diff --git a/features/esm_SAM.py b/features/esm_SAM.py
index 4693571..d6ea6fd 100644
--- a/features/esm_SAM.py
+++ b/features/esm_SAM.py
@@ -1,3 +1,4 @@
+import numpy as np
 import pandas as pd
 
 import features.esm
@@ -18,7 +19,6 @@ GROUP_QUESTIONNAIRES_BY = [
     "participant_id",
     "device_id",
     "esm_session",
-    "questionnaire_id",
 ]
 # Each questionnaire occurs only once within each esm_session on the same device within the same participant.
 
@@ -51,7 +51,7 @@ def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
 
     df_esm_events = df_esm_events.join(
         df_esm_event_work_related[
-            GROUP_QUESTIONNAIRES_BY.append("event_work_related")
+            GROUP_QUESTIONNAIRES_BY + ["event_work_related"]
         ].set_index(GROUP_QUESTIONNAIRES_BY)
     )
 
@@ -59,7 +59,7 @@ def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
     df_esm_event_time = convert_event_time(df_esm_sam_clean)
 
     df_esm_events = df_esm_events.join(
-        df_esm_event_time[GROUP_QUESTIONNAIRES_BY.append("event_time")].set_index(
+        df_esm_event_time[GROUP_QUESTIONNAIRES_BY + ["event_time"]].set_index(
             GROUP_QUESTIONNAIRES_BY
         )
     )
@@ -89,30 +89,14 @@ def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataF
         )
     ]
     # Calculate mean of threat and challenge subscales for each ESM session.
-    df_esm_event_threat_challenge_mean = (
-        df_esm_event_threat_challenge.groupby(GROUP_QUESTIONNAIRES_BY)
-        .esm_user_answer_numeric.agg("mean")
-        .reset_index()
-        .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
-    )
-    # Rename questionnaire ID to indicate their names.
-    df_esm_event_threat_challenge_mean[
-        "event_subscale"
-    ] = df_esm_event_threat_challenge_mean.questionnaire_id.astype(
-        "category"
-    ).cat.rename_categories(
-        {
-            QUESTIONNAIRE_ID_SAM.get("event_threat"): "threat_mean",
-            QUESTIONNAIRE_ID_SAM.get("event_challenge"): "challenge_mean",
-        }
-    )
-    # Pivot a table so that each ESM session is represented by one row with threat and challenge means as two columns.
-    df_esm_event_threat_challenge_mean_wide = pd.pivot(
-        df_esm_event_threat_challenge_mean,
-        index=GROUP_QUESTIONNAIRES_BY,
-        columns=["event_subscale"],
-        values=["esm_numeric_mean"],
-    )
+    df_esm_event_threat_challenge_mean_wide = pd.pivot_table(df_esm_event_threat_challenge, index=["participant_id","device_id", "esm_session"], columns=["questionnaire_id"], values=["esm_user_answer_numeric"], aggfunc="mean")
+    # Drop unnecessary column values.
+    df_esm_event_threat_challenge_mean_wide.columns = df_esm_event_threat_challenge_mean_wide.columns.get_level_values(1)
+    df_esm_event_threat_challenge_mean_wide.columns.name = None
+    df_esm_event_threat_challenge_mean_wide.rename(columns={
+        QUESTIONNAIRE_ID_SAM.get("event_threat"): "threat_mean",
+        QUESTIONNAIRE_ID_SAM.get("event_challenge"): "challenge_mean"
+    }, inplace=True)
     return df_esm_event_threat_challenge_mean_wide
 
 
@@ -168,6 +152,7 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
     # Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm,
     # but only the numeric types of questions and answers.
     # Since this was of "datetime" type, convert these specific answers here again.
+    df_esm_event_duration["event_duration_info"] = np.nan
     df_esm_event_duration[
         df_esm_event_duration.event_duration.isna()
     ] = df_esm_event_duration[df_esm_event_duration.event_duration.isna()].assign(