Example Smart Health database data

Example Smart Health database data#

This page demonstrates example data from the Smart Health database.

This data has been generated by the MVP Smart Health pipeline using synthetic data.

The example data for each table can be downloaded under the corresponding heading below.

Fitbit Daily Summary#

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# import libraries
import pandas as pd

# Display all columns
pd.set_option("display.max_columns", None)

# Load the data
daily_summary = pd.read_csv(
    "../../../tests/data/clean/clean_fitbit_summary_2025-12-10.csv"
)

# Show the first five rows
daily_summary.head()
sheffield_id timestamp calories caloriesBMR distance elevation floors minutesSedentary minutesLightlyActive minutesFairlyActive minutesVeryActive steps breathingRate vo2Max dailyRmssd deepRmssd sleep_deep_minutes sleep_light_minutes sleep_rem_minutes sleep_wake_minutes sleep_minutesToFallAsleep sleep_timeInBed SP02_avg SP02_min SP02_max tempCore steps_sum steps_n_samples heart_mean heart_min heart_max heart_n_samples date time filtered_heart_min filtered_heart_max filtered_heart_mean filtered_heart_n_samples
0 p00022 2025-12-10 109 14 110 14 28 68 16 49 22 106 65 76 74 27 107 29 84 50 13 38 81 45 50 101 21272 1440 119.899190 40 200 17280 2025-12-10 00:00:00 40 200 119.899190 17280
1 p00011 2025-12-10 106 36 90 98 102 72 102 71 40 39 66 10 73 70 75 108 65 38 96 73 68 68 63 32 22067 1440 119.939931 40 200 17280 2025-12-10 00:00:00 40 200 119.939931 17280
2 p00098 2025-12-10 96 53 71 40 25 16 85 13 38 104 52 28 75 60 88 92 44 17 53 58 48 62 102 21 20939 1440 119.995081 40 200 17280 2025-12-10 00:00:00 40 200 119.995081 17280
3 p00013 2025-12-10 82 56 101 75 75 62 53 97 42 89 82 103 44 107 57 78 84 66 75 15 20 23 101 95 21533 1440 120.003819 40 200 17280 2025-12-10 00:00:00 40 200 120.003819 17280
4 p00041 2025-12-10 59 103 60 52 61 93 88 32 18 56 100 102 68 41 29 12 45 15 100 79 27 109 47 65 21650 1440 120.111632 40 200 17280 2025-12-10 00:00:00 40 200 120.111632 17280

Download the Daily Summary data here

Fitbit Intraday Activities#

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# Load the data
fb_intraday = pd.read_csv(
    "../../../tests/data/clean/clean_fitbit_intraday_2025-12-10.csv"
)

# Show the first five rows
fb_intraday.head()
sheffield_id timestamp heart_mean heart_min heart_max heart_n_samples steps_sum steps_n_samples date time filtered_heart_min filtered_heart_max filtered_heart_mean filtered_heart_n_samples
0 p00001 2025-12-10 00:05:00 119.400000 44 199 60 64 5 2025-12-10 00:05:00 44 199 119.400000 60
1 p00001 2025-12-10 00:10:00 115.200000 40 200 60 73 5 2025-12-10 00:10:00 40 200 115.200000 60
2 p00001 2025-12-10 00:15:00 118.100000 40 198 60 75 5 2025-12-10 00:15:00 40 198 118.100000 60
3 p00001 2025-12-10 00:20:00 118.583333 44 199 60 54 5 2025-12-10 00:20:00 44 199 118.583333 60
4 p00001 2025-12-10 00:25:00 121.933333 40 197 60 68 5 2025-12-10 00:25:00 40 197 121.933333 60

Download the Intraday activities data here

Intraday LSOA#

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# Load the data
intraday_lsoa = pd.read_csv(
    "../../../tests/data/clean/clean_lsoa_2025-12-10.csv"
)

# Show the first five rows
intraday_lsoa.head()
timestamp sheffield_id latest_LSOA21CD latest_LSOA21NM date time
0 2025-12-10 00:00:00 p00022 E01033264 Sheffield 073D 2025-12-10 00:00:00
1 2025-12-10 00:05:00 p00022 E01033264 Sheffield 073D 2025-12-10 00:05:00
2 2025-12-10 01:10:00 p00022 E01008092 Sheffield 012D 2025-12-10 01:10:00
3 2025-12-10 01:15:00 p00022 E01008088 Sheffield 012B 2025-12-10 01:15:00
4 2025-12-10 02:10:00 p00022 E01008135 Sheffield 005C 2025-12-10 02:10:00

Download the Intraday LSOA data here

Intraday Mobility#

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# Load the data
intraday_mobility = pd.read_csv(
    "../../../tests/data/clean/clean_mobility_2025-12-10.csv"
)

# Show the first five rows
intraday_mobility.head()
timestamp sheffield_id mobility_n_samples dist_travelled mean_speed n_unique_LSOAs date time
0 2025-12-10 00:00:00 p00022 1.0 NaN NaN 1.0 2025-12-10 00:00:00
1 2025-12-10 00:05:00 p00022 20.0 218.923 1.0 1.0 2025-12-10 00:05:00
2 2025-12-10 01:10:00 p00022 7.0 54.981 1.0 1.0 2025-12-10 01:10:00
3 2025-12-10 01:15:00 p00022 26.0 277.903 1.0 3.0 2025-12-10 01:15:00
4 2025-12-10 02:10:00 p00022 3.0 22.992 1.0 1.0 2025-12-10 02:10:00

Download the Intraday Mobility data here

Intraday Environmental#

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# Load the data
intraday_env = pd.read_csv(
    "../../../tests/data/clean/clean_environmental_2025-12-10.csv"
)

# Show the first five rows
intraday_env.head()
sheffield_id timestamp env_n_samples ah4blue ah4dent ah4gp ah4hosp ah4phar ah4gpas ah4no2 ah4so2 ah4pm10 ah4ffood ah4gamb ah4pubs ah4tob ah4leis ah4h ah4g ah4e ah4r ah4ahah ah4ahah_rnk ah4ahah_pct date time
0 p00001 2025-12-10 00:00:00 1.0 1.6619 1.2029 1.2852 0.9864 1.2437 0.1209 15.0331 1.7135 12.2273 0.6958 1.2165 0.4488 0.6797 0.9244 -1.0098 0.7812 0.4064 1.5922 37.6882 40185 95 2025-12-10 00:00:00
1 p00001 2025-12-10 01:00:00 3.0 0.9130 1.2327 0.9656 0.6221 0.8837 0.1338 15.6697 1.6765 12.1112 0.3418 1.7448 0.4101 1.1040 0.7747 -1.4519 0.3853 0.4035 1.6114 30.9563 36714 87 2025-12-10 01:00:00
2 p00001 2025-12-10 02:00:00 6.0 6.4627 2.8773 1.2337 1.0273 1.1583 0.4055 10.7934 1.7629 10.5272 1.5424 2.5883 1.2968 2.6038 6.1835 -0.3200 0.6495 0.0227 0.2211 23.0848 27349 65 2025-12-10 02:00:00
3 p00001 2025-12-10 03:00:00 157.0 6.5558 6.1045 7.2558 3.4741 3.5114 0.4863 9.7712 1.5678 11.3537 3.0673 7.8895 2.9262 7.1773 7.3611 0.9674 0.2425 -0.0668 -0.7730 21.8908 25173 60 2025-12-10 03:00:00
4 p00001 2025-12-10 05:00:00 11.0 2.8817 0.9702 1.5508 1.1829 2.0356 0.3822 9.7202 1.3600 11.1282 0.8830 0.9989 1.7084 1.2665 4.4300 -0.3861 0.1461 -0.2062 0.8909 20.4215 22164 52 2025-12-10 05:00:00

Download the Intraday Envrionmental data here

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