Most commercial tools are parsing iOS health data. However, not all tools are perfect, and most will interpret the data in different manners. It is up to you to validate the findings. The following query will parse additional information to prove if the tool extracted it correctly.

select :

datetime(samples.start_date+978307200,'unixepoch','localtime') as "Start Date",

datetime(samples.end_date+978307200,'unixepoch','localtime') as "End Date", samples.

data_id, case

when data_type = 3 then "weight"

when data_type = 7 then "steps"

when data_type = 8 then "dist in m"

when data_type = 9 then "resting energy"

when data_type = 10 then "active energy"

when data_type = 12 then "flights climbed"

when data_type = 67 then "weekly calori goal"

when data_type = 70 then "watch on"

when data_type = 75 then "stand"

when data_type = 76 then "activity"

when data_type = 79 then "workout"

when data_type = 83 then "some workouts"

end as "activity type“,

quantity AS "Flights"

from samples

left outer join quantity_samples on samples.data_id = quantity_samples.data_id

left outer join unit_strings on quantity_samples.original_unit = unit_strings.RowID

left outer join correlations on samples.data_id = correlations.object

left outer join metadata_values on metadata_values.object_id = samples.data_id

left outer join metadata_keys on metadata_keys.ROWID = metadata_values.key_id

where "Start Date" like '%2017-01-19%'

order by "Start Date" desc