (n = 463).

Characteristic All EUC CASM CASM+ Siga

M  SD or % M  SD or % M  SD or % M  SD or %
n = 132 n = 169 n = 162

Age (years) 58.4  9.2 58.7  9.1 58.7  9.3 57.8  9.3 .618

% Female 49.8% 51.5% 44.6% 53.7% .231

Race .525
American Indian/Alaska Native 6.7% 11.1% 4.9% 4.8%
Asian 1.6% 1.6% 1.9% 1.4%
Black or African American 15.4% 12.7% 14.8% 18.4%
White 72.0% 70.6% 74.1% 70.7%

Latino ethnicity 21.8% 16.8% 25.3% 25.3% .178

Income .241
Less than $49,999 47.3% 50.4% 45.7% 46.0%
$50,000–$89,999 35.2% 36.6% 33.5% 35.7%
$90,000 or more 17.5% 13.0% 20.6% 18.2%

High school or less education 19.1% 13.0% 19.9% 23.6% .069

% Low–moderate health literacy 5.9% 7.6% 6.0% 4.3% .495

Numeracy 4.31  1.0 4.32  0.8 4.21  1.1 4.39  1.0 .720

Computer use .190

Never to 2 h per week 16.3% 15.1% 16.6% 16.6%
3–6 h per week 17.7% 21.2% 20.2% 12.4%
7–8 h per week 6.1% 4.5% 5.4% 8.0%
9 or more hours per week 60.0% 59.1% 57.7% 63.0%

Smoke cigarettes 10.8% 9.1% 10.1% 13.0% .531

Note: EUC = enhanced usual care control condition; CASM/CASM+ = computer-assisted self-management intervention.
a
One-way analysis of variance or chi-square test, as appropriate.
86 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92

Table 3 (p = .006), with Latinos reducing blood pressure more than non-
Number of log-ins to website per month by condition.
Latinos in the CASM/CASM+ conditions while Latinos had less
Month CASM condition CASM+ condition reduction in blood pressure than non-Latinos in the EUC condition.
Mean (SD); Median Mean (SD); Median
3.3. Website use
1 10.45 (9.23); 8 10.86 (9.31); 8
2 7.52 (8.95); 4 7.62 (8.01); 6
3 5.62 (8.14); 2 5.76 (7.15); 3 Website use was relatively high initially and throughout the
4 5.12 (8.01); 1 5.24 (6.72); 3 first 4 months. Following the 4-month assessment, as can be seen
5 5.31 (8.19); 1 5.27 (6.78); 3 in Table 3, visits to the website declined considerably from an
6 4.37 (7.31); 1 4.36 (6.12); 2
average of almost 11 times per month in the initial month to an
7 3.75 (7.34); 0 3.77 (5.56); 1
8 3.86 (7.57); 0 3.53 (5.87); 0 average of fewer than 3 times per month in month 12, with no
9 3.33 (6.79); 0 3.31 (5.80); 0 differences between CASM and CASM+ conditions.
10 3.49 (7.33); 0 3.22 (5.99); 0
11 3.25 (6.97); 0 2.97 (5.86); 0 3.4. Outcomes
12 2.60 (5.76); 0 2.57 (5.22); 0

3.4.1. Behavior change

In intention-to-treat (or imputation so titled because imputa-
or outcome variables, suggesting that the data were missing at tion analyses model the most likely data for those on whom follow-
random and that data-imputation procedures were appropriate. up data are not complete) analyses, the combined CASM/CASM+
conditions improved significantly more than the EUC condition
3.2. Moderator analyses over the 12 months of the program in eating habits (condi-
tion  time chi-square = 9.01, p < .05), fat intake (condition  time
With one exception, none of the hypothesized moderator chi-square = 6.28, p < .05), and physical activity (condition  time
variables were found in hierarchical multiple regression analyses chi-square = 6.01, p < .05), but not medication adherence (con-
to significantly affect either 4- or 12-month treatment outcomes. dition  time chi-square = 0.49, p > .05) (Table 4). The imputation
The exception was that Latino ethnicity was a significant analyses and complete-cases analyses revealed a highly similar
moderator of change in blood pressure (only) at 12 months pattern of significant improvement from baseline to 12 months on

Table 4
Baseline, 4-month, and 12-month behavioral outcomes (estimated means and SEs).

Baseline 4 Months 12 Months Condition Time CT

(M  SE) (M  SE) (M  SE) (chi-square) (chi-square) (chi-square)

I. Control vs. CASM/CASM+

A. Intention to treat
Eating habits 12.64* 83.06* 9.01*
(score; range = 1/worst 3/best)
Control 2.13  .03 2.18  .02 2.23  .03
CASM/CASM+ 2.18  .02 2.31  .01 2.32  .02
Effect size .32 .15
Fat intakea (%; range = 20–50) 3.91* 51.06* 6.28*
Control 35.18  .40 35.11  .41 33.91  .37
CASM/CASM+ 34.86  .28 33.71  .27 33.22  .24
Effect size .24 .09
Phys activityb 1.70 47.93* 6.01*
(Cals/Wk; range = 0–10,000)
Control 3915  294 3704  273 2882  300
CASM/CASM+ 3989  165 4410  169 3242  179
Effect size .23 .09
Medication adherence dichotomized 0.27 8.80* 0.49
(range = 0/nonadherent 1/adherent)
Control .34  .04 .38  .04 .41  .04
CASM/CASM+ .35  .03 .42  .03 .43  .03
Effect size .06 .02
B. Complete cases
Eating habits 9.33* 71.45* 11.82*
(score; range = 1/worst 3/best)
Control 2.13  .03 2.18  .03 2.24  .03
CASM/CASM+ 2.18  .02 2.31  .02 2.31  .02
Effect size .32 .07
Fat intakea (%; range = 20–50) 3.92* 45.12* 4.62c
Control 35.20  .42 35.06  .46 33.91  .41
CASM/CASM+ 34.84  .29 33.69  .30 33.04  .29
Effect size .22 .12
Phys activityb 2.00 35.37* 5.63c
(Cals/Wk; range = 0–10,000)
Control 3953  302 3776  291 2839  320
CASM/CASM+ 4005  169 4512  189 3328  215
Effect size .25 .14
Medication adherence dichotomized 0.06 9.77* 0.41
(range = 0/nonadherent 1/adherent)
Control .34  .04 .40  .05 .44  .05
CASM/CASM+ .34  .03 .40  .03 .41  .03
Effect size .00 .06
 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92 87

Table 4 (Continued )

Baseline 4 Months 12 Months Condition Time CT

(M  SE) (M  SE) (M  SE) (chi-square) (chi-square) (chi-square)

II. CASM vs. CASM+

A. Intention to treat
Eating habits 3.24c 99.88* 0.78
(score; range = 1/worst 3/best)
CASM 2.20  .03 2.34  .02 2.34  .02
CASM+ 2.17  .02 2.28  .02 2.29  .02
Effect size .12 .07
Fat intakea (%; range = 20–50) 0.63 45.54* 0.43
CASM 34.97  .44 33.68  .40 33.32  .37
CASM+ 34.76  .36 33.74  .35 33.12  .31
Effect size .06 .002
Phys activityb 2.20 44.20* 2.16
(Cals/Wk; range = 0–10,000)
CASM 4302  233 4644  234 3307  252
CASM+ 3662  230 4165  243 3174  255
Effect size .06 .16
Medication adherence dichotomized 0.40 9.53* 4.25
(range = 0/nonadherent 1/adherent)
CASM .40  .04 .42  .04 .43  .04
CASM+ .30  .04 .42  .04 .43  .04
Effect size .19 .18
B. Complete cases
Eating habits 1.63 88.86* 0.80
(score; range = 1/worst 3/best)
CASM 2.19  .03 2.33  .03 2.33  .02
CASM+ 2.17  .02 2.29  .02 2.29  .02
Effect size .08 .07
Fat intakea (%; range = 20–50) 0.88 38.68* 0.36
CASM 35.06  .45 33.82  .47 33.36  .46
CASM+ 34.62  .35 33.55  .39 32.73  .34
Effect size .04 .05
Phys activityb 2.21 30.06* 0.77
(Cals/Wk; range = 0–10,000)
CASM 4319  241 4690  266 3519  305
CASM+ 3689  233 4342  269 3144  301
Effect size .10 .08
Medication adherence dichotomized 0.58 5.63* 1.48
(range = 0/nonadherent 1/adherent)
CASM .38  .04 .41  .04 .41  .05
CASM+ .30  .04 .39  .04 .40  .04
Effect size .12 .13

Note: Based on GEE analysis results comparing long-term treatment effects on outcome measures from baseline to 4 and 12 months, and covarying age, education, Latino
ethnicity, and gender at baseline, which were found in univariate analyses to be related to outcomes at baseline. DF(condition) = 1; DF(time) and DF(condition  time) = 2.
CASM/CASM+ = computer-assisted self-management intervention. Ranges were calculated from the present dataset.
*
Significant at p < .05 or less.
a
Outliers were defined as cases reporting > 50% calories from fat; to obtain a normal distribution for this variable, outliers were recoded to 50.
b
Outliers were defined as cases reporting > 10,000 calories per week of exercise; to obtain a normal distribution for this variable, outliers were recoded to 10,000.
c
p < .10.

three of the four outcomes, favoring the CASM conditions over EUC, intervention conditions produced greater reductions in distress
with no significant differences between CASM and CASM+ than the EUC condition (condition  time chi-square = 6.26,
conditions. Effect sizes indicated that the interventions produced p < .05).
strongest gains between baseline and 4 months; improvements in
the EUC condition contributed to smaller effect sizes between 4. Discussion
conditions from 4 to 12 months.
The primary purpose of this paper was to investigate the longer-
3.4.2. Biological outcomes term (12-month) effects of the My Path program relative to a
Participants in the intervention conditions demonstrated relatively stringent enhanced usual care condition. Overall,
consistent, modest improvements on all of the biological outcomes improvement was seen in most measures, but between-group
across the 12-month period, but between-condition differences differences were largely non-significant. The patterns of change
were not statistically significant on any of the measures on varied across outcomes. On the behavioral outcomes, the CASM
imputed or complete-cases analyses (Table 5). and CASM+ conditions improved significantly more than the EUC
condition across 12 months, but effect sizes indicated that
3.4.3. Psychosocial outcomes treatment effects were obtained mostly during the first 4 months.
Intervention participants improved in all psychosocial and On biological outcomes, there were modest improvements across
quality of life measures across the 12 months, with generally larger treatment groups, especially on 10-year CHD risk, but no indication
effect sizes at 4 months than at the 12 month assessment (Table 6). that the CASM/CASM+ interventions were superior to EUC. Finally,
However, partly due to improvements in the EUC, no significant on the psychosocial variables, there was more variability in
differential treatment effects were found, with the exception of outcome patterns, but only one significant difference between
diabetes distress in complete-cases GEE analysis; the combined conditions across the 12-month period, in reductions in diabetes
88 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92

Table 5
Baseline, 4-month, and 12-month biological outcomes (estimated means and SEs).

Baseline 4 Months 12 Months Condition Time CT

(M  SE) (M  SE) (M  SE) (chi-square) (chi-square) (chi-square)

I. Control vs. CASM/CASM+

A. Intention to treat
Body mass (kg/m2; range = 21–61) 0.01 0.73 1.13
Control 34.8  0.6 34.9  0.6 34.8  0.6
CASM/CASM+ 34.9  0.4 34.8  0.4 34.6  0.4
Effect size .17 .12
Hemoglobin A1c (%; range = 5–16) 0.03 10.54* 1.51
Control 8.16  0.16 8.02  0.14 8.04  0.14
CASM/CASM+ 8.14  0.10 8.00  0.09 8.16  0.09
Effect size .00 .11
Lipid ratio (total/HDL; range = 1–11) 2.98 10.21* 1.47
Control 3.81  0.09 3.68  0.0 3.77  0.08
CASM/CASM+ 3.99  0.06 3.88  0.06 3.88  0.06
Effect size .03 .09
BP MAP (mm Hg; range = 62–151) 0.19 11.11* 0.73
Control 96.0  1.0 94.8  0.9 93.4  0.9
CASM/CASM+ 95.1  0.6 94.4  0.6 93.6  0.6
Effect size .05 .09
10-Year CHD risk (%; range = 0–50) 0.51 17.20* 1.59
Control 8.46  0.49 8.10  0.48 8.17  0.48
CASM/CASM+ 9.07  0.38 8.41  0.34 8.51  0.38
Effect size .12 .09
B. Complete cases
Body mass (kg/m2; range = 21–61) 0.05 0.54 2.49
Control 34.8  0.6 34.9  0.6 34.9  0.6
CASM/CASM+ 34.8  0.4 34.7  0.4 34.6  0.4
Effect size .17 .12
Hemoglobin A1c (%; range = 5–16) 0.12 7.36* 0.77
Control 8.09  0.17 7.96  0.14 8.00  0.15
CASM/CASM+ 8.12  0.10 7.97  0.09 8.12  0.10
Effect size .02 .07
Lipid ratio (total/HDL; range = 1–11) 2.17 8.52* 0.65
Control 3.81  0.09 3.70  0.08 3.78  0.09
CASM/CASM+ 3.98  0.06 3.86  0.06 3.87  0.07
Effect size .02 .11
BP MAP (mm Hg; range = 62–151) 0.01 7.16* 1.45
Control 95.9  1.0 94.6  0.9 93.2  1.0
CASM/CASM+ 95.2  0.6 94.5  0.6 94.2  0.7
Effect size .05 .14
10-Year CHD risk (%; range = 0–50) 0.52 11.02* 0.71
Control 8.66  0.54 8.26  0.53 8.21  0.54
CASM/CASM+ 9.13  0.41 8.58  0.37 8.80  0.42
Effect size .06 .04

II. CASM vs. CASM+

A. Intention to treat
Body mass (kg/m2; range = 21–61) 1.30 3.20 0.10
CASM 34.4  0.5 34.4  0.5 34.2  0.5
CASM+ 35.3  0.5 35.2  0.5 35.1  0.6
Effect size .04 .00
Hemoglobin A1c (%; range = 5–16) 1.21 15.70* 0.68
CASM 8.03  0.14 7.89  0.13 8.10  0.14
CASM+ 8.26  0.13 8.10  0.12 8.23  0.13
Effect size .02 .09
Lipid ratio (total/HDL; range = 1–11) 1.01 11.87* 1.43
CASM 3.94  0.09 3.84  0.09 3.79  0.08
CASM+ 4.03  0.09 3.92  0.08 3.97  0.10
Effect size .01 .14
BP MAP (mm Hg; range = 62–151) 0.17 5.59 2.67
CASM 95.2  0.8 94.5  0.8 92.8  0.7
CASM+ 95.0  0.8 94.3  0.8 94.4  0.9
Effect size .00 .15
10-Year CHD risk (%; range = 0–50) 0.39 27.06* 3.63
CASM 9.43  0.59 8.54  0.49 8.66  0.55
CASM+ 8.69  0.48 8.28  0.46 8.35  0.51
Effect size .20 .15
B. Complete cases
Body mass (kg/m2; range = 21–61) 1.01 4.82 0.33
CASM 34.5  0.5 34.4  0.5 34.3  0.5
CASM+ 35.2  0.5 35.1  0.5 34.9  0.6
Effect size .00 .05
Hemoglobin A1c (%; range = 5–16) 1.04 12.90* 2.42
CASM 7.98  0.15 7.89  0.14 8.07  0.16
CASM+ 8.28  0.14 8.05  0.14 8.18  0.14
Effect size .18 .17
 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92 89

Table 5 (Continued )

Baseline 4 Months 12 Months Condition Time CT

(M  SE) (M  SE) (M  SE) (chi-square) (chi-square) (chi-square)

Lipid ratio (total/HDL; range = 1–11) 0.93 12.08* 0.18

CASM 3.94  0.08 3.80  0.08 3.82  0.08
CASM+ 4.03  0.09 3.92  0.09 3.93  0.11
Effect size .05 .03
BP MAP (mm Hg; range = 62–151) 0.24 2.32 2.34
CASM 95.4  0.8 94.6  0.9 93.3  0.9
CASM+ 95.1  0.8 94.4  0.9 95.1  1.1
Effect size .01 .18
10-Year CHD risk (%; range = 0–50) 0.96 11.89* 3.29
CASM 9.66  0.64 8.81  0.53 9.10  0.60
CASM+ 8.57  0.51 8.31  0.51 8.47  0.58
Effect size .25 .15

Note: Based on GEE analysis results comparing long-term treatment effects on outcome measures from baseline to 4 and 12 months, and covarying age, education, Latino
ethnicity, and gender at baseline, which were found in univariate analyses to be related to outcomes at baseline. DF(condition) = 1; DF(time) and DF(condition  time) = 2.
CASM/CASM+ = computer-assisted self-management intervention. Ranges were calculated from the present dataset.
*
Significant at p < .05 or less.

Table 6
Baseline, 4-month, and 12-month psychosocial and quality of life outcomes (estimated means and SEs).

Baseline 4 Months 12 Months Condition Time CT

(M  SE) (M  SE) (M  SE) (chi-square) (chi-square) (chi-square)

I. Control vs. CASM/CASM+

A. Intention to treat
Self-efficacy 3.25a 6.00* 3.70
(score; range = 1/low efficacy 14/high
efficacy)
Control 6.90  .14 6.69  .15 6.91  .16
CASM/CASM+ 7.02  .10 7.09  .09 7.22  .09
Effect size .19 .13
Problem solving 2.35 54.53* 2.61
(score; range = 1/low skill 5/high skill)
Control 2.95  .06 3.03  .06 3.18  .06
CASM/CASM+ 2.99  .04 3.17  .04 3.29  .04
Effect size .16 .10
Supportive resources (score; range = 1/low 2.37 4.76a 1.37
support 5/high support)
Control 1.91  .05 1.93  .06 1.94  .06
CASM/CASM+ 1.97  .04 2.05  .04 2.04  .04
Effect size .12 .08
General health state (score; range = 10/poor 0.00 13.80* 0.45
health 100/excellent health)
Control 68.7  1.4 71.7  1.3 71.1  1.4
CASM/CASM+ 69.0  1.0 71.7  0.9 70.5  1.0
Effect size .02 .06
Diabetes distress (score; range = 1/low 0.17 46.91* 5.47a
distress 6/high distress)
Control 3.00  .11 2.87  .10 2.72  .10
CASM/CASM+ 3.08  .07 2.71  .06 2.66  .06
Effect size .23 .14
B. Complete cases
Self-efficacy (score; range = 1/low 2.16 5.87 4.51a
efficacy 14/high efficacy)
Control 6.94  .15 6.69  .17 6.97  .18
CASM/CASM+ 7.00  .10 7.10  .10 7.22  .10
Effect size .24 .13
Problem solving (score; range = 1/low 1.08 43.39* 3.64
skill 5/high skill)
Control 2.98  .07 3.04  .07 3.21  .06
CASM/CASM+ 2.99  .04 3.19  .04 3.26  .04
Effect size .23 .06
Supportive resources (score; range = 1/low 2.94a 4.05 3.66
support 5/high support)
Control 1.93  .06 1.92  .06 1.94  .06
CASM/CASM+ 1.97  .03 2.05  .04 2.08  .04
Effect size .18 .20
General health state (score; range = 10/poor 0.17 46.91* 5.47a
health 100/excellent health)
Control 68.5  1.5 70.8  1.5 70.9  1.5
CASM/CASM+ 69.0  1.0 71.4  1.0 70.5  1.1
Effect size .01 .06
Diabetes Distress (Score; range = 1/low 0.01 42.54* 6.26*
distress 6/high distress)
Control 2.96  .11 2.85  .11 2.63  .11
90 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92

Table 6 (Continued )

Baseline 4 Months 12 Months Condition Time CT

(M  SE) (M  SE) (M  SE) (chi-square) (chi-square) (chi-square)

CASM/CASM+ 3.07  .07 2.69  .07 2.64  .07

Effect size .26 .10

II. CASM vs. CASM+

A. Intention to treat
Self-efficacy (score; range = 1/low 7.92* 7.01* 4.70a
efficacy 14/high efficacy)
CASM 7.35  .15 7.25  .12 7.41  .11
CASM+ 6.68  .14 6.93  .13 7.02  .13
Effect size .24 .19
Problem solving (score; range = 1/low 2.49 59.04* 3.00
skill 5/high skill)
CASM 3.08  .06 3.22  .05 3.31  .05
CASM+ 2.90  .06 3.12  .06 3.26  .06
Effect size .13 .19
Supportive resources (score; range = 1/low 2.19 8.69* 2.51
support 5/high support)
CASM 2.05  .05 2.09  .05 2.07  .06
CASM+ 1.90  .04 2.01  .05 2.01  .05
Effect size .14 .18
General health state (score; range = 10/poor 4.97* 8.89* 0.72
health 100/excellent health
CASM 70.8  1.3 73.9  1.2 71.9  1.3
CASM+ 67.1  1.5 69.5  1.4 69.0  1.5
Effect size .04 .05
Diabetes distress (score; range = 1/low 7.16* 67.25* 2.93
distress 6/high distress)
CASM 2.88  .10 2.58  .09 2.55  .08
CASM+ 3.29  .10 2.84  .09 2.78  .09
Effect size .15 .18
B. Complete cases
Self-efficacy (score; range = 1/low 10.20* 5.60a 1.22
efficacy 14/high efficacy)
CASM 7.33  .15 7.33  .14 7.49  .13
CASM+ 6.68  .14 6.88  .14 6.97  .14
Effect size .14 .09
Problem solving (score; range = 1/low 1.62 44.19* 4.41
skill 5/high skill)
CASM 3.07  .06 3.25  .06 3.25  .06
CASM+ 2.92  .06 3.12  .06 3.26  .06
Effect size .03 .24
Supportive resources (score; range = 1/low 2.45 10.65* 1.31
support-5/high support)
CASM 2.04  .05 2.10  .05 2.11  .06
CASM+ 1.90  .04 2.00  .05 2.05  .06
Effect size .08 .16
General health state (score; range = 10/poor 4.09* 6.49* 1.41
health 100/excellent health)
CASM 70.6  1.3 73.8  1.3 71.7  1.6
CASM+ 67.3  1.5 69.0  1.6 69.2  1.6
Effect size .09 .05
Diabetes distress (score; range = 1/low 5.88* 58.23* 1.97
distress 6/high distress)
CASM 2.88  .10 2.59  .10 2.49  .09
CASM+ 3.27  .10 2.79  .10 2.78  .10
Effect size .18 .10

Note: Based on GEE analysis results comparing long-term treatment effects on outcome measures from baseline to 4 and 12 months, and covarying age, education, Latino
ethnicity, and gender at baseline, which were found in univariate analyses to be related to outcomes at baseline. DF(condition) = 1; DF(time) and DF(condition  time) = 2.
CASM/CASM+ = computer-assisted self-management intervention. Ranges were calculated from the present dataset.
*
Significant at p < .05 or less.
a
p < (or = ) .10.

distress. These patterns were similar across both complete-cases may be that a considerably more intensive (and costly) interven-
and intent-to-treat imputation analyses. In no analysis did the tion, such as in the DPP [48], is required to improve upon this basic
CASM+ condition improve significantly more than the CASM set of supportive conditions to produce generalizable effects that
condition, a largely self-administered web-based intervention. produce improvement beyond these components and extraneous
The general lack of treatment effects, with the exception of factors. The website use data support this interpretation as they
behavioral outcomes and diabetes distress, suggests that it is demonstrate decreasing website usage over time, despite the
difficult to improve upon reasonably good ‘‘enhanced usual care’’ addition of the modest additional contacts in the CASM+
that included regular assessments, personalized (albeit comput- condition.
er-facilitated) attention, periodic feedback on health behaviors, It is also possible that our decision to select heterogeneous
and access to health plan and community resources in the context patients, to be more similar to those seen in practice, rather than
of an organized care system that had prioritized diabetes care. It only those needing in improvement on our primary health
 R.E. Glasgow et al. / Patient Education and Counseling 87 (2012) 81–92 91

behavior outcomes or on HbA1c, along with attrition limited our 4.1. Practical implications
ability to detect intervention effects.
However, as Schillinger and colleagues demonstrated [2], an Website developers, program implementers, and future re-
increase in the frequency of interactive technology-based strate- search should explore whether a computer-tailored self-manage-
gies linked to nurse care management (i.e., weekly automated ment intervention that is part of the patient health record,
telephone self-management sessions over 9 months) CAN result in delivered through a patient personal health record portal, with
moderate effect sizes across indicators of patient perceptions of patient goals and progress more visible to providers would
their care, quality of life, and behavioral self-management skills. produce stronger results than the present intervention. An
Further, the automated telephone self-management intervention important direction for future research and practice is to identify
in their study was superior to monthly group medical visits across ways to strengthen the sustainability of the Internet intervention
a number of behavioral outcomes. These findings, coupled with our without adversely impacting its reach or substantially increasing
own, suggest that investigation into the necessary frequency and costs [52]. Such approaches might include innovations to more
duration of interactive technology interventions like CASM, and strongly integrate the intervention with primary care or to make
the optimal and most cost-effective balance between human- and the intervention more mobile and available to participants
computer-delivered content, remains a ripe area for future throughout their day [53]. Additional evaluations are also needed
research [17,18]. to evaluate cost-effectiveness, and to understand the linkages
It was encouraging that relapse between the 4- and 12-month between intervention and maintenance processes and outcomes.
assessments was modest, despite no in-person contact between
these assessments. When combined with the results of a parallel
Conflict of interest
randomized study, comparing in-person brief diabetes self-
management education to a mailed DVD intervention with
All authors declare no conflicts of interest.
different but highly similar type 2 diabetes patients from this
same health plan [49], we conclude that automated and computer-
assisted interventions are appealing to diabetes patients, offer a Acknowledgment
number of advantages in terms of accessibility and convenience,
and can produce improvements in behavioral, biologic, quality of This study was supported by grant DK35524 from the National
life, and psychosocial outcomes. For many patients, however, a Institute of Diabetes and Digestive and Kidney Diseases.
more intensive, longer, or a substantially different type of
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