29M quantified self PaJR

PaJR = patient journey record where patient advocate share a patient's updates according to guidance they get from their primary care provider who is supported by a group of volunteer medical professionals (ranging from medical innovators who may be engineers and  medical students, to residents and consultants. Basically a knowledge network). [Currently Implemented as a whatsapp group]

The patient advocate ensures privacy, data management, and continuity of care.

The updates are:

- lifestyle modification related eg. Calorie deficit

- medication adherence related,

- patient education queries,

etc.

The current case is a 29 year old otherwise healthy but obese man having weight of 86.5-87 kg even after being physically active. His height is 5 feet 5 inches, BMI is 32 and below is picture from Google fit (mobile) data about his daily walk (since many months his average is 5km daily).

All the analytics and charting below is done with help of ChatGPT (free version) and google collab (to run python code output given by chat gpt).

Estimated Basal Metabolic Rate (BMR) for this 29-year-old obese man is approximately 1877.22 kcal/day. 

For 1 month of calorie deficit (31 days)  patient maintained average daily total walk of 5km. Checked weight nearly every week - 86.5, 85, 84, 83, 82.5.

These are his daily calorie intake for 31 days. (Calorie counting done manually by patient advocate. )

1. 600 kcal

2. 750 kcal

3. 1200 kcal

4. 1000 kcal

5. 1100 kcal

6. 800 kcal

7. 900 kcal

8. 1700 kcal

9. 1300 kcal

10. 1400 kcal

11. 2000 kcal

12. 1500 kcal

13. 1250 kcal

14. 1500 kcal

15. 1800 kcal

16. 1450 kcal

17. 1150 kcal

18. 1400 kcal

19. 1450 kcal

20. 1500 kcal

21. 1800 kcal

22. 1500 kcal

23. 1400 kcal

24. 1300 kcal

25. 1200 kcal

26. 1250 kcal

27. 1700 kcal

28. 1900 kcal

29. 1150 kcal

30. 1000 kcal

31. 1350 kcal

Patient used to take around 2500 kcal daily before starting intervention. 

Here are some useful graphs. (The data is in reverse order in these graph 1, 2, & 4).

ChatGPT - One fascinating insight from this reverse order calorie counting data is the oscillation between higher and lower calorie days. By visualizing this data as a sine wave graph, with each day represented as a point along the curve, we see a rhythmic pattern emerge. The peaks and troughs represent days of higher and lower calorie intake respectively, creating a visually stunning wave pattern that illustrates the natural ebb and flow of dietary habits. This visualization highlights the balance between indulgence and restraint, offering a captivating depiction of the intricate dance of calorie consumption over time.

In simple words

- trend is useful to see it started Aggressive and plateau at around 1500 kcal

- seasonal indicate that after getting more strict patient is going back to less strict by variation of around -200 to +200 kcal range.

(Dates are incorrect in this graph).

This data also helps to predict calorie intake for next day it may have high inaccuracy. Regression analysis predicted next day calorie intake to be 1561 kcal.

Useful motivating / behavioural nudging messages are also possible which may also have good information to help reduce junk food intake by awareness. Like -

Day 1 - 600 calories intake: Equivalent to approximately 0.7 liters of petrol or 1.3 servings of chowmein.

Day 31 - Calorie deficit: 2500 - 1350 = 1150 calories

1) Equivalent to approximately 10,000 steps of brisk walking.

2) Equivalent to 45 minutes of high-intensity interval training (HIIT).

Requesting to share if any inputs/corrections in comment section below this blog post.

You may also like to check my precious work about fever charts here - https://classworkdecjan.blogspot.com/2021/02/some-tech-for-just-fever-sign.html