[WIP] Prepare a function to classify adherence and illustrate steps in Jupyter Notebook.
junos
parent
224dedaced
commit
d5cd76f05a
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@ -32,7 +32,9 @@ from features.esm import *
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# Only take data from the main part of the study. The pilot data have different structure, there were especially many additions to ESM_JSON.
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# %%
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participants_inactive_usernames = participants.query_db.get_usernames(collection_start=datetime.date.fromisoformat("2020-08-01"))
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participants_inactive_usernames = participants.query_db.get_usernames(
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collection_start=datetime.date.fromisoformat("2020-08-01")
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)
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df_esm_inactive = get_esm_data(participants_inactive_usernames)
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# %%
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@ -47,7 +49,7 @@ df_esm_preprocessed.columns
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# %% [markdown]
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# 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.
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# Traditionally, concordance (adherence) in EMA study is simply calculated as the ratio of (daily) answered EMAs.
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# Traditionally, concordance (adherence) in EMA study is simply calculated as the ratio of (daily) answered EMAs.
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# This is possible for studies with simple EMA design, such that they are presented at fixed schedule and expired within a certain limit.
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#
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# Since EMAs were triggered more flexibly in our study, a different approach is needed.
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@ -59,7 +61,9 @@ df_esm_preprocessed.columns
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# 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.
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# %%
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session_counts = df_esm_preprocessed.groupby(["participant_id","esm_session"]).count()["id"]
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session_counts = df_esm_preprocessed.groupby(["participant_id", "esm_session"]).count()[
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"id"
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]
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# %% [markdown]
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# 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.
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@ -73,9 +77,13 @@ sns.displot(session_counts.to_numpy(), binwidth=1, height=8)
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# ### Unique session IDs
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# %%
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df_session_counts = pd.DataFrame(session_counts).rename(columns={"id": "esm_session_count"})
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df_session_counts = pd.DataFrame(session_counts).rename(
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columns={"id": "esm_session_count"}
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)
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df_session_1 = df_session_counts[(df_session_counts["esm_session_count"] == 1)]
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df_esm_unique_session = df_session_1.join(df_esm_preprocessed.set_index(["participant_id","esm_session"]))
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df_esm_unique_session = df_session_1.join(
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df_esm_preprocessed.set_index(["participant_id", "esm_session"])
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)
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# %%
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df_esm_unique_session["esm_user_answer"].value_counts()
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@ -85,10 +93,14 @@ df_esm_unique_session["esm_user_answer"].value_counts()
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# What do the answers "Ne" represent?
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# %%
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df_esm_unique_session.query("esm_user_answer == 'Ne'")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]].head()
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df_esm_unique_session.query("esm_user_answer == 'Ne'")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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].head()
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# %%
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df_esm_unique_session.loc[df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"].value_counts()
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df_esm_unique_session.loc[
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df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"
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].value_counts()
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# %% [markdown]
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# These are all "first" questions of EMAs which serve as a way to postpone the daytime or evening EMAs.
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@ -103,22 +115,31 @@ df_esm_unique_session.loc[df_esm_unique_session["esm_user_answer"].str.contains(
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# There are some session IDs that only appear twice or three times.
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# %%
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df_session_counts[(df_session_counts["esm_session_count"] < 4) & (df_session_counts["esm_session_count"] > 1)]
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df_session_counts[
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(df_session_counts["esm_session_count"] < 4)
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& (df_session_counts["esm_session_count"] > 1)
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]
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# %% [markdown]
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# Some represent the morning EMAs that only contained three questions.
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# %%
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df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
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df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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]
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# %%
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df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
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df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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]
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# %% [markdown]
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# Others represent interrupted EMA sessions.
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# %%
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df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
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df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[
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["esm_trigger", "esm_instructions", "esm_user_answer"]
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]
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# %% [markdown]
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# ### Long sessions
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@ -127,7 +148,9 @@ df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[[ "esm_tri
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df_session_counts[(df_session_counts["esm_session_count"] > 40)]
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# %%
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df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trigger","datetime_lj", "_id", "username", "device_id"]]
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df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[
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["esm_trigger", "datetime_lj", "_id", "username", "device_id"]
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]
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# %% [markdown]
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# 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).
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@ -135,11 +158,108 @@ df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trig
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# Session IDs should therefore be grouped while taking the device ID into account.
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# %%
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session_counts_device = df_esm_preprocessed.groupby(["participant_id", "device_id", "esm_session"]).count()["id"]
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session_counts_device = df_esm_preprocessed.groupby(
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["participant_id", "device_id", "esm_session"]
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).count()["id"]
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sns.displot(session_counts_device.to_numpy(), binwidth=1, height=8)
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# %% [markdown]
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# ## Other possibilities
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# %% [markdown]
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# There are also answers that describe what happened to a pending question: "Removed%"
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# Prepare a dataframe with session response as determined from other indices.
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# %%
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import numpy as np
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df_session_counts = pd.DataFrame(session_counts_device).rename(
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columns={"id": "esm_session_count"}
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)
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df_session_counts["session_response"] = np.NaN
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session_group_by = df_esm_preprocessed.groupby(
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["participant_id", "device_id", "esm_session"]
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)
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df_session_counts.count()
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# %% [markdown]
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# ### ESM statuses
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# %% [markdown]
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# The status of the ESM can be: 0-new, 1-dismissed, 2-answered, 3-expired, 4-visible, or 5-branched.
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#
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# Which statuses appear in the data?
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# %%
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df_esm_preprocessed["esm_status"].value_counts()
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# %% [markdown]
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# Most of the ESMs were answered (2). We can group all others as unanswered.
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# %%
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contains_status_not_2 = session_group_by.apply(lambda x: (x.esm_status != 2).any())
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df_session_counts.loc[contains_status_not_2, "session_response"] = "esm_unanswered"
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# %%
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df_session_counts.count()
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# %% [markdown]
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# ### Day finished or off
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# %%
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non_session = session_group_by.apply(
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lambda x: (
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(x.esm_user_answer == "DayFinished3421") | (x.esm_user_answer == "DayOff3421")
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).any()
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)
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df_session_counts.loc[non_session, "session_response"] = "day_finished"
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# %%
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df_session_counts.count()
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# %% [markdown]
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# ### Removed
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# %% [markdown]
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# There are also answers that explicitly describe what happened to a pending question that start with "Removed%".
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# %%
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esm_removed = session_group_by.apply(
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lambda x: (x.esm_user_answer.str.contains("Removed")).any()
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)
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# %%
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df_session_counts.loc[esm_removed]
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# %%
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df_session_counts.loc[esm_removed, "session_response"].value_counts()
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# %% [markdown]
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# It turns out that these had been accounted for with ESM statuses.
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# %% [markdown]
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# ### Evening_last
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# %% [markdown]
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# When the evening EMA session comes to an end, the trigger should reflect this, that is, it should say `evening_last`.
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# %%
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finished_sessions = session_group_by.apply(
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lambda x: (x.esm_trigger.str.endswith("_last")).any()
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)
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df_session_counts.loc[finished_sessions, "session_response"] = "esm_finished"
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# %%
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df_session_counts.count()
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# %%
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df_esm_preprocessed["esm_trigger"].value_counts()
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# %%
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sns.displot(
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df_session_counts[df_session_counts.session_response.isna()],
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x="esm_session_count",
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binwidth=1,
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height=8,
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)
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# %%
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@ -1,6 +1,7 @@
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import datetime
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from collections.abc import Collection
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import numpy as np
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import pandas as pd
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from pytz import timezone
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@ -55,3 +56,54 @@ def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
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columns=["esm_trigger"]
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) # The esm_trigger column is already present in the main df.
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return df_esm.join(df_esm_json)
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def classify_sessions_adherence(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
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"""
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For each distinct EMA session, determine how the participant responded to it.
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Possible outcomes are: esm_unanswered
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This is done in several steps.
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#TODO Finish the documentation.
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Parameters
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----------
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df_esm_preprocessed: pd.DataFrame
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A preprocessed dataframe of esm data, which must include the session ID (esm_session).
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Returns
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-------
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some dataframe
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"""
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sessions_grouped = df_esm_preprocessed.groupby(
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["participant_id", "device_id", "esm_session"]
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)
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df_session_counts = pd.DataFrame(sessions_grouped.count()["id"]).rename(
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columns={"id": "esm_session_count"}
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)
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df_session_counts["session_response"] = np.NaN
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esm_not_answered = sessions_grouped.apply(lambda x: (x.esm_status != 2).any())
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df_session_counts.loc[esm_not_answered, "session_response"] = "esm_unanswered"
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non_session = sessions_grouped.apply(
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lambda x: (
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(x.esm_user_answer == "DayFinished3421")
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| (x.esm_user_answer == "DayOff3421")
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).any()
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)
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df_session_counts.loc[non_session, "session_response"] = "day_finished"
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finished_sessions = sessions_grouped.apply(
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lambda x: (x.esm_trigger.str.endswith("_last")).any()
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)
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df_session_counts.loc[finished_sessions, "session_response"] = "esm_finished"
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# TODO Look at evening-evening_last sequence, if everything is caught with finished sessions
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# TODO What can be done about morning EMA, perhaps morning-morning_first (sic!) sequence?
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# TODO What can be done about workday EMA.
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return sessions_grouped.count()
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