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MindLAMP

A Digital Platform for Behavioral Research & Care

MindLAMP is an open-source platform that enables teams to study, adapt, and support behavioral health in ways that are scientifically rigorous and immediately useful in practice. It is designed to support rich measurement, flexible delivery, and reciprocal engagement all within one ecosystem, allowing for transformative advancements in digital research and care.


Addressing Challenges in Research & Care

Research and clinical care face three persistent challenges that limit our ability to understand and support individuals effectively. MindLAMP addresses each through innovative digital solutions:

The Challenge

Measurement Gaps

Traditional assessment relies on infrequent clinical visits that capture only snapshots of a person's experience. Symptoms and behaviors fluctuate daily, yet measurement depends heavily on self-report questionnaires and clinical observations that may be inconsistent, biased, or miss critical trends between appointments.

MindLAMP's Solution

Rich Measurement

MindLAMP captures a comprehensive picture through multiple data streams that work together to fill measurement gaps:

Digital Phenotyping: Real-time monitoring of behavioral patterns through smartphone sensors and self-reported experiences, providing continuous insight into a person's daily life rather than isolated snapshots. This approach combines both active and passive data collection methods.

Active Data: Information collected when individuals actively engage with the app, including:

  • Surveys: Structured questionnaires and assessment instruments that, when combined with scheduling flexibility and contextual sensor data, enable Ecological Momentary Assessments (EMAs)—brief, repeated sampling of experiences and symptoms in real-time within natural environments, capturing how people feel and function as they go about their daily lives
  • Cognitive Assessments: Tasks evaluating domains including memory, attention, processing speed, executive function, and emotion recognition
  • Therapeutic Activities: Interactive exercises such as journaling, skill-building tools, wellness practices, and psychoeducation
  • Metadata: Detailed information about how people interact with these activities—such as response patterns, completion times, engagement timing, and content preferences—offering additional insights into participation and experience

Passive Data: Information gathered automatically in the background through smartphone sensors without requiring any effort from the user, revealing behavioral and health patterns that individuals might not notice or report themselves. Sensors can capture:

  • Location and movement patterns (GPS, device motion, accelerometer)
  • Physical activity (step count, activity recognition, workouts)
  • Sleep patterns
  • Device usage (screen time, app analytics)
  • Social and environmental context (Bluetooth and WiFi proximity, calls and texts)
  • Physiological measures (heart rate and variability, blood pressure, blood glucose, oxygen saturation, body temperature, respiratory rate)
  • Nutrition tracking

Together, these measurement approaches transform sparse clinical data into rich, continuous understanding of health and behavioral patterns.

Explore LAMP Sensors →
Explore LAMP Activities →


Platform Components

MindLAMP is built around four core components. Together they form a complete platform, but each can also be used independently depending on the needs of a study or clinical program.

The App

For Participants

A participant-facing tool for surveys, cognitive tasks, wellness activities, and — when enabled — passive data collection in the background. Available on iOS and Android; customizable per project.

Learn how to use the App →

The Dashboard

For Coordinators

A web-based tool for researchers & clinicians to configure their project implementations, manage participants, and review incoming data with structured views of activity and outcomes.

Learn how to use the Dashboard →

The Server

For Administrators

Securely organizes all collected information using the LAMP Protocol, ensuring reproducibility and extensibility. Foundation for programmatic access and custom tools.

Learn how to use the Server →

Cortex

For Data Analysts

A data processing pipeline that transforms raw inputs into meaningful features and visualizations, enabling researchers and clinicians to interpret complex digital signals.

Learn how to use Cortex →


Proof & Community

MindLAMP is built and sustained through collaboration. Researchers, clinicians, patients, and developers form a community with the shared goal of using digital tools to advance both science and care.

Most teams implement MindLAMP through LAMP CORE, the service we provide to support research and clinical programs. CORE requires a business agreement and includes hosting, support, and consultation. This ensures that implementations are reliable, secure, and aligned with scientific and clinical goals.

Join LAMP CORE →

Because MindLAMP is open source, teams may also adapt the code independently. Projects that take this path should still cite MindLAMP and let us know about their work. Independent implementations, however, are responsible for their own hosting, maintenance, and support.

View MindLAMP on GitHub →

This balance — open and shareable, yet guided through CORE — has allowed MindLAMP to expand globally while staying rooted in community needs. Patients and participants engage with MindLAMP only through these research and clinical programs, using the App as part of their care or study involvement. This structure ensures that the platform evolves in ways that are scientifically rigorous, clinically relevant, and responsive to the people who use it. To see how this works in practice, explore our case studies, which describe how MindLAMP has been implemented across research and clinical settings.

Read Case Studies →