diff --git a/rules/features.smk b/rules/features.smk
index 9fc0fde3..c27db7e8 100644
--- a/rules/features.smk
+++ b/rules/features.smk
@@ -329,7 +329,8 @@ rule preprocess_esm:
     params:
         provider=lambda wildcards: config["PHONE_ESM"]["PROVIDERS"][wildcards.provider_key.upper()],
         provider_key="{provider_key}",
-        sensor_key="phone_esm"
+        sensor_key="phone_esm",
+        scales=lambda wildcards: config["PHONE_ESM"]["PROVIDERS"][wildcards.provider_key.upper()]["SCALES"]
     output: "data/interim/{pid}/phone_esm_features/phone_esm_clean_{provider_key}.csv"
     script:
         "../src/features/phone_esm/straw/preprocess.py"
diff --git a/src/features/phone_esm/straw/preprocess.py b/src/features/phone_esm/straw/preprocess.py
index 8eae5286..97a883ad 100644
--- a/src/features/phone_esm/straw/preprocess.py
+++ b/src/features/phone_esm/straw/preprocess.py
@@ -1,7 +1,7 @@
 from esm_preprocess import *
 from esm_JCQ import reverse_jcq_demand_control_scoring
 
-requested_scales = provider["SCALES"]
+requested_scales = snakemake.params["scales"]
 
 df_esm = pd.read_csv(snakemake.input[0])
 df_esm_preprocessed = preprocess_esm(df_esm)
@@ -20,5 +20,6 @@ for scale in requested_scales:
     mask = df_esm_clean["questionnaire_id"] == questionnaire_id
     if scale.startswith("JCQ"):
         df_esm_clean.loc[mask] = reverse_jcq_demand_control_scoring(df_esm_clean.loc[mask])
+    #TODO Reverse other questionnaires if needed and/or adapt esm_user_score to original scoring.
 
-df_esm_clean.to_csv(snakemake.output[0])
+df_esm_clean.to_csv(snakemake.output[0], index=False)