Burnout and Wellbeing Signals
A burnout signal is an early, honest indicator that overload is building — read from your sustained workload and working patterns, and surfaced as a prompt to pause and rebalance before exhaustion sets in. It is designed to inform your wellbeing, never to police your output.
This is the throughline of TimeCampus: the platform exists to help you do better work and protect the person doing it — the opposite of tools that optimise for squeezing out more output at any cost.
What you'll learn
- Where signals come from
- What triggers a signal
- How signals are presented
- Team-level signals — aggregate by design
- Who sees what
Where signals come from
Signals are read across your work patterns over time. No single day triggers anything — one brutal day is a brutal day. What TimeCampus looks for is sustained shape:
- The structure of your days: focus, meeting, and break hours, interruptions
- The wellbeing markers you record: energy, mood, stress
- The trend across days and weeks, not the snapshot
Because the wellbeing markers come from you, the signal quality follows your honesty: patterns recorded truthfully — including the bad days — are what make early detection possible.
What triggers a signal
Sustained-workload conditions building across consecutive days, for example:
| Pattern building over days | Why it matters |
|---|---|
| Focus hours dropping while meeting hours climb | Deep work is being crowded out — the classic overload slide |
| Break hours trending toward zero | Recovery is disappearing from the day |
| Stress rising while energy falls | The day's cost is exceeding its recharge |
| Interruptions climbing, deep-work sessions shrinking | Work is fragmenting; effort is rising while progress falls |
| Long working hours held across weeks | Pace that works for a sprint, breaking as a default |
How signals are presented
Signals arrive as insights on your Work Patterns view — each names what was detected, describes it plainly, states its impact, and carries a recommendation. For example: "Your focus hours dropped while meetings climbed three days running — consider declining or consolidating meetings this week to protect recovery time."
Three things a signal will never be:
- A push for more output. The recommendation attached to a signal is always toward rebalancing — protecting breaks, reducing load, restoring focus time. Never "work harder."
- A judgment. A signal is information about a trend, not a verdict on you.
- A report to your manager. Signals inform the individual first (see below).
When a signal fires, treat it as the early warning it is: rebalance the coming week — cut a recurring meeting, restore break time, defend a focus block — and watch whether the trend turns in your patterns.
Team-level signals: aggregate by design
For teams, the value of workload signal is planning, not policing. Team-facing views in TimeCampus are aggregate: they answer questions like "is this team's load sustainable?" and "where is deep work being crowded out?" so leads can distribute work more fairly — without exposing any individual's patterns, mood, or stress entries.
- A lead can see that the team's aggregate load is trending unsustainable — and rebalance.
- A lead does not get a dashboard of who felt what on which day.
See Team analytics for the aggregate views, and Resource planning for acting on capacity.
Who sees what
The privacy stance, concretely:
| Data | Who sees it |
|---|---|
| Your daily work patterns (hours, mood, stress) | You |
| Your individual burnout signals and recommendations | You |
| Team workload signal | Aggregate only — no individual breakdown |
There are no screenshots, no keystroke logging, and no idle-time policing anywhere in TimeCampus — signals are derived from the structure of work and what you choose to record, under your control. Full detail in the privacy model and data controls.
Predictive burnout-risk modelling and personalized wellbeing recommendations — going beyond today's deterministic insight rules — are committed roadmap. The signals described on this page are the honest, rule-derived foundation they will build on.
Next steps
- Work Patterns — the daily records signals are read from
- Pomodoro — rebuild protected focus time when a signal fires
- Privacy model — the boundary all of this operates inside