Reporting Patient Generated Health Data and Patient Reported Outcomes With Health Information Technology

Study Overview
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Brief Summary
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This study will assess the feasibility of using patient-centered, commercial off-the-shelf (COTS) health information technology (IT) solutions to collect patient generated health data (PGHD) and patient-reported outcomes (PROs) from diverse, low-income disadvantaged populations. These data will then be mapped and reported in a way that will allow them to be made actionable and used to improve health care quality and delivery. The data mapping will be designed for data collection through technology such as mobile apps and wearables, and will be intended to support integration into interoperable electronic health records (EHRs), clinical information systems, and big data infrastructures.

Detailed Description
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Patient engagement is particularly critical to achieving good chronic disease self-management. This is especially important for disadvantaged patients, who are disproportionately affected by chronic disease. A key component of chronic disease self-management is the ability for patients to record and monitor their ongoing performance on indicator measures. While health IT solutions have been shown to improve chronic disease self-management, adoption and use of costly, specialized technologies among disadvantaged patients is lower than among higher-income populations. In contrast, COTS technologies such as mobile phones are more accessible to and widely adopted by disadvantaged patients, thus bridging the gap of the digital divide. The central research hypothesis posits that 1) low-income, disadvantaged patients both can and will provide high quality PGHD and PROs through COTS-based health IT solutions, and 2) these data can be integrated into clinical systems and used to improve health care quality and delivery. PGHD can be collected through patient interaction with COTS health IT solutions such as mobile health apps and fitness trackers. PROs can be collected via patient response to questionnaire-based PROs measures, or PROMs. These data can be transmitted to clinical information systems, integrated into clinical workflows and used by providers to improve health care quality and delivery. Using a sequential integrated mixed-methods approach, we propose to test the central hypothesis through three specific aims, as follows: Aim 1: To assess the needs and preferences of disadvantaged patients and safety net health care providers regarding the use of health IT for communicating PGHD and PROs. Aim 1 Research Questions: What specific features in COTS solutions meet the needs and preferences of disadvantaged patients for communicating PGHD and PROs to their providers? What PGHD and PROs are deemed most important by providers and patients for improving health care and health outcomes? Answering these questions will inform health IT solution selection, design, usability, and utility; assist with prioritizing PGHD and PROs collection by data element and measure type; and identify potential discrepancies between patients' and providers' perceptions of PGHD and PROs importance. Aim 2: To demonstrate the feasibility of PGHD and PROs collection through COTS health IT solutions in a patient-centered pilot intervention for weight management among disadvantaged patients. Aim 2 Hypothesis: Providing PGHD and PROs through COTS solutions will improve engagement among disadvantaged patients. Secondary outcomes include improving key health indicators (e.g., weight, physical activity) and PROMs (e.g., quality of life, mental health symptoms). Weight management is important in delaying, averting, and reducing the effects of multiple chronic diseases, including diabetes, hypertension, and obesity. A weight management-related intervention also serves as an effective test of PGHD and PROMs collection, due to the existence of numerous COTS solutions which use different methods for tracking common data elements related to weight, physical activity, and fitness. Aim 3: To create an ontology mapping and set of interoperability resources which can be used to support integration of PGHD and PRO into clinical information systems. Aim 3 Hypothesis: PGHD and PROs can be characterized by distinct types, elements, and structures which, once described, may be modeled and mapped to existing vocabularies for health data management. In order to make PGHD and PROs actionable, these data must be integrated into clinical information systems such as electronic health records (EHRs) where it can be used by clinicians in their practice. Creating a translation by matching PGHD and PROs data elements to comparable ones in existing clinical vocabularies will provide a tool to support future data integration into the EHR. Creating a resource set which can be used with multiple EHRs will improve the generalizability and broad usability of the ontology mapping tool.

Official Title
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Engaging Disadvantaged Patients in Sharing Patient Generated Health Data and Patient Reported Outcomes Through Health Information Technology

Conditions
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Obesity

Intervention / Treatment
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* Behavioral: 16-week program
* Behavioral: Patient generated health data


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: BMI of 25.0-39.9, Has a smartphone English or Spanish as primary language assessed at medium health risk according a risk stratification algorithm based on clinical criteria, diagnostic scoring, and health care utilization Exclusion Criteria: Does not meet inclusion criteria

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Recruited participants will be randomized to one of two arms, intervention or control. Both intervention and control groups will engage in a 16-week program where they will receive regular health promotion messaging about (a) food, nutrition, and diet; and (b) exercise and physical activity. Intervention patients will be asked to track patient generated health data elements related to weight management through a mobile health app loaded on their phones and/or through using a fitness tracker, depending on patient preference, and to share that information with the research team. Intervention patients will also be asked to provide answers to patient-reported outcomes measures on a weekly basis. If intervention patients do not have a fitness tracker, a low-cost option will be provided for them. Both iOS and Android phone options will be supported. The app will be selected from a limited set of well-established options such as LoseIt!, MyFitnessPal, Apple Health, or Google Fit.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intervention<br>Intervention patients will engage in a 16-week program where they will receive regular health promotion messaging about (a) food, nutrition, and diet; and (b) exercise and physical activity. Intervention patients will be asked to track patient generated health data (PGHD) elements related to weight management through a mobile health app loaded on their phones and/or through using a fitness tracker, depending on patient preference, and to share that information with the research team. Patient-reported outcomes (PRO) measures will be collected pre-and-post-intervention. Intervention patients will also be asked to provide answers to patient-reported outcomes measures on a weekly basis. | Behavioral: 16-week program<br>* 16-week program where patients will receive regular health promotion messaging about (a) food, nutrition, and diet; and (b) exercise and physical activity.<br>Behavioral: Patient generated health data<br>* Intervention patients will be asked to track patient generated health data and patient reported outcomes. PGHD elements related to weight management will be collected through a mobile health app loaded on their phones and/or through using a fitness tracker, depending on patient preference, and to share that information with the research team. Patient-reported outcomes (PRO) measures will be collected pre-and-post-intervention. Intervention patients will also be asked to provide answers to patient-reported outcomes measures on a weekly basis.<br>|
| Active Comparator: Control<br>Control patients will engage in a 16-week program where they will receive regular health promotion messaging about (a) food, nutrition, and diet; and (b) exercise and physical activity. Patient-reported outcomes measures will be collected pre-and-post-intervention. | Behavioral: 16-week program<br>* 16-week program where patients will receive regular health promotion messaging about (a) food, nutrition, and diet; and (b) exercise and physical activity.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Patient Engagement (Patient Activation Measure) | Patient Engagement will be measured by participant performance on the Patient Activation Measurement (PAM)-13 tool. This validated instrument helps to show patients' motivation for being an active participant in managing their health. Each of the 13 items on the tool is rated on a four-point per-item scale, then converted to a total PAM score. The total PAM score is transformed into a scale score with values that range from 0 to 100 based on the calibration tables for the instrument, with higher numbers reflecting better scores and indicative of increased engagement. The scale score is reported here. | Baseline, Post-Intervention | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Weight Loss | Change in absolute percent weight | 16 weeks | 
| Healthy Days HRQOL-4 Measure | Healthy days will be measured by participant performance on the Health Related Quality of Life Scores (HRQOL)-4 questionnaire. This questionnaire is scored based on participant reported number of days experiencing poor physical or mental health. The scale ranges from 1-30, with lower scores being better in that they indicate fewer poor health days. | 16 weeks | 
| Healthy Days Symptoms Measure | Patient Reported Outcomes Measures, Healthy Days Symptoms Score - lower scores are better, save for Energy where a higher score is better. Minimum value is 0, maximum value is 30. | 16 weeks | 
| Number of Patients Who Responded to Text Messages | Text message response to prompts for weight data. | 16 weeks | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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patient generated health data, patient reported outcome measures, health information technology, mobile health, consumer health informatics