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Clinical Trial Summary

This study evaluates a sensor-controlled digital game (SCDG) to motivate self-management behaviors of weight monitoring and physical activity in older adults with heart failure (HF). Half of the participants will receive the SCDG app and weight monitoring and physical activity sensors and the other half will receive only the weight monitoring and physical activity sensors.

Clinical Trial Description

The primary goal of this study is to obtain initial efficacy data and undertake a comprehensive feasibility assessment of a SCDG intervention that synchronizes with a Bluetooth-enabled weight scale and activity tracker to activate game rewards and feedback based on older adult heart failure (HF) participants' real-time weight monitoring and exercise behaviors.

The SCDG will involve a narrative, the goal of which is to help an avatar in the game avoid rehospitalization by using game points, earned via the participant's real-time behaviors, in game tasks that help maintain the avatar's optimal HF health status. Real-time behaviors of weight-monitoring and physical activity will be tracked by an off-the-shelf sensors and app (Withings). The data from the Withings sensors will then be routed to our SCDG app. The digital game paired with sensors will enable objective tracking of real-time behaviors such as physical activity, and weight monitoring, and provide personalized, contextually relevant feedback (e.g., reduce fluid intake or call doctor for weight gain) to motivate engagement in and generate habit formation of heart failure related self-management behaviors. The goal of this proposal is to demonstrate the feasibility of the SCDG concept with an optimal number of sensors in a small study so as to lay a foundation for scaling this concept to include more relevant sensors in longer, larger studies.

In this study, the initial efficacy of the SCDG intervention for primary outcome of rate of engagement in HF self-management behavior of weight-monitoring and secondary outcomes of physical activity engagement, HF self-management-knowledge, and self-efficacy, HF-functional status, hospitalization, cognitive ability, depression and quality of life will be evaluated. For this study, 44 older adults diagnosed with the New York Heart Association's HF classification I to III from out-patient HF settings in central Texas will be recruited, and randomized to either the SCDG intervention group that will receive sensors tracking weight monitoring and activity and play the SCDG on a mobile smartphone for 12 weeks or a control group that will receive sensors and an app tracking activity and weight monitoring, and standardized written HF educational modules.

Sample size and statistical power. Based on a 1:1 randomization scheme (intervention : control) with 80% power (5% alpha), to detect a difference of 80% versus 50% (intervention and control group) of daily monitoring of weight monitoring and physical activity behaviors would require a sample size of 44 patients per group. 80% was chosen as the cut-off for adequate weight-monitoring at 12 weeks, because HF patients who completed ≥80% of weight diaries were found to have significantly reduced odds for HF-related hospitalizations compared with patients who completed <80% of weight diaries. Also, only 50% of participants in the remote monitoring sensor group recorded their weights >50% of the time. Allowing for 10% attrition, 49 patients per group (N = 98) must be recruited for a fully powered clinical trial of the SCDG intervention for HF behavior of weight-monitoring. Although this study is underpowered, a sample size of 44 is sufficient for a feasibility study to provide findings that can inform implementation of a fully powered study with fewer problems to better test the effectiveness of the SCDG intervention.

Statistical analysis. Descriptive statistics will be computed for the study variables to assess for out-of-range values and variable distributions and to ensure that the data meet statistical assumptions (normality, independence, homogeneity of variance). Baseline characteristics will be compared between the intervention group (IG) and the control group (CG) arms using t-tests for continuous variables and chi-square for categorical variables to determine the balance between arms. Any variable not equivalent between groups at baseline, despite randomization, will be controlled for statistically. The IG and CG will be analyzed using intention-to-treat analysis. Mixed effects models will be used to compare trends in the effect of the SCDG intervention on the primary outcome of days with weight monitoring, and secondary outcomes of days with physical activity, average daily steps, energy expenditure, average distance of physical activity, empathy, HF self-management habits, motivation, knowledge, and self-efficacy, HF-related functional status, hospitalization, and QoL over the multiple data collection time points. Cronbach's α coefficients will be computed for each scale, with α ≥ .70 denoting optimal consistency. The effects of the biological variables of age and sex, and, if applicable, for the effects of other covariates—education level, race/ethnicity, depression, empathy and NYHA HF class will be controlled. Statistical significance will be at α = .05 for primary and secondary analyses. Standard deviation and error estimates will be used to calculate effect sizes for findings.

Missing data. Mixed models can accommodate missing data points often encountered in longitudinal datasets and can model nonlinear individual characteristics. If fewer than 5% of data are missing, the missing data will be replaced using mean substitution. For more extensive missing data, multiple imputation strategies for mixed models with sensitivity analyses to determine the effects of imputation will be utilized. ;

Study Design

Related Conditions & MeSH terms

NCT number NCT03947983
Study type Interventional
Source University of Texas at Austin
Contact Kavita Radhakrishnan, PhD
Phone 512-471-7936
Status Not yet recruiting
Phase N/A
Start date September 1, 2019
Completion date September 30, 2020

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