mHealth Data Integration for Objective Subject State Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current mobile health (mHealth) devices lack the capability to objectively determine a subject's state or condition, relying on subjective patient-reported outcomes or manual observations, which are prone to misreporting and are not feasible for real-time monitoring in clinical trials.
Innovation Solution
A system that continuously collects data from multiple mHealth devices to automatically and objectively determine a subject's state or condition, using integrated data from various sensors and machine-learning algorithms to generate predictive models for accurate classification of subject states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective patient-reported outcomes or manual observations are used to determine subject state, then the system is simple to operate, but the measurement precision and reliability are poor due to misreporting and lack of real-time monitoring
Solution Approach 1:
The system segments the data collection function across multiple independent mHealth devices, each monitoring specific physiological parameters (heart rate, activity, sleep, etc.). This segmentation allows each device to remain simple while the integrated system achieves high measurement precision through multi-parameter analysis for objective subject state determination
Solution Approach 2:
The system employs universal mHealth devices that can monitor multiple physiological parameters simultaneously (heart rate, physical activity, sleep patterns, respiratory rate). This multi-functionality enables comprehensive objective assessment of subject state without requiring separate specialized devices for each parameter, balancing precision with manageable complexity
2Reliability
If multiple mHealth devices are integrated to objectively determine subject state, then the measurement precision and reliability improve, but the device complexity increases due to data synchronization requirements
Solution Approach 1:
The system introduces a centralized server or cloud platform as an intermediary to receive, synchronize, and integrate data from multiple mHealth devices. This intermediary handles the complex tasks of data normalization, timestamp alignment, and parameter correlation, enabling reliable objective subject state determination while keeping individual devices simple
Solution Approach 2:
The system replaces manual observation and subjective reporting with automated sensor-based mechanical and electronic measurement systems. mHealth devices continuously and objectively measure physiological parameters without human intervention, eliminating misreporting bias and enabling real-time reliable monitoring of subject state
3Productivity
If manual observation methods are used to monitor subject condition, then the system is easy to implement, but the productivity and time efficiency are low due to lack of real-time monitoring capability
Solution Approach 1:
The system implements continuous real-time monitoring of subject physiological parameters through automated mHealth devices, replacing intermittent manual observations. This continuous data collection enables immediate detection of subject state changes, improving clinical trial monitoring efficiency and enabling timely interventions without requiring complex manual protocols
Data Source
AI summary
A method for calculating a subject's state or condition comprises integrating data that are captured from multiple sources, storing the integrated data in a first database, calculating time intervals in which to collect an optimal amount of data to predict the subject state, developing a predictive model using a recorded diary or electronic data capture information, testing the model against a portion of the captured data, and applying the predictive model to new data from other sources. The predictive model may determine a subject state that may be a digital bio-marker for a disease condition. A system for predicting subject state is also disclosed.