Combine sound, stages, and watch sensors into a picture you can chart over months.
You already record most nights, and the snore count alone has stopped surprising you. This guide takes you past that. It shows you how to feed the sleep-stage estimator clean data, fold in Apple Watch biometrics, read those signals together against your timeline, and pull everything into a spreadsheet so you can chart the trend across weeks and months. Treat every number here as a personal estimate, not a clinical reading. The point is to spot patterns in your own data and to ask better questions.
Sleep stages in Snore Timeline come from sound. The app listens to your breathing rhythm, breathing regularity, and movement, then estimates whether you are in Light, Deep, or REM sleep, or Awake. Steady, metronome-like breathing points toward Deep sleep, moderate regularity toward Light, and more irregular breathing toward REM. Because the whole estimate rides on hearing your breath, your setup decides how good the data is.
Three things give the estimator the clearest signal:
Longer recordings sharpen the estimate, since several hours let the app observe more complete sleep cycles. The mechanics of how each signal maps to a stage are covered in Sleep Stages, and the hypnogram section explains how to read the stage chart itself.
If you keep seeing long Silence bands, your breathing is reading too soft. Move the phone closer, switch to Standard quality for richer audio, and cut one source of room noise. Each change gives the stage estimator more to work with.
An Apple Watch adds a second, sensor-based view that audio cannot reach. When a watch reports sleep-stage data for the night, Snore Timeline uses the watch's Deep and REM percentages for your sleep score in place of its own audio-derived estimates, since a wrist sensor measures those stages more accurately. Without a watch, the app falls back to its breathing- and movement-based estimates.
A paired watch also surfaces biometrics on your summary and in your export:
This data flows through Apple Health. Snore Timeline reads watch biometrics from Health and works fully with microphone access alone, so the watch connection is an addition rather than a requirement. Health and other permissions come up only when you turn those features on. Setup steps, the full biometrics list, and notes on other wearables live in Apple Watch & Biometrics, with details on the data exchange in the Apple Health and other wearables sections.
One signal in isolation tells you little. The value comes from reading them together for a single night, then carrying that habit across nights. Work through the summary in this order:
Snore Timeline also tracks a Sleep Bank against a 7-hour nightly goal, so you can see accumulated sleep debt rather than judging a single night. Pair that with the weekly trends view and your sleep deficit to read direction over time instead of reacting to one rough night.
The in-app trends answer most questions, but a spreadsheet lets you chart your own metrics and join nights to anything you track outside the app. Snore Timeline exports your data as CSV files bundled in a single ZIP. Build the export like this:
For charting trends across many nights, the Nightly Summary CSV is your main table. It carries one row per night with columns including Night, Sleep Score, Sleep Efficiency, Total Sleep (s), Time to Sleep (s), Light Sleep (s), Deep Sleep (s), REM Sleep (s), Silence (s), Awake (s), WASO (s), Awakenings, Avg Respiratory Rate (bpm), Episode Count, Loudest Peak (dB), Average Peak (dB), Total Snore Events, Total Gasp Events, Total Cough Events, Total Sleep Talking Events, and Total Breathing Disruptions. Durations are in seconds, so divide by 3600 to chart hours.
The supporting files let you go finer:
Timestamps use ISO 8601 with timezone (for example 2024-01-15T22:30:45-08:00) and night identifiers use plain YYYY-MM-DD dates, which sort and filter cleanly. To chart a trend, put Night on the x-axis and the metric you care about on the y-axis: Sleep Score over 90 days, Total Breathing Disruptions per week, or Deep Sleep as a percentage of Total Sleep. The full column reference and audio-bundling options live in Export & Sharing, and the export report section covers building the file.
Automate the pull. Build a Shortcut that opens the export at a set interval so your spreadsheet stays current without manual steps. See Siri, Shortcuts & Widgets.
Every metric in this guide is an estimate for personal insight, not a clinical measurement or a diagnosis. Snore Timeline derives stages, respiratory rate, and breathing disruptions from sound, and reads heart rate and SpO2 from a consumer wearable. Clinical sleep staging uses brain waves, eye movement, and muscle activity, signals that audio cannot capture, so an audio-based stage estimate runs around 70 percent agreement with polysomnography as a rough figure, not a guaranteed accuracy. Acoustic disruption counts skip events that produce no audible recovery, so treat them as a conservative floor. The app does not diagnose sleep apnea or any condition. If your trends concern you, or you notice daytime sleepiness, morning headaches, or a partner reports pauses in your breathing, share the recordings and your export with a healthcare provider and let them interpret it.
Read across nights, not within one. A single rough reading often reflects a noisy room or a phone too far away rather than your sleep. The trend is where the signal lives. The accuracy notes for stages and the guidance on when stages do not appear are worth reading before you put too much weight on any one figure.
