---
# System prepended metadata

title: 4.Apple Heart Study Interpretation
tags: [Lesson 1]

---

###### tags: `Lesson 1`

# 4.Apple Heart Study Interpretation

{%youtube HE5aFTEKwuE %}

## Summary
I think this is a landmark study in the use of wearables for clinical research, but I don’t think we should overvalue that 34% of participants were confirmed to have AF or how the Apple Watch's use as a screening or diagnostic tool. The reason for this is because the study deviated from typical clinical trials that try to prove the effectiveness of devices or drugs in a few significant ways:

* Not everyone received an ECG chest patch, so we don’t know the false-negative rate.
* The inclusion criteria (Apple Watch and iPhone users) created a biased study population.

If wearable research is biased towards a more affluent population, the interventions or discoveries made may be more specifically tailored for this population and may be less effective on a less affluent population. This is similar to why researchers think long and hard about how to enroll minority populations in their clinical trials.

Despite the caveats above, this study broke new ground in significant ways:

* Recruited almost half a million people for the study in 8 months.
* These types of studies lose a lot of participants to follow-up (e.g., over 2000 participants were notified of having irregular pulse rates but only collected data from 450 ECG chest patches.)
* How participants would react to a smartwatch notifying them of an abnormal heart rhythm while it was happening.

I think the study is super exciting in that it's a first attempt to use the capability of wearables to do long term monitoring in a medical context. There’s obviously a lot of hype around studies like this and I want us to be excited about the right things.

## Classification Accuracy
From activity classification to AF detection, many tasks in wearable healthcare involve classification. In the case of the AHS, participants were classified by the “irregular pulse” notification into AF and non-AF groups. One way of discussing **classification accuracy** in binary classification is by looking at precision and recall.

* **precision**: the proportion of all true positive cases that are detected positive by the classifier
* **recall**: the proportion of all positives detected by the classifier that are true positives

This is demonstrated graphically below:

![](https://video.udacity-data.com/topher/2020/March/5e7a3d4b_nd320-c4-l0-precision-recall-wiki/nd320-c4-l0-precision-recall-wiki.png)

Source: Walber. Precision and recall. Nov 2014. CC-BY-SA-4.0 [Link](https://commons.wikimedia.org/wiki/File:Precisionrecall.svg)

## Further Research
Wearables are part of Digital Health, learn more about it [here](https://en.wikipedia.org/wiki/Digital_health).

The wikipedia page for [precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall)

The Framingham Study is a landmark study in cardiovascular health. Many interesting papers came out of it, and I highly recommend exploring them. Start [here with the Wikipedia page](https://en.wikipedia.org/wiki/Framingham_Heart_Study)

If you would like to read up on the AHS on your own, you can find the clinical trial study record detail [here](https://clinicaltrials.gov/ct2/show/NCT03335800).

### Relevant Papers
* **Apple Heart Study** - Perez MV, Mahaffey KW, Hedlin H, et al. "Large-scale assessment of a smartwatch to identify atrial fibrillation." N Engl J Med 2019;381:1909-1917. [Link](https://www.nejm.org/doi/full/10.1056/NEJMoa1901183).
* **Apple Heart Study Response** - Campion Edward W., Jarcho John A.. (2019) "Watched by Apple." N Engl J Med 381:20, 1964-1965. [Link](https://www.nejm.org/doi/full/10.1056/NEJMe1913980).
* **Framingham Study** - Wolf PA, Abbott RD, Kannel WB. "Atrial fibrillation as an independent risk factor for stroke: the Framingham Study." Stroke 1991;22:983-988. [Link](https://www.ahajournals.org/doi/10.1161/01.STR.22.8.983).
* **Wearable Health** - Montgomery, K., Chester, J., & Kopp, K. (2018). "Health Wearables: Ensuring Fairness, Preventing Discrimination, and Promoting Equity in an Emerging Internet-of-Things Environment." Journal of Information Policy, 8, 34-77. doi:10.5325/jinfopoli.8.2018.0034 [Link](https://www.jstor.org/stable/10.5325/jinfopoli.8.2018.0034#metadata_info_tab_contents)

## New Vocabulary
* **Inclusion Criteria**: Characteristics that potential study subjects must have for them to be included in the study.
* **Exclusion Criteria**: Characteristics that disqualify potential study subjects from participating in a clinical study. e.g., Some common ones are being under 18 or pregnant.
* **Classification Accuracy**: A metric for evaluating the performance of a classifier -- the fraction of classifications that are correct. For rare events (like atrial fibrillation), this metric is unsuitable. For example, a classifier that classifies every data point as healthy would have a classification accuracy of 99%, as around 1 percent of the population has atrial fibrillation, but would be relatively useless.
* **Precision**: The fraction of positive classifications that are correct.
* **Recall**: The fraction of positive elements that are classified correctly as positive.