This clinical trial evaluates the usefulness of a self-monitoring platform for tracking medication safety events and concerns in patients with lung, colorectal, breast, and prostate cancer. Patients receiving oral anticancer agents often encounter challenges in managing complex treatment regimens, potentially life-threatening toxicities, and drug-drug and drug-food interactions at home. To achieve the goal of medication safety, they need to become "vigilant partners" in medication and toxicity self-monitoring, including timely reporting of medication events to clinicians when their care transitions back home. In this study, patients use an online self-monitoring platform to track their experiences or concerns about taking their medications, including their experiences with symptoms. This platform may be a useful way for patients to track problems they have when taking their medications at home and may help them take better care of their health.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Enrollment
80
University of Michigan Comprehensive Cancer Center
Ann Arbor, Michigan, United States
RECRUITINGSelf-reported system usability
Self-reported system usability will be assessed using the 10-item System Usability Scale. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 6 months
Perceived usefulness
Qualitative data, including brief interview data will be analyzed and summarized through content analysis.
Time frame: Up to 6 months
Ease of use
Qualitative data, including brief interview data will be analyzed and summarized through content analysis.
Time frame: Up to 6 months
Attitudes toward use
Qualitative data, including brief interview data will be analyzed and summarized through content analysis.
Time frame: Up to 6 months
Intention to use and continuous use of the system over time
Qualitative data, including brief interview data will be analyzed and summarized through content analysis.
Time frame: Up to 12 months
Self-reported patient engagement
Self-reported patient engagement will be measured by the 9-item Twente Engagement with Ehealth Technologies Scale (TWEETS), including three dimensions, behavioral engagement, cognitive engagement, and affective engagement. Will use the overall TWEETS score to evaluate patient engagement in this study, rather than analyzing each dimension separately. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables. Will use repeated measures analysis of variance to assess the changes in mean scores of patient engagement. Univariate associations between baseline personal and clinical factors and patient engagement at each time point will be assessed using correlation analyses (Pearson or Spearman), Chi-square tests, or non-parametric Mann-Whitney U tests, depending on the variable types. Multiple linear regressions will be conducted to assess associations between patient engagement and outcomes.
Time frame: Up to 6 months
System usage over time
Will be objectively recorded by the system logs and analyzed using Google Analytics (dashboard only), including parameters like the number of logins, number of tab pages clicked, number of events or concerns self-tracked, and more. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 12 months
Patient activation levels
Patient activation levels will be measured by the Short Form of Patient Activation Measure. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 6 months
Medication self-management ability
Medication self-management ability will be measured by the Measure of Medication Self-Management. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 6 months
Symptom distress
Symptom distress will be measured by the 19-item MD Anderson Symptom Inventory. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 6 months
Health related quality of life
Health-related quality of life will be measured by the 27-item Functional Assessment of Cancer Therapy - General. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 6 months
Number of emergency room visits
Will be measured by both self-report and chart review. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 12 months
Number of hospitalizations
Will be measured by both self-report and chart review. Will use descriptive statistics, such as mean and standard deviation for continuous variables, and frequency and percentage for categorical variables.
Time frame: Up to 12 months
User experiences with the system
Participants' user experiences with the system, such as perceived facilitators or barrios to system use, along with their suggestions for system improvement, will be collected through a brief individual interview. Qualitative data, including brief interview data will be analyzed and summarized through content analysis.
Time frame: Up to 6 months
Self-tracked, structured medication safety events or concerns
To understand the nature and causes of events and concerns, self-tracked, structured medication safety events or concerns tracking data from the system, such as the number and types of events or concerns self-tracked, will be summarized using descriptive statistics. The free-text tracking data from the system will be analyzed and summarized through content analysis or natural language processing techniques, depending on data volume.
Time frame: Up to 12 months
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