Dashboard

The dashboard is the fastest way to understand your recent training pattern. It combines summary cards, fitness status, and configurable overview metrics.

Use it when you want to answer:

• How much have I trained recently?
• Is my load rising, stable, or falling?
• Which sport is driving the trend?
• Do I need to look deeper at activities, PMC, power, pace, or heart rate?

Before you begin

• Import enough recent activities for the selected time range to be meaningful.
• Configure thresholds and load-source priority before comparing intensity-based metrics.
• Use one sport at a time when running, cycling, and swimming rely on different thresholds.

Trend review workflow

1. Select the intended time range and sport.
2. Read fitness status as a trend, not a diagnosis or readiness verdict.
3. Identify the card or chart that changed.
4. Open the activities behind that change.
5. Continue to the relevant PMC, power, pace, or heart-rate page before changing the plan.

Trainingload.ai

Dashboard trend review

Confirm time and sport filters before comparing fitness status and overview cards with the activities behind them.

Plan

4 week block

Load

steady build

Activity

matched

AI review

ready

Filters

The dashboard supports global filters:

• Time range: switch between recent periods.
• Sport type: focus on running, cycling, swimming, or all activity.

When you change filters, the cards and charts update to match the selected context.

Fitness status

Fitness status cards summarize the current training picture. They are meant for scanning, not final decisions.

Common signals include:

• Long-term load and fitness trend.
• Recent fatigue.
• Readiness/form context.
• Training consistency.

For the underlying model, read:

• Chronic Training Load
• Acute Training Load
• Training Stress Balance

Overview cards

Overview cards summarize activity volume and performance-related metrics. Depending on your data, they can include distance, duration, elevation, heart rate, power, cadence, and other training fields.

Use overview cards to spot changes, then open the relevant page for detail:

• Activities for the workouts behind the numbers.
• PMC for load trend.
• Power for FTP, CP, NP, VI, and power curve context.
• Pace for running pace, GAP, NGP, and best-distance views.
• Heart Rate for HR response, zones, EF, and decoupling.

Edit layout

On desktop, the dashboard supports editing the layout of key sections. Use this when you want the dashboard to match your training focus.

Examples:

• Put load and consistency first during base training.
• Put power or pace metrics first during a race-specific block.
• Keep recovery and fatigue visible when returning from a high-load week.

After editing, save the layout so the dashboard opens in the same structure next time.

How to interpret dashboard changes

Do not overreact to one card changing for one day. A better workflow:

1. Check whether the time range and sport filter are correct.
2. Open recent activities behind the change.
3. Compare load with subjective fatigue.
4. Use AI Coach if you want a plan-aware interpretation.

The dashboard is your starting point. The decision should still consider the plan, recent workouts, and how you feel.

Verify the result

• The active filters match the question you intended to answer.
• The visible activities and totals cover the same date and sport scope.
• A trend can be traced back to specific completed activities.
• Threshold-dependent metrics use current profile values.
• A saved desktop layout remains after reopening the dashboard.

Troubleshooting

• Cards are empty: widen the time range, clear the sport filter, and confirm activities are Ready.
• A total differs from the activity list: compare time zone, date range, sport filter, and processing status.
• Load or status changed unexpectedly: check threshold history and load-source priority before assuming the training changed.
• A layout did not persist: save the edit and confirm the account is signed in before refreshing.
• One card looks extreme: open the underlying activity and check sensor quality before acting on the outlier.

Next steps

• Training Analysis Workflow
• Activity Detail
• Training Load