feat: introduce fitness dashboard overview and enhance activity metrics

- Added new functions to build fitness dashboard visualizations, including weekly training volume and training type distribution charts.
- Updated the `charts.py` router to include a new endpoint for the fitness dashboard, integrating data from activity metrics.
- Refactored existing activity-related functions to improve modularity and maintainability.
- Updated frontend components to reflect the new fitness terminology and integrate the fitness dashboard overview, enhancing user experience.

backend/data_layer/activity_metrics.py

@@ -330,24 +330,30 @@ def calculate_training_frequency_7d(profile_id: str) -> Optional[int]:
         return int(row['session_count']) if row else None
 
 
-def calculate_quality_sessions_pct(profile_id: str) -> Optional[int]:
+def calculate_quality_sessions_pct(profile_id: str, days: int = 28) -> Optional[int]:
-    """Calculate percentage of quality sessions (good or better) last 28 days"""
+    """Anteil qualitativ guter Sessions (quality_label) im Zeitfenster ``days``."""
+    if days < 1:
+        days = 28
+    cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
     with get_db() as conn:
         cur = get_cursor(conn)
-        cur.execute("""
+        cur.execute(
+            """
             SELECT
                 COUNT(*) as total,
                 COUNT(*) FILTER (WHERE quality_label IN ('excellent', 'very_good', 'good')) as quality_count
             FROM activity_log
             WHERE profile_id = %s
-              AND date >= CURRENT_DATE - INTERVAL '28 days'
+              AND date >= %s
-        """, (profile_id,))
+            """,
+            (profile_id, cutoff),
+        )
 
         row = cur.fetchone()
-        if not row or row['total'] == 0:
+        if not row or row["total"] == 0:
             return None
 
-        pct = (row['quality_count'] / row['total']) * 100
+        pct = (row["quality_count"] / row["total"]) * 100
         return int(pct)
 
 
@@ -1222,3 +1228,128 @@ def get_training_parameters_ki_glossary_data(profile_id: str) -> Dict[str, Any]:
         "parameters": rows,
         "meta": {"count": len(rows), "scope": "global_active_catalog"},
     }
+
+
+# ============================================================================
+# Chart payloads (Phase 0c / Layer 1) — gemeinsam mit charts-Router und Layer-2b-Bundles
+# ============================================================================
+
+
+def build_training_volume_chart_payload(profile_id: str, weeks: int) -> Dict[str, Any]:
+    """
+    Wöchentliches Trainingsvolumen (Minuten) — gleiche Logik wie GET /api/charts/training-volume.
+    """
+    if weeks < 4:
+        weeks = 4
+    if weeks > 52:
+        weeks = 52
+
+    cutoff = (datetime.now() - timedelta(weeks=weeks)).strftime("%Y-%m-%d")
+
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute(
+            """SELECT
+                DATE_TRUNC('week', date) as week_start,
+                SUM(duration_min) as total_minutes,
+                COUNT(*) as session_count
+               FROM activity_log
+               WHERE profile_id=%s AND date >= %s
+               GROUP BY week_start
+               ORDER BY week_start""",
+            (profile_id, cutoff),
+        )
+        rows = cur.fetchall()
+
+    if not rows:
+        return {
+            "chart_type": "bar",
+            "data": {"labels": [], "datasets": []},
+            "metadata": {
+                "confidence": "insufficient",
+                "data_points": 0,
+                "message": "Keine Aktivitätsdaten vorhanden",
+            },
+        }
+
+    labels = [row["week_start"].strftime("KW %V") for row in rows]
+    values = [safe_float(row["total_minutes"]) for row in rows]
+
+    confidence = calculate_confidence(len(rows), weeks * 7, "general")
+
+    return {
+        "chart_type": "bar",
+        "data": {
+            "labels": labels,
+            "datasets": [
+                {
+                    "label": "Trainingsminuten",
+                    "data": values,
+                    "backgroundColor": "#1D9E75",
+                    "borderColor": "#085041",
+                    "borderWidth": 1,
+                }
+            ],
+        },
+        "metadata": serialize_dates(
+            {
+                "confidence": confidence,
+                "data_points": len(rows),
+                "avg_minutes_week": round(sum(values) / len(values), 1) if values else 0,
+                "total_sessions": sum(row["session_count"] for row in rows),
+            }
+        ),
+    }
+
+
+def build_training_type_distribution_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
+    """
+    Trainingstyp-Verteilung — gleiche Logik wie GET /api/charts/training-type-distribution.
+    """
+    dist_data = get_training_type_distribution_data(profile_id, days)
+
+    if dist_data["confidence"] == "insufficient":
+        return {
+            "chart_type": "pie",
+            "data": {"labels": [], "datasets": []},
+            "metadata": {
+                "confidence": "insufficient",
+                "data_points": 0,
+                "message": "Keine Trainingstypen-Daten",
+            },
+        }
+
+    labels = [item["category"] for item in dist_data["distribution"]]
+    values = [item["count"] for item in dist_data["distribution"]]
+
+    colors = [
+        "#1D9E75",
+        "#3B82F6",
+        "#F59E0B",
+        "#EF4444",
+        "#8B5CF6",
+        "#10B981",
+        "#F97316",
+        "#06B6D4",
+    ]
+
+    return {
+        "chart_type": "pie",
+        "data": {
+            "labels": labels,
+            "datasets": [
+                {
+                    "data": values,
+                    "backgroundColor": colors[: len(values)],
+                    "borderWidth": 2,
+                    "borderColor": "#fff",
+                }
+            ],
+        },
+        "metadata": {
+            "confidence": dist_data["confidence"],
+            "total_sessions": dist_data["total_sessions"],
+            "categorized_sessions": dist_data["categorized_sessions"],
+            "uncategorized_sessions": dist_data["uncategorized_sessions"],
+        },
+    }

backend/data_layer/fitness_interpretation.py

@@ -0,0 +1,167 @@
+"""
+KPI-Kacheln für Layer-2b Fitness-Dashboard (Issue #53).
+
+Ausgabe für KpiTilesOverview; ``keys`` = Platzhalter-Registry-Referenzen.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+
+def _verdict(status: str) -> str:
+    if status == "good":
+        return "Gut"
+    if status == "warn":
+        return "Hinweis"
+    return "Achtung"
+
+
+def _minutes_status(minutes: Optional[int]) -> str:
+    if minutes is None:
+        return "warn"
+    if 150 <= minutes <= 300:
+        return "good"
+    if minutes < 150:
+        return "warn" if minutes >= 90 else "bad"
+    return "warn"
+
+
+def _quality_status(pct: Optional[int]) -> str:
+    if pct is None:
+        return "warn"
+    if pct >= 60:
+        return "good"
+    if pct >= 40:
+        return "warn"
+    return "bad"
+
+
+def _score_status(score: Optional[int]) -> str:
+    if score is None:
+        return "warn"
+    if score >= 70:
+        return "good"
+    if score >= 50:
+        return "warn"
+    return "bad"
+
+
+def _vo2_status(trend: Optional[float]) -> str:
+    if trend is None:
+        return "warn"
+    if trend > 0.5:
+        return "good"
+    if trend >= -0.5:
+        return "warn"
+    return "bad"
+
+
+def build_fitness_dashboard_kpi_tiles(
+    summary: Dict[str, Any],
+    minutes_7d: Optional[int],
+    quality_pct: Optional[int],
+    quality_window_days: int,
+    activity_score: Optional[int],
+    vo2_trend: Optional[float],
+    top_focus: Optional[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    spw = summary.get("sessions_per_week")
+    try:
+        spw_f = float(spw) if spw is not None else None
+    except (TypeError, ValueError):
+        spw_f = None
+    spw_s = f"{spw_f:.1f}".replace(".", ",") if spw_f is not None else "—"
+
+    m_status = _minutes_status(minutes_7d)
+    q_status = _quality_status(quality_pct)
+    s_status = _score_status(activity_score)
+    v_status = _vo2_status(vo2_trend)
+
+    tiles: List[Dict[str, Any]] = [
+        {
+            "key": "minutes_week",
+            "category": "Minuten (7 Tage)",
+            "icon": "⏱",
+            "value": f"{minutes_7d} min" if minutes_7d is not None else "—",
+            "sublabel": "WHO: 150–300 min/Woche",
+            "status": m_status,
+            "verdict": _verdict(m_status),
+            "hoverTop": "Summe Trainingsminuten (letzte 7 Tage)",
+            "hoverBody": "Gleiche Quelle wie Platzhalter training_minutes_week.",
+            "keys": ["training_minutes_week", "activity_score"],
+        },
+        {
+            "key": "sessions_per_week",
+            "category": "Sessions / Woche",
+            "icon": "📅",
+            "value": spw_s,
+            "sublabel": f"Fenster: {summary.get('days_analyzed', '—')} Tage",
+            "status": "good",
+            "verdict": "Gut",
+            "hoverTop": "Durchschnittliche Sessions pro Woche",
+            "hoverBody": "Aus activity_summary (activity_log im gewählten Zeitraum).",
+            "keys": ["activity_summary"],
+        },
+        {
+            "key": "quality_pct",
+            "category": "Qualitätssessions",
+            "icon": "✓",
+            "value": f"{quality_pct} %" if quality_pct is not None else "—",
+            "sublabel": f"Anteil «gut+» · {quality_window_days} Tage",
+            "status": q_status,
+            "verdict": _verdict(q_status),
+            "hoverTop": "Anteil Sessions mit guter Qualitätslabel-Klassifikation",
+            "hoverBody": "Entspricht quality_sessions_pct (Fenster wie gewählt).",
+            "keys": ["quality_sessions_pct"],
+        },
+        {
+            "key": "activity_score",
+            "category": "Activity-Score",
+            "icon": "🎯",
+            "value": str(activity_score) if activity_score is not None else "—",
+            "sublabel": "Ausrichtung an gewichteten Fokusbereichen",
+            "status": s_status,
+            "verdict": _verdict(s_status) if activity_score is not None else "Hinweis",
+            "hoverTop": "Gewichteter Score (0–100)",
+            "hoverBody": "Ohne gewichtete Aktivitäts-Fokusbereiche kein Score.",
+            "keys": ["activity_score"],
+        },
+        {
+            "key": "vo2_trend",
+            "category": "VO₂max-Trend",
+            "icon": "🫁",
+            "value": f"{vo2_trend:+.1f}" if vo2_trend is not None else "—",
+            "sublabel": "28-Tage-Trend (geschätzt)",
+            "status": v_status,
+            "verdict": _verdict(v_status) if vo2_trend is not None else "Hinweis",
+            "hoverTop": "Trend der VO₂max-Schätzung aus Aktivitätsdaten",
+            "hoverBody": "Wie vo2max_trend_28d im Data Layer.",
+            "keys": ["vo2max_trend_28d"],
+        },
+    ]
+
+    if top_focus:
+        prog = top_focus.get("progress")
+        prog_s = f"{prog} %" if prog is not None else "—"
+        w = top_focus.get("weight")
+        try:
+            w_s = f"{float(w):.0f} %" if w is not None else "—"
+        except (TypeError, ValueError):
+            w_s = "—"
+        tiles.append(
+            {
+                "key": "top_focus",
+                "category": "Schwerpunkt-Fokus",
+                "icon": "🔭",
+                "value": str(top_focus.get("label") or "—"),
+                "sublabel": f"Fortschritt {prog_s} · Gewicht {w_s}",
+                "status": "good",
+                "verdict": "Gut",
+                "hoverTop": "Höchstgewichteter Fokusbereich",
+                "hoverBody": "Aus focus_area_definitions + Nutzer-Gewichtungen.",
+                "keys": ["top_focus_area_name", "top_focus_area_progress"],
+            }
+        )
+
+    return tiles

backend/data_layer/fitness_viz.py

@@ -0,0 +1,125 @@
+"""
+Layer 2b: Fitness-Hub — ein Bundle für die Aktivitäts-/Fitness-UI (Issue #53).
+
+Single Source: activity_metrics + dieselben Hilfsfunktionen wie Chart-Endpunkte A1/A2.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, Optional
+
+from db import get_db, get_cursor
+from data_layer.activity_metrics import (
+    build_training_type_distribution_chart_payload,
+    build_training_volume_chart_payload,
+    calculate_activity_score,
+    calculate_training_minutes_week,
+    calculate_quality_sessions_pct,
+    calculate_vo2max_trend_28d,
+    get_activity_summary_data,
+)
+from data_layer.fitness_interpretation import build_fitness_dashboard_kpi_tiles
+from data_layer.scores import get_top_focus_area
+
+
+def _iso(d: Any) -> Optional[str]:
+    if d is None:
+        return None
+    if hasattr(d, "isoformat"):
+        return d.isoformat()[:10]
+    return str(d)[:10]
+
+
+def _has_activity_entries(profile_id: str) -> bool:
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute(
+            "SELECT 1 FROM activity_log WHERE profile_id=%s LIMIT 1",
+            (profile_id,),
+        )
+        return cur.fetchone() is not None
+
+
+def _last_activity_date(profile_id: str) -> Optional[str]:
+    with get_db() as conn:
+        cur = get_cursor(conn)
+        cur.execute(
+            "SELECT MAX(date) AS d FROM activity_log WHERE profile_id=%s",
+            (profile_id,),
+        )
+        row = cur.fetchone()
+        if not row or row["d"] is None:
+            return None
+        return _iso(row["d"])
+
+
+def get_fitness_dashboard_viz_bundle(profile_id: str, days: int) -> Dict[str, Any]:
+    """
+    Bundle für Fitness-Übersicht: KPI-Kacheln + eingebettete Chart-Payloads (Chart.js-Format).
+
+    ``days``: Analysefenster für Zusammenfassung; >=9999 = lange Historie (max. 3650 Tage).
+    """
+    if not _has_activity_entries(profile_id):
+        return {
+            "confidence": "insufficient",
+            "has_activity_entries": False,
+            "message": "Noch keine Aktivitätsdaten",
+            "kpi_tiles": [],
+            "summary": {},
+            "charts": {},
+            "meta": {"layer_1": "activity_metrics", "layer_2b": "fitness_viz"},
+        }
+
+    all_history = days >= 9999
+    eff_days = 3650 if all_history else max(7, min(int(days), 3650))
+
+    summary = get_activity_summary_data(profile_id, eff_days)
+
+    weeks_vol = max(4, min(52, (min(eff_days, 365) + 6) // 7))
+    dist_days = min(90, max(7, min(eff_days, 365)))
+
+    volume_chart = build_training_volume_chart_payload(profile_id, weeks_vol)
+    type_chart = build_training_type_distribution_chart_payload(profile_id, dist_days)
+
+    quality_days = dist_days
+    quality_pct = calculate_quality_sessions_pct(profile_id, quality_days)
+    minutes_7d = calculate_training_minutes_week(profile_id)
+    activity_score = calculate_activity_score(profile_id)
+    vo2_trend = calculate_vo2max_trend_28d(profile_id)
+    top_focus = get_top_focus_area(profile_id)
+
+    kpi_tiles = build_fitness_dashboard_kpi_tiles(
+        summary,
+        minutes_7d,
+        quality_pct,
+        quality_days,
+        activity_score,
+        vo2_trend,
+        top_focus,
+    )
+
+    conf = summary.get("confidence") or "medium"
+    if summary.get("activity_count", 0) == 0:
+        conf = "insufficient"
+
+    return {
+        "confidence": conf,
+        "has_activity_entries": True,
+        "days_requested": days,
+        "effective_window_days": eff_days,
+        "training_volume_weeks_used": weeks_vol,
+        "training_type_dist_days_used": dist_days,
+        "last_updated": _last_activity_date(profile_id),
+        "summary": summary,
+        "kpi_tiles": kpi_tiles,
+        "interpretation_tiles": [],
+        "charts": {
+            "training_volume": volume_chart,
+            "training_type_distribution": type_chart,
+        },
+        "meta": {
+            "layer_1": "activity_metrics",
+            "layer_2b": "fitness_viz",
+            "issue": "53-layer-2b-fitness",
+        },
+    }

backend/routers/charts.py

@@ -33,6 +33,7 @@ from data_layer.body_metrics import (
 )
 from data_layer.body_viz import get_body_history_viz_bundle
 from data_layer.nutrition_viz import get_nutrition_history_viz_bundle
+from data_layer.fitness_viz import get_fitness_dashboard_viz_bundle
 from data_layer.nutrition_metrics import (
     get_nutrition_average_data,
     get_protein_targets_data,
@@ -44,13 +45,14 @@ from data_layer.nutrition_metrics import (
 )
 from data_layer.activity_metrics import (
     get_activity_summary_data,
-    get_training_type_distribution_data,
     calculate_training_minutes_week,
     calculate_quality_sessions_pct,
     calculate_proxy_internal_load_7d,
     calculate_monotony_score,
     calculate_strain_score,
-    calculate_ability_balance
+    calculate_ability_balance,
+    build_training_volume_chart_payload,
+    build_training_type_distribution_chart_payload,
 )
 from data_layer.recovery_metrics import (
     get_sleep_duration_data,
@@ -288,6 +290,26 @@ def get_nutrition_history_viz(
     return serialize_dates(bundle)
 
 
+@router.get("/fitness-dashboard-viz")
+def get_fitness_dashboard_viz(
+    days: int = Query(
+        default=28,
+        ge=7,
+        le=9999,
+        description="Analysefenster in Tagen (9999 = lange Historie)",
+    ),
+    session: dict = Depends(require_auth),
+) -> Dict:
+    """
+    Layer 2b: Fitness-Übersicht — KPI-Kacheln + Volumen- und Typ-Verteilungs-Charts.
+
+    Daten aus activity_metrics (gleiche Payloads wie training-volume / training-type-distribution).
+    """
+    profile_id = session["profile_id"]
+    bundle = get_fitness_dashboard_viz_bundle(profile_id, days)
+    return serialize_dates(bundle)
+
+
 @router.get("/circumferences")
 def get_circumferences_chart(
     max_age_days: int = Query(default=90, ge=7, le=365),
@@ -1051,66 +1073,7 @@ def get_training_volume_chart(
         Chart.js bar chart with weekly training minutes
     """
     profile_id = session['profile_id']
-
+    return build_training_volume_chart_payload(profile_id, weeks)
-    from db import get_db, get_cursor
-    with get_db() as conn:
-        cur = get_cursor(conn)
-        cutoff = (datetime.now() - timedelta(weeks=weeks)).strftime('%Y-%m-%d')
-
-        # Get weekly aggregates
-        cur.execute(
-            """SELECT
-                DATE_TRUNC('week', date) as week_start,
-                SUM(duration_min) as total_minutes,
-                COUNT(*) as session_count
-               FROM activity_log
-               WHERE profile_id=%s AND date >= %s
-               GROUP BY week_start
-               ORDER BY week_start""",
-            (profile_id, cutoff)
-        )
-        rows = cur.fetchall()
-
-    if not rows:
-        return {
-            "chart_type": "bar",
-            "data": {
-                "labels": [],
-                "datasets": []
-            },
-            "metadata": {
-                "confidence": "insufficient",
-                "data_points": 0,
-                "message": "Keine Aktivitätsdaten vorhanden"
-            }
-        }
-
-    labels = [row['week_start'].strftime('KW %V') for row in rows]
-    values = [safe_float(row['total_minutes']) for row in rows]
-
-    confidence = calculate_confidence(len(rows), weeks * 7, "general")
-
-    return {
-        "chart_type": "bar",
-        "data": {
-            "labels": labels,
-            "datasets": [
-                {
-                    "label": "Trainingsminuten",
-                    "data": values,
-                    "backgroundColor": "#1D9E75",
-                    "borderColor": "#085041",
-                    "borderWidth": 1
-                }
-            ]
-        },
-        "metadata": serialize_dates({
-            "confidence": confidence,
-            "data_points": len(rows),
-            "avg_minutes_week": round(sum(values) / len(values), 1) if values else 0,
-            "total_sessions": sum(row['session_count'] for row in rows)
-        })
-    }
 
 
 @router.get("/training-type-distribution")
@@ -1131,52 +1094,7 @@ def get_training_type_distribution_chart(
         Chart.js pie chart with training categories
     """
     profile_id = session['profile_id']
-
+    return build_training_type_distribution_chart_payload(profile_id, days)
-    dist_data = get_training_type_distribution_data(profile_id, days)
-
-    if dist_data['confidence'] == 'insufficient':
-        return {
-            "chart_type": "pie",
-            "data": {
-                "labels": [],
-                "datasets": []
-            },
-            "metadata": {
-                "confidence": "insufficient",
-                "data_points": 0,
-                "message": "Keine Trainingstypen-Daten"
-            }
-        }
-
-    labels = [item['category'] for item in dist_data['distribution']]
-    values = [item['count'] for item in dist_data['distribution']]
-
-    # Color palette for training categories
-    colors = [
-        "#1D9E75", "#3B82F6", "#F59E0B", "#EF4444",
-        "#8B5CF6", "#10B981", "#F97316", "#06B6D4"
-    ]
-
-    return {
-        "chart_type": "pie",
-        "data": {
-            "labels": labels,
-            "datasets": [
-                {
-                    "data": values,
-                    "backgroundColor": colors[:len(values)],
-                    "borderWidth": 2,
-                    "borderColor": "#fff"
-                }
-            ]
-        },
-        "metadata": {
-            "confidence": dist_data['confidence'],
-            "total_sessions": dist_data['total_sessions'],
-            "categorized_sessions": dist_data['categorized_sessions'],
-            "uncategorized_sessions": dist_data['uncategorized_sessions']
-        }
-    }
 
 
 @router.get("/quality-sessions")

docs/README.md

@@ -27,6 +27,7 @@ Dieser Ordner ist **immer mit Git versioniert**. Er ergänzt **`.claude/docs/`**
 | `issue-51-prompt-page-assignment.md` |
 | `issue-52-blood-pressure-dual-targets.md` |
 | `issue-53-phase-0c-multi-layer-architecture.md` |
+| `issue-fitness-dashboard-layer2b.md` |
 | `issue-54-dynamic-placeholder-system.md` |
 | `issue-55-dynamic-aggregation-methods.md` |
 | `issue-76-training-quality-goal-list-filter.md` |
@@ -56,4 +57,4 @@ Themen-Übersicht (lokal): **`.claude/docs/GITEA_ISSUES_INDEX.md`**
 
 ---
 
-**Stand:** 2026-04-08
+**Stand:** 2026-04-19

docs/issues/issue-fitness-dashboard-layer2b.md

@@ -0,0 +1,54 @@
+# Fitness-Dashboard (Layer 2b) – Abnahme & technische Zuordnung
+
+**Status:** umgesetzt (Frontend + Backend)  
+**Bezug:** Issue #53 (Phase 0c) – Layer 1 → Layer 2b Bundle → UI nur Darstellung  
+**Stand:** 2026-04-19
+
+---
+
+## Ziel
+
+- Eine **Fitness-Übersicht** auf `/activity` (Capture-Hub: „Fitness“), die **keine parallelen Berechnungen** im Client führt.
+- **Single Source of Truth:** `data_layer/activity_metrics` (und Scores/Focus wie bei den Platzhaltern), identische Chart-Payloads wie die bestehenden Chart-Endpunkte A1/A2.
+
+---
+
+## Backend
+
+| Bestandteil | Pfad / Endpoint |
+|-------------|-----------------|
+| Chart-Payloads (A1/A2) | `build_training_volume_chart_payload`, `build_training_type_distribution_chart_payload` in `backend/data_layer/activity_metrics.py` |
+| KPI-Kacheln (Struktur für UI) | `backend/data_layer/fitness_interpretation.py` → `build_fitness_dashboard_kpi_tiles` |
+| Bundle | `backend/data_layer/fitness_viz.py` → `get_fitness_dashboard_viz_bundle(profile_id, days)` |
+| API | `GET /api/charts/fitness-dashboard-viz?days=7…9999` in `backend/routers/charts.py` |
+
+**Hinweise:**
+
+- `days >= 9999` wählt eine **lange Historie** für die Zusammenfassung (analog Ernährungs-Bundle).
+- `calculate_quality_sessions_pct(profile_id, days)` unterstützt ein variables Fenster (wird auch vom Quality-Chart genutzt).
+
+---
+
+## Frontend
+
+| Bestandteil | Pfad |
+|-------------|------|
+| API-Client | `getFitnessDashboardViz(days)` in `frontend/src/utils/api.js` |
+| Darstellung | `frontend/src/components/FitnessDashboardOverview.jsx` |
+| Einbindung | `frontend/src/pages/ActivityPage.jsx` (oben, vor Tabs) |
+| Navigation Capture | `frontend/src/config/captureNav.js` – Label **Fitness**, Route `/activity` |
+
+---
+
+## Erweiterungen (optional)
+
+- Weitere Charts aus A3–A8 ins Bundle ziehen (weiterhin nur Payload-Referenz, keine Duplikat-Logik im Router).
+- Gitea-Issue anlegen/verknüpfen, falls formale Nachverfolgung gewünscht.
+
+---
+
+## Abnahme-Checkliste
+
+- [x] Bundle liefert `has_activity_entries`, `summary`, `kpi_tiles`, `charts.training_volume`, `charts.training_type_distribution`, `meta`.
+- [x] Keine clientseitige Neuberechnung der KPIs aus Rohlisten.
+- [x] `/api/charts/training-volume` und `/training-type-distribution` nutzen dieselben Builder wie das Bundle.

frontend/src/components/FitnessDashboardOverview.jsx

@@ -0,0 +1,211 @@
+import { useState, useEffect } from 'react'
+import {
+  BarChart,
+  Bar,
+  XAxis,
+  YAxis,
+  Tooltip,
+  ResponsiveContainer,
+  CartesianGrid,
+  PieChart,
+  Pie,
+} from 'recharts'
+import { api } from '../utils/api'
+import KpiTilesOverview from './KpiTilesOverview'
+
+const PERIODS = [
+  { v: 7, label: '7 Tage' },
+  { v: 28, label: '28 Tage' },
+  { v: 90, label: '90 Tage' },
+  { v: 9999, label: 'Gesamt' },
+]
+
+/**
+ * Layer 2b: Kennzahlen und Charts nur aus GET /api/charts/fitness-dashboard-viz (activity_metrics).
+ */
+export default function FitnessDashboardOverview() {
+  const [period, setPeriod] = useState(28)
+  const [viz, setViz] = useState(null)
+  const [loading, setLoading] = useState(true)
+  const [err, setErr] = useState(null)
+
+  useEffect(() => {
+    let cancelled = false
+    setLoading(true)
+    setErr(null)
+    api
+      .getFitnessDashboardViz(period)
+      .then((v) => {
+        if (!cancelled) setViz(v)
+      })
+      .catch((e) => {
+        if (!cancelled) setErr(e.message || 'Laden fehlgeschlagen')
+      })
+      .finally(() => {
+        if (!cancelled) setLoading(false)
+      })
+    return () => {
+      cancelled = true
+    }
+  }, [period])
+
+  if (loading) {
+    return (
+      <div className="card section-gap">
+        <div className="card-title">Fitness-Übersicht</div>
+        <div className="spinner" style={{ margin: 24 }} />
+      </div>
+    )
+  }
+
+  if (err) {
+    return (
+      <div className="card section-gap">
+        <div className="card-title">Fitness-Übersicht</div>
+        <div style={{ color: 'var(--danger)' }}>{err}</div>
+      </div>
+    )
+  }
+
+  if (!viz?.has_activity_entries) {
+    return (
+      <div className="card section-gap">
+        <div className="card-title">Fitness-Übersicht</div>
+        <p style={{ fontSize: 12, color: 'var(--text3)', lineHeight: 1.45 }}>
+          Noch keine Aktivitätsdaten. Sobald du Trainings erfasst oder importierst, erscheinen Kennzahlen und
+          Diagramme hier.
+        </p>
+      </div>
+    )
+  }
+
+  const vol = viz.charts?.training_volume
+  const typ = viz.charts?.training_type_distribution
+  const volRows = (vol?.data?.labels || []).map((name, i) => ({
+    name,
+    min: vol?.data?.datasets?.[0]?.data?.[i] ?? 0,
+  }))
+  const pieLabels = typ?.data?.labels || []
+  const pieVals = typ?.data?.datasets?.[0]?.data || []
+  const pieColors = typ?.data?.datasets?.[0]?.backgroundColor || []
+  const pieData = pieLabels.map((name, i) => ({
+    name,
+    value: pieVals[i],
+    fill: pieColors[i] || '#888780',
+  }))
+
+  const kpiTiles = (viz.kpi_tiles || []).map((t) => ({
+    ...t,
+    sublabel:
+      typeof t.sublabel === 'string' && t.sublabel.length > 42 ? `${t.sublabel.slice(0, 40)}…` : t.sublabel,
+  }))
+
+  const eff = viz.effective_window_days
+  const wUsed = viz.training_volume_weeks_used
+  const dTyp = viz.training_type_dist_days_used
+
+  return (
+    <div className="card section-gap">
+      <div className="card-title" style={{ display: 'flex', flexWrap: 'wrap', alignItems: 'center', gap: 12 }}>
+        <span>Fitness-Übersicht</span>
+        <label style={{ fontSize: 12, fontWeight: 500, color: 'var(--text3)', display: 'flex', alignItems: 'center', gap: 8 }}>
+          Zeitraum
+          <select
+            className="form-input"
+            style={{ maxWidth: 140, padding: '6px 10px', fontSize: 13 }}
+            value={period}
+            onChange={(e) => setPeriod(Number(e.target.value))}
+          >
+            {PERIODS.map((p) => (
+              <option key={p.v} value={p.v}>
+                {p.label}
+              </option>
+            ))}
+          </select>
+        </label>
+      </div>
+
+      <p style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 10 }}>
+        Kennzahlen und Charts nutzen dieselbe Berechnung wie die KI-Platzhalter (Aktivitäts-Data-Layer). Zusammenfassung
+        ca. <strong>{eff}</strong> Tage · Volumen-Chart <strong>{wUsed}</strong> Wochen · Typ-Verteilung{' '}
+        <strong>{dTyp}</strong> Tage
+        {viz.last_updated ? (
+          <>
+            {' '}
+            · letzte Aktivität <strong>{viz.last_updated}</strong>
+          </>
+        ) : null}
+        .
+      </p>
+
+      <KpiTilesOverview tiles={kpiTiles} heading="Kennzahlen" />
+
+      <div
+        style={{
+          display: 'grid',
+          gridTemplateColumns: 'repeat(auto-fit, minmax(280px, 1fr))',
+          gap: 16,
+          marginTop: 8,
+        }}
+      >
+        <div>
+          <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
+            Trainingsvolumen (Minuten / Woche)
+          </div>
+          {volRows.length >= 1 ? (
+            <ResponsiveContainer width="100%" height={200}>
+              <BarChart data={volRows} margin={{ top: 4, right: 8, bottom: 0, left: -12 }}>
+                <CartesianGrid stroke="var(--border)" strokeDasharray="3 3" />
+                <XAxis dataKey="name" tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} interval={0} angle={-35} textAnchor="end" height={48} />
+                <YAxis tick={{ fontSize: 9, fill: 'var(--text3)' }} tickLine={false} />
+                <Tooltip
+                  contentStyle={{
+                    background: 'var(--surface)',
+                    border: '1px solid var(--border)',
+                    borderRadius: 8,
+                    fontSize: 11,
+                  }}
+                  formatter={(v) => [`${Math.round(v)} min`, 'Volumen']}
+                />
+                <Bar dataKey="min" fill="#1D9E75" radius={[3, 3, 0, 0]} name="Minuten" />
+              </BarChart>
+            </ResponsiveContainer>
+          ) : (
+            <div style={{ fontSize: 12, color: 'var(--text3)' }}>Keine Wochendaten im gewählten Fenster.</div>
+          )}
+        </div>
+
+        <div>
+          <div style={{ fontSize: 12, fontWeight: 600, color: 'var(--text3)', marginBottom: 6 }}>
+            Training nach Kategorie
+          </div>
+          {pieData.length >= 1 ? (
+            <ResponsiveContainer width="100%" height={200}>
+              <PieChart>
+                <Pie
+                  data={pieData}
+                  dataKey="value"
+                  nameKey="name"
+                  cx="50%"
+                  cy="50%"
+                  outerRadius={72}
+                  label={({ name, percent }) => `${name} ${(percent * 100).toFixed(0)}%`}
+                />
+                <Tooltip
+                  contentStyle={{
+                    background: 'var(--surface)',
+                    border: '1px solid var(--border)',
+                    borderRadius: 8,
+                    fontSize: 11,
+                  }}
+                />
+              </PieChart>
+            </ResponsiveContainer>
+          ) : (
+            <div style={{ fontSize: 12, color: 'var(--text3)' }}>Keine kategorisierten Sessions im Fenster.</div>
+          )}
+        </div>
+      </div>
+    </div>
+  )
+}

frontend/src/config/captureNav.js

@@ -56,7 +56,7 @@ export const CAPTURE_HUB_TILES = [
   },
   {
     icon: '🏋️',
-    label: 'Aktivität',
+    label: 'Fitness',
     sub: 'Training manuell oder Apple Health importieren',
     to: '/activity',
     color: '#D4537E',

frontend/src/pages/ActivityPage.jsx

@@ -3,6 +3,7 @@ import { Upload, Pencil, Trash2, Check, X, CheckCircle } from 'lucide-react'
 import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, CartesianGrid } from 'recharts'
 import { api } from '../utils/api'
 import UsageBadge from '../components/UsageBadge'
+import FitnessDashboardOverview from '../components/FitnessDashboardOverview'
 import TrainingTypeSelect from '../components/TrainingTypeSelect'
 import BulkCategorize from '../components/BulkCategorize'
 import dayjs from 'dayjs'
@@ -912,7 +913,12 @@ export default function ActivityPage() {
 
   return (
     <div className="capture-page">
-      <h1 className="page-title">Aktivität</h1>
+      <h1 className="page-title">Fitness</h1>
+      <p style={{ fontSize: 12, color: 'var(--text3)', marginTop: -8, marginBottom: 12 }}>
+        Auswertung (Data-Layer) und Erfassung an einem Ort.
+      </p>
+
+      <FitnessDashboardOverview />
 
       <div className="tabs" style={{overflowX:'auto',flexWrap:'nowrap'}}>
         <button className={'tab'+(tab==='list'?' active':'')} onClick={()=>setTab('list')}>Verlauf</button>

frontend/src/pages/History.jsx

@@ -328,10 +328,10 @@ function InsightBox({ insights, slugs, onRequest, loading }) {
   const [expanded, setExpanded] = useState(null)
   const relevant = insights?.filter(i=>slugs.includes(i.scope))||[]
   const LABELS = {gesamt:'Gesamt',koerper:'Komposition',ernaehrung:'Ernährung',
-    aktivitaet:'Aktivität',gesundheit:'Gesundheit',ziele:'Ziele',
+    aktivitaet:'Fitness',gesundheit:'Gesundheit',ziele:'Ziele',
     pipeline:'🔬 Mehrstufige Analyse',
     pipeline_body:'Pipeline Körper',pipeline_nutrition:'Pipeline Ernährung',
-    pipeline_activity:'Pipeline Aktivität',pipeline_synthesis:'Pipeline Synthese',
+    pipeline_activity:'Pipeline Fitness',pipeline_synthesis:'Pipeline Synthese',
     pipeline_goals:'Pipeline Ziele'}
   return (
     <div style={{marginTop:14}}>
@@ -535,7 +535,7 @@ function BodySection({ profile, insights, onRequest, loadingSlug, filterActiveSl
       <BodyGoalsStrip grouped={groupedGoals} />
 
       <p style={{ fontSize: 11, color: 'var(--text3)', lineHeight: 1.45, marginBottom: 10 }}>
-        Daten und Kennzahlen aus dem Backend-Bundle (gleiche Quelle wie Platzhalter). Training: <strong>Verlauf → Aktivität</strong>.
+        Daten und Kennzahlen aus dem Backend-Bundle (gleiche Quelle wie Platzhalter). Training: <strong>Verlauf → Fitness</strong>.
       </p>
 
       {viz?.meta?.layer_2a_alignment && (
@@ -1101,7 +1101,7 @@ function NutritionSection({ profile, insights, onRequest, loadingSlug, filterAct
 function ActivitySection({ activities, insights, onRequest, loadingSlug, filterActiveSlugs, globalQualityLevel }) {
   const [period, setPeriod] = useState(30)
   if (!activities?.length) return (
-    <EmptySection text="Noch keine Aktivitätsdaten." to="/activity" toLabel="Aktivität erfassen"/>
+    <EmptySection text="Noch keine Aktivitätsdaten." to="/activity" toLabel="Fitness erfassen"/>
   )
   const cutoff = dayjs().subtract(period,'day').format('YYYY-MM-DD')
 
@@ -1132,7 +1132,7 @@ function ActivitySection({ activities, insights, onRequest, loadingSlug, filterA
 
   return (
     <div>
-      <SectionHeader title="🏋️ Aktivität" to="/activity" toLabel="Alle Einträge" lastUpdated={activities[0]?.date}/>
+      <SectionHeader title="🏋️ Fitness" to="/activity" toLabel="Alle Einträge" lastUpdated={activities[0]?.date}/>
       <PeriodSelector value={period} onChange={setPeriod}/>
 
       {/* Issue #31: Show active global quality filter */}
@@ -1518,7 +1518,7 @@ function RecoverySection({ insights, onRequest, loadingSlug, filterActiveSlugs }
 const TABS = [
   { id:'body',        label:'⚖️ Körper'     },
   { id:'nutrition',   label:'🍽️ Ernährung'  },
-  { id:'activity',    label:'🏋️ Aktivität'  },
+  { id:'activity',    label:'🏋️ Fitness'  },
   { id:'recovery',    label:'😴 Erholung'    },
   { id:'correlation', label:'🔗 Korrelation' },
   { id:'photos',      label:'📷 Fotos'       },

frontend/src/utils/api.js

@@ -639,6 +639,8 @@ export const api = {
   getBodyHistoryViz:                  (days=90)  => req(`/charts/body-history-viz?days=${days}`),
   /** Layer 2b: Verlauf Ernährung — Kennzahlen, Reihen, TDEE, Wochen-Chart (nutrition_metrics) */
   getNutritionHistoryViz:             (days=90)  => req(`/charts/nutrition-history-viz?days=${days}`),
+  /** Layer 2b: Fitness-Übersicht — KPI + Volumen/Typ-Charts (activity_metrics) */
+  getFitnessDashboardViz:             (days=28)  => req(`/charts/fitness-dashboard-viz?days=${days}`),
   getEnergyBalanceChart:              (days=28)  => req(`/charts/energy-balance?days=${days}`),
   getProteinAdequacyChart:            (days=28)  => req(`/charts/protein-adequacy?days=${days}`),
   getNutritionConsistencyChart:       (days=28)  => req(`/charts/nutrition-consistency?days=${days}`),