Smart Goal Setting for Depression Management
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Solution Overview
Problem
Current treatments for depression often come with side effects, leading to low adherence among individuals, and existing systems lack personalized goal-setting capabilities for depression management.
Innovation Solution
A computer-based system that uses data from similar demographics to set individualized, smart goals for users by monitoring and learning from their performance in categories like sleep, diet, exercise, and medication compliance, adjusting goals based on the achievements of the top percentile within their cohort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If antidepressant drugs are used to treat depression, then depression symptoms can be combated, but side effects occur and adherence decreases
Solution Approach 1:
The patent introduces a computer-based goal-setting system as an intermediary between depression treatment and patient adherence. This system processes multiple data points (sleep, diet, exercise, screen time, social interaction, academic performance) to generate personalized goals that indirectly support depression management without requiring patients to take medication, thus avoiding side effects while maintaining treatment effectiveness
Solution Approach 2:
The system enables patients to self-manage their depression through personalized goal-setting based on their own data. By allowing patients to set and track their own goals across multiple life domains, the system empowers them to take control of their mental health without relying solely on medication, thereby reducing side effect exposure while maintaining reliable treatment outcomes
2Adaptability or versatility
If generic goal-setting systems are used for depression management, then basic tracking is provided, but personalized and feasible goals cannot be achieved
Solution Approach 1:
The patent applies local quality by creating personalized goals specific to each patient's unique circumstances rather than using uniform goals for all. The system analyzes individual data across multiple domains (sleep, diet, exercise, etc.) and generates tailored goals that reflect each patient's specific needs, capabilities, and lifestyle, thereby achieving both personalization and precise feasibility assessment
Solution Approach 2:
The system dynamically adjusts goal parameters based on continuously collected data and cohort performance. By changing goal difficulty, targets, and focus areas according to individual progress and comparative cohort analysis, the system maintains both high personalization and accurate feasibility measurement, allowing goals to evolve as patients improve
3Productivity
If data collection is limited to single time point, then initial assessment is possible, but continuous monitoring and goal adjustment cannot be performed
Solution Approach 1:
The patent implements continuous data collection and monitoring across multiple time points, enabling ongoing assessment of patient progress and real-time goal adjustment. The system continuously gathers data on sleep, diet, exercise, and other domains, maintaining an up-to-date view of patient status without interruption, thereby improving management efficiency while eliminating delays in responding to patient changes
Solution Approach 2:
The system establishes continuous feedback loops where collected data immediately informs goal adjustments and treatment recommendations. By processing ongoing data streams and comparing current performance against cohort benchmarks, the system provides timely feedback to both patients and providers, enabling rapid adaptation to changing patient needs without time loss
Data Source
AI summary
A depression scoring system obtains data from a large population of individuals including data about each of a plurality of different activities in relevant categories, and information about each of the individuals. Cohorts of individuals are defined as individuals who have similar statistical characteristics such as similar age, socioeconomic status, and sex. For each of the cohorts, a distribution of the data is obtained, and the scores of the top n %, e.g., 25% is set as a goal for the remaining members of the cohort. These goals are incrementally set and personalized for individual users to easily meet. This process is repeated constantly.