Here's a quick video overview of How Apple Watch Sleep Apnea Detect...
A polysomnography is an overnight sleep study conducted in a specia...
The Apnea-Hypopnea Index (AHI) is a key metric used to measure slee...
#### The 4% Hypopnea Rule
The 4% hypopnea rule (also called the ...
Abdominal belts (respiratory effort belts) used in sleep studies me...
Junichiro Hayano from the University of Nagoya was able to demonstr...
Estimating Breathing
Disturbances and Sleep Apnea
Risk from Apple Watch
September 2024
Overview
Users can measure Breathing Disturbances during sleep tracking with Apple Watch
Series9 or later and Apple Watch Ultra 2 (excluding Apple Watch SE) to better
understand interruptions in their respiratory patterns. Apple Watch also uses this
datainan algorithm that detects signs of moderate to severe sleep apnea.
Sleep apnea is a common and treatable disorder that negatively impacts people’s health
and quality of life. Despite advances in public awareness about the importance of sleep,
most cases of sleep apnea remain undiagnosed. Apple Watch can track Breathing
Disturbances during sleep and provide notifications of possible sleep apnea if Breathing
Disturbances values reach a level associated with moderate to severe sleep apnea.
This article describes the development and validation of
the Sleep Apnea Notification Feature.Apple developed the
AppleWatch algorithm, which uses accelerometer data to
estimate Breathing Disturbances, with a large and diverse
training set of adults undergoing simultaneous recordings
from Apple Watch and a ground truth reference. This
included either in-lab polysomnography (PSG) or home
sleep apnea test (HSAT) kits, which measure apnea and
hypopnea events — the foundation of the apnea-hypopnea
index (AHI).
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 2
Introduction
Sleep apnea is a treatable sleep disorder characterized by repeated disturbances in breathing during sleep.
Untreated sleep apnea can increase the risk of health issues such as high blood pressure, heart attack,
and stroke. The health implications of sleep apnea are most significant for moderate to severe disease,
defined byan apnea-hypopnea index (AHI) — a measure of the average number of apnea or hypopnea
events per hour over a recording interval — of 15 or more. Clinical guidelines indicate that moderate to
severesleep apnea warrants treatment regardless of symptoms and that treatment for mild sleep apnea
is recommended if symptomatic (Epstein et al. 2009). Fortunately, well-established treatment options are
available for this condition, such as positive airway pressure devices (for example, CPAP), dental appliances,
and surgical options.
The prevalence of moderate to severe sleep apnea is estimated at approximately 10% of the global adult
population, based on a strictscoring convention known as the 4% hypopnea rule (Benjafield et al. 2019).
Research also suggests that up to 80% of sleep apnea cases remain undiagnosed (Peppard etal.
2013).While some people experience symptoms such as daytime drowsiness or gasping for breath at
night, other people may not have any symptoms.
Accelerometer-Based Breathing Analysis
Technical and Feature Description
Apple Watch tracks movement with triaxial accelerometer signals, which capture coarse motion of the
body as well as fine movements including motion associated with breathing.Apple developed an algorithm
that uses the accelerometer time series data to classify Breathing Disturbances that occur during sleep
tracking, which are temporary interruptions in the breathing pattern.Figure 1 demonstrates the relationship of
the Apple Watch accelerometer data (X, Y, and Z) alongside more traditional methods of identifying
disruptions to breathing, including abdomen belts and airflow meters.
The Sleep Apnea Notification Feature contains two components: a nightly Breathing Disturbances
measurement and a notification sent to the user if the Breathing Disturbances values are elevated over a
30-day period, indicating signs of potential moderate to severe sleep apnea. Note that neither component is
intended or cleared for use by people who are already diagnosed with sleep apnea. Breathing Disturbances
aren’t equivalent to the AHI.
Human-scored events indicate interruptions in the respiratory pattern alternating with recovery breaths.
Figure 1. Apple Watch Accelerometer Signals and Ground Truth Respiration Signals
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 3
Initial use of this feature requires a short onboarding process and sleep tracking to be enabled on
Apple Watch. Once active, Breathing Disturbances values can be viewed in the Health app.Tracking
Breathing Disturbances helps users understand their sleep and the behaviors or lifestyle factors that may
affect their Breathing Disturbances levels.The notification feature alerts users who may be at risk of moderate
to severe sleep apnea. An example of Breathing Disturbance data viewed in the iPhone Health app is shown
in Figure 2. In this example, the majority of nights were classified as not elevated, so the displayed period is
summarized as Not Elevated.
Figure 2. Breathing Disturbance Data in the Health App on iPhone
To l e a r n m o re a bo u t u si n g t he S l e ep fe at u re i n th e H e al t h a pp o n Ap p l e Wa t c h, v i s i t support.apple.com/en-us/
HT211685.
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 4
Preclinical Design and Algorithm Testing
Apple conducted research studies to develop the Breathing Disturbances metric and the associated sleep
apnea notification algorithm.Adult participants from multiple research sites provided informed consent via
protocols approved by an institutional review board (IRB). To enhance performance generalizability, Apple
recruited a diverse population of research participants across various demographic factors (age, biological
sex, race, ethnicity, and BMI) and evaluated sleep in both at-home and in-laboratory sleeping environments.
In addition, the studies included a broad range of sleep apnea severity, from normal (fewer than five apnea
and hypopnea events per hour) to severe (more than 30 events per hour).
Adult research participants wore an Apple Watch while reference recordings were conducted simultaneously
from in-laboratory PSG (one night) or at-home HSAT recordings (one to four nights).In each case, the
reference device recordings followed a standard clinical approach.Certified PSG technologists scored the
clinical recordings according to American Academy of Sleep Medicine (AASM) standards to provide reference
labels of each apnea and hypopnea event.It’s important to note that current AASM clinical standards allow
multiple definitions for scoring sleep apnea events.Both the training and validation studies used the strictest
scoring definition, which requires a 4% oxygen desaturation for hypopneas.
The design phase of algorithm development consisted of 3936 nights of at-home and in-lab PSG recordings
from 2160 participants.Some participants contributed multiple nights of recordings.The performance
testing phase included an additional 7220 nights from 2542 participants (see Table 1).None of the data
from the testing set was used in the design phase. That is, the testing set was sequestered and unseen
by the algorithm during the training phase.Participants self-reported race and ethnicity in the design and
testing sets, respectively, as White (70.7% and 69.1%), Black (9.0% and 8.9%), Asian (11.4% and 13.5%),
and Hispanic (7.2% and 7.6%).
The algorithm output of Breathing Disturbances is expressed as a continuous variable, which has units of
events per hour.The sleep apnea notification algorithm assesses the Breathing Disturbance data every
30 days (non-rolling), starting 30 days after onboarding. When at least 10 sleep recordings (not required
tobesequential) with Breathing Disturbances values occur within a given 30-day period, the notification
algorithm checks if at least 50% of these values are elevated.If so, the algorithm surfaces a notification of
possible sleep apnea to the user; otherwise, it remains silent.The algorithm also stays silent if there are fewer
than 10 nights with a Breathing Disturbances value in a 30-day window, as the data is insufficient for analysis.
Whether or not a notification is surfaced, the sleep apnea notification algorithm will remain active and attempt
to analyze data after each 30-day window.
The operating point on a receiver operating characteristic (ROC) curve reflects the trade-offs incurred when
choosing a threshold for a binary classifier to balance sensitivity and specificity goals.Sensitivity, or the true
positive rate, refers to the percentage of participants with moderate to severe sleep apnea who are correctly
identified by the algorithm.Specificity refers to the percentage of those without moderate to severe sleep
apnea who wouldn’t receive a notification. The operating point on the ROC curve was intentionally chosen to
favor specificity, understanding that operating point positions with high specificity have lower sensitivity.This
choice supported the goal of minimizing the false positive risk, which is particularly important for a feature
designed to repeatedly check for signs of possible sleep apnea over time (and in consideration of the
cumulative false positive rate) while simultaneously maintaining an impactful true positive rate.
In the sequestered algorithm testing data set, notification performance was 66.6% for sensitivity and 95.9%
for specificity.
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 5
To understand potential confounders that could impact algorithm performance, additional regression
analyses were conducted.The results suggested no significant impact of sex, BMI, race, ethnicity, periodic
limb movement index (PLMI), co-sleeper status, or sleep efficiency on algorithm performance.
Ta b le 1. Pa r t i c ip a n t C ha r ac t er i st i c s i n P re c li n i ca l D ev el o p me n t
Clinical Validation
Apple validated the finished algorithm separately using data collected in a prospective clinical study to
support submissions to the FDA and other global medical device regulators.Each participant provided written
informed consent to participate in the study, defined by an IRB-approved protocol.In this study, participants
wore Apple Watch forup to 30 nights and underwent a minimum of two nights of HSAT recordings with a type
3 HSAT device, which served as the ground truth for sleep apnea status.A centralscoringteamof certified
PSG technologists scored the HSAT data according to the AASM clinical standards, using the 4%
desaturation hypopnea scoringrule.Each HSAT night was scored separately by two technologists who were
blinded to each other and the algorithm.A certified sleep clinician, blinded to the algorithm, reviewed the raw
data and the two technologists’ scoring reports, adjudicating any disagreement between scorers using pre-
specified escalation rules. Ground truth status for notification performance was defined as the higher value of
two HSAT nights.
The characteristics of the participants in clinical validation are given in Table 2.
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 6
Design
Testing
Participants (4702)
2160
2542
Nights (11,156)
3936
7220
Age (years)
49.1 (16.6)
48.7 (15.1)
Sex (% female)
53
50
BMI (kg/m
2
)
29.6 (7.3)
29.4 (7.1)
Sleep Apnea Categories
Normal (AHI < 5)
1425
1143
Mild (AHI ≥ 5 to < 15)
423
617
Moderate (AHI ≥ 15 to < 30)
202
384
Severe (AHI ≥ 30)
110
398
PLMI (% ≥ 15)*
22.0
22.2
Data is presented as mean (SD) for continuous variables and % for categorical variables.% refers to nights (not participants), and recordings from individual
participants with multiple nights were used only in either the design or testing phases (a single participant was never used in both). Periodic limb movement
index (PLMI) data was available for nights with PSG reference recordings (in lab).Body mass index (BMI) is calculated as units of kilograms (kg) over meters
squared (m
2
).
* Based on in-lab PSG studies only.
Ta b le 2. P a r t ic i p a n t C ha r ac t er i st i c s i n C l in i ca l Va l i da t io n
The study population included broad representation across demographic factors and sleep apnea severity,
including an enriched population that exceeds the real-world prevalence of approximately 10% for moderate
to severe sleep apnea, based on the strict 4% scoring rule.Therefore, to reflect the real-world sensitivity
andspecificity of the notification algorithm, the performance of each group was re-weighted based on
approximate prevalence data in adults. The rationale for pre-specifying weights is informed by epidemiology
research (Franklin and Lindberg 2015; Benjafield et al. 2019), which shows that normal AHI values are by
far the most common, followed by mild-range AHI values.To compute overall specificity for the combined
group of normal and mild cases, analysis included a conservative five-to-one weighting ratio. In contrast,
the prevalence imbalance between moderate and severe is much less pronounced; thus, sensitivity was
calculated using a simple weighting ratio of one-to-one for moderate and severe cases.
The primary endpoints for the notification performance are:
• Sensitivity of the notification for those with a ground truth indication of moderate or severe sleep apnea
• Specificity of the notification for those with a ground truth indication of normal or mild sleep apnea
The ground truth sleep apnea category for each participant was defined for the primary endpoint using the
higher AHI value of two HSAT nights.
The primary endpoint hypothesis associated with the notification accuracy was pre-specified as follows:
• H
0
: sensitivity < 50% or specificity < 85%
• H
1
: sensitivity ≥ 50% and specificity ≥ 85%
To t es t t he n u l l h y p ot h e s i s, H
0
, estimates of the overall sensitivity and overall specificity were computed
usingthe estimates of the individual diagnostic category performance and weights as outlined above.
H
0
wasrejected if the lower confidence bounds for sensitivity and specificity were at least 50% and
85%, respectively.
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 7
Validation
Participants (no.)
1499
Nights (no.)
24 (7)
Age (years)
46 (14)
Sex (% female)
56.5
BMI (kg/m
2
)
32 (8)
Sleep apnea categories
Normal (AHI < 5)
559
Mild (AHI ≥ 5 to < 15)
362
Moderate (AHI ≥ 15 to < 30)
216
Severe (AHI ≥ 30)
201
Data is presented as mean (SD) for continuous variables and % for categorical variables.Body mass index (BMI) is calculated as units of
kilograms (kg) over meters squared (m
2
).
A total of 1499 participants were enrolled, with 1448 completing the study. The sensitivity was 66.3%
(95% CI: 62.2% to 70.3%), and the specificity was 98.5% (95% CI: 98.0% to 99.0%), demonstrating that
thefeature meets the design objectives to confidently identify sleep apnea while minimizing false positives.
Ta b l e 3 s h o w s t h e p e r f o r m a n c e o f s p e c i f i c i t y ( fo r n o r m a l a n d m i l d c a t e g o r i e s ) a n d s e n s i t i v i t y ( fo r m o d e r a t e
and severe categories). It’s important to note that the specificity was 100% (95% CI: 99.7 to 100%) for the
normal category, indicating that all participants with a “positive” algorithm result had at least mild sleep
apnea.Also, sensitivity was higher in the severe category at 89.1% (95% CI: 83.7% to 93.2%), indicating
thatthe large majority of severe cases were identified.
Subgroup analyses indicated that the sensitivity and specificity were comparable when analyzed by age, sex,
race, ethnicity, and BMI subgroups.No serious adverse events were observed.
Table 3. Primary Endpoints: Sensitivity and Specificity
A secondary endpoint analysis was conducted on the accuracy of individual night Breathing Disturbances
values, where ground truth was defined by the HSAT reference device compared with participants’ aligned
time wearing Apple Watch. Figure 3 shows the evaluation method for assessing performance.The green
funnel-shaped zone is based on the principle that error tolerance grows as the true AHI value grows,
similar tothe approach used for blood sugar (Jendrike et al. 2017). For example, an error of 3 points is less
meaningful whenthe true AHI is 50 (severe sleep apnea) than when the true AHI is 5 (borderline mild sleep
apnea).Apercentage-based error is also deemed unsuitable because it would place undue restrictions
at a very low AHI. For example, a Breathing Disturbances value of 2, when the true AHI = 1, meaning
an error of+1, would be a 100% error. The green funnel bounds are symmetric around the identity line.
Of the 1305 participants who had at least one paired watch and an HSAT data value, 1193 (91.4%) had
Breathing Disturbances measurements within the green performance zone.
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 8
Algorithm Result
Value
Two-Sided 95% Confidence Interval
Sensitivity for moderate category
89/205 (43.4%)
(36.5%, 50.5%)
Sensitivity for severe category
164/184 (89.1%)
(83.7%, 93.2%)
Weighted overall sensitivity
66.3%
(62.2%, 70.3%)
Specificity for normal category
543/543 (100.0%)
(99.3%, 100.0%)
Specificity for mild category
315/346 (91.0%)
(87.5%, 93.8%)
Weighted overall specificity
98.5%
(98.0%, 99.0%)
Weighted average sensitivity = (1/2)(moderate sensitivity) + (1/2)(severe sensitivity)!
Weighted average specificity = (5/6)(normal specificity) + (1/6)(mild specificity)
Figure 3. Funnel Region to Assess Breathing Disturbances Accuracy
Discussion
The Sleep Apnea Notification Feature is a software-based medical device that analyzes Breathing
Disturbance data collected nightly by the Apple Watch accelerometer sensor. It surfaces a notification if
there’s sufficient information suggesting moderate to severe sleep apnea (based on multiple elevations
over a 30-day period containing at least 10 days of Breathing Disturbances values). Tracking Breathing
Disturbances during sleep can also help users discover patterns over time that may provide insight into
the quality of their sleep. For example, various lifestyle factors, such as alcohol, exercise, and changes
in medications or body weight, can influence Breathing Disturbances levels over time.The performance
accuracy of this notification was validated in engineering studies and confirmed to meet performance
requirements in a separate formal clinical validation study submitted to the FDA and other global medical
device regulatory authorities as part of the software marketing authorization.
The sleep apnea algorithm is trained and tested on large and diverse populations, and the details provided in
this report can help guide users and their care providers toward confident interpretations. This feature doesn’t
take into account a user’s symptoms, and the Health app’s user interface (UI) makes it clear that if users are
experiencing symptoms, they should speak to their physician.The Health app also clarifies that the absence
of a notification isn’t intended to indicate the absence of sleep apnea. Most notably, the feature has the
capacity to accurately track sleep apnea risk over time in ways not possible with traditional means of
breathing assessments, such as PSG and HSAT, which focus on only one or a few nights.
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 9
0
0
10
20
30
40
50
60
70
80
90
100
10 20 30 40 50 60 70 80 90 100
| | | | | | | | | | |
| | | | | | | | | | |
Ground truth AHI (events per hour)
Breathing Disturbances (events per hour)
Conclusion
Apple Watch includes a wide range of features that focus on health, fitness, safety, and staying connected.
The Sleep Apnea Notification Feature sends users a notification if multiple nights of elevated Breathing
Disturbances values suggest possible sleep apnea. The Health app provides an informative report on iPhone
for users to discuss with their healthcare provider. Breathing Disturbance data available in the Health app also
enables users to relate their lifestyle factors to their quality of sleep.
References
Epstein, Lawrence J., David Kristo,Patrick J. Strollo Jr.,Norman Friedman,Atul Malhotra,Susheel P. Patil,Kannan Ramar,Robert
Rogers,Richard J. Schwab,and Edward M. Weaver. 2009.“Clinical Guideline for the Evaluation, Management and Long-Term Care of
Obstructive Sleep Apnea in Adults.” Journal of Clinical Sleep Medicine 5, no. 3 (June): 263–276. pubmed.ncbi.nlm.nih.gov/19960649.
Benjafield, Adam V., Najib T. Ayas, Peter R. Eastwood, Raphael Heinzer, Mary S. M. Ip, Mary J. Morrell, Carlos M. Nunez, Sanjay R. Patel,
Thomas Penzel, and Jean-Louis Pépin. 2019. “Estimation of the Global Prevalence and Burden of Obstructive Sleep Apnoea: A Literature-
Based Analysis.” Lancet Respiratory Medicine 7, no. 8 (August): 687–698. doi.org/10.1016/S2213-2600(19)30198-5.
Peppard, Paul E., Terry Young, Jodi H. Barnet, Mari Palta, Erika W. Hagen, and Khin Mae Hla. 2013. “Increased Prevalence of Sleep-Disordered
Breathing in Adults.” American Journal of Epidemiology 177, no. 9 (May): 1006–1014. doi.org/10.1093/aje/kws342.
Franklin, Karl A. and Eva Lindberg. 2015. “Obstructive Sleep Apnea Is a Common Disorder in the Population—A Review on the Epidemiology
of Sleep Apnea.” Journal of Thoracic Disease 7, no. 8 (August): 1311–1322. doi.org/10.3978/j.issn.2072-1439.2015.06.11.
Jendrike, Nina, Annette Baumstark, Ulrike Kamecke, Cornelia Haug, and Guido Freckmann. 2017. “ISO 15197: 2013 Evaluation of a Blood
Glucose Monitoring System’s Measurement Accuracy.” Journal of Diabetes Science and Technology 11, no. 6 (November): 1275–1276.
doi.org/10.1177/1932296817727550.
Estimating Breathing Disturbances and Sleep Apnea Risk from Apple Watch | September 2024 10
Here's a quick video overview of How Apple Watch Sleep Apnea Detection Works
[](https://www.youtube.com/watch?v=dN2MSmw8wCM)
A polysomnography is an overnight sleep study conducted in a specialized sleep lab where multiple body functions are monitored simultaneously while you sleep. Here's what it involves:
Key measurements:
- Brain activity (EEG)
- Eye movements (EOG)
- Muscle activity/tone (EMG)
- Heart rhythm (ECG)
- Breathing rate and oxygen levels
- Body position and movements
The Apnea-Hypopnea Index (AHI) is a key metric used to measure sleep apnea severity. It represents the average number of breathing pauses (apneas) and partial breathing reductions (hypopneas) that occur per hour of sleep.
Here's how the numbers break down:
AHI Score Interpretation:
- Less than 5: Normal
- 5 to 14.9: Mild sleep apnea
- 15 to 29.9: Moderate sleep apnea
- 30 or more: Severe sleep apnea
#### The 4% Hypopnea Rule
The 4% hypopnea rule (also called the "4% desaturation criterion") is one of the standard definitions for scoring hypopneas during sleep studies. According to this rule, a hypopnea is scored when:
1. There is a ≥30% reduction in airflow from baseline
2. This reduction lasts for ≥10 seconds
3. The event is accompanied by a ≥4% drop in oxygen saturation (SpO2)
#### Example
- If baseline SpO2 is 96%, a drop to 92% or lower would qualify
- The oxygen desaturation usually occurs shortly after the airflow reduction
#### Comparison to Arousal Rule
This is different from the alternative "arousal" rule where hypopneas can be scored with:
- ≥30% airflow reduction
- ≥10 seconds duration
- Either a ≥3% oxygen desaturation OR an arousal
#### Clinical Implications
The 4% rule is generally considered:
- More specific but less sensitive than the arousal rule
- Results in lower AHI scores
- May lead to fewer diagnosed cases of sleep apnea
- Can affect treatment eligibility
*Note: The choice of scoring rules can significantly impact diagnosis and should be specified in sleep study reports.*
Junichiro Hayano from the University of Nagoya was able to demonstrate that you could detect sleep apnea with a wearable watch device with photoplethysmography (PPG) - a non-invasive optical measurement technique that detects blood volume changes in the microvascular tissue.
Paper: https://pmc.ncbi.nlm.nih.gov/articles/PMC7652322/pdf/pone.0237279.pdf
Abdominal belts (respiratory effort belts) used in sleep studies measure respiratory movements and effort through the following:
Primary Measurements
- Expansion and contraction of the abdomen during breathing
- Changes in tension of the belt as the abdomen moves
- Timing and pattern of breathing efforts