diff --git a/activity_sleep.ipynb b/activity_sleep.ipynb
index 77e2f17..84571f0 100644
--- a/activity_sleep.ipynb
+++ b/activity_sleep.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "5b2b0060",
    "metadata": {},
    "outputs": [],
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 16,
    "id": "52f55dde",
    "metadata": {},
    "outputs": [],
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 17,
    "id": "826e5af0",
    "metadata": {},
    "outputs": [],
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
    "id": "2cbef16d",
    "metadata": {},
    "outputs": [],
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 19,
    "id": "d3ec20f3",
    "metadata": {},
    "outputs": [],
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 20,
    "id": "845cc713",
    "metadata": {},
    "outputs": [],
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 21,
    "id": "05da5fe7",
    "metadata": {},
    "outputs": [],
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 298,
+   "execution_count": 22,
    "id": "6a8888ce",
    "metadata": {},
    "outputs": [],
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 299,
+   "execution_count": 23,
    "id": "47ffa998",
    "metadata": {},
    "outputs": [
@@ -1273,7 +1273,7 @@
        "[30 rows x 21 columns]"
       ]
      },
-     "execution_count": 299,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1284,7 +1284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 300,
+   "execution_count": 24,
    "id": "ce818c76",
    "metadata": {},
    "outputs": [
@@ -1315,7 +1315,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 300,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1342,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 25,
    "id": "41080d47",
    "metadata": {},
    "outputs": [
@@ -1377,7 +1377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "id": "cf54e6c7",
    "metadata": {},
    "outputs": [
@@ -1416,13 +1416,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 27,
    "id": "e2246df2",
    "metadata": {},
    "outputs": [
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1443,38 +1443,84 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "filtered_df_combined_4h_before_sleep = df_combine_activities_4_hours_before_sleep[\n",
-    "    (df_combine_activities_4_hours_before_sleep[\"activity_calories\"].notna()) #&\n",
-    "    #(df_combine_activities_4_hours_before_sleep['sleep_duration_needed_delta'].abs() < pd.Timedelta(hours=0.5))\n",
+    "    (df_combine_activities_4_hours_before_sleep[\"activity_calories\"].notna())\n",
     "]\n",
+    "\n",
+    "blue_count = len(filtered_df_combined_4h_before_sleep)\n",
+    "\n",
     "plt.figure(figsize=(8, 6))\n",
     "plt.scatter(\n",
     "    filtered_df_combined_4h_before_sleep['activity_heart_rate_average'],\n",
     "    filtered_df_combined_4h_before_sleep['sleep_score'],\n",
     "    alpha=0.7,\n",
-    "    color='blue'\n",
+    "    color='blue',\n",
+    "    label=f'Training <4h vor Schlaf (n={blue_count})'\n",
     ")\n",
     "filtered_df_combined_more_than_4h_before_sleep = df_combined[\n",
-    "     (df_combined[\"activity_calories\"].notna()) &\n",
-    "     (df_combined[\"bedtime_activity_ending_delta\"] > pd.Timedelta(hours=4))#&\n",
-    "     #(df_combined['sleep_duration_needed_delta'].abs() < pd.Timedelta(hours=0.5))\n",
-    " ]\n",
+    "    (df_combined[\"activity_calories\"].notna()) &\n",
+    "    (df_combined[\"bedtime_activity_ending_delta\"] > pd.Timedelta(hours=4))  # &\n",
+    "]\n",
     "\n",
+    "red_count = len(filtered_df_combined_more_than_4h_before_sleep)\n",
     "plt.scatter(\n",
     "    filtered_df_combined_more_than_4h_before_sleep['activity_heart_rate_average'],\n",
     "    filtered_df_combined_more_than_4h_before_sleep['sleep_score'],\n",
     "    alpha=0.7,\n",
     "    color='red',\n",
+    "    label=f'Training ≥4h vor Schlaf (n={red_count})'\n",
     ")\n",
     "\n",
     "plt.xlabel('Activity Heart Rate Average')\n",
     "plt.ylabel('HRV Status That Night')\n",
     "plt.ylim(60, 110)\n",
     "plt.grid(True)\n",
+    "plt.legend(title='Gruppe', loc='best')\n",
     "plt.show()\n",
     "\n",
+    "print(f'Number of blue points: {blue_count}')\n",
+    "print(f'Number of red points: {red_count}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "52f93b1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "counts = {\n",
+    "    \"Kein Training\": len(df_combined[(df_combined[\"activity_calories\"].isna())]),\n",
+    "    \"Training weniger als 4h vor Schlaf\": len(df_combined[(df_combined[\"activity_calories\"].notna()) & (\n",
+    "        df_combined[\"bedtime_activity_ending_delta\"] < pd.Timedelta(hours=4))]),\n",
+    "    \"Training mehr als 4h vor Schlaf\": len(df_combined[(df_combined[\"activity_calories\"].notna()) & (\n",
+    "        df_combined[\"bedtime_activity_ending_delta\"] >= pd.Timedelta(hours=4))]),\n",
+    "}\n",
     "\n",
-    "print(f'Number of blue points: {len(filtered_df_combined_4h_before_sleep)}')\n",
-    "print(f'Number of red points: {len(filtered_df_combined_more_than_4h_before_sleep)}')\n"
+    "labels = list(counts.keys())\n",
+    "sizes = list(counts.values())\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "plt.pie(\n",
+    "    sizes,\n",
+    "    labels=[f\"{lab} ({cnt})\" for lab, cnt in zip(labels, sizes)],\n",
+    "    autopct=\"%1.1f%%\",\n",
+    "    startangle=90,\n",
+    ")\n",
+    "plt.title(\n",
+    "    \"Verteilung: Kein Training / Training <4h vor Schlaf / Training ≥4h vor Schlaf\")\n",
+    "plt.axis(\"equal\")\n",
+    "plt.show()"
    ]
   }
  ],
diff --git a/data/sleep_activity_merged_365.csv b/data/sleep_activity_merged_365.csv
deleted file mode 100644
index 494fe0a..0000000
--- a/data/sleep_activity_merged_365.csv
+++ /dev/null
@@ -1,366 +0,0 @@
-Date,Score,Resting_heart_rate,Body_Battery,Breathing,HFV_status,Sleepquality,Sleepduration_h,Sleepduration_min,Need_for_sleep_h,Need_for_Sleep_min,Bedtime,Wake-up time,UniqueDate,Activity_type,Distance_km,Calories,Steps,Total increase,Total descent,Number of rounds,Training Stress Score®,Heart rate,Cadence (running),stride length,Average vertical ratio,vertical movement,Ground contact time,Cadence (running).1,Max. cadence (running).1,Normalized Power® (NP®),Maximum performance (20 min),Performance,Respiratory rate,Maximum heart rate,Max. cadence (running),Aerobics TE,Max. power,Maximum temperature,Maximum respiratory rate,MaxHeight,Minimum temperature,Minimum respiratory rate,MinHeight,Minutes_in_Motion
-2025-09-30,77,44,55,11.38,83,Ausreichend,6.78,407,7.00,420,11:22 PM,6:09 AM,2025-09-30,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-29,73,46,62,12.00,84,Ausreichend,9.10,546,8.67,520,10:52 PM,8:02 AM,2025-09-29,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-28,34,47,21,14.96,84,Schlecht,6.57,394,7.67,460,12:34 AM,8:09 AM,2025-09-28,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-27,93,41,67,11.13,92,Ausgezeichnet,8.53,512,7.67,460,10:39 PM,7:20 AM,2025-09-27,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-26,97,42,71,11.15,90,Ausgezeichnet,7.83,470,7.67,460,10:07 PM,5:57 AM,2025-09-26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-25,94,43,64,10.92,87,Ausgezeichnet,7.52,451,7.67,460,10:25 PM,5:57 AM,2025-09-25,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-24,80,43,63,10.83,87,Gut,7.58,455,7.17,430,12:20 AM,7:56 AM,2025-09-24,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-23,90,44,63,12.50,88,Ausgezeichnet,7.82,469,8.00,480,11:36 PM,7:36 AM,2025-09-23,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-22,76,46,54,12.62,91,Ausreichend,10.02,601,8.50,510,10:01 PM,8:33 AM,2025-09-22,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-21,99,42,87,10.79,95,Ausgezeichnet,8.62,517,8.00,480,9:08 PM,5:45 AM,2025-09-21,Laufen,42.65,2817,43684,107,105,43,,148,160,0.97,9.5,9.3,285,,,262,,251,30,165,184,5,397,36,41,56,26,19,33,275
-2025-09-20,72,45,43,12.53,86,Ausreichend,6.25,375,7.67,460,9:06 PM,3:33 AM,2025-09-20,Laufen,3.72,246,3304,9,12,11,,144,161,1.14,9.2,10.7,268,,,307,,303,30,161,173,2.8,407,33,38,47,30,20,40,20
-2025-09-19,82,44,51,11.81,87,Gut,8.12,487,7.50,450,10:40 PM,7:01 AM,2025-09-19,Radfahren,12.81,210,,125,,15,,116,,,,,,92,117,127,132,120,,134,,1.5,164,,,59,,,4,30
-2025-09-18,93,40,67,11.59,90,Ausgezeichnet,8.25,495,7.33,440,10:39 PM,6:54 AM,2025-09-18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-17,90,43,63,11.17,91,Ausgezeichnet,8.92,535,7.00,420,10:17 PM,7:21 AM,2025-09-17,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-16,85,40,66,10.58,90,Gut,9.45,567,8.67,520,9:21 PM,7:11 AM,2025-09-16,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-15,100,38,88,10.00,90,Ausgezeichnet,7.95,477,9.00,540,10:57 PM,6:55 AM,2025-09-15,Laufen,5.60,350,4922,21,23,6,,133,162,1.13,9,10.4,266,,,299,,297,26,151,172,2.8,407,33,39,437,28,20,421,31
-2025-09-14,16,42,,16.69,85,Schlecht,1.27,76,8.00,480,5:43 AM,6:59 AM,2025-09-14,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-13,80,43,60,11.71,93,Gut,7.42,445,8.00,480,12:22 AM,8:00 AM,2025-09-13,Laufen,9.01,549,7442,21,24,10,,144,167,1.2,8.2,9.9,266,,,341,,326,32,171,185,3.5,439,30,41,435,25,21,421,45
-2025-09-12,94,40,73,10.94,94,Ausgezeichnet,7.85,471,8.00,480,10:10 PM,6:02 AM,2025-09-12,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-11,93,39,75,10.60,91,Ausgezeichnet,7.73,464,8.00,480,10:14 PM,5:58 AM,2025-09-11,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-10,91,42,63,10.97,91,Ausgezeichnet,7.92,475,8.00,480,10:08 PM,6:03 AM,2025-09-10,Laufen,9.33,573,8234,28,23,,,135,166,1.12,8.6,9.9,265,,,298,,297,29,147,172,3.1,366,34,37,432,27,17,421,50
-2025-09-09,89,39,75,11.18,89,Gut,7.95,477,8.00,480,10:02 PM,5:59 AM,2025-09-09,Laufen,8.17,491,6632,25,29,10,,143,167,1.22,7.9,9.8,266,,,332,,325,32,162,191,3.6,423,31,41,437,25,22,420,40
-2025-09-08,59,46,39,13.22,89,Schlecht,8.00,480,8.00,480,11:02 PM,7:17 AM,2025-09-08,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-07,84,44,57,11.09,96,Gut,7.80,468,8.00,480,11:59 PM,7:50 AM,2025-09-07,Laufen,14.13,873,13034,42,45,15,,136,166,1.08,9.2,10.2,258,,,290,,289,29,146,202,3.4,390,31,38,437,24,16,421,79
-2025-09-06,97,38,79,10.47,95,Ausgezeichnet,7.95,477,8.50,510,11:37 PM,7:35 AM,2025-09-06,Laufen,39.71,1092,7148,286,17,33,,135.5,164,1.09,9,10.1,268,86,105,236.5,206,227.5,30,158,169,3.2,353,31,41,427,29,17,1,52
-2025-09-05,76,44,49,11.39,93,Ausreichend,6.85,411,8.00,480,11:23 PM,6:30 AM,2025-09-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-09-04,84,43,59,11.00,96,Gut,7.08,425,8.00,480,10:55 PM,6:00 AM,2025-09-04,Laufen,8.02,511,7218,25,23,9,,137,165,1.11,9,10.2,271,,,298,,296,30,150,178,3.1,416,35,40,445,29,21,436,44
-2025-09-03,86,42,52,11.14,97,Gut,7.72,463,8.00,480,10:18 PM,6:04 AM,2025-09-03,Mixed Martial Arts,,326,,,,1,,93,,,,,,,,,,,19,139,,0.6,,,33,,,11,,
-2025-09-02,95,40,63,11.00,98,Ausgezeichnet,7.83,470,8.00,480,10:06 PM,6:01 AM,2025-09-02,Laufen,9.87,605,8374,20,27,13,,141,169,1.17,8.1,9.7,264,,,318,,313,32,157,177,3.6,428,30,44,435,25,22,421,50
-2025-09-01,92,42,81,10.81,98,Ausgezeichnet,8.07,484,8.67,520,9:53 PM,6:03 AM,2025-09-01,Radfahren,18.55,301,,62,,15,,117,,,,,,83,91,170,181,160,,138,,2.2,212,,,28,,,-6,33
-2025-08-31,84,45,55,11.42,96,Gut,7.37,442,8.00,480,11:39 PM,7:04 AM,2025-08-31,Radfahren,76.72,1829,,523,498,17,154.2,135.5,,,,,,85.5,123.5,187,200,177,29.5,153,,3.5,936,24,37,717,15,14,426,76
-2025-08-30,84,46,51,10.77,95,Gut,8.08,485,7.67,460,11:20 PM,7:28 AM,2025-08-30,Laufen,16.04,986,14488,55,54,17,,137,165,1.1,9.4,10.5,258,,,301,,298,28,148,177,3.5,435,29,37,440,23,16,420,89
-2025-08-29,89,42,61,10.93,96,Gut,8.03,482,8.00,480,10:01 PM,6:03 AM,2025-08-29,Laufen,9.74,594,8692,34,28,21,,134,164,1.1,9.1,10.1,269,,,308,,296,32,160,193,3.1,523,34,40,436,26,18,422,54
-2025-08-28,93,41,74,10.56,95,Ausgezeichnet,7.75,465,8.00,480,10:03 PM,6:00 AM,2025-08-28,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-08-27,91,44,70,10.72,95,Ausgezeichnet,8.02,481,8.00,480,9:58 PM,6:00 AM,2025-08-27,Radfahren,31.33,494,,48,,8,,120,,,,,,85,95,167,170,156,,141,,2.5,186,,,18,,,13,55
-2025-08-26,86,39,63,11.00,94,Gut,6.83,410,8.00,480,11:09 PM,6:01 AM,2025-08-26,Laufen,8.54,514,7102,22,23,14,,143,167,1.19,8.3,9.8,263,,,335,,320,30,168,183,3.5,443,32,40,437,30,17,422,43
-2025-08-25,87,41,86,12.53,,Gut,7.27,436,8.83,530,10:40 PM,6:02 AM,2025-08-25,Mixed Martial Arts,,332,,,,1,,97,,,,,,,,,,,20,153,,1,,,33,,,12,,
-2025-08-24,68,46,47,12.03,93,Ausreichend,5.92,355,8.00,480,1:44 AM,7:52 AM,2025-08-24,Laufen,14.04,896,12856,45,43,15,,140,163,1.09,8.9,9.7,271,,,283,,281,,154,171,3.6,382,31,,439,25,,422,79
-2025-08-23,87,44,62,11.60,92,Gut,7.43,446,7.67,460,10:56 PM,6:29 AM,2025-08-23,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-08-22,98,39,63,10.67,91,Ausgezeichnet,8.23,494,7.67,460,9:42 PM,6:00 AM,2025-08-22,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-08-21,96,41,69,10.55,89,Ausgezeichnet,8.72,523,8.00,480,9:15 PM,5:58 AM,2025-08-21,Radfahren,23.09,432,,230,,23,,131,,,,,,89,107,191,206,176,,158,,3,272,,,83,,,2,43
-2025-08-20,87,43,77,10.07,89,Gut,7.80,468,8.00,480,9:45 PM,5:52 AM,2025-08-20,Laufen,7.81,489,6766,21,23,8,,138,164,1.15,8.6,10,266,,,300,,298,,151,174,3.2,420,30,,447,26,,436,41
-2025-08-19,84,43,61,10.90,89,Gut,7.12,427,8.00,480,11:14 PM,6:27 AM,2025-08-19,Laufen,9.28,590,8302,27,30,10,,140,161,1.11,9.2,10.4,269,,,292,,290,26,154,166,3.1,359,38,39,437,30,20,419,52
-2025-08-18,84,42,81,10.99,91,Gut,7.20,432,7.33,440,10:35 PM,6:00 AM,2025-08-18,Mixed Martial Arts,,452,,,,1,,116,,,,,,,,,,,23,158,,2.1,,,37,,,13,,
-2025-08-17,68,47,54,12.06,91,Ausreichend,7.33,440,8.00,480,11:59 PM,7:54 AM,2025-08-17,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-08-16,84,46,58,11.50,94,Gut,7.40,444,8.00,480,12:08 AM,7:37 AM,2025-08-16,Laufen,10.05,630,8122,21,31,11,,153,169,1.22,7.7,9.7,259,,,328,,324,31,168,179,4.1,412,29,41,438,25,19,419,49
-2025-08-15,77,45,56,11.41,94,Ausreichend,7.05,423,8.00,480,11:11 PM,6:28 AM,2025-08-15,Radfahren,42.40,1013,,575,557,9,82.6,124,,,,,,80,109,165,186,138,26,154,,3.1,713,30,41,952,24,12,423,106
-2025-08-14,96,40,56,10.07,94,Ausgezeichnet,8.03,482,8.00,480,9:47 PM,5:49 AM,2025-08-14,Radfahren,23.56,529,,408,,21,,133,,,,,,86,117,206,207,172,,165,,3.2,291,,,318,,,4,54
-2025-08-13,88,42,62,10.02,93,Gut,7.72,463,8.00,480,9:22 PM,5:06 AM,2025-08-13,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-08-12,92,42,58,10.08,90,Ausgezeichnet,7.68,461,8.00,480,10:45 PM,6:27 AM,2025-08-12,Laufen,9.07,576,7442,14,26,12,,150,162,1.2,8.3,10.2,267,,,316,,308,31,169,175,3.8,408,39,47,435,32,14,419,46
-2025-08-11,88,42,73,10.35,88,Gut,8.30,498,8.67,520,9:40 PM,6:01 AM,2025-08-11,Mixed Martial Arts,,431,,,,1,,105,,,,,,,,,,,23,156,,1.5,,,38,,,12,,
-2025-08-10,76,42,69,10.90,87,Ausreichend,6.85,411,8.00,480,10:46 PM,6:01 AM,2025-08-10,Radfahren,70.08,1770,,1052,1062,15,152.6,132,,,,,,80,128,180,210,158,29,156,,4,823,29,43,,21,16,411,164
-2025-08-09,76,46,55,11.04,87,Ausreichend,7.85,471,8.00,480,11:34 PM,7:45 AM,2025-08-09,Laufen,11.42,708,10254,27,26,12,,141,161,1.1,9.2,10.4,272,,,290,,289,30,151,169,3.2,389,35,43,435,29,19,422,64
-2025-08-08,94,41,45,10.00,82,Ausgezeichnet,8.13,488,8.00,480,9:50 PM,5:58 AM,2025-08-08,Radfahren,42.10,907,,217,212,9,81.2,139,,,,,,88,117,192,191,175,31,171,,3.3,689,33,41,488,24,14,424,77
-2025-08-07,91,44,61,10.29,76,Ausgezeichnet,8.43,506,8.00,480,9:34 PM,6:01 AM,2025-08-07,Wandern,1.55,88,1914,6,3,2,,75,80,0.83,,,,,,,,,,89,114,0.2,,,,427,,,422,18
-2025-08-06,88,44,60,10.31,78,Gut,8.08,485,8.00,480,9:53 PM,5:59 AM,2025-08-06,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-08-05,84,45,51,10.00,80,Gut,7.60,456,7.50,450,10:23 PM,6:02 AM,2025-08-05,Laufen,9.01,571,7700,22,27,11,,147,160,1.16,8.7,10.1,265,,,306,,293,32,168,212,3.5,386,36,45,437,29,17,421,48
-2025-08-04,93,40,66,10.00,83,Ausgezeichnet,8.17,490,7.67,460,9:51 PM,6:01 AM,2025-08-04,Radfahren,16.38,563,,676,,43,,130,,,,,,89,101,193,199,173,,162,,3.2,262,,,318,,,14,57
-2025-08-03,94,40,52,10.00,84,Ausgezeichnet,8.32,499,8.00,480,11:16 PM,7:36 AM,2025-08-03,Radfahren,17.10,318,,132,,24,,119,,,,,,89,103,168,167,154,,148,,2.3,262,,,100,,,,36
-2025-08-02,41,44,17,13.00,83,Schlecht,6.75,405,9.00,540,2:17 AM,9:08 AM,2025-08-02,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-08-01,36,46,14,13.00,88,Schlecht,4.20,252,8.00,480,12:38 AM,5:29 AM,2025-08-01,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-07-31,90,40,70,10.00,96,Ausgezeichnet,7.95,477,8.00,480,10:11 PM,6:09 AM,2025-07-31,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-07-30,83,43,61,10.00,94,Gut,7.33,440,8.00,480,10:43 PM,6:06 AM,2025-07-30,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-07-29,94,39,68,10.00,95,Ausgezeichnet,7.55,453,8.00,480,10:28 PM,6:01 AM,2025-07-29,Laufen,9.02,541,7158,20,19,11,,150,166,1.24,7.9,9.9,261,,,334,,324,32,168,176,3.8,423,31,42,439,26,18,423,44
-2025-07-28,88,42,70,10.00,93,Gut,7.97,478,8.00,480,10:05 PM,6:04 AM,2025-07-28,Radfahren,31.50,583,,266,,25,,125,,,,,,89,109,185,206,168,,153,,3.1,273,,,83,,,,60
-2025-07-27,82,45,59,11.00,93,Gut,7.85,471,8.33,500,11:56 PM,7:48 AM,2025-07-27,Laufen,10.63,677,9676,17,17,11,,141,163,1.09,9,10,275,,,283,,282,31,153,170,3.2,352,30,44,432,21,13,423,60
-2025-07-26,78,44,46,11.00,92,Ausreichend,7.03,422,7.67,460,12:32 AM,7:41 AM,2025-07-26,Radfahren,33.43,765,,621,,30,,140,,,,,,85,110,193,202,185,,154,,3.5,269,,,374,,,,72
-2025-07-25,88,39,63,10.00,93,Gut,8.22,493,7.67,460,10:03 PM,6:19 AM,2025-07-25,Laufen,9.65,586,8460,32,33,21,,136,161,1.11,8.7,9.7,283,,,301,,286,30,161,188,3.2,511,31,39,433,28,18,418,53
-2025-07-24,97,40,63,10.00,93,Ausgezeichnet,8.50,510,8.00,480,9:33 PM,6:04 AM,2025-07-24,Radfahren,19.64,583,,641,,25,,129,,,,,,90,108,185,206,168,,156,,3.1,276,,,421,,,1,60
-2025-07-23,96,40,75,10.00,92,Ausgezeichnet,8.07,484,8.00,480,10:00 PM,6:04 AM,2025-07-23,Radfahren,11.12,196,,159,,10,,111,,,,,,93,100,162,158,151,,136,,2,211,,,83,,,5,23
-2025-07-22,84,41,54,11.00,92,Gut,7.22,433,8.00,480,11:10 PM,6:25 AM,2025-07-22,Laufen,9.03,572,8172,33,30,20,,137,158,1.07,8.7,9.2,270,,,276,,261,,159,236,3.1,618,37,,438,30,,422,53
-2025-07-21,96,39,83,10.00,95,Ausgezeichnet,8.20,492,8.00,480,9:44 PM,6:03 AM,2025-07-21,Radfahren,25.24,444,,129,,21,,134,,,,,,89,121,209,206,172,,168,,3.2,289,,,28,,,-6,45
-2025-07-20,75,43,44,12.00,93,Ausreichend,7.58,455,8.00,480,11:31 PM,7:30 AM,2025-07-20,Radfahren,14.86,312,,118,,13,8.8,109,,,,,,92,112,120.5,118,114.5,,126,,1.3,162,,,223,,,120,21
-2025-07-19,79,45,52,11.00,90,Ausreichend,7.28,437,8.00,480,11:32 PM,6:49 AM,2025-07-19,Laufen,5.01,265,4584,27,18,6,,136,162,1.08,9.2,10.2,270,,,287,,284,28,158,167,2.2,364,32,37,439,28,21,416,28
-2025-07-18,93,41,67,10.00,92,Ausgezeichnet,7.75,465,8.00,480,10:12 PM,5:58 AM,2025-07-18,Laufen,9.27,571,8270,28,26,10,,137,162,1.11,9,10.2,276,,,288,,287,27,153,166,3,369,37,38,436,31,20,422,52
-2025-07-17,83,43,53,11.00,91,Gut,7.35,441,8.00,480,10:39 PM,6:01 AM,2025-07-17,Radfahren,65.61,1607,,687,,78,76.4,130.5,,,,,,84.5,125.5,196,208,181,32,163,,3.7,831,,41,516,,16,-6,72
-2025-07-16,94,42,65,10.00,94,Ausgezeichnet,7.83,470,8.00,480,10:11 PM,6:01 AM,2025-07-16,Laufen,9.01,542,7856,34,35,10,,133,161,1.14,8.9,10.3,275,,,297,,295,31,151,208,3.1,435,30,43,470,25,15,450,49
-2025-07-15,93,40,67,10.00,94,Ausgezeichnet,7.55,453,8.00,480,10:26 PM,6:00 AM,2025-07-15,Radfahren,31.15,750,,,,7,59.1,111.5,,,,,,83,94,128,205,119.5,29,160,,3.2,280,,42,,,14,,35
-2025-07-14,81,40,66,11.00,92,Gut,7.58,455,8.00,480,9:59 PM,5:53 AM,2025-07-14,Laufen,10.01,603,8286,27,33,16,,146,161,1.18,8.3,9.8,274,,,320,,303,33,168,175,3.7,461,36,41,437,29,15,423,52
-2025-07-13,50,48,24,12.00,93,Schlecht,7.90,474,8.50,510,10:53 PM,7:15 AM,2025-07-13,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-07-12,76,39,70,10.00,95,Ausreichend,6.03,362,8.00,480,9:22 PM,3:28 AM,2025-07-12,Wandern,24.51,2398,41586,2491,571,1,,106,80,0.65,,,,,,,,,,152,235,2.2,,,,,,,524,354
-2025-07-11,75,44,42,11.00,95,Ausreichend,6.78,407,8.67,520,11:32 PM,6:22 AM,2025-07-11,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-07-10,88,42,63,11.00,95,Gut,7.52,451,8.00,480,10:27 PM,6:01 AM,2025-07-10,Radfahren,67.94,1656,,1196,1183,14,,135,,,,,,,,,,,28,165,,3.8,,29,39,,16,13,416,177
-2025-07-09,90,39,80,10.00,94,Ausgezeichnet,7.62,457,8.00,480,10:20 PM,5:59 AM,2025-07-09,Laufen,7.02,434,6220,21,17,8,,136,162,1.12,8.8,9.9,271,,,289,,287,,151,228,3.1,364,29,,447,22,,433,38
-2025-07-08,89,36,68,10.00,94,Gut,7.63,458,8.00,480,10:10 PM,5:58 AM,2025-07-08,Radfahren,29.09,718,,,,6,59.7,134,,,,,,87,97,191,196,177,29,165,,3.2,273,,41,,,15,,60
-2025-07-07,82,37,62,11.00,94,Gut,6.63,398,8.00,480,11:23 PM,6:02 AM,2025-07-07,Radfahren,13.49,289,,,,3,15,103,,,,,,86,98,135,135,134,,112,,1.4,138,,,,,,,31
-2025-07-06,70,43,39,12.00,96,Ausreichend,7.23,434,8.50,510,2:30 AM,10:01 AM,2025-07-06,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-07-05,79,41,75,11.00,100,Ausreichend,7.35,441,8.67,520,11:30 PM,6:58 AM,2025-07-05,Radfahren,5.17,112,,,,2,3.6,109,,,,,,83,92,90,,89,,120,,0.6,121,,,,,,,17
-2025-07-04,70,43,47,11.00,100,Ausreichend,5.93,356,8.67,520,11:58 PM,6:01 AM,2025-07-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-07-03,95,39,61,9.00,102,Ausgezeichnet,7.60,456,8.00,480,10:20 PM,5:58 AM,2025-07-03,Mixed Martial Arts,29.37,1569,,,,7,69.9,147,,,,,,81,97,207,222,183,33.5,184,,4.3,348,,46,,,13,,30
-2025-07-02,98,38,60,10.00,102,Ausgezeichnet,7.85,471,8.00,480,10:04 PM,5:58 AM,2025-07-02,Radfahren,7.99,212,,,,2,23.2,124,,,,,,84,91,225,,188,27,166,,2.4,341,,47,,,16,,17
-2025-07-01,84,42,59,11.00,101,Gut,7.27,436,8.00,480,10:59 PM,6:21 AM,2025-07-01,Wandern,1.81,104,2378,11,14,2,,72,85,0.76,,,,,,,,,,84,153,0.3,,,,427,,,421,23
-2025-06-30,85,43,75,10.00,101,Gut,7.42,445,8.50,510,10:18 PM,6:02 AM,2025-06-30,Mixed Martial Arts,,449,,,,1,,116,,,,,,,,,,,24,165,,2.2,,,41,,,12,,
-2025-06-29,89,40,73,10.00,98,Gut,7.23,434,8.00,480,11:12 PM,6:30 AM,2025-06-29,Radfahren,57.89,1250,,1116,1111,12,,127,,,,,,,,,,,28,153,,3.4,,30,43,985,22,12,320,148
-2025-06-28,82,42,71,11.00,98,Gut,7.10,426,8.00,480,12:04 AM,7:16 AM,2025-06-28,Laufen,12.02,780,11184,41,41,13,,145,162,1.06,9.2,10,279,,,284,,282,28,156,169,3.5,370,38,38,436,30,13,423,70
-2025-06-27,85,42,58,10.00,94,Gut,7.38,443,8.00,480,10:54 PM,6:17 AM,2025-06-27,Radfahren,28.56,681,,,,6,62.4,134,,,,,,85,101,186,188,166,30,163,,3.2,253,,44,,,15,,60
-2025-06-26,97,39,66,10.00,92,Ausgezeichnet,8.35,501,8.00,480,9:15 PM,5:52 AM,2025-06-26,Mixed Martial Arts,,456,,,,1,,123,,,,,,,,,,,25,156,,2.3,,,36,,,11,,
-2025-06-25,82,44,63,10.00,91,Gut,7.33,440,8.00,480,10:43 PM,6:03 AM,2025-06-25,Laufen,1.40,84,1288,1,3,2,,122,158,1.08,9.7,10.7,276,,,280,,280,29,135,165,1.5,326,34,36,429,31,15,423,8
-2025-06-24,92,41,63,10.00,90,Ausgezeichnet,7.62,457,8.00,480,10:43 PM,6:27 AM,2025-06-24,Laufen,8.65,537,7198,38,50,13,,151,161,1.18,8.3,9.8,276,,,322,,307,32,171,179,3.8,439,35,42,439,32,15,419,45
-2025-06-23,88,44,61,11.00,87,Gut,7.72,463,8.00,480,10:07 PM,5:58 AM,2025-06-23,Mixed Martial Arts,,278,,,,1,,92,,,,,,,,,,,19,126,,0.3,,,29,,,11,,
-2025-06-22,89,41,81,10.00,89,Gut,7.18,431,8.67,520,11:18 PM,6:30 AM,2025-06-22,Radfahren,101.27,1934,,652,644,21,,143,,,,,,,,,,,32,165,,4.2,,31,41,706,21,17,425,201
-2025-06-21,74,46,52,12.00,81,Ausreichend,6.68,401,8.50,510,11:56 PM,7:02 AM,2025-06-21,Laufen,9.38,594,9424,52,49,10,,132,158,0.99,9.8,9.9,293,,,260,,259,26,145,171,2.8,350,38,37,436,29,15,418,60
-2025-06-20,73,48,44,11.00,81,Ausreichend,6.80,408,8.67,520,10:54 PM,6:00 AM,2025-06-20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-06-19,89,40,80,10.00,83,Gut,7.13,428,8.00,480,10:51 PM,6:00 AM,2025-06-19,Radfahren,1317.93,1766,,1324,1317,75,,117.5,,,,,,,,,,,30,159,,4,,31,40,,21,18,441,96
-2025-06-18,78,44,58,10.00,82,Ausreichend,6.73,404,8.00,480,9:54 PM,4:38 AM,2025-06-18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-06-17,83,46,49,10.00,84,Gut,7.57,454,8.00,480,10:30 PM,6:06 AM,2025-06-17,Schwimmen,653.71,824,,,,55,59.8,115,,,,,,82,104,182,191,168,29,162,,3.3,244,,40,,,16,,36
-2025-06-16,94,41,89,9.00,86,Ausgezeichnet,7.53,452,9.00,540,10:34 PM,6:07 AM,2025-06-16,Mixed Martial Arts,,478,,,,1,,117,,,,,,,,,,,23,161,,2.2,,,36,,,11,,
-2025-06-15,51,44,18,13.00,85,Schlecht,6.22,373,8.83,530,12:49 AM,7:43 AM,2025-06-15,Wandern,2.92,165,3934,50,48,3,,86,95,0.76,,,,,,,,,,103,156,0.4,,,,462,,,422,40
-2025-06-14,82,46,54,10.00,84,Gut,7.58,455,8.00,480,11:18 PM,7:00 AM,2025-06-14,Radfahren,82.28,2238,,1586,1489,17,,145,,,,,,,,,,,32,170,,5,,45,42,,27,18,431,209
-2025-06-13,98,42,63,9.00,85,Ausgezeichnet,7.92,475,7.67,460,10:04 PM,6:00 AM,2025-06-13,Radfahren,34.74,741,,193,187,7,,138,,,,,,,,,,,32,162,,3.5,,37,41,492,32,19,428,75
-2025-06-12,89,42,65,10.00,86,Gut,7.78,467,7.67,460,10:15 PM,6:03 AM,2025-06-12,Radfahren,18.61,438,,,,4,22.7,106,,,,,,84,96,121,122,118,,124,,2.1,267,,,,,,,52
-2025-06-11,99,44,63,10.00,84,Ausgezeichnet,8.10,486,7.33,440,9:54 PM,6:00 AM,2025-06-11,Wandern,2.00,109,2720,17,18,3,,75,90,0.76,,,,,,,,,,88,103,0.3,,,,432,,,422,28
-2025-06-10,91,42,80,10.00,82,Ausgezeichnet,7.47,448,8.00,480,10:32 PM,6:00 AM,2025-06-10,Radfahren,11.93,221,,,,3,8.9,98,,,,,,80,107,100,100,100,,111,,1.2,105,,,,,,,30
-2025-06-09,79,47,70,11.00,79,Ausreichend,8.98,539,8.00,480,10:33 PM,7:34 AM,2025-06-09,Radfahren,7.96,166,,,,2,4.4,95,,,,,,79,93,70,72,69,,106,,0.7,158,,,,,,,30
-2025-06-08,35,56,10,14.00,79,Schlecht,5.12,307,9.00,540,12:51 AM,6:10 AM,2025-06-08,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-06-07,,,,,,,,,,,,,2025-06-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-06-06,94,43,65,10.00,86,Ausgezeichnet,8.58,515,9.00,540,9:31 PM,6:07 AM,2025-06-06,Laufen,100.50,6376,113144,803,797,101,,128,135,0.87,8.7,7.6,313,,,207,,176,,161,250,5,388,29,,573,19,,428,808
-2025-06-05,67,40,44,10.00,83,Ausreichend,5.03,302,8.00,480,1:54 AM,6:56 AM,2025-06-05,Wandern,2.57,139,3186,5,3,3,,87,102,0.81,,,,,,,,,,96,122,0.4,,,,427,,,423,31
-2025-06-04,84,45,62,11.00,76,Gut,7.85,471,8.00,480,10:17 PM,6:11 AM,2025-06-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-06-03,78,44,55,10.00,78,Ausreichend,6.32,379,7.67,460,12:11 AM,6:31 AM,2025-06-03,Laufen,10.02,644,9148,44,39,11,,136,154,1.09,9,9.8,294,,,283,,270,,167,176,3.4,479,31,,13,28,,1,59
-2025-06-02,99,40,68,11.00,79,Ausgezeichnet,7.88,473,8.00,480,10:04 PM,5:58 AM,2025-06-02,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-06-01,73,45,41,12.00,79,Ausreichend,6.02,361,8.00,480,2:51 AM,9:00 AM,2025-06-01,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-31,82,45,71,11.00,81,Gut,7.57,454,7.67,460,1:58 AM,9:33 AM,2025-05-31,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-30,92,44,88,12.00,79,Ausgezeichnet,9.15,549,8.00,480,12:20 AM,9:30 AM,2025-05-30,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-29,42,51,23,12.00,81,Schlecht,5.38,323,8.00,480,3:20 AM,9:01 AM,2025-05-29,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-28,92,44,63,10.00,89,Ausgezeichnet,7.63,458,8.00,480,10:22 PM,6:00 AM,2025-05-28,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-27,88,44,75,10.00,89,Gut,7.42,445,8.00,480,10:22 PM,5:58 AM,2025-05-27,Radfahren,29.53,740,,,,6,77.2,146,,,,,,82,100,204,221,183,,168,,3.5,343,,,,,,,60
-2025-05-26,88,44,78,10.00,87,Gut,7.20,432,8.00,480,10:44 PM,5:58 AM,2025-05-26,Laufen,31.89,1182,9596,28,23,16,53.6,139,158,1.03,9.2,9.7,293,88,113,234.5,203,222,30,167,176,3.3,393,28,46,435,22,14,421,53
-2025-05-25,81,46,74,10.00,87,Gut,7.77,466,8.50,510,11:03 PM,6:51 AM,2025-05-25,Laufen,12.02,769,11934,29,20,13,,133,155,1,9.3,9.2,306,,,254,,246,,146,250,3.2,358,28,,432,20,,418,77
-2025-05-24,73,48,38,12.00,87,Ausreichend,6.35,381,8.00,480,11:23 PM,5:54 AM,2025-05-24,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-23,85,45,52,10.00,91,Gut,7.35,441,8.00,480,10:31 PM,6:07 AM,2025-05-23,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-22,96,43,68,10.00,90,Ausgezeichnet,7.63,458,8.00,480,10:29 PM,6:07 AM,2025-05-22,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-21,98,45,52,10.00,91,Ausgezeichnet,8.02,481,8.00,480,10:00 PM,6:02 AM,2025-05-21,Laufen,7.33,450,6770,14,7,15,,135,165,1.06,8.6,9.2,273,,,289,,279,31,160,192,3,504,29,39,444,21,17,435,42
-2025-05-20,79,44,57,11.00,92,Ausreichend,7.20,432,8.00,480,10:36 PM,6:02 AM,2025-05-20,Radfahren,19.15,457,,,,4,37.7,125,,,,,,84,90,171,179,164,28,149,,2.5,217,,35,,,19,,41
-2025-05-19,88,43,64,10.00,94,Gut,7.08,425,8.00,480,10:55 PM,6:02 AM,2025-05-19,Radfahren,37.34,1215,7902,26,21,15,73.1,138,163,1.02,7.8,8.2,300,87,100,237.5,201,220,30.5,170,182,3.4,374,29,42,432,22,17,414,54
-2025-05-18,82,45,63,11.00,95,Gut,7.17,430,7.67,460,10:56 PM,6:07 AM,2025-05-18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-17,87,44,71,10.00,94,Gut,7.45,447,8.00,480,10:32 PM,6:07 AM,2025-05-17,Laufbandtraining,25.20,1211,24804,,,27,,129.5,157,1.035,8.65,9.2,306.5,,,260,,249,28.5,143,169,2.5,380,,41,,,14,,79
-2025-05-16,79,44,,11.00,95,Ausreichend,6.95,417,8.00,480,10:55 PM,6:07 AM,2025-05-16,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-15,83,42,48,10.00,95,Gut,6.82,409,8.33,500,11:17 PM,6:14 AM,2025-05-15,Radfahren,27.14,636,,,,6,47.8,131,,,,,,85,96,159,169,154,,146,,2.9,228,,,,,,,60
-2025-05-14,85,41,67,10.00,93,Gut,7.30,438,8.00,480,10:40 PM,6:10 AM,2025-05-14,Radfahren,56.60,974,,266,258,12,,136,,,,,,,,,,,30,158,,3.2,,25,38,488,20,16,410,112
-2025-05-13,94,44,53,10.00,93,Ausgezeichnet,7.47,448,8.00,480,10:07 PM,5:37 AM,2025-05-13,Radfahren,25.54,642,,,,6,60.5,138,,,,,,82,118,192,206,180,31,157,,3.4,484,,40,,,15,,52
-2025-05-12,84,42,65,10.00,92,Gut,7.28,437,7.67,460,10:26 PM,5:54 AM,2025-05-12,Laufen,10.05,620,9382,37,40,11,,136,157,1.06,9.3,10.1,284,,,275,,274,26,148,162,3.1,359,31,38,431,28,12,409,60
-2025-05-11,86,47,62,11.00,91,Gut,7.52,451,8.67,520,11:46 PM,7:26 AM,2025-05-11,Radfahren,20.77,527,,,,5,60.4,140,,,,,,83,97,211,201,178,32,167,,3.2,345,,42,,,16,,43
-2025-05-10,92,43,67,10.00,92,Ausgezeichnet,7.52,451,8.00,480,10:52 PM,6:24 AM,2025-05-10,Radfahren,118.61,2435,,1927,1905,24,,133,,,,,,,,,,,29,159,,4,,27,40,,14,13,414,318
-2025-05-09,83,41,68,10.00,86,Gut,7.08,425,8.50,510,10:10 PM,5:15 AM,2025-05-09,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-08,76,44,56,11.00,83,Ausreichend,6.60,396,8.00,480,10:24 PM,5:14 AM,2025-05-08,Laufbandtraining,14.00,768,12624,,,14,,140,159,1.09,9,10.1,290,,,276,,275,28,150,164,3.1,346,,41,,,15,,80
-2025-05-07,97,41,67,10.00,84,Ausgezeichnet,7.75,465,8.67,520,9:29 PM,5:14 AM,2025-05-07,Laufbandtraining,8.22,484,6766,,,9,,146,156,1.18,8.8,10.2,276,,,304,,290,31,170,191,3.4,454,,39,,,22,,42
-2025-05-06,78,43,65,10.00,81,Ausreichend,6.18,371,8.00,480,10:49 PM,5:00 AM,2025-05-06,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-05,90,45,66,10.00,78,Ausgezeichnet,7.48,449,8.00,480,10:25 PM,5:55 AM,2025-05-05,Laufbandtraining,10.00,582,8490,,,10,,145,160,1.09,9.3,10.3,278,,,286,,280,31,170,191,3.5,470,,43,,,22,,53
-2025-05-04,84,45,87,11.00,78,Gut,8.58,515,9.00,540,10:19 PM,6:57 AM,2025-05-04,Radfahren,22.52,559,,,,5,58.1,144,,,,,,80,92,201,219,180,34,168,,3.5,332,,49,,,18,,46
-2025-05-03,35,50,21,12.00,76,Schlecht,3.48,209,8.50,510,3:54 AM,7:27 AM,2025-05-03,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-05-02,80,48,54,10.00,76,Gut,8.73,524,8.33,500,10:23 PM,7:13 AM,2025-05-02,Radfahren,62.45,1208,,277,278,13,,139,,,,,,89,126,,,,30,161,,3.6,,29,39,490,20,18,413,120
-2025-05-01,89,43,72,10.00,74,Gut,7.57,454,8.00,480,11:01 PM,6:42 AM,2025-05-01,Radfahren,140.39,3279,,2750,2742,30,,144,,,,,,79,127,,,,31.5,167,,5,,32,46,,20,15,424,187
-2025-04-30,91,45,61,11.00,70,Ausgezeichnet,8.38,503,8.33,500,9:23 PM,6:00 AM,2025-04-30,Laufen,10.02,652,9524,27,24,11,,143,158,1.04,9.9,10.6,287,,,274,,273,27,160,169,3.4,464,36,38,446,26,17,433,61
-2025-04-29,87,48,53,10.00,69,Gut,7.85,471,8.00,480,10:24 PM,6:15 AM,2025-04-29,Radfahren,21.54,523,,,,5,49.6,141,,,,,,83,95,187,170,169,32,166,,3.2,346,,44,,,20,,45
-2025-04-28,83,45,77,10.00,71,Gut,7.05,423,8.33,500,11:03 PM,6:15 AM,2025-04-28,Mixed Martial Arts,,3,,,,1,,79,,,,,,,,,,,13,94,,2,,,15,,,12,,
-2025-04-27,89,46,65,11.00,70,Gut,7.72,463,8.00,480,10:15 PM,5:59 AM,2025-04-27,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-26,78,50,45,12.00,71,Ausreichend,8.13,488,8.00,480,10:05 PM,6:15 AM,2025-04-26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-25,85,51,52,12.00,74,Gut,8.40,504,8.00,480,9:56 PM,6:30 AM,2025-04-25,Laufen,5.01,314,4930,19,16,6,,126,157,1.01,9.6,9.7,290,,,257,,255,,138,165,2.6,311,26,,11,23,,5,32
-2025-04-24,78,50,42,11.00,77,Ausreichend,7.52,451,8.00,480,10:40 PM,6:29 AM,2025-04-24,Wandern,2.98,170,3812,13,11,3,,72,88,0.8,,,,,,,,,,121,205,0.7,,,,9,,,2,36
-2025-04-23,77,48,47,11.00,76,Ausreichend,8.23,494,8.00,480,10:12 PM,6:31 AM,2025-04-23,Laufen,8.51,540,7962,31,26,9,,137,159,1.06,9.3,10.1,284,,,280,,278,29,157,165,3.3,354,26,38,10,22,17,4,50
-2025-04-22,73,44,67,11.00,77,Ausreichend,8.65,519,7.67,460,10:09 PM,7:05 AM,2025-04-22,Laufen,8.11,536,6962,52,44,9,,152,160,1.16,8.9,10.4,278,,,319,,315,34,174,201,4,541,31,48,29,26,21,3,44
-2025-04-21,89,47,46,11.00,73,Gut,8.67,520,8.00,480,10:24 PM,7:05 AM,2025-04-21,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-20,87,45,57,11.00,73,Gut,8.68,521,8.00,480,10:16 PM,7:05 AM,2025-04-20,Wandern,12.20,955,18276,591,584,1,,102,87,0.74,,,,,,,,,,133,250,1.6,,,,,,,,175
-2025-04-19,93,45,67,10.00,72,Ausgezeichnet,8.45,507,8.33,500,10:36 PM,7:05 AM,2025-04-19,Wandern,11.32,848,16830,622,615,1,,98,80,0.73,,,,,,,,,,145,235,1.3,,,,,,,,162
-2025-04-18,90,47,75,11.00,69,Ausgezeichnet,9.60,576,9.00,540,9:02 PM,6:45 AM,2025-04-18,Wandern,9.40,541,12042,208,213,10,,87,97,0.8,,,,,,,,,,127,163,0.9,,,,,,,927,116
-2025-04-17,42,49,25,11.00,69,Schlecht,3.77,226,8.67,520,11:14 PM,3:00 AM,2025-04-17,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-16,76,50,48,10.00,75,Ausreichend,6.45,387,8.00,480,11:42 PM,6:14 AM,2025-04-16,Radfahren,7.77,206,,,,2,25.3,140,,,,,,84,98,219,,181,28,174,,2.7,323,,49,,,15,,16
-2025-04-15,72,50,31,12.00,77,Ausreichend,7.43,446,7.67,460,10:47 PM,6:15 AM,2025-04-15,Radfahren,27.63,662,,,,6,66.8,140,,,,,,82,96,183,197,161,31,163,,3.4,303,,40,,,16,,60
-2025-04-14,90,46,59,10.00,83,Ausgezeichnet,8.28,497,8.00,480,9:43 PM,6:01 AM,2025-04-14,Mixed Martial Arts,,541,,,,1,,117,,,,,,,,,,,24,166,,2.5,,,39,,,12,,
-2025-04-13,84,49,60,10.00,83,Gut,7.68,461,8.50,510,11:19 PM,7:00 AM,2025-04-13,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-12,74,49,38,11.00,82,Ausreichend,8.28,497,8.00,480,10:27 PM,6:45 AM,2025-04-12,Radfahren,71.85,1437,,572,530,15,,134,,,,,,83,125,,,,29,168,,3.5,,31,41,722,15,17,407,172
-2025-04-11,79,48,52,10.00,84,Ausreichend,7.43,446,7.67,460,10:51 PM,6:18 AM,2025-04-11,Radfahren,26.29,578,,,,6,40,127,,,,,,86,95,145,155,138,,146,,2.7,158,,,,,,,60
-2025-04-10,94,45,72,10.00,87,Ausgezeichnet,8.25,495,8.00,480,10:06 PM,6:21 AM,2025-04-10,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-09,86,43,50,11.00,76,Gut,7.98,479,8.00,480,10:00 PM,6:09 AM,2025-04-09,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-08,91,45,70,10.00,75,Ausgezeichnet,8.12,487,8.33,500,10:02 PM,6:10 AM,2025-04-08,Radfahren,15.27,451,4280,24,17,7,14.7,98,87,0.82,,,,83,102,130,134,128,,127,153,1.8,152,,,433,,,418,33
-2025-04-07,81,46,64,11.00,74,Gut,7.75,465,8.00,480,10:13 PM,6:00 AM,2025-04-07,Wandern,2.41,105,2894,27,26,3,,81,90,0.85,,,,,,,,,,96,113,0.2,,,,444,,,422,28
-2025-04-06,80,46,41,12.00,75,Gut,8.07,484,8.00,480,11:35 PM,7:45 AM,2025-04-06,Wandern,4.51,282,6078,106,106,5,,80,82,0.78,,,,,,,,,,118,219,0.4,,,,506,,,423,61
-2025-04-05,73,46,33,12.00,79,Ausreichend,7.88,473,8.00,480,11:20 PM,7:26 AM,2025-04-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-04,91,41,48,10.00,77,Ausgezeichnet,8.12,487,8.67,520,11:20 PM,7:27 AM,2025-04-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-03,46,43,17,16.00,77,Schlecht,6.05,363,8.00,480,12:59 AM,7:26 AM,2025-04-03,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-02,87,43,46,11.00,88,Gut,7.23,434,8.00,480,9:39 PM,5:07 AM,2025-04-02,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-04-01,81,44,56,11.00,85,Gut,6.97,418,7.33,440,11:36 PM,6:56 AM,2025-04-01,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-31,98,41,62,10.00,82,Ausgezeichnet,8.03,482,8.00,480,10:05 PM,6:13 AM,2025-03-31,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-30,82,41,77,11.00,76,Gut,6.80,408,8.50,510,11:11 PM,6:01 AM,2025-03-30,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-29,54,50,28,13.00,74,Schlecht,7.13,428,8.00,480,11:24 PM,7:00 AM,2025-03-29,Laufen,4.10,248,3668,28,39,5,,132,156,1.11,9.2,10.3,285,,,319,,290,26,147,164,2.3,796,29,34,35,26,17,4,24
-2025-03-28,88,42,58,10.00,78,Gut,8.50,510,8.00,480,10:29 PM,7:01 AM,2025-03-28,Radfahren,204.20,4131,,4091,4027,42,,130,,,,,,74,119,,,,28,159,,3.6,,32,44,,17,13,9,262
-2025-03-27,87,46,66,10.00,78,Gut,8.98,539,8.33,500,10:01 PM,7:00 AM,2025-03-27,Laufen,8.03,509,6986,48,44,9,,144,159,1.14,9,10.5,282,,,310,,305,28,163,190,3.3,468,34,39,35,26,17,2,44
-2025-03-26,66,43,45,12.00,77,Ausreichend,8.67,520,8.67,520,9:44 PM,6:59 AM,2025-03-26,Radfahren,53.74,896,,813,802,11,,115,,,,,,76,134,,,,26,139,,2.1,,34,33,94,23,12,6,129
-2025-03-25,76,50,58,11.00,81,Ausreichend,8.63,518,8.67,520,10:21 PM,7:00 AM,2025-03-25,Radfahren,100.53,2295,,2601,2601,21,,128,,,,,,69,124,,,,28,158,,3.7,,39,40,,10,12,23,306
-2025-03-24,59,48,28,13.00,83,Schlecht,7.68,461,9.00,540,10:11 PM,6:04 AM,2025-03-24,Radfahren,78.89,1763,,1449,1440,16,,135,,,,,,79,139,,,,30,161,,3.9,,38,39,928,19,14,8,198
-2025-03-23,81,46,60,11.00,90,Gut,8.27,496,8.00,480,10:39 PM,7:00 AM,2025-03-23,Radfahren,86.70,2396,,1908,1896,18,,139,,,,,,75,126,,,,30,167,,3.9,,30,41,646,15,17,7,125
-2025-03-22,79,46,47,10.00,93,Ausreichend,7.63,458,8.00,480,10:48 PM,6:34 AM,2025-03-22,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-21,94,42,57,10.00,95,Ausgezeichnet,7.90,474,8.00,480,10:01 PM,5:56 AM,2025-03-21,Radfahren,30.05,732,,,,7,83.3,139,,,,,,79,98,199,204,171,32,163,,3.5,262,,44,,,19,,63
-2025-03-20,80,44,56,11.00,94,Gut,7.85,471,8.67,520,10:26 PM,6:21 AM,2025-03-20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-19,82,47,57,10.00,94,Gut,7.77,466,7.50,450,10:56 PM,6:44 AM,2025-03-19,Radfahren,107.16,1915,,636,619,22,,134,,,,,,84,128,,,,28,167,,3.7,,23,41,738,10,15,411,243
-2025-03-18,89,43,59,10.00,92,Gut,7.35,441,8.00,480,9:32 PM,4:53 AM,2025-03-18,Laufen,1482.45,753,6726,32,27,72,,117.5,161,1.1,8.7,9.7,278,,,283,,281,27,153,173,3.1,372,28,36,429,16,20,404,35
-2025-03-17,84,44,61,10.00,89,Gut,7.63,458,8.00,480,10:19 PM,5:57 AM,2025-03-17,Radfahren,33.29,815,,,,7,81.3,140,,,,,,82,94,186,203,171,32,163,,3.7,302,,42,,,14,,70
-2025-03-16,97,41,77,10.00,86,Ausgezeichnet,7.88,473,8.00,480,11:13 PM,7:07 AM,2025-03-16,Radfahren,60.04,1239,,,,13,51.2,133,,,,,,85,99,142,155,150,,152,,3.3,159,,,,,,,132
-2025-03-15,80,44,58,10.00,84,Gut,7.18,431,7.50,450,12:12 AM,7:27 AM,2025-03-15,Laufen,12.33,778,11246,42,37,13,,145,161,1.09,9.5,10.6,271,,,293,,291,29,157,173,3.6,370,27,38,429,18,14,410,70
-2025-03-14,96,41,58,10.00,84,Ausgezeichnet,9.00,540,8.00,480,9:25 PM,6:25 AM,2025-03-14,Radfahren,27.65,662,,,,6,67,137,,,,,,78,89,183,197,161,31,161,,3.4,310,,44,,,14,,60
-2025-03-13,73,44,58,11.00,80,Ausreichend,8.30,498,8.00,480,9:56 PM,6:15 AM,2025-03-13,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-12,75,49,51,11.00,78,Ausreichend,7.17,430,8.00,480,10:30 PM,5:53 AM,2025-03-12,Radfahren,25.02,594,,,,6,66.9,138,,,,,,80,105,189,197,157,31,162,,3.2,310,,41,,,15,,55
-2025-03-11,78,48,37,11.00,80,Ausreichend,7.32,439,8.00,480,10:35 PM,5:56 AM,2025-03-11,Radfahren,35.21,808,,,,8,75,138,,,,,,79,93,170,188,154,31,160,,3.5,287,,39,,,16,,76
-2025-03-10,84,48,46,10.00,83,Gut,7.50,450,8.00,480,10:26 PM,5:56 AM,2025-03-10,Schwimmen,395.83,712,,,,39,54.6,114,,,,,,78,92,176,173,155,31,161,,3.1,259,,43,,,17,,25
-2025-03-09,87,46,59,11.00,84,Gut,7.72,463,8.00,480,11:21 PM,7:15 AM,2025-03-09,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-08,92,44,67,10.00,84,Ausgezeichnet,7.58,455,8.00,480,11:32 PM,7:07 AM,2025-03-08,Wandern,14.04,892,15358,247,251,15,,120.5,128.5,0.89,9.3,10.4,273,,,293,,289,31,171,185,3.7,436,28,42,676,21,22,410,60
-2025-03-07,78,44,45,10.00,84,Ausreichend,7.40,444,8.00,480,10:39 PM,6:12 AM,2025-03-07,Laufen,8.59,552,7494,37,35,30,,148,160,1.11,8.8,9.7,272,,,294,,284,,168,228,3.7,532,28,,430,20,,410,47
-2025-03-06,86,45,54,11.00,87,Gut,7.65,459,8.00,480,10:31 PM,6:12 AM,2025-03-06,Krafttraining,9.63,654,8388,36,32,11,,118,158,1.15,8.8,10.1,273,,,296,,290,,170,236,3.9,385,30,,432,22,,411,29
-2025-03-05,81,47,54,10.00,89,Gut,7.43,446,8.00,480,10:12 PM,5:56 AM,2025-03-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-03-04,90,43,61,10.00,90,Ausgezeichnet,7.93,476,8.00,480,11:13 PM,7:10 AM,2025-03-04,Laufen,9.03,609,8096,32,27,10,,152,161,1.11,9,10,275,,,285,,284,,169,165,3.8,387,28,,424,15,,405,50
-2025-03-03,96,42,68,10.00,90,Ausgezeichnet,7.90,474,8.00,480,10:04 PM,6:00 AM,2025-03-03,Laufen,6.20,396,5466,22,18,7,,140,159,1.13,8.9,10.1,274,,,288,,286,,162,174,3.2,455,27,,426,16,,409,34
-2025-03-02,72,41,57,10.00,92,Ausreichend,5.72,343,7.17,430,1:08 AM,6:57 AM,2025-03-02,Mixed Martial Arts,,195,80,,,2,,110.5,,,,,,,,,,,23,158,,1,,,31,,,17,,5
-2025-03-01,94,42,61,10.00,95,Ausgezeichnet,9.13,548,8.00,480,9:47 PM,7:01 AM,2025-03-01,Wandern,4.72,246,5978,23,16,5,,73,103,0.8,,,,,,,,,,86,192,0.8,,,,431,,,410,57
-2025-02-28,81,42,62,9.00,96,Gut,7.23,434,7.17,430,10:44 PM,6:09 AM,2025-02-28,Wandern,2.68,56,3404,15,13,3,,59,106,0.79,,,,,,,,,,80,119,0.1,,,,430,,,415,32
-2025-02-27,82,40,51,10.00,94,Gut,6.00,360,7.33,440,9:57 PM,4:06 AM,2025-02-27,Krafttraining,3.53,140,4396,18,15,5,,73.5,105,0.81,,,,,,,,,,103,122,0.1,,,,430,,,417,28
-2025-02-26,92,42,55,9.00,96,Ausgezeichnet,7.92,475,7.33,440,10:14 PM,6:16 AM,2025-02-26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-02-25,99,39,46,10.00,96,Ausgezeichnet,8.07,484,7.00,420,10:00 PM,6:05 AM,2025-02-25,Wandern,5.86,252,7326,19,18,6,,67,98,0.81,,,,,,,,,,83,114,0.6,,,,426,,,410,70
-2025-02-24,93,39,44,10.00,95,Ausgezeichnet,8.25,495,7.50,450,9:57 PM,6:12 AM,2025-02-24,Wandern,3.53,185,4458,22,12,4,,72,102,0.8,,,,,,,,,,90,167,0.5,,,,431,,,417,42
-2025-02-23,98,40,41,10.00,94,Ausgezeichnet,8.03,482,7.00,420,10:47 PM,6:58 AM,2025-02-23,Wandern,3.73,193,4750,18,21,4,,75,98,0.8,,,,,,,,,,101,154,0.5,,,,431,,,417,45
-2025-02-22,97,40,51,10.00,93,Ausgezeichnet,7.63,458,8.00,480,9:30 PM,5:08 AM,2025-02-22,Wandern,3.81,145,4902,22,20,4,,67,96,0.8,,,,,,,,,,94,117,0.2,,,,430,,,417,46
-2025-02-21,88,38,66,13.00,85,Gut,8.10,486,7.00,420,10:36 PM,6:59 AM,2025-02-21,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-02-20,83,42,67,11.00,86,Gut,6.75,405,8.00,480,10:10 PM,4:55 AM,2025-02-20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-02-19,90,45,61,12.00,84,Ausgezeichnet,7.60,456,8.00,480,10:12 PM,5:51 AM,2025-02-19,Radfahren,29.44,914,1984,,,9,50.2,118.6666667,155,1.04,9.5,9.9,287,80,94,193.5,150,190,,143,163,2.9,259,,,,,,,32
-2025-02-18,89,42,62,12.00,85,Gut,8.10,486,8.00,480,10:10 PM,6:19 AM,2025-02-18,Radfahren,27.54,665,,,,6,77.2,140,,,,,,81,127,179,185,162,33,160,,3.6,430,,44,,,22,,60
-2025-02-17,86,43,71,11.00,85,Gut,7.72,463,8.00,480,10:02 PM,5:55 AM,2025-02-17,Schwimmen,427.00,603,2010,,,40,,105.6666667,155,0.56,12.5,6.9,317,,,144,,134,,147,163,1.5,284,,,,,,,15
-2025-02-16,90,42,74,11.00,85,Ausgezeichnet,7.55,453,8.83,530,11:24 PM,6:58 AM,2025-02-16,Radfahren,70.19,1424,,,,15,103.6,126,,,,,,82,106,126,130,124,,147,,3.5,147,,,,,,,162
-2025-02-15,53,42,26,11.00,84,Schlecht,6.32,379,8.00,480,12:38 AM,7:07 AM,2025-02-15,Laufen,12.06,749,10820,41,29,13,,146,160,1.1,9.4,10.6,280,,,281,,278,29,165,188,3.6,392,27,39,433,12,21,407,68
-2025-02-14,91,43,70,11.00,92,Ausgezeichnet,7.57,454,8.00,480,10:14 PM,5:55 AM,2025-02-14,Radfahren,35.81,795,,,,8,78.9,133,,,,,,86,108,157,180,144,,161,,3.7,186,,,,,,,80
-2025-02-13,79,45,47,12.00,90,Ausreichend,6.93,416,7.50,450,10:51 PM,5:47 AM,2025-02-13,Radfahren,40.15,873,,,,9,76,133,,,,,,84,132,144,152,138,,153,,3.5,369,,,,,,,91
-2025-02-12,99,41,66,12.00,88,Ausgezeichnet,8.57,514,8.00,480,8:59 PM,5:56 AM,2025-02-12,Mixed Martial Arts,,401,,,,1,,105,,,,,,,,,,,22,151,,2,,,34,,,11,,
-2025-02-11,86,41,57,11.00,85,Gut,7.53,452,7.33,440,10:24 PM,5:56 AM,2025-02-11,Krafttraining,2.04,373,2530,,,4,,115,155,1.04,9.6,9.9,290,,,236,,217,,141,165,2,315,,,,,,,20
-2025-02-10,95,41,66,11.00,84,Ausgezeichnet,7.87,472,8.00,480,10:24 PM,6:16 AM,2025-02-10,Schwimmen,575.00,238,,,,41,,97,,,,,,,,,,,,144,,1.1,,,,,,,,10
-2025-02-09,93,42,64,12.00,84,Ausgezeichnet,8.07,484,8.00,480,11:23 PM,7:27 AM,2025-02-09,Radfahren,72.87,1453,,,,15,102.7,126,,,,,,84,110,122,125,120,,145,,3.6,206,,,,,,,170
-2025-02-08,90,43,75,12.00,82,Ausgezeichnet,7.85,471,8.00,480,11:19 PM,7:11 AM,2025-02-08,Laufen,13.03,811,11282,41,35,14,,148,155,1.12,9.2,10.3,277,,,283,,271,30,169,179,4,566,28,33,429,15,26,409,73
-2025-02-07,77,44,45,15.00,80,Ausreichend,6.42,385,8.00,480,11:30 PM,5:55 AM,2025-02-07,Krafttraining,2.63,333,2488,,,5,,115.5,153,1.33,8.6,11.4,274,,,308,,302,,146,161,2,378,,,,,,,18
-2025-02-06,80,49,43,12.00,83,Gut,7.35,441,8.00,480,11:04 PM,6:25 AM,2025-02-06,Radfahren,39.07,850,,,,8,74.5,133,,,,,,84,129,145,151,139,,157,,3.4,364,,,,,,,88
-2025-02-05,93,44,62,12.00,84,Ausgezeichnet,7.40,444,8.00,480,10:31 PM,5:55 AM,2025-02-05,Mixed Martial Arts,,480,,,,1,,117,,,,,,,,,,,24,166,,2.4,,,44,,,12,,
-2025-02-04,86,44,54,13.00,83,Gut,7.63,458,7.67,460,10:40 PM,6:18 AM,2025-02-04,Radfahren,27.30,625,,,,6,64.7,137,,,,,,83,107,164,173,152,,164,,3.7,283,,,,,,,60
-2025-02-03,91,44,65,11.00,84,Ausgezeichnet,7.83,470,8.00,480,10:06 PM,5:57 AM,2025-02-03,Schwimmen,604.00,730,3936,,,53,,108,153,1.2,9.1,10.9,285,,,280,,272,,145,160,2.2,389,,,,,,,18
-2025-02-02,80,47,42,12.00,85,Gut,7.33,440,7.67,460,11:56 PM,7:30 AM,2025-02-02,Radfahren,50.07,1008,,,,11,70.1,129,,,,,,81,92,126,133,124,,156,,3.2,3631,,,,,,,115
-2025-02-01,86,46,48,12.00,88,Gut,8.18,491,7.67,460,11:32 PM,7:43 AM,2025-02-01,Laufen,10.03,646,9006,35,29,11,,150,158,1.11,9.3,10.4,281,,,276,,272,36,170,167,3.7,364,27,43,431,14,29,410,58
-2025-01-31,93,42,54,12.00,89,Ausgezeichnet,7.98,479,7.33,440,10:18 PM,6:18 AM,2025-01-31,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-01-30,89,46,64,13.00,87,Gut,9.17,550,7.67,460,9:09 PM,6:20 AM,2025-01-30,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-01-29,81,47,40,13.00,90,Gut,8.23,494,7.50,450,9:43 PM,5:57 AM,2025-01-29,Krafttraining,3.68,454,3024,,,5,,125.5,151,1.26,8.8,11,276,,,293,,281,,154,170,2.6,387,,,,,,,23
-2025-01-28,92,41,64,12.00,85,Ausgezeichnet,8.32,499,7.50,450,9:59 PM,6:24 AM,2025-01-28,Radfahren,18.93,384,,,,4,30.3,117,,,,,,84,106,134,136,122,,157,,2.4,469,,,,,,,45
-2025-01-27,94,41,54,12.00,83,Ausgezeichnet,8.20,492,7.67,460,9:52 PM,6:04 AM,2025-01-27,Schwimmen,275.00,184,,,,25,,83,,,,,,,,,,,,143,,0.4,,,,,,,,5
-2025-01-26,93,43,71,13.00,81,Ausgezeichnet,8.85,531,8.00,480,11:02 PM,7:53 AM,2025-01-26,Radfahren,20.62,409,,,,5,27,118,,,,,,84,91,120,124,116,,133,,2.3,127,,,,,,,49
-2025-01-25,78,45,54,13.00,80,Ausreichend,8.00,480,8.00,480,11:18 PM,7:28 AM,2025-01-25,Wandern,25.51,714,7868,75,49,11,25.1,98,91,0.77,,,,79,87,119,125,113,,134,120,2.2,127,,,466,,,415,62
-2025-01-24,65,46,50,13.00,77,Ausreichend,8.62,517,8.50,510,10:54 PM,8:14 AM,2025-01-24,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-01-16,79,44,54,10.00,91,Ausreichend,6.63,398,8.00,480,10:21 PM,5:05 AM,2025-01-16,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-01-15,92,42,55,12.00,93,Ausgezeichnet,7.97,478,8.00,480,10:02 PM,6:01 AM,2025-01-15,Krafttraining,4.00,500,3964,,,5,,118,158,0.94,9.9,9.2,290,,,227,,216,,159,177,2.3,362,,,,,,,21
-2025-01-14,80,42,41,11.00,90,Gut,7.62,457,7.50,450,10:39 PM,6:19 AM,2025-01-14,Radfahren,21.15,539,,,,5,54.8,130,,,,,,89,100,161,166,147,30,158,,3.2,321,,41,,,15,,53
-2025-01-13,94,44,63,12.00,90,Ausgezeichnet,7.78,467,7.67,460,10:32 PM,6:19 AM,2025-01-13,Mixed Martial Arts,,494,,,,1,,114,,,,,,,,,,,23,170,,2.6,,,40,,,10,,
-2025-01-12,87,43,51,12.00,90,Gut,9.22,553,8.00,480,10:50 PM,8:04 AM,2025-01-12,Radfahren,55.74,1323,,,,12,95,123,,,,,,79,131,124,128,122,,143,,3.3,717,,,,,,,153
-2025-01-11,79,47,50,11.00,87,Ausreichend,8.22,493,8.00,480,11:20 PM,7:44 AM,2025-01-11,Wandern,7.27,800,8006,33,16,9,,108.3333333,131.5,0.94,9.4,9.9,283,,,251,,248,,153,167,2.5,346,,,432,,,413,29
-2025-01-10,84,42,53,11.00,86,Gut,7.57,454,8.00,480,10:33 PM,6:16 AM,2025-01-10,Radfahren,24.22,583,,,,5,47.2,126,,,,,,85,125,136,144,130,,149,,2.9,544,,,,,,,64
-2025-01-09,81,40,59,13.00,84,Gut,7.22,433,7.67,460,10:01 PM,5:15 AM,2025-01-09,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-01-08,82,43,50,13.00,82,Gut,8.48,509,8.00,480,9:27 PM,5:57 AM,2025-01-08,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-01-07,83,48,43,12.00,80,Gut,8.23,494,8.00,480,9:39 PM,5:55 AM,2025-01-07,Laufen,9.01,459,7318,28,21,10,,154,165,1.22,7.8,9.6,262,,,304,,302,33,167,177,3.4,398,27,43,426,11,26,408,45
-2025-01-06,88,45,57,13.00,81,Gut,7.57,454,8.83,530,10:57 PM,6:39 AM,2025-01-06,Krafttraining,4.00,544,4144,,,6,,116,158,1.18,8.9,10.2,275,,,275,,266,,149,173,2.2,374,,,,,,,24
-2025-01-05,76,45,39,12.00,79,Ausreichend,6.68,401,8.67,520,2:13 AM,8:55 AM,2025-01-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2025-01-04,68,45,46,10.00,81,Ausreichend,6.53,392,8.00,480,1:07 AM,7:57 AM,2025-01-04,Laufen,17.01,1080,16794,38,39,18,,141,162,1.01,9.4,9.5,278,,,251,,249,,152,172,3.6,348,23,,430,13,,411,104
-2025-01-03,84,46,60,12.00,81,Gut,7.90,474,8.00,480,10:58 PM,6:56 AM,2025-01-03,Krafttraining,3.30,567,3536,,,5,,121,157,1.14,9.1,10.3,278,,,279,,259,,150,167,2.2,385,,,,,,,22
-2025-01-02,91,44,80,12.00,82,Ausgezeichnet,9.22,553,8.83,530,9:39 PM,6:54 AM,2025-01-02,Radfahren,55.51,1183,,,,12,88.5,126,,,,,,76,96,133,135,132,28,142,,3.2,3683,,34,,,16,,128
-2025-01-01,56,46,20,12.00,83,Schlecht,6.83,410,8.00,480,1:12 AM,8:10 AM,2025-01-01,Laufen,11.29,699,10368,91,81,13,,125.5,127.5,0.925,8.2,9.8,261,,,296,,289,31,174,180,3.8,396,26,42,432,9,21,409,35
-2024-12-31,83,44,56,12.00,84,Gut,7.17,430,8.00,480,10:14 PM,5:25 AM,2024-12-31,Skifahren,19.19,410,,2007,3652,7,,102.5,,,,,,,,,,,,147,,0.8,,,,,,,,37
-2024-12-30,83,45,52,13.00,85,Gut,7.62,457,8.00,480,10:15 PM,5:52 AM,2024-12-30,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-29,85,45,69,12.00,86,Gut,7.93,476,8.50,510,11:17 PM,7:27 AM,2024-12-29,Laufen,10.03,610,8352,35,29,11,,152,168,1.19,7.8,9.5,265,,,303,,300,30,167,193,4,445,27,41,430,10,18,406,50
-2024-12-28,73,47,39,12.00,84,Ausreichend,6.52,391,7.67,460,1:12 AM,7:55 AM,2024-12-28,Wandern,11.32,942,13648,268,272,15,,104.75,116.3333333,0.93,8.9,10.4,271,,,293,,283,,153,228,2.5,324,,,,,,924,33
-2024-12-27,87,46,49,13.00,82,Gut,7.57,454,8.00,480,11:37 PM,7:22 AM,2024-12-27,Radfahren,60.81,1262,,,,13,92.5,123,,,,,,80,102,127,130,121,,139,,3.2,186,,,,,,,140
-2024-12-26,94,44,81,12.00,83,Ausgezeichnet,8.92,535,8.00,480,10:00 PM,6:55 AM,2024-12-26,Krafttraining,5.68,618,4872,,,7,,126,160,1.24,8.7,10.7,268,,,298,,292,,157,167,2.5,356,,,,,,,25
-2024-12-25,66,44,30,11.00,83,Ausreichend,5.97,358,8.00,480,1:13 AM,7:20 AM,2024-12-25,Laufen,7.46,460,6518,26,21,8,,144,163,1.13,8.5,9.7,273,,,285,,278,31,165,182,3.3,455,29,40,429,13,21,408,40
-2024-12-24,90,45,49,11.00,88,Ausgezeichnet,8.07,484,7.67,460,11:21 PM,7:26 AM,2024-12-24,Wandern,4.42,125,5660,30,29,5,,61,98,0.79,,,,,,,,,,76,167,0.2,,,,439,,,422,53
-2024-12-23,80,44,68,12.00,87,Gut,7.27,436,8.00,480,11:56 PM,7:15 AM,2024-12-23,Krafttraining,2.16,329,2328,,,4,,111.5,162,1.05,8.3,8.8,283,,,256,,247,26,137,174,1.3,304,,39,,,12,,18
-2024-12-22,79,47,51,11.00,89,Ausreichend,8.18,491,8.00,480,11:17 PM,7:55 AM,2024-12-22,Radfahren,41.44,858,,,,9,64.9,129,,,,,,85,139,133,147,127,,154,,3.2,357,,,,,,,96
-2024-12-21,65,47,35,11.00,92,Ausreichend,7.78,467,8.00,480,11:38 PM,7:30 AM,2024-12-21,Laufen,14.64,929,13694,60,55,15,,144,161,1.07,9.2,9.8,277,,,266,,264,,154,171,3.6,362,27,,434,17,,412,85
-2024-12-20,89,41,57,12.00,88,Gut,7.08,425,8.00,480,10:30 PM,5:35 AM,2024-12-20,Seilspringen,,37,,,,12,,123,,,,,,,,,,,,139,,0.7,,,,,,,,3
-2024-12-19,98,43,68,11.00,87,Ausgezeichnet,8.15,489,8.00,480,9:54 PM,6:04 AM,2024-12-19,Radfahren,19.12,409,,,,4,,120,,,,,,85,116,141,137,122,,143,,2.6,225,,,,,,,47
-2024-12-18,84,44,60,11.00,86,Gut,7.32,439,8.00,480,10:21 PM,6:00 AM,2024-12-18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-17,82,43,60,12.00,84,Gut,7.25,435,8.00,480,11:13 PM,6:29 AM,2024-12-17,Laufen,7.45,464,6364,23,19,8,,146,161,1.16,8.4,10,272,,,285,,283,31,165,174,3.3,394,28,40,431,10,17,411,40
-2024-12-16,88,41,66,10.00,82,Gut,7.33,440,8.50,510,10:55 PM,6:16 AM,2024-12-16,Mixed Martial Arts,,673,10,,,1,,132,,,,,,,,,,,29,169,,3.2,,,41,,,9,,
-2024-12-15,81,43,74,11.00,81,Gut,6.65,399,8.83,530,11:55 PM,6:41 AM,2024-12-15,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-14,44,46,22,12.00,80,Schlecht,6.55,393,8.00,480,1:43 AM,8:18 AM,2024-12-14,Radfahren,65.92,1130,,,,14,,129,,,,,,77,114,130,135,130,29,144,,3,186,,37,,,12,,150
-2024-12-13,85,45,60,12.00,90,Gut,7.90,474,8.00,480,10:34 PM,6:32 AM,2024-12-13,Laufen,13.36,824,11826,44,37,14,,143,159,1.12,9.6,10.9,276,,,287,,285,31,163,219,3.5,381,28,40,430,11,14,410,75
-2024-12-19,98,43,68,11.00,87,Ausgezeichnet,8.15,489,8.00,480,9:54 PM,6:04 AM,2024-12-19,Radfahren,19.12,409,,,,4,,120,,,,,,85,116,141,137,122,,143,,2.6,225,,,,,,,47
-2024-12-18,84,44,60,11.00,86,Gut,7.32,439,8.00,480,10:21 PM,6:00 AM,2024-12-18,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-17,82,43,60,12.00,84,Gut,7.25,435,8.00,480,11:13 PM,6:29 AM,2024-12-17,Laufen,7.45,464,6364,23,19,8,,146,161,1.16,8.4,10,272,,,285,,283,31,165,174,3.3,394,28,40,431,10,17,411,40
-2024-12-16,88,41,66,10.00,82,Gut,7.33,440,8.50,510,10:55 PM,6:16 AM,2024-12-16,Mixed Martial Arts,,673,10,,,1,,132,,,,,,,,,,,29,169,,3.2,,,41,,,9,,
-2024-12-15,81,43,74,11.00,81,Gut,6.65,399,8.83,530,11:55 PM,6:41 AM,2024-12-15,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-14,44,46,22,12.00,80,Schlecht,6.55,393,8.00,480,1:43 AM,8:18 AM,2024-12-14,Radfahren,65.92,1130,,,,14,,129,,,,,,77,114,130,135,130,29,144,,3,186,,37,,,12,,150
-2024-12-13,85,45,60,12.00,90,Gut,7.90,474,8.00,480,10:34 PM,6:32 AM,2024-12-13,Laufen,13.36,824,11826,44,37,14,,143,159,1.12,9.6,10.9,276,,,287,,285,31,163,219,3.5,381,28,40,430,11,14,410,75
-2024-12-12,86,45,58,12.00,87,Gut,8.32,499,7.17,430,9:38 PM,6:22 AM,2024-12-12,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-11,88,41,52,12.00,87,Gut,7.17,430,7.67,460,9:49 PM,4:59 AM,2024-12-11,Laufen,5.24,313,4564,17,17,6,,138,159,1.14,8.8,10.2,275,,,282,,276,29,165,174,2.9,434,29,42,427,12,17,409,29
-2024-12-10,84,41,50,11.00,87,Gut,7.30,438,8.00,480,11:01 PM,6:25 AM,2024-12-10,Laufen,10.04,604,8592,26,21,11,,143,160,1.16,8.8,10.5,276,,,288,,287,29,155,180,3.4,449,29,35,427,15,14,409,54
-2024-12-09,90,40,57,12.00,90,Ausgezeichnet,7.90,474,8.50,510,10:06 PM,6:01 AM,2024-12-09,Mixed Martial Arts,,384,,,,1,,103,,,,,,,,,,,22,162,,2.1,,,38,,,11,,
-2024-12-08,71,41,57,12.00,89,Ausreichend,8.22,493,8.67,520,9:42 PM,6:15 AM,2024-12-08,Radfahren,50.02,997,,,,11,,115,,,,,,76,97,122,125,119,27,137,,3,137,,34,,,15,,117
-2024-12-07,75,41,70,14.00,91,Ausreichend,6.30,378,8.00,480,1:26 AM,8:13 AM,2024-12-07,Laufen,7.39,466,6684,29,26,8,,139,159,1.1,9.2,10.4,279,,,285,,283,29,152,171,3,350,28,37,431,15,13,410,42
-2024-12-06,80,50,47,12.00,87,Gut,7.88,473,8.00,480,11:26 PM,7:22 AM,2024-12-06,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-05,94,45,63,12.00,89,Ausgezeichnet,7.95,477,8.00,480,9:50 PM,5:48 AM,2024-12-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-04,74,45,50,12.00,89,Ausreichend,7.78,467,8.33,500,9:21 PM,5:31 AM,2024-12-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-12-03,92,41,62,12.00,89,Ausgezeichnet,7.58,455,8.00,480,10:43 PM,6:20 AM,2024-12-03,Mixed Martial Arts,,667,12,,,1,,139,,,,,,,,,,,30,172,,3.5,,,44,,,12,,
-2024-12-02,91,43,66,12.00,88,Ausgezeichnet,8.72,523,8.33,500,9:50 PM,6:39 AM,2024-12-02,Mixed Martial Arts,,643,,,,1,,139,,,,,,,,,,,29,174,,3.3,,,42,,,12,,
-2024-12-01,78,44,52,12.00,87,Ausreichend,6.57,394,8.33,500,11:31 PM,6:15 AM,2024-12-01,Laufen,6.01,382,5600,6,10,7,,135,157,1.07,9.7,10.6,279,,,272,,270,26,152,170,2.7,453,30,37,323,27,20,315,36
-2024-11-30,79,46,53,11.00,88,Ausreichend,7.20,432,8.00,480,10:56 PM,6:20 AM,2024-11-30,Mixed Martial Arts,,632,,,,1,,136,,,,,,,,,,,29,175,,3.4,,,42,,,12,,
-2024-11-29,89,45,62,13.00,89,Gut,8.23,494,8.00,480,10:55 PM,7:11 AM,2024-11-29,Mixed Martial Arts,,648,,,,1,,143,,,,,,,,,,,30,174,,3.6,,,43,,,12,,
-2024-11-28,87,45,65,11.00,90,Gut,9.02,541,8.00,480,9:57 PM,7:00 AM,2024-11-28,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-27,97,43,64,12.00,82,Ausgezeichnet,8.25,495,7.33,440,10:31 PM,7:00 AM,2024-11-27,Mixed Martial Arts,,677,20,,,1,,144,,,,,,,,,,,29,179,,3.5,,,42,,,11,,
-2024-11-26,82,43,69,12.00,85,Gut,7.68,461,8.83,530,10:31 PM,6:29 AM,2024-11-26,Mixed Martial Arts,,745,,,,1,,132,,,,,,,,,,,27,173,,3.2,,,41,,,11,,
-2024-11-25,87,41,63,12.00,86,Gut,7.63,458,8.00,480,10:41 PM,6:30 AM,2024-11-25,Mixed Martial Arts,,833,,,,1,,141,,,,,,,,,,,29,179,,4,,,44,,,11,,
-2024-11-24,86,41,82,11.00,87,Gut,7.32,439,8.00,480,10:53 PM,6:30 AM,2024-11-24,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-23,93,42,93,13.00,86,Ausgezeichnet,9.72,583,7.33,440,8:55 PM,6:40 AM,2024-11-23,Mixed Martial Arts,,909,,,,1,,147,,,,,,,,,,,31,179,,4.1,,,45,,,16,,
-2024-11-22,30,45,18,12.00,84,Schlecht,2.00,120,7.00,420,2:49 AM,4:51 AM,2024-11-22,Laufen,5.99,380,6786,6,5,6,,129,156,0.88,10.8,9.6,304,,,227,,229,26,137,170,2.2,366,33,34,322,32,18,312,44
-2024-11-21,44,46,17,11.00,85,Schlecht,7.18,431,7.67,460,10:53 PM,6:55 AM,2024-11-21,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-20,93,41,53,12.00,93,Ausgezeichnet,8.92,535,8.00,480,9:31 PM,6:26 AM,2024-11-20,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-19,79,42,56,12.00,89,Ausreichend,6.65,399,8.00,480,10:46 PM,5:27 AM,2024-11-19,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-18,92,41,66,12.00,88,Ausgezeichnet,7.85,471,8.50,510,10:32 PM,6:25 AM,2024-11-18,Mixed Martial Arts,,463,,,,1,,122,,,,,,,,,,,24,167,,2.6,,,41,,,10,,
-2024-11-17,85,45,44,12.00,88,Gut,6.98,419,7.00,420,12:51 AM,7:55 AM,2024-11-17,Radfahren,21.49,519,,,,5,,124,,,,,,82,121,130,140,123,28,139,,2.8,186,,34,,,17,,58
-2024-11-16,63,41,33,11.00,88,Ausreichend,4.92,295,7.67,460,11:32 PM,4:29 AM,2024-11-16,Laufen,6.97,452,6714,24,18,17,,142,154,1.01,9.3,9.2,278,,,281,,248,31,169,195,3.1,505,29,43,429,17,23,410,44
-2024-11-15,74,44,50,11.00,88,Ausreichend,8.07,484,7.67,460,10:06 PM,6:36 AM,2024-11-15,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-14,94,41,65,11.00,90,Ausgezeichnet,8.55,513,8.00,480,9:58 PM,6:32 AM,2024-11-14,Laufen,7.08,434,6596,27,24,18,,134,154,1.04,9.4,9.6,276,,,276,,255,31,165,243,3,619,27,39,429,15,22,412,44
-2024-11-13,67,43,47,12.00,92,Ausreichend,7.33,440,7.67,460,10:38 PM,6:11 AM,2024-11-13,Radfahren,15.05,362,,,,4,,114,,,,,,74,103,134,151,122,27,132,,2.5,254,,34,,,14,,42
-2024-11-12,92,40,57,14.00,92,Ausgezeichnet,7.98,479,7.67,460,10:11 PM,6:11 AM,2024-11-12,Laufen,6.65,471,5622,21,20,28,,139,157,1.17,8.9,10.3,269,,,305,,291,34,172,175,3.6,375,,50,428,,23,410,20
-2024-11-11,94,42,68,12.00,86,Ausgezeichnet,8.22,493,8.00,480,9:58 PM,6:15 AM,2024-11-11,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-10,91,41,67,11.00,87,Ausgezeichnet,7.80,468,8.00,480,11:40 PM,7:28 AM,2024-11-10,Laufen,10.77,698,9230,39,35,12,,150,157,1.16,9.2,10.9,273,,,296,,294,36,166,164,4.1,444,,44,431,,28,411,59
-2024-11-09,73,47,44,12.00,85,Ausreichend,7.53,452,7.67,460,11:15 PM,7:18 AM,2024-11-09,Radfahren,22.74,504,,,,5,,122,,,,,,75,98,137,154,126,29,151,,2.9,464,,39,,,19,,56
-2024-11-08,92,39,42,12.00,86,Ausgezeichnet,7.62,457,7.67,460,11:14 PM,6:51 AM,2024-11-08,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-07,90,40,37,13.00,84,Ausgezeichnet,8.37,502,7.33,440,11:11 PM,7:33 AM,2024-11-07,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-06,81,41,45,13.00,83,Gut,8.62,517,8.67,520,10:40 PM,7:33 AM,2024-11-06,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-05,59,53,17,13.00,81,Schlecht,5.70,342,7.50,450,10:33 PM,4:31 AM,2024-11-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-11-04,93,42,64,13.00,84,Ausgezeichnet,8.95,537,8.50,510,9:03 PM,6:12 AM,2024-11-04,Mixed Martial Arts,,420,16,,,1,,111,,,,,,,,,,,24,159,,2,,,37,,,11,,
-2024-11-03,65,45,44,12.00,81,Ausreichend,7.12,427,8.00,480,12:50 AM,8:09 AM,2024-11-03,Wandern,4.36,238,5390,24,19,5,,91,105,0.81,,,,,,,,,,116,111,0.9,,,,431,,,417,51
-2024-11-02,92,43,71,11.00,83,Ausgezeichnet,8.28,497,8.67,520,11:35 PM,7:57 AM,2024-11-02,Laufen,18.52,1222,17028,33,55,19,,153,157,1.08,10.2,11.3,281,,,288,,287,33,163,206,4.3,391,,41,427,,19,398,109
-2024-11-01,78,44,63,11.00,81,Ausreichend,6.55,393,8.67,520,2:06 AM,8:39 AM,2024-11-01,Laufen,7.19,451,6106,25,17,8,,144,158,1.17,9,10.7,273,,,301,,296,29,173,170,3.4,431,,42,431,,16,410,39
-2024-10-31,83,42,80,12.00,79,Gut,6.90,414,9.00,540,11:52 PM,6:46 AM,2024-10-31,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-30,70,47,39,11.00,78,Ausreichend,5.72,343,8.50,510,12:17 AM,6:05 AM,2024-10-30,Laufbandtraining,5.03,295,3988,,,6,,143,156,1.29,8.6,10.8,274,,,308,,300,,163,168,3.1,369,,,,,,,26
-2024-10-29,82,43,62,12.00,83,Gut,6.15,369,7.67,460,12:32 AM,6:47 AM,2024-10-29,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-28,74,43,53,12.00,85,Ausreichend,5.97,358,7.17,430,12:30 AM,6:28 AM,2024-10-28,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-27,91,43,71,11.00,85,Ausgezeichnet,8.83,530,8.33,500,9:33 PM,6:27 AM,2024-10-27,Radfahren,52.21,1243,6432,31,29,17,,133,155,1.14,8.6,9.8,279,80,91,210.5,130,198.5,27.5,171,189,3.4,507,,39,431,,12,412,66
-2024-10-26,81,44,52,12.00,79,Gut,8.03,482,8.00,480,11:44 PM,7:47 AM,2024-10-26,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-25,85,43,53,11.00,79,Gut,7.68,461,8.00,480,10:13 PM,6:14 AM,2024-10-25,Radfahren,16.64,427,,,,4,,116,,,,,,65,80,143,154,134,29,138,,2.6,240,,35,,,19,,43
-2024-10-24,87,44,53,12.00,79,Gut,8.92,535,8.00,480,9:45 PM,6:42 AM,2024-10-24,Mixed Martial Arts,,493,,,,1,,116,,,,,,,,,,,25,152,,2.3,,,35,,,11,,
-2024-10-23,82,44,54,11.00,80,Gut,7.38,443,8.33,500,10:51 PM,6:14 AM,2024-10-23,Laufen,5.01,296,4132,10,17,6,,133,157,1.2,8.8,10.9,271,,,301,,299,26,152,162,3,379,,35,431,,17,411,27
-2024-10-22,83,43,65,12.00,79,Gut,7.15,429,8.33,500,11:20 PM,6:29 AM,2024-10-22,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-21,87,47,59,11.00,78,Gut,7.33,440,8.00,480,10:36 PM,6:12 AM,2024-10-21,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-20,69,47,27,13.00,81,Ausreichend,9.17,550,7.00,420,9:57 PM,7:08 AM,2024-10-20,Laufen,5.33,333,4396,18,21,6,,145,159,1.21,8.7,10.5,272,,,310,,306,,167,179,3.3,408,,,431,,,411,28
-2024-10-19,65,45,53,11.00,89,Ausreichend,7.92,475,8.00,480,11:45 PM,7:47 AM,2024-10-19,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-18,79,46,50,12.00,89,Ausreichend,7.02,421,7.67,460,11:33 PM,6:39 AM,2024-10-18,Radfahren,45.82,820,,,,10,,114,,,,,,77,91,122,128,118,,137,,3,137,,,,,,,96
-2024-10-17,100,41,62,11.00,89,Ausgezeichnet,8.30,498,8.00,480,9:51 PM,6:10 AM,2024-10-17,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-16,84,44,51,11.00,89,Gut,7.47,448,7.67,460,10:35 PM,6:12 AM,2024-10-16,Radfahren,20.93,304,,,,5,,95,,,,,,77,91,97,100,92,,108,,2,116,,,,,,,44
-2024-10-15,83,42,66,12.00,90,Gut,8.05,483,8.33,500,10:21 PM,6:27 AM,2024-10-15,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-14,91,41,75,12.00,90,Ausgezeichnet,7.77,466,8.00,480,10:28 PM,6:15 AM,2024-10-14,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-13,90,41,84,11.00,90,Ausgezeichnet,7.88,473,7.17,430,9:47 PM,5:45 AM,2024-10-13,Laufen,21.25,1416,18420,30,35,22,,157,164,1.15,8.7,10,266,,,296,,294,,177,178,5,353,,,409,,,395,113
-2024-10-12,76,46,42,12.00,89,Ausreichend,7.13,428,8.00,480,12:43 AM,8:03 AM,2024-10-12,Wandern,5.57,301,6836,25,20,6,,90,106,0.82,,,,,,,,,,105,167,1.1,,,,428,,,409,64
-2024-10-11,79,42,52,12.00,92,Ausreichend,7.10,426,8.00,480,11:20 PM,6:27 AM,2024-10-11,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-10,84,43,51,11.00,92,Gut,6.80,408,8.00,480,11:22 PM,6:12 AM,2024-10-10,Laufbandtraining,5.56,300,4342,,,6,,136,156,1.28,8.6,11,270,,,309,,305,,154,167,3,376,,,,,,,28
-2024-10-09,86,41,61,13.00,91,Gut,7.58,455,7.67,460,9:53 PM,6:12 AM,2024-10-09,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-08,93,41,63,11.00,92,Ausgezeichnet,7.15,429,8.00,480,11:20 PM,6:30 AM,2024-10-08,Radfahren,32.58,564,,,,7,,110,,,,,,80,140,134,144,116,,157,,2.8,337,,,,,,,64
-2024-10-07,77,45,60,11.00,90,Ausreichend,6.78,407,7.00,420,11:27 PM,6:30 AM,2024-10-07,Mixed Martial Arts,,576,,,,1,,140,,,,,,,,,,,,,,,,,,,,,,
-2024-10-06,92,42,73,13.00,87,Ausgezeichnet,8.30,498,8.33,500,11:36 PM,7:57 AM,2024-10-06,Laufen,7.47,486,6402,23,20,8,,147,159,1.16,8.9,10.4,277,,,293,,291,,159,167,3.5,356,,,431,,,411,40
-2024-10-05,86,46,60,12.00,77,Gut,8.02,481,8.00,480,11:56 PM,7:57 AM,2024-10-05,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-04,87,46,58,11.00,75,Gut,8.68,521,8.50,510,9:26 PM,6:13 AM,2024-10-04,Radfahren,43.53,807,,,,9,,115,,,,,,80,101,114,115,113,,133,,2.9,244,,,,,,,98
-2024-10-03,81,43,57,11.00,76,Gut,7.12,427,8.33,500,11:05 PM,6:13 AM,2024-10-03,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-02,94,42,69,11.00,77,Ausgezeichnet,7.78,467,8.33,500,10:25 PM,6:12 AM,2024-10-02,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
-2024-10-01,79,43,66,12.00,77,Ausreichend,7.68,461,8.00,480,10:30 PM,6:12 AM,2024-10-01,Radfahren,,278,,,,1,,107,,,,,,,,,,,,135,,1.4,,,,,,,,
\ No newline at end of file
diff --git a/term-paper/documentation.tex b/term-paper/documentation.tex
index b7813d0..64b455a 100644
--- a/term-paper/documentation.tex
+++ b/term-paper/documentation.tex
@@ -12,7 +12,7 @@
 % header
 % ------------------------
 \documentclass[a4paper,12pt]{scrartcl}
-\linespread {1.25}
+\linespread{1.25}
 
 %-------------------------
 % packages and config
@@ -27,41 +27,138 @@
 %-------------------------
 % title
 %-------------------------
+\thispagestyle{empty}
 \input{title}
 
-\section{Einleitung}
-\citeauthor{Student2022} führt aus Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua \cite{Student2022}. 
+\newpage
+\pagenumbering{Roman}
+
+\tableofcontents
+\listoffigures
+
+\newpage
+
+\pagenumbering{arabic}
+\section{Einleitung}
+Nach einem intensiven Training am Abend fühlen sich viele Menschen erschöpft, können aber schlecht einschlafen. Diese Arbeit untersucht, ob Training am Abend mit geringem zeitlichem Abstand zum Zubettgehen einen messbaren Einfluss auf die Schlafqualität hat. Auf Basis der Erkenntnisse sollen potenzielle negative Effekte von späterer körperlicher Aktivität auf den Schlaf identifiziert und daraus Empfehlungen für optimale Trainingszeiten abgeleitet werden. Durch die Wahl eines geeigneteren Trainingszeitpunkts soll über längere Zeit eine bessere Leistungsfähigkeit und Schlafqualität erzielt werden können.
+
+\section{Beschreibung zum Datensatz}
+Der analysierte Datensatz umfasst einen Zeitraum von 52 Wochen und wurde mithilfe einer Garmin-Sportuhr gemessen.
+Insgesamt beinhaltet der Datensatz 294 Trainingseinheiten sowie 365 Schlafaufzeichnungen einer Testperson.
+Die Trainingseinheiten umfassen unterschiedliche Aktivitäten, darunter Laufen, Radfahren, Mixed Martial Arts, Gehen, Wandern, Schwimmen, Krafttraining, Skifahren, Seilspringen und Boxen. 
+
+Die Garmin-Uhr zeichnet über 20 Merkmale (Features) einer Aktivität auf.
+Für diese Analyse haben wurden die relevanten Features Pace (Tempo), Kalorienverbrauch, Trainingsdauer und durchschnittliche Atem- und Herzfrequenz ausgewählt. 
+Bei den Schlafdaten wurden die Features Schlaf-Score, Gesamtdauer, Einschlafzeit, Ruhefrequenz, Herzratenvariabilität (HRV), Atmung und ermitteltes Schlafbedürfnis berücksichtigt. 
+Die Messwerte wurden mit den Sensoren der Garmin-Uhr aufgezeichnet, wobei die Features Score und Schlafbedürfnis auf Berechnungen von Garmin beruhen. 
+Laut Hersteller wird der Schlaf-Score auf Basis einer Kombination der Schlafdauer, Schlafarchitektur, Stressdaten, Unterbrechungen während der Nacht sowie anderen Faktoren berechnet \cite{noauthor_schlafuberwachung_nodate}.
+
+\subsection{Weitere Einflussfaktoren}
+Die Schlafqualität wird von vielen Faktoren beeinflusst.
+Da entsprechende Daten zu solchen Faktoren in unserem Datensatz nicht vorhanden sind, beschränkt sich die Analyse auf die Trainings- und Schlafdaten.
+Dies führt  jedoch zu einer potenziellen Ungenauigkeit in den Ergebnissen, was bei der Interpretation der Ergebnisse beachtet werden muss.
+Beispiele für solche Faktoren sind folgende:
+
+\textbf{Ernährung:}
+Studien belegen, dass Diäten und Ernährung einen Einfluss auf die Schlafqualität haben. 
+So konnte eine Abhängigkeit des REM-Schlafs bei kohlenhydratreichen und ballaststoffarmen Mahlzeiten festgestellt werden \cite{st-onge_effects_2016}.
+Auch die Einnahme von Alkohol kann die Schlafqualität negativ beeinflussen \cite{gardiner_effect_2025}.
+Ein weiterer Aspekt ist der Zeitpunkt der letzten Koffeineinnahme, die sich negativ auf den Schlaf auswirken kann.
+Es wird empfohlen, die späteste Einnahme auf mindestens sechs Stunden vor dem Schlafengehen zu beschränken \cite{drake_caffeine_2013}.
+
+\textbf{Aktivitätstyp und Chronotyp:} 
+Ob eine Person ein Morgen- oder Abendtyp ist, beeinflusst die Wirkung von spätabendlichem Training.
+Studien zeigen, dass spätes Training nur dann negative Auswirkungen auf den Schlaf hat, wenn ein Morgenmensch abends trainiert.
+Abendtypen sind hiervon hingegen kaum betroffen \cite{vitale_sleep_2017}. 
+
+\textbf{Körperliche Aktivierung:} 
+Bis die Körpertemperatur aufgrund der Belastung zu den Normalwerten zurückkehrt, dauert es mindestens zwei Stunden \cite{kim_effects_2023}.
+Eine weitere Studie zeigt auf, dass die Schlafqualität negativ beeinflusst wird, wenn das Training weniger als vier Stunden vor dem Zubettgehen stattgefunden hat \cite{leota_dose-response_2025}.
+
+\section{Methodik}
+Für die Datenanalyse wurd eien Jupyter Notebook\footnote{\url{https://gitea.fhgr.ch/hollbacdario/cds-1011-health-data-analysis}} in Python verwendet.
+Die Rohdaten wurden aus der Garmin Webapplikation im CSV-Format exportiert und anschliessend in Python mit dem Modul \texttt{pandas} eingelesen.
+Nach dem Import erfolgte eine Bereinigung der Daten, um sicherzustellen, dass alle zeitlichen und numerischen Features für weitere Berechnungen verwendet werden können. 
+
+Für die Analyse von zeitabhängigen Zusammenhängen wurden die Uhrzeiten in Sekunden seit Mitternacht umgerechnet und die Zeitdifferenzen in Sekunden gespeichert.
+Basierend auf dem Trainingsbeginn und der Trainingsdauer wurde die Endzeit jeder Aktivität als neues Feature berechnet und hinzugefügt.
+Anschliessend wurden die Schlaf- und Aktivitätsdatensätze über das Datum zusammengefügt.
+Dabei wurde ein Left-Join auf den Schlafdatensatz angewendet, sodass ein kombinierter Datensatz entstanden ist, der für jeden Tag Schlafdaten enthält und, sofern vorhanden, die dazugehörigen Aktivitätsdaten. Falls an einem Tag mehrere Aktivitäten vorhanden waren, wurde die Aktivität mit der spätesten Endzeit berücksichtigt.
+Für die Analyse wurde der kombinierte Datensatz in drei Kategorien eingeteilt: 
+
+\vspace{-0.5em}
+\begin{enumerate}
+    \item Kein Training am entsprechenden Tag \vspace{-0.5em}
+    \item Training mehr als vier Stunden vor dem Schlafengehen \vspace{-0.5em}
+    \item Training weniger als vier Stunden vor dem Schlafengehen
+\end{enumerate}
+Die Kategorien weisen eine unterschiedliche Anzahl an Einträgen auf (siehe \cref{fig:verteilung_trainingsaktivitaeten} im Anhang).
+
+Mithilfe des Python-Moduls \texttt{seaborn} wurde anschliessend eine Korrelationsmatrix sowohl für alle Aktivitäten als auch speziell für Aktivitäten mit einer Endzeit spätestens vier Stunden vor dem Schlafengehen erstellt.
+Für die Visualisierung des Zusammenhangs zwischen Herzfrequenz im Training und nächtlicher Herzratenvariabilität wurde mit \texttt{matplotlib} ein Streudiagramm erstellt.
+
 
-\section{Forschungsfragen und Methodik}
-Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
 
 \section{Resultate}
-Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
+Die Analyse der Korrelationsmatrix aller Aktivitäten (siehe \cref{fig:korrelationsmatrix_aller_aktivitaeten}) zeigt keine signifikanten Zusammenhänge zwischen den Features der Schlaf- und Trainingsdaten.
 
-\begin{table}[ht]
-\centering
-\begin{tabular}{l l}
-Lorem & ipsum \\\hline
-dolor sit amet & 66 \\
-consetetur sadipscing elitr & 99 \\
-\end{tabular}
-\caption{Lorem ipsum}
-\label{tab:lorem}
-\end{table}
+In der Korrelationsmatrix der Aktivitäten, die vier Stunden vor dem Schlafengehen stattfanden (siehe~\cref{fig:korrelationsmatrix_aktivitaeten_vor_schlaf}), lassen sich hingegen leichte Zusammenhänge erkennen. 
+Dies deutet darauf hin, dass Aktivitäten kurz vor dem Schlafengehen einen messbaren Einfluss auf die Schlafqualität haben können.
 
-%\begin{figure}
-%  \includegraphics[width=\linewidth]{ipsum.jpg}
-%  \caption{Lorem ipsum}
-%  \label{fig:ipsum}
-%\end{figure}
-%Lorem ipsum dolor sit amet figure \ref{fig:ipsum}.
+Das Streudiagramm (siehe~\cref{fig:herzratenvariabilitaet}) visualisiert den Zusammenhang zwischen der durchschnittlichen Herzfrequenz während einer Aktivität und der Herzratenvariabilität im anschliessenden Schlaf. 
+Die blauen Punkte, welche Aktivitäten innerhalb von vier Stunden vor dem Schlaf darstellen, zeigen einen leichten positiven Trend auf. 
+Mit steigender Herzfrequenz im Training nimmt tendenziell auch die Herzratenvariabilität im Schlaf zu. 
+Die roten Punkte, welche alle anderen Aktivitäten repräsentieren, erscheinen dagegen zufällig verteilt und weisen keinen erkennbaren Trend auf.
+
+Insgesamt deuten die Ergebnisse darauf hin, dass vor allem intensive Aktivitäten, die  kurz vor dem Zubettgehen stattfinden, die Schlafqualität beeinflussen können.
 
 \section{Diskussion}
-Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
+Die durchgeführte Analyse zeigt auf, dass sich ein Zusammenhang zwischen abendlichen Aktivitäten und der Schlafqualität andeutet, insbesondere bei intensiven Aktivitäten, die maximal vier Stunden vor dem Zubettgehen endeten. 
+Der Effekt zeigt sich vor allem in der Herzratenvariabilität und bestätigt teilweise die in der Literatur beschriebenen Ergebnisse, dass körperliche Aktivitäten vor dem Schlaf den Erholungsprozess beeinflussen können.
+
+Die festgestellten Zusammenhänge sind allerdings schwach und erlauben somit keine kausalen Schlussfolgerungen. 
+Weitere Faktoren wie Stress, Ernährung und Tagesform könnten die Ergebnisse verfälscht haben. 
+Da die Analyse mit einem Datensatz von nur einer Person und in einem begrenzten Zeitraum stattgefunden hat, sind die Resultate als explorativ zu interpretieren.
+
+Aus den Ergebnissen ergibt sich die Schlussfolgerung, dass intensive, spätabendliche Aktivitäten für eine bessere Leistungsfähigkeit und Erholung vermieden werden sollten.
 
 %-------------------------
 % literature
 %-------------------------
+\newpage
+\renewcommand{\refname}{Literaturverzeichnis} % For article
 \bibliography{library}
 
+%-------------------------
+% appendix
+%-------------------------
+
+\newpage
+\section{Anhang}
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.7\linewidth]{pie_plot.png}
+    \caption{Verteilung der Trainingsaktivitäten}
+    \label{fig:verteilung_trainingsaktivitaeten}
+\end{figure}
+\begin{figure}[H]
+    \centering
+    \includegraphics[width=0.8\linewidth]{korrelations_matrix_alle_aktivitaeten.png}
+    \caption{Korrelationsmatrix aller Aktivitäten}
+    \label{fig:korrelationsmatrix_aller_aktivitaeten}
+\end{figure}
+\begin{figure} [H]
+    \centering
+    \includegraphics[width=0.8\linewidth]{korrelations_matrix_4h_vor_schlaf.png}
+    \caption{Korrelationsmatrix mit Aktivitäten vier Stunden vor dem Schlaf}
+    \label{fig:korrelationsmatrix_aktivitaeten_vor_schlaf}
+\end{figure}
+\begin{figure} [H]
+    \centering
+    \includegraphics[width=0.7\linewidth]{scatter_plot.png}
+    \caption{Streudiagramm von der Herzfrequenz im Training und der Herzratenvariabilität im Schlaf}
+    \label{fig:herzratenvariabilitaet}
+\end{figure}
+
+
 \end{document}
\ No newline at end of file
diff --git a/term-paper/korrelations_matrix_4h_vor_schlaf.png b/term-paper/korrelations_matrix_4h_vor_schlaf.png
new file mode 100644
index 0000000..582ffb2
Binary files /dev/null and b/term-paper/korrelations_matrix_4h_vor_schlaf.png differ
diff --git a/term-paper/korrelations_matrix_alle_aktivitaeten.png b/term-paper/korrelations_matrix_alle_aktivitaeten.png
new file mode 100644
index 0000000..3a56a38
Binary files /dev/null and b/term-paper/korrelations_matrix_alle_aktivitaeten.png differ
diff --git a/term-paper/library.bib b/term-paper/library.bib
index 00c2fad..885fb53 100644
--- a/term-paper/library.bib
+++ b/term-paper/library.bib
@@ -1,8 +1,148 @@
-@article{Student2022,
-   author={Toth Yannick },
-   title={My Title},
-   journal={My Journal},
-   volume={8},
-   year=2022,
-   pages={175-191}
-}
\ No newline at end of file
+
+@article{vitale_sleep_2017,
+	title = {Sleep quality and high intensity interval training at two different times of day: A crossover study on the influence of the chronotype in male collegiate soccer players},
+	volume = {34},
+	issn = {0742-0528},
+	url = {https://doi.org/10.1080/07420528.2016.1256301},
+	doi = {10.1080/07420528.2016.1256301},
+	shorttitle = {Sleep quality and high intensity interval training at two different times of day},
+	abstract = {The influence of the chronotype on the sleep quality in male collegiate soccer players in response to acute high intensity interval training ({HIIT}) performed at two different times of day was evaluated. The sleep quality was poorer in the morning-type than in the evening-type players after the evening {HIIT} session, whereas no significant changes in the sleep quality of the two chronotypes after the morning {HIIT} session was observed. The results suggest that an athlete’s chronotype should be taken into account when scheduling training sessions and to promote faster recovery processes.},
+	pages = {260--268},
+	number = {2},
+	journaltitle = {Chronobiology International},
+	author = {Vitale, Jacopo A. and Bonato, Matteo and Galasso, Letizia and La Torre, Antonio and Merati, Giampiero and Montaruli, Angela and Roveda, Eliana and Carandente, Franca},
+	urldate = {2025-10-17},
+	date = {2017-02-07},
+	pmid = {27906554},
+	keywords = {Actigraphy, chronotype, high intensity interval training, sleep, soccer, time of day},
+}
+
+@article{vitale_sleep_2017-1,
+	title = {Sleep quality and high intensity interval training at two different times of day: A crossover study on the influence of the chronotype in male collegiate soccer players},
+	volume = {34},
+	issn = {0742-0528},
+	url = {https://doi.org/10.1080/07420528.2016.1256301},
+	doi = {10.1080/07420528.2016.1256301},
+	shorttitle = {Sleep quality and high intensity interval training at two different times of day},
+	abstract = {The influence of the chronotype on the sleep quality in male collegiate soccer players in response to acute high intensity interval training ({HIIT}) performed at two different times of day was evaluated. The sleep quality was poorer in the morning-type than in the evening-type players after the evening {HIIT} session, whereas no significant changes in the sleep quality of the two chronotypes after the morning {HIIT} session was observed. The results suggest that an athlete’s chronotype should be taken into account when scheduling training sessions and to promote faster recovery processes.},
+	pages = {260--268},
+	number = {2},
+	journaltitle = {Chronobiology International},
+	author = {Vitale, Jacopo A. and Bonato, Matteo and Galasso, Letizia and La Torre, Antonio and Merati, Giampiero and Montaruli, Angela and Roveda, Eliana and Carandente, Franca},
+	urldate = {2025-10-17},
+	date = {2017-02-07},
+	pmid = {27906554},
+	note = {Publisher: Taylor \& Francis
+\_eprint: https://doi.org/10.1080/07420528.2016.1256301},
+	keywords = {Actigraphy, chronotype, high intensity interval training, sleep, soccer, time of day},
+}
+
+@article{gardiner_effect_2025,
+	title = {The effect of alcohol on subsequent sleep in healthy adults: A systematic review and meta-analysis},
+	volume = {80},
+	issn = {1087-0792},
+	url = {https://www.sciencedirect.com/science/article/pii/S1087079224001345},
+	doi = {10.1016/j.smrv.2024.102030},
+	shorttitle = {The effect of alcohol on subsequent sleep in healthy adults},
+	abstract = {Alcohol is commonly consumed prior to bedtime with the belief that it facilitates sleep. This systematic review and meta-analysis investigated the impact of alcohol on the characteristics of night-time sleep, with the intent to identify the influence of the dose and timing of alcohol intake. A systematic search of the literature identified 27 studies for inclusion in the analysis. Changes in sleep architecture were observed, including a delay in the onset of rapid eye movement ({REM}) sleep and a reduction in the duration of {REM} sleep. A dose-response relationship was identified such that disruptions to {REM} sleep occurred following consumption of a low dose of alcohol (≤0.50 g∙kg−1 or approximately two standard drinks) and progressively worsened with increasing doses of alcohol. Reductions in sleep onset latency and latency to deep sleep (i.e., non-rapid eye movement stage three (N3)) were only observed following the consumption of a high dose of alcohol (≥0.85∙g kg−1 or approximately five standard drinks). The effect of alcohol on the remaining characteristics of sleep could not be determined, with large uncertainty observed in the effect on total sleep time, sleep efficiency, and wake after sleep onset. The results of the present study suggest that a low dose of alcohol will negatively impact (i.e., reduce) {REM} sleep. It appears that high doses of alcohol may shorten sleep onset latency, however this likely exacerbates subsequent {REM} sleep disruption. Future work on personal and environmental factors that affect alcohol metabolism, and any differential effects of alcohol due to sex is encouraged.},
+	pages = {102030},
+	journaltitle = {Sleep Medicine Reviews},
+	shortjournal = {Sleep Medicine Reviews},
+	author = {Gardiner, Carissa and Weakley, Jonathon and Burke, Louise M. and Roach, Gregory D. and Sargent, Charli and Maniar, Nirav and Huynh, Minh and Miller, Dean J. and Townshend, Andrew and Halson, Shona L.},
+	urldate = {2025-10-16},
+	date = {2025-04-01},
+	keywords = {Ethanol, Hypnotic, Sedative, Sleep behaviours, Sleep disruption, Sleep recommendations, Sleepiness},
+}
+
+@article{kredlow_effects_2015,
+	title = {The effects of physical activity on sleep: a meta-analytic review},
+	volume = {38},
+	issn = {1573-3521},
+	url = {https://doi.org/10.1007/s10865-015-9617-6},
+	doi = {10.1007/s10865-015-9617-6},
+	shorttitle = {The effects of physical activity on sleep},
+	abstract = {A significant body of research has investigated the effects of physical activity on sleep, yet this research has not been systematically aggregated in over a decade. As a result, the magnitude and moderators of these effects are unclear. This meta-analytical review examines the effects of acute and regular exercise on sleep, incorporating a range of outcome and moderator variables. {PubMed} and {PsycINFO} were used to identify 66 studies for inclusion in the analysis that were published through May 2013. Analyses reveal that acute exercise has small beneficial effects on total sleep time, sleep onset latency, sleep efficiency, stage 1 sleep, and slow wave sleep, a moderate beneficial effect on wake time after sleep onset, and a small effect on rapid eye movement sleep. Regular exercise has small beneficial effects on total sleep time and sleep efficiency, small-to-medium beneficial effects on sleep onset latency, and moderate beneficial effects on sleep quality. Effects were moderated by sex, age, baseline physical activity level of participants, as well as exercise type, time of day, duration, and adherence. Significant moderation was not found for exercise intensity, aerobic/anaerobic classification, or publication date. Results were discussed with regards to future avenues of research and clinical application to the treatment of insomnia.},
+	pages = {427--449},
+	number = {3},
+	journaltitle = {Journal of Behavioral Medicine},
+	shortjournal = {J Behav Med},
+	author = {Kredlow, M. Alexandra and Capozzoli, Michelle C. and Hearon, Bridget A. and Calkins, Amanda W. and Otto, Michael W.},
+	urldate = {2025-10-16},
+	date = {2015-06-01},
+	keywords = {Exercise, Insomnia, Physical activity, Sleep, Sleep quality},
+}
+
+@online{noauthor_schlafuberwachung_nodate,
+	title = {Schlafüberwachung | Gesundheitswissenschaft | Garmin-Technologie | Garmin},
+	url = {https://www.garmin.com/de-CH/garmin-technology/health-science/sleep-tracking/},
+	abstract = {Die erweiterte Schlafüberwachung auf kompatiblen Garmin-Geräten berücksichtigt mehrere Faktoren, damit du deinen Schlaf besser verstehen kannst.},
+	urldate = {2025-10-16},
+}
+
+@article{drake_caffeine_2013,
+	title = {Caffeine Effects on Sleep Taken 0, 3, or 6 Hours before Going to Bed},
+	volume = {09},
+	url = {https://jcsm.aasm.org/doi/10.5664/jcsm.3170},
+	doi = {10.5664/jcsm.3170},
+	abstract = {Study Objective:Sleep hygiene recommendations are widely disseminated despite the fact that few systematic studies have investigated the empirical bases of sleep hygiene in the home environment. For example, studies have yet to investigate the relative effects of a given dose of caffeine administered at different times of day on subsequent sleep.Methods:This study compared the potential sleep disruptive effects of a fixed dose of caffeine (400 mg) administered at 0, 3, and 6 hours prior to habitual bedtime relative to a placebo on self-reported sleep in the home. Sleep disturbance was also monitored objectively using a validated portable sleep monitor.Results:Results demonstrated a moderate dose of caffeine at bedtime, 3 hours prior to bedtime, or 6 hours prior to bedtime each have significant effects on sleep disturbance relative to placebo (p {\textbackslash}textless 0.05 for all).Conclusion:The magnitude of reduction in total sleep time suggests that caffeine taken 6 hours before bedtime has important disruptive effects on sleep and provides empirical support for sleep hygiene recommendations to refrain from substantial caffeine use for a minimum of 6 hours prior to bedtime.Citation:Drake C; Roehrs T; Shambroom J; Roth T. Caffeine effects on sleep taken 0, 3, or 6 hours before going to bed. J Clin Sleep Med 2013;9(11):1195-1200.},
+	pages = {1195--1200},
+	number = {11},
+	journaltitle = {Journal of Clinical Sleep Medicine},
+	author = {Drake, Christopher and Roehrs, Timothy and Shambroom, John and Roth, Thomas},
+	urldate = {2025-10-16},
+	date = {2013-11-15},
+	keywords = {Caffeine, insomnia, sleep habits, sleep hygiene, stimulant},
+}
+
+@article{st-onge_effects_2016,
+	title = {Effects of Diet on Sleep Quality},
+	volume = {7},
+	issn = {2161-8313},
+	url = {https://www.sciencedirect.com/science/article/pii/S2161831322007803},
+	doi = {10.3945/an.116.012336},
+	abstract = {There is much emerging information surrounding the impact of sleep duration and quality on food choice and consumption in both children and adults. However, less attention has been paid to the effects of dietary patterns and specific foods on nighttime sleep. Early studies have shown that certain dietary patterns may affect not only daytime alertness but also nighttime sleep. In this review, we surveyed the literature to describe the role of food consumption on sleep. Research has focused on the effects of mixed meal patterns, such as high-carbohydrate plus low-fat or low-carbohydrate diets, over the short term on sleep. Such studies highlight a potential effect of macronutrient intakes on sleep variables, particularly alterations in slow wave sleep and rapid eye movement sleep with changes in carbohydrate and fat intakes. Other studies instead examined the intake of specific foods, consumed at a fixed time relative to sleep, on sleep architecture and quality. Those foods, specifically milk, fatty fish, tart cherry juice, and kiwifruit, are reviewed here. Studies provide some evidence for a role of certain dietary patterns and foods in the promotion of high-quality sleep, but more studies are necessary to confirm those preliminary findings.},
+	pages = {938--949},
+	number = {5},
+	journaltitle = {Advances in Nutrition},
+	shortjournal = {Advances in Nutrition},
+	author = {St-Onge, Marie-Pierre and Mikic, Anja and Pietrolungo, Cara E},
+	urldate = {2025-10-16},
+	date = {2016-09-01},
+	keywords = {{REM}, carbohydrate, cherry, dairy, diet, glycemic index, kiwi, sleep},
+}
+
+@article{leota_dose-response_2025,
+	title = {Dose-response relationship between evening exercise and sleep},
+	volume = {16},
+	issn = {2041-1723},
+	doi = {10.1038/s41467-025-58271-x},
+	abstract = {Public health guidelines recommend exercise as a key lifestyle intervention for promoting and maintaining healthy sleep function and reducing disease risk. However, strenuous evening exercise may disrupt sleep due to heightened sympathetic arousal. This study examines the association between strenuous evening exercise and objective sleep, using data from 14,689 physically active individuals who wore a biometric device during a one-year study interval (4,084,354 person-nights). Here we show later exercise timing and higher exercise strain are associated with delayed sleep onset, shorter sleep duration, lower sleep quality, higher nocturnal resting heart rate, and lower nocturnal heart rate variability. Regardless of strain, exercise bouts ending ≥4 hours before sleep onset are not associated with changes in sleep. Our results suggest evening exercise-particularly involving high exercise strain-may disrupt subsequent sleep and nocturnal autonomic function. Individuals aiming to improve sleep health may benefit from concluding exercise at least 4 hours before sleep onset or electing lighter strain exercises within this window.},
+	pages = {3297},
+	number = {1},
+	journaltitle = {Nature Communications},
+	shortjournal = {Nat Commun},
+	author = {Leota, Josh and Presby, David M. and Le, Flora and Czeisler, Mark {\'E} and Mascaro, Luis and Capodilupo, Emily R. and Wiley, Joshua F. and Drummond, Sean P. A. and Rajaratnam, Shantha M. W. and Facer-Childs, Elise R.},
+	date = {2025-04-15},
+	pmid = {40234380},
+	pmcid = {PMC12000559},
+	keywords = {Adult, Circadian Rhythm, Exercise, Female, Heart Rate, Humans, Male, Middle Aged, Sleep, Sleep Quality, Time Factors, Young Adult},
+}
+
+@article{kim_effects_2023,
+	title = {Effects of exercise timing and intensity on physiological circadian rhythm and sleep quality: a systematic review},
+	volume = {27},
+	issn = {2733-7545},
+	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC10636512/},
+	doi = {10.20463/pan.2023.0029},
+	shorttitle = {Effects of exercise timing and intensity on physiological circadian rhythm and sleep quality},
+	abstract = {[Purpose] Humans show near-24-h physiological and behavioral rhythms, which encompass the daily cycle of sleep and wakefulness. Exercise stimulates circadian rhythms, including those of cortisol, melatonin, and core body temperature, and affects sleep quality. We systematically reviewed studies that examined the effects of exercise intensity and timing on physiological circadian rhythms and sleep quality. [Methods] In this systematic review, we used the online databases {PubMed}, Science Direct, Web of Science, and Embase. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Two independent and experienced systematic reviewers performed the search and selected relevant studies. The participant, intervention, comparison, and outcome characteristics were: (1) adults; (2) exercise treatment; (3) no exercise treatment or different types of exercise (pre-exercise baseline); (4) cortisol, melatonin, or core body temperature measurement, and subjective or objective sleep quality assessments. [Results] We identified 9 relevant articles involving 201 participants (77.1\% of whom were male). Our review revealed that short-term evening exercise delayed melatonin rhythm and increased nocturnal core body temperature; however, no negative effects on non-rapid eye movement sleep and sleep efficiency were observed. Moreover, no differences in sleep quality were observed between acute high-intensity and moderate-intensity exercises. With long exercise durations, the core body temperature tended to increase and return to baseline levels at 30–120 min. [Conclusion] Our review showed that short-term evening exercise and high-intensity exercise did not have a significant negative effect on sleep quality but physiological circadian rhythm tended to alter. Longterm morning exercise tended to decrease cortisol concentrations after awakening and improve sleep quality. Future studies should examine the effects of long-term exercise timing and intensity on circadian rhythm and sleep.},
+	pages = {52--63},
+	number = {3},
+	journaltitle = {Physical Activity and Nutrition},
+	shortjournal = {Phys Act Nutr},
+	author = {Kim, Nahyun and Ka, Soonjo and Park, Jonghoon},
+	urldate = {2025-10-16},
+	date = {2023-09},
+	pmid = {37946447},
+	pmcid = {PMC10636512},
+}
diff --git a/term-paper/packages_and_configuration.tex b/term-paper/packages_and_configuration.tex
index 6455130..6d7f9f3 100644
--- a/term-paper/packages_and_configuration.tex
+++ b/term-paper/packages_and_configuration.tex
@@ -3,9 +3,10 @@
 %-------------------------
 % imports
 % ------------------------
-\usepackage[english, ngerman]{babel}
-\usepackage[left=3cm,top=2.5cm,right=2.5cm,bottom=2.cm]{geometry}
+\usepackage[ngerman]{babel}
+\usepackage[left=3cm,top=2.5cm,right=2.5cm,bottom=2cm]{geometry}
 \usepackage[T1]{fontenc}
+\usepackage[utf8]{inputenc}
 \usepackage{abstract}
 \usepackage{hyperref}
 \usepackage{apacite}
@@ -15,10 +16,30 @@
 \usepackage{newtxmath}
 \setkomafont{disposition}{\bfseries}
 
+\usepackage{graphicx}
+\usepackage{float}
+\usepackage{cleveref}
+\usepackage{fancyhdr}
+\usepackage{lipsum}
+\usepackage{lmodern}
+\usepackage{etoolbox}
+\usepackage{url}
+\usepackage[nottoc, numbib, notlof]{tocbibind}
+\crefname{figure}{Abbildung}{Abbildungen}
+\Crefname{figure}{Abbildung}{Abbildungen}
+\crefname{table}{Tabelle}{Tabellen}
+\Crefname{table}{Tabelle}{Tabellen}
+
 %-------------------------
 % configuration
 % ------------------------
 
+\pagestyle{fancy}
+\fancyhf{}
+\fancyfoot[R]{\fontsize{10pt}{12pt}\selectfont \thepage}
+\setlength{\footskip}{1cm} 
+\renewcommand{\headrulewidth}{0pt}
+
 % paragraph indent
 \setlength{\parindent}{0pt}
 
diff --git a/term-paper/pie_plot.png b/term-paper/pie_plot.png
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diff --git a/term-paper/title.tex b/term-paper/title.tex
index 238ee12..294f4dc 100644
--- a/term-paper/title.tex
+++ b/term-paper/title.tex
@@ -1,9 +1,7 @@
 % !TEX root = documentation.tex
 
-
 \titlehead{BSc Computational and Data Science\\CDS1011 Einführung in Data Science\\Dozent: Prof. Corsin Capol\hfill}
-\title{Data Conneiseurs}
-\subtitle{blablabla}
+\title{Zusammenhang zwischen Trainingszeitpunkt und Schlafqualität}
 \author[1,*]{Yannick Toth}
 \author[1]{Arif Hizlan}
 \author[1]{Dario Hollbach}
@@ -13,5 +11,8 @@
 \maketitle
 
 \begin{abstract}
-Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum.
-\end{abstract}
\ No newline at end of file
+Diese Arbeit untersucht den Zusammenhang zwischen Trainingszeitpunkt und Schlafqualität auf Basis von Aktivitäts- und Schlafdaten einer Testperson über den Zeitraum von 52 Wochen. 
+Die Analyse zeigt, dass Aktivitäten, die weniger als vier Stunden vor dem Zubettgehen stattfanden, lediglich einen schwach messbaren, nicht kausalen Einfluss auf die Schlafqualität aufweisen.
+\end{abstract}
+
+\thispagestyle{empty}
\ No newline at end of file