How we measure what matters.
Teamaet's signal system is grounded in two of the most rigorously validated frameworks in sport and clinical psychology. Here's how we translate theory into practice — and how we protect athletes at every step.
Two frameworks. One system.
Self-Determination Theory (Deci & Ryan, 2000) identifies three basic psychological needs — Autonomy, Competence, and Relatedness — that drive sustained motivation and wellbeing. When these needs are satisfied, athletes thrive. When they're frustrated, burnout, disengagement, and depression follow. Critically, SDT constructs behave as leading indicators: declining self-determination precedes burnout, not the other way around (Lonsdale & Hodge, 2011).
Acceptance & Commitment Therapy (ACT) tracks how athletes relate to stress and cognitive load — not to eliminate difficulty, but to build the psychological flexibility to perform through it. ACT informs our stress response and stressor stacking signals, and guides the intervention exercises our AI agents deliver.
Together, these frameworks produce nine calibrated signals that update with every check-in, giving athletes and coaches a quantified, longitudinal picture of cognitive performance.
Signal → Framework Mapping
From check-in to signal.
Each signal is calculated fresh from every check-in using a multi-step process designed for accuracy, not speed.
Structured Check-In
Athletes complete a brief daily check-in — five core questions plus three rotating behavioral questions mapped to the ABC framework (Ambition, Belonging, Craft). Questions are designed to capture cognitive state without clinical language. Check-ins can be typed, tapped, or spoken aloud via voice input with AI extraction.
Signal Calculation
Each signal is computed from weighted combinations of check-in responses and historical data. Every signal produces a value (0–100), a confidence score (how much data backs it), and a volatility indicator (how much it's changing). Confidence increases as more check-ins accumulate, ensuring early signals are treated appropriately.
Pattern Recognition
Signal trends are tracked over daily, weekly, and monthly periods. The dashboard surfaces the most urgent patterns — declining relatedness, compounding stress, sudden readiness drops — and ranks them by urgency, impact, and context. AI agents provide coaching, explanations, and interventions calibrated to each athlete's current state.
The ABC vocabulary.
SDT's three needs — Autonomy, Competence, Relatedness — are powerful but clinical. For coaches, we translate them into a vocabulary that maps directly to how they already think about athlete development.
Ambition
SDT: Autonomy
Drive, purpose, and intrinsic motivation. Maps to the SDT need for autonomy — the sense that effort is self-directed and meaningful.
Belonging
SDT: Relatedness
Connection, trust, and team cohesion. Maps to the SDT need for relatedness — the social bonds that sustain motivation under pressure.
Craft
SDT: Competence
Skill mastery and confidence. Maps to the SDT need for competence — the belief that effort leads to growth and capability.
Coaches interact with ABC scores — never raw psychological data. The translation layer ensures insights are actionable without requiring clinical training, and preserves athlete privacy by operating at the signal level rather than the disclosure level.
AI that supports, never diagnoses.
Our AI agents are purpose-built for specific tasks. They never make clinical diagnoses, never label athletes, and never share private data with coaches.
Cognitive Coach
Adaptive mental training conversations grounded in SDT. Personalizes skill routing based on which psychological needs are currently frustrated — not generic advice.
Intervention Engine
Delivers ACT-based micro-interventions — values clarification, defusion, grounding — calibrated to the athlete's current cognitive state and signal trajectory.
Explanation Assistant
Translates signal data into plain, athlete-safe language. Athletes can ask 'Why is my readiness low?' and receive a grounded, jargon-free answer.
Query Assistant
Coaches ask natural language questions about their team's cognitive state. The AI synthesizes patterns across the roster — never exposing individual journal entries or private data.
AI Safety Guardrails
All AI outputs use athlete-safe language — no clinical diagnoses or labels
Coaches see aggregated signals and trends, never raw journal text or private reflections
AI agents operate on signal data, not raw check-in responses — an abstraction layer protects privacy
Every AI interaction is grounded in the athlete's actual data, not generic advice
Athletes control what they share — journal entries and reflections are private by default
Token usage is tracked for cost control and to prevent runaway AI consumption
Athletes own their data.
Privacy isn't a feature — it's a structural commitment baked into every layer of the system.
What Athletes See
- Their own 9 cognitive signals and trends
- Personalized AI coaching and interventions
- Private journal with 6 entry types and tags
- Group check-ins and shared goals
- Full data export anytime (GDPR compliant)
- Complete control over what coaches can see
What Coaches See
- Team readiness overview and member list
- Individual signal trends and trajectories
- Alerts calibrated to each athlete's baseline
- ABC-vocabulary insights (Ambition, Belonging, Craft)
- Natural language team queries via AI assistant
- Aggregated patterns — never raw journal text
What No One Else Sees
- Private journal entries and reflections
- Raw check-in response text
- Individual AI coaching conversations
- Voice check-in recordings (processed, then discarded)
- Personal notes and media attachments
- Data is never sold, shared with third parties, or used for advertising
Development, not surveillance.
No clinical diagnoses
Teamaet is not a clinical tool. It does not diagnose mental health conditions, prescribe treatment, or replace professional mental health care. Signals indicate cognitive performance patterns — they are development tools, not medical instruments.
No weaponization of data
Cognitive signal data must never be used to justify roster decisions, scholarship reductions, or punitive action. The system is designed to surface athletes who need support — not to create performance dossiers.
Indirect measurement by design
Athletes are never asked to self-diagnose or disclose vulnerability directly. Check-in questions capture cognitive state through behavioral indicators — focus, energy, connection — not clinical symptom inventories. This reduces the stigma barrier that causes 96% of depressed athletes to go undetected in traditional screening.
Coach notes with ethical tension flagging
When coaches record private observations about athletes, the system flags potential ethical tensions — observations that could be misused if taken out of context. This built-in guardrail encourages reflective coaching practice.
Full data portability
Athletes can export their complete data history at any time in standard formats. If an athlete leaves the program, their data goes with them. GDPR-compliant deletion removes all personal data on request.
Read the research.
Our white papers explain the evidence base behind Teamaet — including the honest limitations.