An Interactive Simulation to Change Outcome Expectancies and Intentions in Adults With Type 2 Diabetes: Within-Subjects Experiment

Gibson, Bryan; Yingling, Leah; Bednarchuk, Alisa; Janamatti, Ashwini; Oakley-Girvan, Ingrid; Allen, Nancy A.

doi:10.2196/diabetes.8069

Cited by 9 publications

(4 citation statements)

References 30 publications

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“… 21 Furthermore, Wu et al 22 reported a significant association with better self-management (diet, exercise, medication, blood sugar, and foot care over the past 7 days). Other studies by Williams and Bond 23 and Gibson et al 24 reported a similar relationship. When we consider outcome expectancy as a proxy for expected utility, there is a clear link between time preference and outcome expectancy in economic theory.…”

Section: Introductionsupporting

confidence: 53%

Time preference, outcome expectancy, and self-management in patients with type 2 diabetes

Karl¹,

Holle²,

Schwettmann³

et al. 2018

PPA

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BackgroundPatient self-management is crucial to prevent complications and mortality in type 2 diabetes. From an economic perspective, time preference predicts short-sighted decision making and thus might help to explain non-adherence to self-anagement recommendations. However, recent studies on this association have shown mixed results.PurposeIn this study, we tested whether the combination of time preference and outcome expectancy can improve the predictions of self-management behavior.Patients and methodsData from 665 patients with type 2 diabetes were obtained from the cross-sectional KORA (Cooperative Health Research in the Region of Augsburg) GEFU 4 study. Time preference and outcome expectancy were measured by one question each, which were answered on a 4-point Likert scale. Their association with six self-managing behaviors was tested in logistic and linear regression analyses. Likewise, we examined the association between self-management and the interaction of outcome expectancy and time preference.ResultsA high time preference was associated with a significantly lower sum of self-management behaviors (β=−0.29, 95% CI [−0.54, −0.04]). Higher outcome expectancy was associated with a higher self-management score (β=0.21, 95% CI [−0.03, 0.45]). The interaction model showed that low time preference was only associated with better self-management when combined with a high outcome expectancy (β=0.05, 95% CI [−0.28, 0.39] vs β=0.27, 95% CI [−0.09, 0.63]).ConclusionTime preference and outcome expectancy are interrelated predictors of patient self-management and could be used to identify and to intervene on patients with a potentially poor self-management.

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Section: Introductionsupporting

confidence: 53%

Time preference, outcome expectancy, and self-management in patients with type 2 diabetes

Karl¹,

Holle²,

Schwettmann³

et al. 2018

PPA

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“…These additional features that support sensemaking should be offered on self-monitoring devices in real-time to help patients understand their data and draw accurate conclusions that lead to behavior change and increased awareness. Recent work has shown that simulated data demonstrating the acute effect of PA on blood glucose may even be enough to change PA-related outcome expectancies and behavioral intentions among adults with T2DM [24].…”

Section: Discussionmentioning

confidence: 99%

An Evaluation of Digital Health Tools for Diabetes Self-Management in Hispanic Adults: Exploratory Study

Yingling¹,

Allen²,

Litchman³

et al. 2019

JMIR Diabetes

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Background Although multiple self-monitoring technologies for type 2 diabetes mellitus (T2DM) show promise for improving T2DM self-care behaviors and clinical outcomes, they have been understudied in Hispanic adult populations who suffer disproportionately from T2DM. Objective The objective of this study was to evaluate the acceptability, feasibility, and potential integration of wearable sensors for diabetes self-monitoring among Hispanic adults with self-reported T2DM. Methods We conducted a pilot study of T2DM self-monitoring technologies among Hispanic adults with self-reported T2DM. Participants (n=21) received a real-time continuous glucose monitor (RT-CGM), a wrist-worn physical activity (PA) tracker, and a tablet-based digital food diary to self-monitor blood glucose, PA, and food intake, respectively, for 1 week. The RT-CGM captured viewable blood glucose concentration (mg/dL) and PA trackers collected accelerometer-based data, viewable on the device or an associated tablet app. After 1 week of use, we conducted a semistructured interview with each participant to understand experiences and thoughts on integration of the data from the devices into a technology-facilitated T2DM self-management intervention. We also conducted a brief written questionnaire to understand participants’ self-reported T2DM history and past experience using digital health tools for T2DM self-management. Feasibility was measured by device utilization and objective RT-CGM, PA tracker, and diet logging data. Acceptability and potential integration were evaluated through thematic analysis of verbatim interview transcripts. Results Participants (n=21, 76% female, 50.4 [SD 11] years) had a mean self-reported hemoglobin A 1c of 7.4 [SD 1.8] mg/dL and had been diagnosed with T2DM for 7.4 [SD 5.2] years (range: 1-16 years). Most (89%) were treated with oral medications, whereas the others self-managed through diet and exercise. Nearly all participants (n=20) used both the RT-CGM and PA tracker, and 52% (11/21) logged at least one meal, with 33% (7/21) logging meals for 4 or more days. Of the 8 possible days, PA data were recorded for 7.1 [SD 1.8] days (range: 2-8), and participants averaged 7822 [SD 3984] steps per day. Interview transcripts revealed that participants felt most positive about the RT-CGM as it unveiled previously unknown relationships between lifestyle and health and contributed to changes in T2DM-related thoughts and behaviors. Participants felt generally positive about incorporating the wearable sensors and mobile apps into a future intervention if support were provided by a health coach or health care provider, device training were provided, apps were tailored to their language and culture, and content were both actionable and delivered on a single platform. Conclusions Sensor-based tools for facilitating T2DM self-monitoring appear to be a feasible and acceptabl...

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“…S1; ref. 33) that demonstrated acute PA (i.e., walking)-related glucose reduction. Next, study staff worked with the participants to calculate their targeted heart rate zone (sex and age adjusted) for light-, moderate-, and high-intensity PA.…”

Section: Pa Education Sessionmentioning

confidence: 99%

Using Continuous Glucose Monitoring to Motivate Physical Activity in Overweight and Obese Adults: A Pilot Study

Liao

Basen-Engquist

Urbauer

et al. 2020

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: Regular physical activity (PA) is associated with a lower risk of several types of cancers. However, two-thirds of overweight/obese adults are not sufficiently active; this, in combination with the unfavorable effect of excess body weight, puts them at a greater risk for cancer. One reason that these individuals do not engage in enough PA may be their lack of motivation to change their current behavior due to the perception of putting in effort for possible future gain without obvious short-term benefits. There is a need for innovative ways to help individuals recognize the immediate health benefits of PA and thus increase their motivation.Methods: This pilot intervention tested a PA education module that included a one-on-one counseling session highlighting the acute effects of PA on glucose patterns, followed by a 10-day selfmonitoring period with a continuous glucose monitor (CGM) and a Fitbit tracker. Participants rated the acceptability of the education module on a 5-point Likert scale and completed surveys assessing stages of change for motivational readiness.Results: Nineteen overweight/obese adults (84% female) completed the study. Participants gave high ratings to the counseling session for improving their PA-related knowledge (mean ¼ 4.22), increasing motivation (mean ¼ 4.29), and providing personally relevant information (mean ¼ 4.35). The summary acceptability scores for the self-monitoring period were 4.46 for CGM and 4.51 for Fitbit. Participants reported a significant decrease in the precontemplation stage and an increase in the action stage (P < 0.05).Conclusions: CGM is a feasible tool for PA interventions. Impact: Information from CGM could be used as biologicalbased feedback to motivate PA.See all articles in this CEBP Focus section, "Modernizing Population Science."

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An Interactive Simulation to Change Outcome Expectancies and Intentions in Adults With Type 2 Diabetes: Within-Subjects Experiment

Cited by 9 publications

References 30 publications

Time preference, outcome expectancy, and self-management in patients with type 2 diabetes

Time preference, outcome expectancy, and self-management in patients with type 2 diabetes

An Evaluation of Digital Health Tools for Diabetes Self-Management in Hispanic Adults: Exploratory Study

Using Continuous Glucose Monitoring to Motivate Physical Activity in Overweight and Obese Adults: A Pilot Study

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