diff --git a/config/models.py b/config/models.py
index 43da431..d1b864b 100644
--- a/config/models.py
+++ b/config/models.py
@@ -232,6 +232,17 @@ class ESM(Base, AWAREsensor):
     esm_session = Column(Integer, nullable=False)
     esm_notification_id = Column(Integer, nullable=False)
     esm_expiration_threshold = Column(SmallInteger)
+    ESM_TYPE = {'text': 1,
+                'radio': 2,
+                'checkbox': 3,
+                'likert': 4,
+                'quick_answers': 5,
+                'scale': 6,
+                'datetime': 7,
+                'pam': 8,
+                'number': 9,
+                'web': 10,
+                'date': 11}
 
 
 class Imperfection(Base):
diff --git a/exploration/expl_esm_adherence.py b/exploration/expl_esm_adherence.py
index 0d2aaf6..c86d9eb 100644
--- a/exploration/expl_esm_adherence.py
+++ b/exploration/expl_esm_adherence.py
@@ -184,7 +184,7 @@ import numpy as np
 df_session_counts = pd.DataFrame(session_counts_device).rename(
     columns={"id": "esm_session_count"}
 )
-df_session_counts["session_response"] = np.NaN
+df_session_counts["session_response"] = np.nan
 session_group_by = df_esm_preprocessed.groupby(
     ["participant_id", "device_id", "esm_session"]
 )
diff --git a/features/esm.py b/features/esm.py
index e40d4f6..b75645e 100644
--- a/features/esm.py
+++ b/features/esm.py
@@ -116,7 +116,7 @@ def classify_sessions_by_completion(df_esm_preprocessed: pd.DataFrame) -> pd.Dat
     df_session_counts = pd.DataFrame(sessions_grouped.count()["id"]).rename(
         columns={"id": "esm_session_count"}
     )
-    df_session_counts["session_response"] = np.NaN
+    df_session_counts["session_response"] = np.nan
 
     # 1. Identify all ESMs with status other than answered.
     esm_not_answered = sessions_grouped.apply(
@@ -249,7 +249,11 @@ def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
             [ANSWER_DAY_FINISHED, ANSWER_DAY_OFF, ANSWER_SET_EVENING]
         )
     ]
-    df_esm_clean = df_esm_clean.assign(
+    df_esm_clean["esm_user_answer_numeric"] = np.nan
+    esm_type_numeric = [ESM.ESM_TYPE.get("radio"),
+                        ESM.ESM_TYPE.get("scale"),
+                        ESM.ESM_TYPE.get("number")]
+    df_esm_clean[df_esm_clean["esm_type"].isin(esm_type_numeric)] = df_esm_clean[df_esm_clean["esm_type"].isin(esm_type_numeric)].assign(
         esm_user_answer_numeric=lambda x: x.esm_user_answer.str.slice(stop=1).astype(
             int
         )