This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
Regular walking is a recommended but underused self-management strategy for individuals with type 2 diabetes mellitus (T2DM).
To test the impact of a simulation-based intervention on the beliefs, intentions, knowledge, and walking behavior of individuals with T2DM. We compared two versions of a brief narrated simulation. The experimental manipulation included two components: the presentation of the expected effect of walking on the glucose curve; and the completion of an action plan for walking over the next week. Primary hypotheses were (1) intervention participants’ walking (minutes/week) would increase more than control participants’ walking, and (2) change in outcome expectancies (beliefs) would be a function of the discrepancy between prior beliefs and those presented in the simulation. Secondary hypotheses were that, overall, behavioral intentions to walk in the coming week and diabetes-related knowledge would increase in both groups.
Individuals were randomly assigned to condition. Preintervention measures included self-reported physical activity (International Physical Activity Questionnaire [IPAQ] 7-day), theory of planned behavior-related beliefs, and knowledge (Diabetes Knowledge Test). During the narrated simulation we measured individuals’ outcome expectancies regarding the effect of exercise on glucose with a novel drawing task. Postsimulation measures included theory of planned behavior beliefs, knowledge, and qualitative impressions of the narrated simulation. The IPAQ 7-day was readministered by phone 1 week later. We used a linear model that accounted for baseline walking to test the main hypothesis regarding walking. Discrepancy scores were calculated between the presented outcome and individuals’ prior expectations (measured by the drawing task). A linear model with an interaction between intervention status and the discrepancy score was used to test the hypothesis regarding change in outcome expectancy. Pre–post changes in intention and knowledge were tested using paired
Of 65 participants, 33 were in the intervention group and 32 in the control group. We excluded 2 participants from analysis due to being extreme outliers in baseline walking. After adjustment for baseline difference in age and intentions between groups, intervention participants increased walking by 61.0 minutes/week (SE 30.5,
This study suggests that a brief, Internet-ready, simulation-based intervention can improve knowledge, beliefs, intentions, and short-term behavior in individuals with T2DM.
Type 2 diabetes mellitus (T2DM) affects approximately 24 million people in the United States, and is associated with significant morbidity and early mortality [
There are many reasons why individuals with T2DM may not perform an appropriate self-management behavior such as being active. In this study we used a brief, narrated simulation to address two factors that we believe are amenable to an informatics intervention: inaccurate mental models of the effects of behavior on the disease [
The intervention in this study was based on simulated glucose curves. Glucose curves represent an individual’s variation in plasma glucose through a day. Prior work suggests that glucose curves may be useful as an interface for educational and motivational interventions. Small trials of participants with type 1 diabetes have shown that classroom education using simulated glucose curves positively affects knowledge [
According to the theory of planned behavior, an individual’s intention to perform a behavior is a function of their beliefs. In this study we focused on a particular type of belief:
Prior work has shown that outcome expectancies are related to self-care behaviors in individuals with T2DM [
While the beliefs included in the theory of planned behavior have been shown to predict the intentions of individuals with T2DM to be physically active [
The intervention version of our simulation guided participants through writing an action plan for walking while concurrently mentally simulating the planned behavior. In this plan participants indicated where, when, with whom, and for how long they would walk for each day in the next week.
Our hypotheses in this trial were that (1) individuals viewing the intervention version of the narrated simulation would report more walking in the subsequent week than control participants would, and (2) changes in outcome expectancies for intervention participants would vary as a function of the discrepancy between the effect presented in the simulation and the individual’s prior beliefs. Finally, we hypothesized that, overall, both groups would increase their behavioral intentions to walk in the subsequent week and their diabetes-related knowledge.
We recruited participants between March 2010 and August 2011 at the George E. Whalen Department of Veterans Affairs Medical Center (Salt Lake City, UT, USA) in primary care clinics, diabetes education and weight management classes, a biweekly diabetes exercise group at the University of Utah, a community diabetes health fair, and via an email to a diabetes-related listserv.
Our inclusion criteria were that participants be between 30 and 70 years of age, have a diagnosis of T2DM, and be able to speak English fluently. Participants with a diagnosis of dementia or severe mental disease, using insulin, or having microvascular or macrovascular complications of diabetes were excluded. The rationale for these last two criteria was 2-fold: first, the content of the narrated simulation is geared toward individuals taking oral medications, and second, we wanted to minimize the risk of walking-induced hypoglycemia, foot ulceration, or a cardiac event. Initial recruitment efforts were exclusively among veterans at the Salt Lake City Veterans Administration Healthcare System, aged 40–60 years; however, due to slow recruitment, in June 2010 we expanded recruitment to the larger community and a wider age range.
The study was conducted in a location convenient to the participant. These locations included the Salt Lake City VA library, a room adjacent to the exercise room at the diabetes exercise group, a table at a diabetes health fair, a meeting room at a public library, and a private office. All meetings were between the principal investigator (BG) and individual participants.
The narrated simulation is based on simulated glucose curves [
Participants were shown one of two versions of the simulation. The intervention version and the control version were identical through the first 8 minutes and 30 seconds (
Concepts included in the narrated simulation and their timing.
Concept | Timing (minutes |
What is the glucose curve? | 1:40 |
When is blood sugar highest and when is it lowest? | 0:20 |
How do meals affect the glucose curve? | 0:30 |
What is the dawn phenomenon? | 0:30 |
What is the safe range of blood sugar? | 0:40 |
What is hemoglobin A1c? | 0:15 |
How does the blood sugar curve change (over years) as A1c increases? | 1:40 |
Why is high blood sugar bad for you? (Includes photographs of individuals with microvascular complications) | 1:40 |
How are changes in A1c associated with complications? | 0:20 |
What can you do today to control your blood sugar? | 0:35 |
Procedures in the simulation for the intervention and control groups. Boxes with a gray background show intervention-specific components. Duration is in minutes and seconds.
At this point in the narrated simulation, participants were shown a glucose curve of an individual “who has had diabetes for a few years,” and the voiceover asked them to imagine that the curve was their glucose curve from yesterday. Using a paper copy of the curve on the screen (
Simulated glucose curve used in the drawing task.
The control version of the narrated simulation ended after the two drawing tasks. In the intervention version of the narrated simulation, after completing each drawing task, viewers were shown the expected change in the curve. They were then guided by the voiceover to complete a paper plan of their walking over the next week: how many days they would walk, on which days they would walk, how long each walk would be, in what location they would walk, at what time of day, with whom, and any preparatory actions they would take to facilitate the plan (eg, put walking shoes in their car) (
Walking plan to be completed by intervention participants.
We hypothesized that two components of the simulation might increase behavioral intentions for both groups. First, in the elicitation of individual’s outcome expectancies via the drawing task, the potential outcome of exercise is framed as an upward counterfactual (how could things have been better: “what
After obtaining informed consent from the participants, we collected the following measures: (1) demographic information (
Participants then watched the narrated simulation on a laptop computer while wearing headphones. During the narrated simulation, all participants completed the two drawing tasks described above. To minimize demand effects, the investigator left the room while participants watched the animation; most questionnaires were administered by paper. However, since the IPAQ was going to be readministered by phone a week later, this questionnaire was administered orally by the investigator during the in-person meeting.
After participants watched the narrated simulation, the 14-item theory of planned behavior-related questionnaire and both diabetes-related knowledge tests were repeated. In addition, to measure the degree to which participants felt that the information in the animation was personally relevant, participants answered two 7-point Likert-type questions: “I think the glucose curves in the movie were related to
To conclude the in-person meeting, we asked participants about their qualitative impressions of the narrated simulation: what they liked and did not like, if there were parts of the simulation they found confusing, and if there were concepts they would like to see presented in this manner that were not included in the narrated simulation. These questions were administered orally.
We contacted participants by phone 1 week later and readministered the IPAQ measure of physical activity over the last 7 days [
We performed all analyses using R version 2.10.0, freely available statistical computing software [
To test our primary hypothesis (that the intervention version of the narrated simulation would more positively affect individuals’ walking), we used a linear model with intervention status and preanimation walking (minutes/week) as the covariates. We adjusted for significant between-group differences in age and a near-significant difference in baseline behavioral intent (see
To test our second hypothesis (that among intervention participants change in outcome expectancies [beliefs] would be a function of the discrepancy between prior beliefs and those presented in the narrated simulation), we first needed to calculate the change in outcome expectancy and then calculate a score reflecting the discrepancy between the presented outcome and the individual’s expected outcome. Once these scores were calculated, we used a linear model with an interaction between the discrepancy score and intervention status as a covariate after adjusting for age and baseline intent.
Outcome expectancies were measured using the following questions on the theory of planned behavior questionnaire: “Walking for at least 30 minutes will lower my blood sugar,” and “Walking for at least 30 minutes/day, 5 days a week
We calculated the outcome expectancy discrepancy score by measuring the difference between the presented change in the glucose curve and the individual’s outcome expectancy elicited in the drawing task. We scored each dimension of the individual’s outcome expectancy (direction, duration, and magnitude) according to whether the individual’s outcome expectation was negative, neutral, or positive. For example, if the decrease in the individual’s drawn curve was greater in magnitude than the decrease in the presented curve (positive expectancy), this dimension was scored 1. If the magnitude of the participant’s expectation was the same as the presented curve, the score was 0 (accurate understanding). If the drawn magnitude was less than the presented curve, the participant was scored –1 (negative expectancy). Since the direction of the change in the curve could only increase or decrease, individuals were scored 1 if their drawing reflected a decrease (a positive expectancy and accurate understanding) and –1 if their drawing reflected an increase in blood glucose postexercise (negative expectancy). The discrepancy score used in the regression is the sum of all the dimension scores for both drawing tasks with a possible range of –6 to 6.
To test our secondary hypotheses (that, overall, both versions of the narrated simulation would positively affect behavioral intentions and knowledge), we used paired
Finally, we conducted an exploratory analysis to inform future work by examining participants’ responses to the qualitative questions of what they liked and did not like in the narrated simulation, what they found confusing, and what they would like to see in future versions for recurrent themes. We also examined the proportion of individuals who reported thinking about the glucose curves in the next week and the context in which they reported thinking about them.
Distribution of outcome expectancy discrepancy scores.
Baseline characteristics of control and intervention groups.
Characteristic | Intervention group |
Control group |
|
|
|
.87 | |||
Male | 20 | 21 | ||
Female | 13 | 11 | ||
Veterans, na | 10 | 12 | .72 | |
Age (years), median (range)b | 56 (34–70) | 61 (36–70) | .02 | |
Years since diagnosis, median (range)b | 7 (.02–20) | 8.5 (.12–19) | .96 | |
Hemoglobin A1c, median (range)b | 7.0 (5.6–11.8) | 6.9 (6.1–10.3) | .63 | |
Diabetes numeracy (scale of 0–10), median (range)b | 8 (1–10) | 8 (2–10) | .34 | |
Frequency of self-monitoring (times/week), median (range)b | 5 (0.1–21) | 2.75 (0–21) | .13 | |
Have email?, na | 29 | 29 | .96 | |
Frequency of non-job email use (x/week), median (range)b | 14 (0–14) | 14 (0–14) | .65 | |
Have a personal health record?, na | 12 | 10 | .86 | |
Nonwalking physical activity (metabolic equivalents × minutes/week), median (range)b | 960 (0–8820) | 512 (0–8640) | .12 | |
Walking (minutes/week), median (range)b | 90 (0–1080) | 145(0–2100) | .27 | |
Knowledge (Diabetes Knowledge Test, scale of 0–14), median (range)b | 12 (5–14) | 12 (6–14) | .55 | |
Behavioral intention (scale of 1–7), median (range)b | 5 (1–7) | 6 (1–7) | .08 |
a Chi-square test.
b Kruskal-Wallis test.
Our first and most clinically significant hypothesis was supported: intervention participants increased walking time more than control participants. After taking into account baseline walking and adjusting for age and baseline behavioral intent, the mean effect of the intervention was an increase of 61.0 minutes (SE 30.5,
Change in walking by condition. Box: 1st-3rd quartile, whiskers: 1.5*interquartile range, circles: outliers.
Our second hypothesis was supported: among intervention participants, the discrepancy between the individuals’ prior beliefs and the presented outcomes was associated with their change in outcome expectancy. The coefficient for the interaction between intervention status and discrepancy score was –.25 (SE .07,
Our secondary hypotheses were also supported: both groups increased behavioral intentions, mean difference 0.66 on a scale of 7 (
Summary of hypotheses and results.
Hypothesis | Model | Coefficient | SE |
|
|
|
Walking will increase more in intervention participants | Linear model regressing postintervention walking on intervention status, preintervention walking adjusted for age and preintervention intent | 61.0 | 30.5 | 1.9 | 58 | .05 |
Among intervention participants, change in outcome expectancy will be a function of the discrepancy between prior beliefs and the presented outcome | Linear model regressing the change in outcome expectancy on an interaction term between intervention status and discrepancy score, adjusted for age and preintervention intent | –.25 | .07 | –3.213 | 57 | <.01 |
Summary of hypotheses and results
Hypothesis | Model | Mean difference |
|
|
|
Both group will increase in behavioral intention | Paired |
0.66 | 4.5 | 62 | <.001 |
Both groups will increase in diabetes-related knowledge | Paired |
0.38 | 2.4 | 62 | .02 |
Means (SD) for all outcome measures pre- and postintervention.
Outcome measure | Intervention status | Pre intervention | Postintervention |
Walking (minutes) | Intervention | 182.9 (245) | 230.3 (262) |
Control | 203.5 (203) | 185.6 (193) | |
Outcome expectancy (scale 1–7) | Intervention | 6.07 (1.1) | 6.56 (.82) |
Control | 6.37 (.89) | 6.69(.55) | |
Behavioral intent (scale 1–7) | Intervention | 4.79 (1.62) | 5.62 (1.80) |
Control | 5.53 (1.60) | 6.03 (1.24) | |
Knowledge (scale 1–14) | Intervention | 11.15 (2.3) | 11.71 (2.14) |
Control | 11.29 (1.95) | 11.48 (2.18) |
We coded responses to qualitative questions into general themes and determined the proportion of each theme. When asked “What were the things that you liked about the simulation?” 31/65 of participants’ responses were coded as
When asked “Were there things you did not like about the simulation?” most participants (52/65) answered “No.” Of those who provided specific negative feedback (13/65), 4 reported that the simulation contained “nothing new” or was “not interesting.” A total of 2 participants, both of whom worked nights and slept during the day, reported feeling that the content of the simulation was not relevant to them. In addition, 3 reported not liking the music or voiceover, 1 reported not liking the glucose curves, 1 reported not liking the drawing task, 1 reported not liking the numeracy test, and 1 thought the simulation was too slow in the beginning.
When asked “Were there parts of the simulation you found confusing or that brought up questions in your mind?” most participants (59/65) answered “No.” Of those who provided specific feedback, 3 reported finding the drawing task confusing and 2 reported not understanding the meaning of the curves.
When asked “Are there things that were not in the simulation that you would like to see in a simulation like this?” 9 participants commented they would like to see the effect of different foods on the glucose curve, 5 wanted more information about how the disease progresses over time and whether it is reversible, 4 commented that they would like to see numbers on the curves, 3 commented that they would like to see more answers to the test questions addressed in the narrated simulation (not all the questions on the knowledge tests were addressed in the simulation), 2 commented that they would like to see the effect of insulin, and 2 control participants wanted to see the effect of exercise on the curve.
Although there was a small difference in the proportion of individuals who reported thinking about the glucose curves in the week following the simulation by condition (27/33 intervention participants, 22/32 controls), this difference was not significant (χ2
1 = .88,
This study had two main findings. First, intervention participants who completed an action plan for walking in the next week reported significantly more walking in the subsequent week than control participants. This findings is congruent with a large number of both laboratory and clinical studies that have found a positive impact of implementation intentions and action plans [
Our second main finding was that intervention participants’ beliefs changed in accordance with the discrepancy between their prior beliefs and the outcomes presented in the simulation. The idea that computerized simulations could change outcome expectancies was suggested by Bandura in 1999 [
We are aware of only one other study involving glucose curves to promote physical activity among individuals with T2DM. Allen et al randomly assigned 52 individuals to one-on-one educational sessions [
The results of this study highlight the potential for the translation of specific evidence from the psychology literature into the design of informatics-based behavioral interventions. We used an action planning intervention to facilitate subsequent action in intervention participants. This technique holds great promise to facilitate health-related behaviors, particularly in mobile phone-based interventions. In fact, recent evidence has shown that sending text message reminders of planned actions further facilitates the desired action [
This study has several strengths. First, we employed prior findings in the psychological literature to design a brief, self-contained intervention and conducted a hypothesis-driven test of the efficacy of components of the intervention. Second, our use of glucose curves for both the presentation and elicitation of outcomes allowed for the measurement of individuals’ outcome expectancies across three dimensions: the magnitude, duration, and direction of the effect. We believe this method is superior to the more common Likert scale measures of belief, and that a computer-based version of this drawing task could further improve upon the discrepancy score used in this study. A limitation of the discrepancy score used in this study is that it does not account for differences in the magnitude and duration of the individual’s expectation (a larger discrepancy reflects a more inaccurate belief than a smaller discrepancy). A better measure of the discrepancy would be the difference in the area under the curve between the individual’s curve and the presented outcome. This was not feasible using the complex curves drawn on paper in this study, but a computer-based version of the drawing task could easily calculate this difference.
This study has limitations. First, our primary outcome measure, physical activity, was measured by self-report. Since all participants used the same measure, we do not believe this undermines the results; however, the true magnitude of the effect of our intervention on subsequent physical activity needs to be determined with objective measures in future work. Additionally, some of our participants did not represent the target population for this intervention: some participants possessed adequate diabetes-related numeracy, had positive outcome expectancies and intentions for exercise, were knowledgeable about their disease, and were already physically active. We plan to address this issue in the future by integrating the intervention into diabetes education classes in target populations, particularly groups with newly diagnosed T2DM and low diabetes numeracy. The third limitation of this study was that the tests used to measure knowledge were not well aligned with the simulation’s presentation of content. We developed the simulation around gists we considered important based on theory [
The next generation of this intervention will test the effectiveness of personalizing the feedback provided in an interactive phone-based intervention. A phone-based intervention may facilitate integration of the simulation into the user’s daily life, may be easier to access than traditional diabetes education, which reaches a limited population [
In this study we tested a simple form of a computer-based simulation. Participants’ outcome expectancies changed in accordance with the discrepancy between their prior beliefs and the presented outcomes. In combination with action planning, the simulation positively affected short-term behavior.
Intervention simulation.
Control simulation.
Theory of Planned Behavior Questionnaire.
International Physical Activity Questionnaire
type 2 diabetes mellitus
None declared.