DIGITAL MEDICINE RESEARCH INSIGHTS
Personalised Modelling of Routine Variability and Affective States
Multimodal smartphone sensor data offers profound insights into real-world behavioral patterns linked to anxiety and depression. Our approach uses non-negative matrix factorization (NMF) to extract individual-specific routines and their weekly variability. Generalized linear models (GLMs) then associate this variability with self-reported affective states, with GPT-40 translating these findings into actionable self-regulation insights.
Executive Impact: Unlocking Behavioral Insights for Mental Health
Our research pioneers a novel approach to understanding the complex interplay between daily routines and mental well-being, leveraging advanced AI to provide unprecedented personalized insights.
99.76%
NMF Models Capture Dominant Routines
<0.001
Strong Between-Group Differences in Depression (PHQ-2)
p=0.0078
Significant Between-Group Differences in Anxiety (GAD-2)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Personalized Routine Modeling
Behavioral Patterns & Mental Health
AI-Driven Insights & Self-Regulation
Enterprise Process Flow
Captured Sensor Data (Triplets)
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NMF Decomposition (Optimal Rank)
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Decomposed Routines & Variability
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Population-Level Routine Labeling
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Individual GLMs (Anxiety & Depression)
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LLM Interpretation
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Group-Based Analysis
99.76%
Of NMF decompositions resulted in Rank 1, indicating a single dominant routine captured for most participants, effectively summarizing a week of behavioral data.
| Negative Correlation (Stable Routines Link to Higher Symptoms) | Positive Correlation (Variable Routines Link to Higher Symptoms) |
|---|---|
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p=0.0078
Statistically significant between-group differences found in GAD-2 scores when grouping individuals by routine variability patterns.
LLM Interprets Personalised GLM Results for Self-Regulation
The LLM (GPT-40) effectively translated complex GLM results into user-accessible language, providing actionable insights. For instance, when an individual's daily routine involves high and consistent stillness—especially in the morning and afternoon—anxiety levels tend to be higher. This indicates prolonged physical inactivity may contribute to elevated anxiety.
Additionally, patterns of high or erratic audio/media playing durations (especially in the afternoon and evening) are associated with higher anxiety, suggesting that media-heavy engagement in the latter half of the day may impact mental states.
The LLM also provided tailored suggestions, such as breaking up prolonged stillness with Pomodoro techniques or hourly reminders, and moderating and scheduling digital engagement by using app timers and screen-free blocks. It also recommended balancing audio/media use throughout the day with calming music and incorporating active transitions like a 10-minute walk after lunch.
Quantifying the Impact: ROI Calculator
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Our Implementation Roadmap
We guide you through a structured process to seamlessly integrate our AI-powered behavioral analysis into your enterprise, ensuring maximum impact and measurable outcomes.
Phase 01: Discovery & Data Integration
Initial consultation, data source identification, and secure integration of behavioral data platforms. Define key metrics and objectives.
Phase 02: Personalized Model Development
NMF-driven routine extraction, individual GLM construction, and initial LLM-based insight generation. Pilot with a select user group.
Phase 03: Validation & Refinement
Continuous validation against mental health outcomes, user feedback integration, and model refinement for improved accuracy and actionable insights.
Phase 04: Deployment & Scaling
Full-scale deployment of the AI-driven system, integration into existing wellness platforms, and ongoing monitoring and support.
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