stress_at_work_analysis/exploration/expl_esm.py

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# %%
import os
import sys

import seaborn as sns

nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
    sys.path.append(nb_dir)
import participants.query_db
from features.esm import *

# %% [markdown]
# # ESM data

# %% [markdown]
# Only take data from the main part of the study. The pilot data have different structure, there were especially many additions to ESM_JSON.

# %%
participants_inactive_usernames = participants.query_db.get_usernames(collection_start=datetime.date.fromisoformat("2020-08-01"))
df_esm_inactive = get_esm_data(participants_inactive_usernames)

# %%
df_esm_preprocessed = preprocess_esm(df_esm_inactive)
df_esm_preprocessed.head()

# %%
df_esm_preprocessed.columns

# %% [markdown]
# # Concordance

# %% [markdown]
# The purpose of concordance is to count the number of EMA sessions that a participant answered in a day and possibly compare it to some maximum number of EMAs that could theoretically be presented for that day.
# Traditionally, concordance (adherence) in EMA study is simply calculated as the ratio of (daily) answered EMAs. 
# This is possible for studies with simple EMA design, such that they are presented at fixed schedule and expired within a certain limit.
#
# Since EMAs were triggered more flexibly in our study, a different approach is needed.

# %% [markdown]
# ## Session IDs

# %% [markdown]
# One approach would be to count distinct session IDs which are incremented for each group of EMAs. However, since not every question answered counts as a fulfilled EMA, some unique session IDs should be eliminated first.

# %%
session_counts = df_esm_preprocessed.groupby(["participant_id","esm_session"]).count()["id"]

# %% [markdown]
# Group data by participant_id and esm_session and count the number of instances (by id). Session counts are therefore counts of how many times a specific session ID appears *within* a specific participant.
#
# In the plot below, it is impossible to distinguish whether a specific count appears many times within the same or across different participants.

# %%
sns.displot(session_counts.to_numpy(), binwidth=1, height=8)

# %% [markdown]
# ### Unique session IDs

# %%
df_session_counts = pd.DataFrame(session_counts).rename(columns={"id": "esm_session_count"})
df_session_1 = df_session_counts[(df_session_counts["esm_session_count"] == 1)]
df_esm_unique_session = df_session_1.join(df_esm_preprocessed.set_index(["participant_id","esm_session"]))

# %%
df_esm_unique_session["esm_user_answer"].value_counts()

# %% [markdown]
# The "DayFinished3421" tag marks the last EMA, where the participant only marked "I finished with work for today" and did not answer any questions.
# What do the answers "Ne" represent?

# %%
df_esm_unique_session.query("esm_user_answer == 'Ne'")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]].head()

# %%
df_esm_unique_session.loc[df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"].value_counts()

# %% [markdown]
# These are all "first" questions of EMAs which serve as a way to postpone the daytime or evening EMAs.

# %% [markdown]
# The other answers signify expired or interrupted EMAs.

# %% [markdown]
# ### "Almost" unique session IDs

# %% [markdown]
# There are some session IDs that only appear twice or three times.

# %%
df_session_counts[(df_session_counts["esm_session_count"] < 4) & (df_session_counts["esm_session_count"] > 1)]

# %% [markdown]
# Some represent the morning EMAs that only contained three questions.

# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]

# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]

# %% [markdown]
# Others represent interrupted EMA sessions.

# %%
df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]

# %% [markdown]
# ### Long sessions

# %%
df_session_counts[(df_session_counts["esm_session_count"] > 40)]

# %%
df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trigger","datetime_lj", "_id", "username", "device_id"]]

# %% [markdown]
# Both, session ID and \_ID (and others) reset on application reinstall. Here, it can be seen that the application was reinstalled on 2 April (actually, the phone was replaced as reported by the participant).
#
# Session IDs should therefore be grouped while taking the device ID into account.

# %%
session_counts_device = df_esm_preprocessed.groupby(["participant_id", "device_id", "esm_session"]).count()["id"]
sns.displot(session_counts_device.to_numpy(), binwidth=1, height=8)

# %% [markdown]
# ## Other possibilities

# %% [markdown]
# There are also answers that describe what happened to a pending question: "Removed%"