I want to send health metrics from Apple Health to AirTable daily.
Here’s the data I receive:
Health Metrics.json (20.7 KB)
I want to be able to insert this data BY DATE in AirTable.
Here’s my AirTable table structure:
Here’s the CSV of the table structure:
Health-Export.csv (692 Bytes)
I have tried so many things like aggregator but I can’t figure out how to group data by data and easily import it into AirTable 
Thank you 
Max
Welcome to the Make community!
Yes, that is possible. You’ll need a minimum of seven modules:
Number of operations used can be calculated as follows:
2N + 5, where N = number of days
Give it a go and let us know if you have any issues!
Thank you so much for your response…
I am trying but I can’t get to the same outcome:
blueprint.json (13.2 KB)
I’m sure I’m doing something wrong but I have no idea where I’m missing the mark.
Would appreciate your help to finalize this @samliew
Thanks again.
Max
You can copy and paste this module export into your scenario. This will paste the modules shown in my screenshots above.
Copy the code below by clicking the copy button when you mouseover the top-right of the code block
Enter your scenario editor. Press ESC to close any dialogs. Press CTRLV to paste in the canvas.
Click on each imported module and save it. You may need to remap some variables.
{
"subflows": [
{
"flow": [
{
"id": 122,
"module": "json:ParseJSON",
"version": 1,
"parameters": {
"type": ""
},
"mapper": {
"json": "[\n {\n \"data\": {\n \"metrics\": [\n {\n \"units\": \"min\",\n \"data\": [\n {\n \"qty\": 175.2,\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"source\": \"Maxime’s Apple Watch|Maxime’s iPhone (2)\"\n },\n {\n \"qty\": 157.2,\n \"source\": \"Maxime’s Apple Watch|Maxime’s iPhone (2)\",\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"source\": \"Maxime’s Apple Watch|Maxime’s iPhone (2)\",\n \"qty\": 42\n }\n ],\n \"name\": \"apple_stand_time\"\n },\n {\n \"name\": \"environmental_audio_exposure\",\n \"units\": \"dBASPL\",\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"qty\": 65.32677315827898\n },\n {\n \"qty\": 67.72967896245953,\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"qty\": 57.53758667137109\n }\n ]\n },\n {\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"source\": \"Maxime’s Apple Watch\",\n \"qty\": 7820.55833648738\n },\n {\n \"qty\": 7840.295105315788,\n \"source\": \"Maxime’s Apple Watch\",\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"source\": \"Maxime’s Apple Watch\",\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"qty\": 2883.743318197767\n }\n ],\n \"name\": \"basal_energy_burned\",\n \"units\": \"kJ\"\n },\n {\n \"data\": [\n {\n \"Max\": 188,\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"Min\": 44,\n \"source\": \"Maxime’s Apple Watch|Oura\",\n \"Avg\": 70.31713024401918\n },\n {\n \"Avg\": 68.09904099697327,\n \"source\": \"Maxime’s Apple Watch|Oura\",\n \"Min\": 46,\n \"date\": \"2024-04-16 00:00:00 +0200\",\n \"Max\": 187\n },\n {\n \"Max\": 121,\n \"Min\": 45,\n \"Avg\": 53.31467240624592,\n \"source\": \"Maxime’s Apple Watch|Oura\",\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ],\n \"units\": \"count/min\",\n \"name\": \"heart_rate\"\n },\n {\n \"units\": \"min\",\n \"name\": \"apple_exercise_time\",\n \"data\": [\n {\n \"qty\": 149,\n \"source\": \"Maxime’s Apple Watch|Maxime’s iPhone (2)\",\n \"date\": \"2024-04-15 00:00:00 +0200\"\n },\n {\n \"source\": \"Maxime’s Apple Watch|Maxime’s iPhone (2)\",\n \"qty\": 126,\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"qty\": 33,\n \"source\": \"Maxime’s Apple Watch|Maxime’s iPhone (2)\",\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ]\n },\n {\n \"units\": \"count/min\",\n \"name\": \"resting_heart_rate\",\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"qty\": 54\n },\n {\n \"qty\": 49,\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"qty\": 55\n }\n ]\n },\n {\n \"units\": \"count/min\",\n \"name\": \"cardio_recovery\",\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"qty\": 46.906116489353515\n },\n {\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"qty\": 47.76198855717249\n }\n ]\n },\n {\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"qty\": 1371.327000000008,\n \"source\": \"Maxime’s Apple Watch|Oura\"\n },\n {\n \"source\": \"Maxime’s Apple Watch|Oura\",\n \"date\": \"2024-04-16 00:00:00 +0200\",\n \"qty\": 1262.67600000001\n },\n {\n \"source\": \"Maxime’s Apple Watch|Oura\",\n \"qty\": 205.75899999999965,\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ],\n \"units\": \"kcal\",\n \"name\": \"active_energy\"\n },\n {\n \"data\": [\n {\n \"qty\": 66.10866383644904,\n \"date\": \"2024-04-15 00:00:00 +0200\"\n },\n {\n \"date\": \"2024-04-16 00:00:00 +0200\",\n \"qty\": 83.756633292232\n },\n {\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"qty\": 67.26853073582659\n }\n ],\n \"name\": \"heart_rate_variability\",\n \"units\": \"ms\"\n },\n {\n \"name\": \"walking_heart_rate_average\",\n \"data\": [\n {\n \"qty\": 101,\n \"date\": \"2024-04-15 00:00:00 +0200\"\n },\n {\n \"qty\": 99.5,\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"qty\": 97,\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ],\n \"units\": \"count/min\"\n },\n {\n \"units\": \"count\",\n \"name\": 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00:00:00 +0200\",\n \"qty\": 47.355000000000004\n },\n {\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"qty\": 47.41\n }\n ],\n \"units\": \"ml/(kg·min)\",\n \"name\": \"vo2_max\"\n },\n {\n \"data\": [\n {\n \"date\": \"2024-04-16 00:00:00 +0200\",\n \"qty\": 500\n }\n ],\n \"units\": \"m\",\n \"name\": \"six_minute_walking_test_distance\"\n },\n {\n \"name\": \"walking_asymmetry_percentage\",\n \"units\": \"%\",\n \"data\": [\n {\n \"qty\": 0.06666666666666667,\n \"date\": \"2024-04-15 00:00:00 +0200\"\n },\n {\n \"date\": \"2024-04-16 00:00:00 +0200\",\n \"qty\": 1.4000000000000001\n },\n {\n \"qty\": 0,\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ]\n },\n {\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"qty\": 5.116320000000001\n },\n {\n \"qty\": 4.809599999999999,\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"qty\": 5.22,\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ],\n \"name\": \"walking_speed\",\n \"units\": \"km/hr\"\n },\n {\n \"name\": 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\"dietary_water\",\n \"data\": [\n {\n \"source\": \"Hidrate\",\n \"qty\": 2740,\n \"date\": \"2024-04-15 00:00:00 +0200\"\n },\n {\n \"qty\": 2100,\n \"date\": \"2024-04-16 00:00:00 +0200\",\n \"source\": \"Hidrate\"\n }\n ]\n },\n {\n \"name\": \"apple_stand_hour\",\n \"units\": \"count\",\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"qty\": 13\n },\n {\n \"qty\": 14,\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"qty\": 2,\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ]\n },\n {\n \"units\": \"dBASPL\",\n \"data\": [\n {\n \"qty\": 61.80808572463909,\n \"date\": \"2024-04-15 00:00:00 +0200\"\n },\n {\n \"qty\": 61.13966854774309,\n \"date\": \"2024-04-16 00:00:00 +0200\"\n },\n {\n \"date\": \"2024-04-17 00:00:00 +0200\",\n \"qty\": 52.59199673288058\n }\n ],\n \"name\": \"headphone_audio_exposure\"\n },\n {\n \"units\": \"hr\",\n \"name\": \"sleep_analysis\",\n \"data\": [\n {\n \"inBedEnd\": \"2024-04-15 07:14:04 +0200\",\n \"sleepStart\": \"2024-04-14 21:28:34 +0200\",\n \"deep\": 1.65,\n \"date\": \"2024-04-15 07:00:34 +0200\",\n \"sleepEnd\": \"2024-04-15 07:14:04 +0200\",\n \"source\": \"Oura\",\n \"inBedStart\": \"2024-04-14 21:28:34 +0200\",\n \"asleep\": 0,\n \"inBed\": 9.758333333333333,\n \"rem\": 2.275,\n \"core\": 4.733333333333335,\n \"awake\": 1.0666666666666667\n },\n {\n \"core\": 3.5666666666666673,\n \"date\": \"2024-04-16 06:59:58 +0200\",\n \"rem\": 1.9333333333333336,\n \"asleep\": 0,\n \"awake\": 1.7666666666666666,\n \"source\": \"Oura\",\n \"sleepStart\": \"2024-04-15 21:48:58 +0200\",\n \"sleepEnd\": \"2024-04-16 07:16:58 +0200\",\n \"inBedStart\": \"2024-04-15 21:48:58 +0200\",\n \"deep\": 2.1916666666666664,\n \"inBedEnd\": \"2024-04-16 07:16:58 +0200\",\n \"inBed\": 9.466666666666667\n },\n {\n \"inBedEnd\": \"2024-04-17 07:00:58 +0200\",\n \"date\": \"2024-04-17 06:55:03 +0200\",\n \"sleepEnd\": \"2024-04-17 07:15:03 +0200\",\n \"source\": \"Maxime’s iPhone (2)|Oura\",\n \"sleepStart\": \"2024-04-16 22:13:03 +0200\",\n \"asleep\": 0,\n \"deep\": 1.2833333333333334,\n \"rem\": 2.008333333333333,\n \"core\": 3.8166666666666664,\n \"awake\": 1.8999999999999997,\n \"inBedStart\": \"2024-04-16 21:26:52 +0200\",\n \"inBed\": 9.155843916667832\n }\n ]\n },\n {\n \"data\": [\n {\n \"date\": \"2024-04-15 00:00:00 +0200\",\n \"qty\": 3.7899122865576493\n },\n {\n \"date\": \"2024-04-16 00:00:00 +0200\",\n \"qty\": 3.370777486156842\n },\n {\n \"qty\": 2.695698925243911,\n \"date\": \"2024-04-17 00:00:00 +0200\"\n }\n ],\n \"name\": \"physical_effort\",\n \"units\": \"kcal/hr·kg\"\n }\n ]\n }\n }\n]"
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It worked 
Thanks @samliew you’re a genius 
Now can you help me insert the text line by line into AirTable?
I generating a CSV but it doesn’t map the column names:
And I also can’t find the different bundles afterwards, there’s only one set of data 
Sorry I’m a bit of a noob when it comes to these things…
Thanks again for all your help!
Max
Based on your metrics, there are 27 bundles (columns).
I added a “Date” column in the first position, so now there are 28 columns.
Following the first column, the remaining columns are ordered the same order as the 27 metrics in your original JSON. The first column is apple_stand_time
Just map the respective column in each of your Airtable “Create a Record” fields.
There are 3 bundles when you Parse CSV — this means your Airtable will run 3 times, and should add 3 records. As long as you map the columns correctly it should just work.
Hey @samliew,
Can I hire you to help me finish this project?
I’m 90% there but I’m wasting a lot of time trying to do this when I feel like you could do it super fast…
