This guide sets out the evidence behind a calming scroll app that gives every short video its own color and tone. Each design choice is graded against the research, from the colors and pitches on screen to the pace of the feed and the rhythm that plays while a viewer lingers. Parts 1 to 6 build the rules, and Part 7 runs them in a working scroll lab.
FIG 0 · The calm arc. Seven screen colors blended in OKLCH, each with the notes it carries in the synchronized sequence (Part 6). Chroma falls at every step while lightness stays moderate.
01Colors and feelings
People everywhere broadly agree on what colors mean; 4,598 people in 30 nations matched one global pattern at r = .881. The mood effects themselves are small, depend on context and are often confounded with brightness and saturation2. Several famous findings failed replication3,4,5. Lowering saturation matters more than choosing the right hue.
02A calming sequence
Seven steps that never repeat a hue category back to back: muted coral, soft gold, sage, muted teal, dusty blue, lavender-grey and misty blue-grey. Chroma falls at each step and lightness stays moderate. Each color holds for 60 to 90 seconds with a 10 to 20 second crossfade in OKLCH, paced to about 6 breaths per minute. Evening room lighting, and the app's night colors, stay dim and leave out blue and teal light.
03Pitch and calm
No scientific color-to-note table exists, and the historical systems contradict each other. The best-supported calming sound is slow, quiet, low to middle in register and consonant6,7. It works best when tempo, register and loudness descend together and the piece ends in silence. A descending D-major pentatonic line over a D drone, slowing from 72 to 48 BPM, applies these rules.
What holds up, and what does not
Each claim is rated on four levels: strong, moderate, weak or unsupported. Ratings reflect sample sizes, replication status and whether effects survived correction for publication bias.
How colors make people feel
Color meaning is broadly shared across cultures. Color effects on the body are smaller, and they depend more on saturation and brightness than on hue.
1.1 What the strongest data show
The largest cross-cultural dataset is the International Color-Emotion Association Survey. Jonauskaite and colleagues tested 4,598 participants from 30 nations who spoke 22 native languages, asking them to link 20 emotion concepts from the Geneva Emotion Wheel to 12 color terms1. Each nation's pattern closely matched the global pattern, with a mean similarity of r = .881.
Nation still predicted associations beyond the global pattern. Nations that were closer in language or geography had more similar patterns, and a machine-learning classifier identified a participant's nation at 34.4% accuracy1. Red and black were rated the most emotional colors and brown the least. Pink was linked to love, yellow to joy and black to sadness1.
TABLE 1.1 · International Color-Emotion Association Survey1
Measure
Value
Participants
4,598
Nations
30
Native languages
22
Emotion concepts × color terms
20 × 12
Mean similarity, nation vs global
r = .88
Classifier accuracy for nation
34.4%
Meaning is not mood
These studies record what people think a color means. What a color does to the body is a separate question, and the two often diverge.
1.2 Hue, saturation and brightness
Valdez and Mehrabian used Munsell color chips and the Pleasure-Arousal-Dominance (PAD) model. Saturation and brightness had strong, consistent effects, summarized by the standardized regression weights in Fig 1.18. Brightness effects were nearly identical for chromatic and achromatic colors. Blue, blue-green, green, red-purple, purple and purple-blue were the most pleasant hues, and yellow and green-yellow the least pleasant8.
Wilms and Oberfeld controlled all three dimensions on an LED display. They showed 27 chromatic colors and 3 brightness-matched achromatic colors to 62 participants while recording skin conductance and heart rate. Saturated, bright colors produced higher self-rated arousal and stronger skin conductance responses, and arousal rose from blue and green to red2. The authors stress that many earlier studies failed to control brightness or saturation2.
A virtual-reality study of red and blue rooms complicates the brightness story. Darker rooms produced higher heart-rate arousal regardless of hue, the opposite direction from small color patches26. A separate study found cognitive performance and emotion indifferent to ambient color27.
Saturation (chroma) is the most reliable lever, because lower chroma lowers arousal. Keep brightness moderate. Very bright colors raise arousal in some paradigms, very dark immersive scenes raise it in others, and darkness lowers pleasure2,8,26. Hue is secondary. Cool hues are slightly less arousing than red at matched chroma and lightness2.
1.3 Four theories of color meaning
Ecological valence
People like colors to the degree they like the objects associated with them, such as clear skies and clean water versus feces and rotting food. A weighted affective valence estimate explained 80% of the variance in average US color preferences (r = .893) and outperformed cone-contrast and color-appearance models28.
Color in context
Color meaning depends on context. Red signals danger or failure in achievement settings and attraction in romantic ones29. The theory is coherent, but several of its flagship predictions have not replicated well (section 1.5).
Biological and evolutionary
These accounts point to red faces in anger, redness in sexual signaling, and blue sky and green vegetation as safe environments. Jonauskaite and colleagues read some shared pairings, such as a red face in anger, this way1. The accounts are plausible for a few associations and hard to test directly.
Learned association
Language, symbolism and tradition shape color meaning, such as red as good fortune in China. Linguistic proximity predicts the similarity of national patterns, which supports a learned layer on top of shared tendencies1.
1.4 Surfaces and screens versus colored light
Physiological color effects are small and fragile. Luminance easily confounds them, because a red and a blue stimulus are rarely equally bright. Studies that equate luminance still find saturation and brightness effects on skin conductance, while hue effects on heart rate are inconsistent across studies2.
Colored light differs from colored surfaces. Light changes the illuminance at the eye and the pupil size, and short wavelengths also drive the melanopsin-based circadian system that regulates melatonin30. A surface or screen color changes only the visual scene.
Individual differences
Associations vary with nation and language1, with gender2, and with age and current mood. Among people with Northern European ancestry, red-green color vision deficiency affects about 1 in 12 males and 1 in 200 females31. A calming sequence should never rely on red-green contrast alone.
1.5 Replication record of famous color effects
Claim
Original evidence
What followed
Verdict
Red impairs intellectual performance
Early laboratory studies of achievement contexts
A meta-analysis of 67 effect sizes from 38 samples found early studies much larger than later ones, and no evidential value after correction for publication bias3.
Unsupported
Red increases attractiveness
Laboratory rating studies
Men rating women: d = 0.26 (95% CI 0.12 to 0.40, N = 2,961, I² = 89%). Women rating men: d = 0.13 (N = 2,739), with clear upward bias10. Student replications (n = 640) found no effect of red backgrounds on women's ratings of men11.
59 inmates randomly assigned to pink or grey cells showed no difference in aggression5. In a 46-student study, a pink room lowered self-reported anxiety but left grip strength and motor precision unchanged15.
Unsupported
1.6 Color atlas
Each card pairs a typical saturated version of the color (top) with a calmer, lower-chroma version (bottom). Meters show relative tendencies at typical saturation and lightness; they shift as chroma and lightness change.
Designing a calming color sequence
One constraint applies throughout. Each consecutive color comes from a different hue category. No controlled study has tested a multi-hue calming sequence, so the principles below extrapolate from the evidence on single colors, light and music.
2.1 What the evidence directly supports
Twelve volunteers completed the Montreal Imaging Stress Task and then lay under blue or white light while ECG and EEG were recorded. Under blue light, stress reached its minimum about 1.1 minutes into the block, compared with about 3.5 minutes under white light. The advantage disappeared after 3.5 to 5 minutes9. Color effects therefore look front-loaded and transient, which favors changing colors every minute or two.
Low chroma reduces arousal2,8, and very dark immersive scenes can raise it26. Slow, minor-mode, soft music is matched with darker, greyer and bluer colors, so a congruent audiovisual pairing for calm moves toward cooler, less saturated colors19.
Time to minimum stress after a stressor
FIG 2.1 · Blue vs white light. Minutes from the start of the relaxation block to the minimum stress level; N = 12, preliminary9.
2.2 Design principles
2.3 Safety and color blending
Safety and accessibility
Photosensitive epilepsy. WCAG 2.3.1 prohibits content that flashes more than three times in any one-second period unless it stays below the general and red flash thresholds34. Crossfades of 10 to 20 seconds sit far below flash rates. Never strobe or pulse saturated red.
Color vision deficiency. The sequences change lightness and chroma as well as hue, so they still read as a progressive change for people with red-green deficiency31.
Evening light. Short-wavelength light is the strongest driver of melatonin suppression30. Keep blue and cyan light out of the hours before sleep and use dim, long-wavelength light instead.
Why interpolation space matters
sRGB blend
OKLCH hue arc
FIG 2.2 · Soft gold to dusty blue. A straight sRGB blend drains through grey at the midpoint. The OKLCH blend keeps chroma and lightness controlled and passes through green and teal instead.
2.4 Sequence A · Screen
FIG 2.3 · Sequence A. The bar under each name is OKLCH chroma on a 0 to 0.12 scale. Confidence: moderate for the direction (falling chroma, warm to cool, moderate lightness); low for the specific hues, timings and the iso-principle opening.
Step
Category
Color
OKLCH (L, C, h)
Dwell + fade
Rationale
2.5 Sequence B · Evening ambient lighting
The same logic with blue and cyan light removed and progressive dimming. Tiles fade toward the page background to show relative intensity; on-screen colors only approximate LED output. The app uses this sequence for its night colors (7.5).
FIG 2.4 · Sequence B. Long wavelengths and low intensity reduce the melanopsin-driven alerting response30; dimming replaces falling chroma as the main lever. Confidence: moderate for avoiding short-wavelength light and dimming; low for the hue order.
Is there a pitch for each color?
No universal assignment exists. Historical systems disagree with each other, and the physics-based mapping has no perceptual basis. The reliable links are broader cross-modal tendencies shared by most listeners.
3.1 Historical color-to-note systems
FIG 3.1 · Four systems, twelve notes. Newton (Opticks, 1704, Dorian scale from D)39; Castel (ocular harpsichord, about 1734 to 1742) and Rimington (colour organ, 1893), as reproduced in secondary historical sources16; Scriabin (Prometheus, 1910)40. Hatched cells are notes the system left unassigned. Hover a cell for the color name.
The systems contradict each other. C is violet for Newton, blue for Castel, and red for Scriabin and Rimington. Red falls on D for Newton, on G for Castel, and on C for Scriabin and Rimington.
Newton chose his seven spectral bands by musical analogy. He split the spectrum into segments proportional to the intervals of a Dorian scale, placing orange and indigo at the half steps39. Scriabin's assignments follow the circle of fifths (C red, G orange, D yellow, A green, E sky blue, B blue, F♯ bright blue), so his rainbow encodes key relationships rather than pitch height40.
Kandinsky's color timbres
From Concerning the Spiritual in Art (1911)41. His scheme sits closer to modern findings, with yellow high and bright and dark blue low, than any note table.
3.2 The physics mapping: dividing light by 240
The method converts wavelength to frequency (f = c / λ) and halves that frequency 40 times, dividing by about 1.0995 × 1012, to land in the audible range.
The visible spectrum, about 400 to 700 nm, spans a frequency ratio of only 1.75, less than one octave, so the whole rainbow collapses into roughly G4 to F5. The choice of 40 octaves is arbitrary, hue perception is not linear in wavelength, and purples are not spectral at all.
The mapping also runs against perception. It puts red lowest and violet highest, while listeners match the lightest hue, yellow, with high pitch and dark blue with low pitch17,18.
Color
λ (nm)
Light (Hz)
÷ 240
Nearest note
FIG 3.2 · The rainbow in less than an octave. Each marker shows a spectral color transposed down 40 octaves; the whole visible range fits between G4 and F5.
3.3 Synesthesia (chromesthesia)
Sound-color synesthetes are more consistent over time and more specific in their color choices than non-synesthetes. They still follow the same underlying mappings as everyone else: higher pitch with lighter color, and timbre affecting chroma17. Ward and colleagues concluded that synesthesia uses cross-modal mechanisms common to all people17.
Different synesthetes do not agree on which color goes with which note. A shared true color for C does not exist even among synesthetes.
3.4 Cross-modal tendencies in everyone
Pitch and lightness. Higher pitch maps to lighter, brighter colors, in non-synesthetes and synesthetes alike17,18.
Loudness and saturation. Louder music maps to more saturated, redder and darker colors. Across 34 excerpts rated by 30 non-synesthetes, loudness correlated with saturation at rs = .64 and with redness at rs = .7720.
Tempo and mode. In the US and Mexico, faster major-mode music matched lighter, more saturated, yellower colors. Slower minor-mode music matched darker, greyer, bluer colors19.
Emotion as the mediator. Removing emotional content eliminated every significant correlation between lower-level features. Arousal and valence explained 58% of the variance in color dimensions on average, against 42.6% for perceptual features20. Color choices for Bach preludes could be predicted from music-emotion and color-emotion ratings21.
3.5 An evidence-based color-to-pitch range map
FIG 3.3 · Color qualities and congruent pitch regions. Log-frequency axis. High saturation in any hue is not pitch-specific; it pairs with louder, more percussive, faster sound20.
Which pitches and sounds calm people
4.1 What the evidence supports
Two multilevel meta-analyses of 104 randomized trials (327 effect sizes, 9,617 participants) found that music interventions reduce physiological and psychological stress (Fig 4.1)22. Music therapy delivered by qualified therapists showed a larger effect across 47 studies42. A Cochrane review found that music listening reduced preoperative anxiety43.
Tempo is the main lever. Bernardi, Porta and Sleight recorded 24 young adults, half of them musicians, across six music styles. Breathing, blood pressure and heart rate rose with faster tempi and simpler rhythms, and a two-minute pause lowered heart rate, blood pressure and minute ventilation below baseline6. The authors concluded that tempo drives most of music's arousal effect and that relaxation is clearest during a pause6.
Across studies of vocal and musical expression, calm and tenderness are carried by slow tempo, low sound level, lower and less variable pitch, soft timbre, legato articulation and slow, smooth onsets7. Relaxing music before a laboratory stressor changed autonomic recovery but did not dampen the cortisol response, so music is not a uniform buffer44. Ambient music whose loudness swelled at a slowed, personalized breathing rate lowered breathing rate, heart rate and skin conductance38.
Effect sizes for stress reduction (Cohen's d)
FIG 4.1 · Music and stress. Interventions: 104 RCTs22. Therapy: 47 studies, 76 effect sizes42. Reference lines mark conventional small (0.2), medium (0.5) and large (0.8) effects.
4.2 How scientific are the popular frequency claims
4.3 Best-supported parameters for calm
Parameter
Recommendation
Evidence
A pitch arc that eases into calm
The sequence opens near the listener's state and then slows, descends, softens and lengthens its notes over a sustained drone, one phrase per breath6,7,36. The scale is D-major pentatonic (D, E, F♯, A, B). It contains no semitones, so any combination of its notes stays consonant. Standard tuning (A4 = 440 Hz) is used, since the evidence for alternative tunings is weak24.
FIG 5.1 · Seven phases, four parameters. Each panel has its own axis. Dots are melody notes; the shaded line is the drone. Phase 7 ends in two minutes of silence.
Each phrase lasts one breath. Amplitude swells on the inhale (about 4 seconds) and decays on the exhale (about 6 seconds), following the amplitude-envelope approach in breathing-paced sound38. The melody falls about an octave and a half overall.
Phase
Notes (Hz)
Tempo
Values
Level
Breaths
One synchronized color and sound timeline
Colors move from warm, more saturated and lighter toward cool, desaturated and mid-light. The sound moves from brighter, faster, higher and louder toward darker, slower, lower and softer. These are the directions found for emotion-mediated music-color matching19,20.
FIG 6.1 · The integrated timeline. Seven 90-second steps (10.5 minutes), then the final color holds while the audio fades into two minutes of silence. Block height tracks loudness.
Step
Time
Color
Notes (Hz)
Tempo
Level
Transition
Evidence-based
Lower chroma reduces arousal. Slower tempo, softer sound and silence reduce physiological arousal. Music reduces stress overall. Slow, minor music is matched with greyer, bluer and darker colors. Breathing-paced sound slows breathing.
Extrapolated
The iso-principle applied to color; the specific hue order, dwell times and crossfade lengths; synchronizing the two channels; and the specific notes and decibel levels. None of these has been tested as a combined sequence.
A calm arc for a 15-minute scroll
How many short videos does a person see in 15 minutes, and how can each one carry its own color and pitch while the session as a whole still follows the calm arc?
7.1 How many videos fit in 15 minutes?
At 5 to 10 s per video≈12090 to 180 videos in 15 minutes. 5,000 simulated sessions all landed between 112 and 128.
Measured pace≈35 to 60About 15 to 27 s per video in donated histories and timed sessions46,47.
Typical session≈8 minMean session length across three user groups in 107 donated histories46.
Videos seen in 15 minutes
FIG 7.1 · Assumed versus measured pace. The thick inner band on the first row is the spread of 5,000 simulated sessions. Measured rows are converted from videos per session and session length46,47,48.
At 5 to 10 seconds per video, a 15-minute session covers 90 to 180 videos. The midpoint pace of 7.5 seconds gives 120.
Measured behavior is slower. TikTok's browsing-history export records when each video starts, so researchers infer viewing time from the gap between consecutive entries46. In 107 donated histories, sessions averaged about 8 minutes, and session sizes in the paper's robustness analysis ran from about 24 to 32 videos, or roughly 3 to 4 videos per minute46. In timed 10-minute browsing sessions, 68 participants averaged 22 to 31 videos47. Together these imply about 35 to 60 videos in 15 minutes.
The 5-to-10-second assumption therefore describes a fast skimmer who leaves most videos early. Most participants in a 347-person data-donation study watched only 30% to 50% of their videos to the end49. The median video in another donated dataset ran 23 seconds, with the middle half between 11 and 60 seconds50. Swipes cluster near the start and the end of a video. In one crowdsourced study, 29% of swipes came in the first fifth of a video and 42% in the last fifth51. Industry panels put a typical session near 11 minutes, though those figures are not peer reviewed52.
7.2 Scroll lab: one color and one note per video
The session splits into seven groups that follow the calm arc from Part 6. Inside each group, colors and notes are drawn at random under rules that keep the overall trajectory calming. Every draw produces a new sequence.
Pace
Sequence
Listen
Loop
#1 / 1200:00
F♯4369.99 Hz
Red · G1 Meet#D2796A
Resting Scroll or tap to continue
Sound paused Tap to turn sound on
Scroll, swipe up, or press ↓ or Space for the next video. Stay on a video to linger. Esc ends the loop.
Press Play or Preview to hear the sequence, or Scroll this loop to watch it like a feed.
Scroll tape · one tile per video
120 videos · seed 20261020
Note per video
dot color = video color · band = drone
Chroma (OKLCH C)
Level (relative dB)
0 → −14 dB
Learning loop
Memory kept for this visit
No loops recorded yet. Press Scroll this loop and move through the feed. Stay longer on any video that feels good; the next loop will bring it back more often.
Remembered pages
Loop history
How the randomizer works
Hue. Each group allows three neighboring hue categories with fixed weights. The same category never appears twice in a row. Neighbors on the hue wheel are favored over skips, and a return to the category from two videos earlier is damped to avoid ping-pong.
Exact color. The hue is the closer of two random draws inside the category band. Lightness follows the group's base value with a small category offset, yellow lighter and teal or blue darker, and moves at most 0.06 per swipe. Chroma falls from 0.115 to 0.035 across the session with small jitter and stays inside the sRGB gamut.
Note. Each group has a three-note pool from D-major pentatonic. Lighter colors favor higher notes, a cross-modal match17,18, and choices drift lower within each group. A note repeats at most twice, leaps stay within a perfect fifth, and the last video resolves to the tonic, D3.
Sound and motion. Level falls from 0 to −14 dB, about 55 to 41 dBA from a calibrated start. Each video's note sounds without a break for as long as the video stays on screen. A swipe hands it to the next note through a 0.8-second equal-power crossfade. Colors crossfade over 20% of the dwell time, at most 1.5 seconds, and the preview crossfades its notes over the same span.
Pace fit. During a loop the lab compares the viewer's average time per video with the plan after every 10 videos. A remaining plan too short for 15 minutes at that pace is rebuilt as more, shorter videos on the same group clock, down to an average of 1.5 seconds per video. Videos already watched keep their place.
Group
Time
Hue categories (weight)
Note pool
Chroma
7.3 Learning from how long people stay
Dwell time is a useful but ambiguous signal. Recommender systems use it beyond clicks once it is normalized across devices and contexts53, and watch-time models remove the bias that longer videos earn longer watch times before learning from it54.
Here the main bias is position. Later pages are calmer and the viewer may be drowsier, so dwell drifts upward through a loop. Each page is compared with the median of its 16 neighbors on a log scale. It counts as a linger when it is at least 1.5 times that median and its robust z-score reaches 2.
A long stay can mean liking, calm, distraction or sleep. The lab cannot tell these apart, so every long stay counts, and the decay described below lets a stray distraction fade. Time spent in another tab or app is not counted. Three minutes without input is logged as possible sleep. The note keeps sounding, and that page counts once like any other linger.
TikTok exploits what it has learned about a user for roughly 30% to 50% of the videos it shows55, and users rate sessions with novel content higher than sessions close to their history47. The lab caps remembered pages at 25% of each group and draws the rest fresh.
Each remembered page keeps a Beta score of lingers against non-lingers. A random draw from that score decides whether the page returns: proven favorites usually come back, uncertain ones sometimes do, and pages that stop drawing lingers fade out. This is Thompson sampling56. Hue categories and notes that drew lingers in a group also get a bounded weight boost (0.6 to 1.8 times) in fresh draws. Scores decay 15% per loop, so old favorites make room for new ones.
What the sandbox shows
Across 300 simulated viewers, each with one favorite hue and one favorite note, remembered pages drew lingers about twice as often as fresh pages (about 16% against 8.5%). By the tenth loop about a fifth of pages came from memory and the favorite hue's share rose from 14% to 18%. The calm arc caps how far the feed can drift: each group allows only three hue categories and never repeats one back to back.
Where the memory lives
Remembered pages stay private to you and are saved in this browser. Remembered pages return in their original group, so every new loop still follows the calm arc.
7.4 The linger rhythm
The page's note keeps sounding for as long as a viewer stays. Past the page's planned time it starts to breathe, easing to half level and rising again once per breath, each breath a little longer than the last. The schedule follows breathing-pacing research, because no controlled study of tone-interval schedules for hypnosis was found.
Slow breathing, under 10 breaths per minute and most often 6, raises heart-rate variability and self-rated relaxation, although the 15 studies behind that conclusion are small and none is randomized57. Resonance-frequency protocols step breathing down by 0.5 breaths per minute, from 6.5 to 4.5, hold each rate for 2 minutes and favor a longer exhale58. Whether people breathed at 6 or 12 per minute, a short inhale with a long exhale (ratio 0.42) produced more relaxation than the reverse59.
Gradual leading works. Device-guided breathing plays one tone for inhaling and one for exhaling and lengthens mainly the exhale tone; in one case report breathing fell from about 16 to 6 per minute during 15-minute daily sessions60. Music whose loudness swelled at 75% of a listener's breathing rate slowed breathing as well38.
Rhythm can ease the way into sleep. Rocking at 0.25 Hz, one cycle every 4 seconds, eased the transition into sleep and boosted spindles and slow oscillations61. Twenty minutes of breathing at 6 per minute before bed shortened sleep onset in people with insomnia62. Sleep playlists are slower and softer than music in general63.
Predictability matters too. Responses fade with repetition, and a novel stimulus restores them, a process called dishabituation64. Each change in the rhythm should therefore stay small.
Hypnosis research offers no timing rule. Repetitive drumming did not lower cortisol more than meditation music, and its dreamlike experiences depended on the instructions given65. People respond to suggestion almost as much without a hypnotic induction as with one, and expectancy explains much of who responds66. No controlled study supports Fibonacci-style or other special interval sequences, and a direct test of the iso-principle, with 9 participants, found no clear advantage67.
Breath length per repeat
FIG 7.2 · Breath length per repeat. Reference lines mark 6 s (the 10-per-minute slow-breathing ceiling), 10 s (6 per minute) and 13.3 s (4.5 per minute, the bottom of the adult resonance range)57,58.
Why this rhythm
The rhythm should meet the viewer before it leads, so the first breath lasts 5 seconds. That is 12 breaths per minute, the slow end of the 12 to 20 per minute that adults breathe at rest68. A shorter first breath would sit inside or above ordinary resting breathing and offer nothing to follow. A first breath of 10 seconds would jump straight to the 6-per-minute target, a step of 50% or more from a resting pace and far larger than the steps that pacing protocols use58. Each breath then stretches by 6%, close to the 8% steps used in resonance-frequency protocols58, so no single change is large enough to pull attention back through dishabituation64. The stretch slows to 3% at 10 seconds, the 6-per-minute rate with the most support37,57, and holds at 12 seconds, inside the adult resonance range58. The exhale grows faster than the inhale, because a short inhale with a long exhale relaxed people more than the reverse59. Gradual lengthening is also how device-guided breathing slowed one user from about 16 to 6 breaths per minute60.
The schedule
Start at 5 seconds per breath (12 per minute), the slow end of normal resting breathing68, so the rhythm meets the viewer before it leads.
Stretch each breath by 6% until it reaches 10 seconds (6 per minute), then by 3% until 12 seconds (5 per minute), and hold there.
Shape each breath as an ease down to half level (−6 dB) for the exhale and a rise back to full level for the inhale. The note never falls silent. The inhale share falls from 40% to 30% over 8 breaths, so the exhale grows most.
Hold the page's level from breath to breath while the screen dims slightly.
Rest after 3 minutes without input. The note keeps breathing, the screen is allowed to sleep, the page records possible sleep, and any input wakes it.
Breath
Elapsed
Length
Per min
In / out
7.5 Day and night
At night the app trades the screen colors of Sequence A for the evening colors of Sequence B (2.5). A sideways swipe switches: right for night, left for day. Everything else in a session stays as it is.
Each video keeps its group, note, timing and place in the session; only its color changes. It takes its group's Sequence B color, turned by up to 6° of hue according to where its day color sits in its hue family, so neighboring videos still differ a little.
Dimming replaces falling chroma as the main lever. Each group's color is dimmed to its Sequence B intensity, from 1.00 for warm white to 0.05 for the deep red night-light, and the session follows one luminance curve through those levels, stepping geometrically from one group's center to the next. The curve never rises, so no video is brighter than the one before it. Dimming scales the light itself, which keeps the hue and lowers OKLCH lightness and chroma by the cube root of the dimming factor.
No hue from 160° to 300°, teal and cyan through blue, appears at night. Text keeps a contrast of at least 4.5 : 1 and turns light on the darker videos. The sound, the pace fit, the linger rhythm and the learning loop work the same by day and by night, and the app notes which videos were seen in night colors.
Day
Night
FIG 7.3 · One session by day and by night. Each tile is one group at its center: by day the calm arc, by night Sequence B dimmed along the session's curve, from warm white to a deep red night-light. Confidence: moderate for keeping short-wavelength light out and dimming before sleep, from studies of light exposure30; low for how much a phone screen's colors change that response.
How people find it
Faint arrows at the left and right edges show the swipe and fade out once it has been used, and a short notice names the mode as it changes. The choice carries over to the next session and can also be set in Settings. The Done for now screen explains both modes.
Full sequence
#
Start
Dwell
Group
Hue
Color
OKLCH
Note
Hz
Level
Fade
Source
CSV export
Sources
Numbers in the text link here. Secondary and non-peer-reviewed sources are marked.
Jonauskaite, D., Abu-Akel, A., Dael, N., et al. “Universal patterns in color-emotion associations are further shaped by linguistic and geographic proximity,” Psychological Science 31(10). 2020. https://doi.org/10.1177/0956797620948810, pp. 1245–1260.
Wilms, L., & Oberfeld, D. “Color and emotion: Effects of hue, saturation, and brightness,” Psychological Research 82(5). 2018. https://doi.org/10.1007/s00426-017-0880-8, pp. 896–914.
Gnambs, T. “Limited evidence for the effect of red color on cognitive performance: A meta-analysis,” Psychonomic Bulletin & Review 27(6). 2020. https://doi.org/10.3758/s13423-020-01772-1, pp. 1374–1382.
Steele, K. M. “Failure to replicate the Mehta and Zhu (2009) color-priming effect on anagram solution times,” Psychonomic Bulletin & Review 21(3). 2014. https://doi.org/10.3758/s13423-013-0548-3, pp. 771–776.
Genschow, O., Noll, T., Wänke, M., et al. “Does Baker-Miller pink reduce aggression in prison detention cells? A critical empirical examination,” Psychology, Crime & Law 21(5). 2015. https://doi.org/10.1080/1068316X.2014.989172, pp. 482–489.
Bernardi, L., Porta, C., & Sleight, P. “Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: The importance of silence,” Heart 92(4). 2006. https://doi.org/10.1136/hrt.2005.064600, pp. 445–452.
Juslin, P. N., & Laukka, P. “Communication of emotions in vocal expression and music performance: Different channels, same code?,” Psychological Bulletin 129(5). 2003. https://doi.org/10.1037/0033-2909.129.5.770, pp. 770–814.
Valdez, P., & Mehrabian, A. “Effects of color on emotions,” Journal of Experimental Psychology: General 123(4). 1994. https://doi.org/10.1037/0096-3445.123.4.394, pp. 394–409.
Minguillon, J., Lopez-Gordo, M. A., Renedo-Criado, D. A., et al. “Blue lighting accelerates post-stress relaxation: Results of a preliminary study,” PLOS ONE 12(10). 2017. https://doi.org/10.1371/journal.pone.0186399, article e0186399.
Lehmann, G. K., Elliot, A. J., & Calin-Jageman, R. J. “Meta-analysis of the effect of red on perceived attractiveness,” Evolutionary Psychology 16(4). 2018. https://doi.org/10.1177/1474704918802412, article 1474704918802412.
Wagge, J. R., Baciu, C., Banas, K., et al. “A demonstration of the Collaborative Replication and Education Project: Replication attempts of the red-romance effect,” Collabra: Psychology 5(1). 2019. https://doi.org/10.1525/collabra.177, article 5.
Mehta, R., & Zhu, R. “Blue or red? Exploring the effect of color on cognitive task performances,” Science 323(5918). 2009. https://doi.org/10.1126/science.1169144, pp. 1226–1229.
Lichtenfeld, S., Elliot, A. J., Maier, M. A., et al. “Fertile green: Green facilitates creative performance,” Personality and Social Psychology Bulletin 38(6). 2012. https://doi.org/10.1177/0146167212436611, pp. 784–797.
Lichtenfeld, S., Maier, M. A., Buechner, V. L., et al. “Ambient green and creativity,” Creativity Research Journal 30(3). 2018. https://doi.org/10.1080/10400419.2018.1488197, pp. 305–309.
Profusek, P. J., & Rainey, D. W. “Effects of Baker-Miller pink and red on state anxiety, grip strength, and motor precision,” Perceptual and Motor Skills 65(3). 1987. https://doi.org/10.2466/pms.1987.65.3.941, pp. 941–942.
Day, S. “Synesthesia: history” (color organs of Castel, Rimington and others), daysyn.com. n.d. http://www.daysyn.com/history.html. Secondary source.
Ward, J., Huckstep, B., & Tsakanikos, E. “Sound-colour synaesthesia: To what extent does it use cross-modal mechanisms common to us all?,” Cortex 42(2). 2006. https://doi.org/10.1016/S0010-9452(08)70352-6, pp. 264–280.
Spence, C. “Crossmodal correspondences: A tutorial review,” Attention, Perception, & Psychophysics 73(4). 2011. https://doi.org/10.3758/s13414-010-0073-7, pp. 971–995.
Palmer, S. E., Schloss, K. B., Xu, Z., et al. “Music-color associations are mediated by emotion,” Proceedings of the National Academy of Sciences 110(22). 2013. https://doi.org/10.1073/pnas.1212562110, pp. 8836–8841.
Whiteford, K. L., Schloss, K. B., Helwig, N. E., et al. “Color, music, and emotion: Bach to the blues,” i-Perception 9(6). 2018. https://doi.org/10.1177/2041669518808535, article 2041669518808535.
Isbilen, E. S., & Krumhansl, C. L. “The color of music: Emotion-mediated associations to Bach’s Well-Tempered Clavier,” Psychomusicology: Music, Mind, and Brain 26(2). 2016. https://doi.org/10.1037/pmu0000147, pp. 149–161.
de Witte, M., Spruit, A., van Hooren, S., et al. “Effects of music interventions on stress-related outcomes: A systematic review and two meta-analyses,” Health Psychology Review 14(2). 2020. https://doi.org/10.1080/17437199.2019.1627897, pp. 294–324.
Garcia-Argibay, M., Santed, M. A., & Reales, J. M. “Efficacy of binaural auditory beats in cognition, anxiety, and pain perception: A meta-analysis,” Psychological Research 83(2). 2019. https://doi.org/10.1007/s00426-018-1066-8, pp. 357–372.
Weijs, M. L., Jonauskaite, D., Reutimann, R., et al. “Effects of environmental colours in virtual reality: Physiological arousal affected by lightness and hue,” Royal Society Open Science 10(10). 2023. https://doi.org/10.1098/rsos.230432, article 230432.
von Castell, C., Stelzmann, D., Oberfeld, D., et al. “Cognitive performance and emotion are indifferent to ambient color,” Color Research & Application 43. 2018. https://doi.org/10.1002/col.22168, pp. 65–74.
Palmer, S. E., & Schloss, K. B. “An ecological valence theory of human color preference,” Proceedings of the National Academy of Sciences 107(19). 2010. https://doi.org/10.1073/pnas.0906172107, pp. 8877–8882.
Elliot, A. J., & Maier, M. A. “Color psychology: Effects of perceiving color on psychological functioning in humans,” Annual Review of Psychology 65. 2014. https://doi.org/10.1146/annurev-psych-010213-115035, pp. 95–120.
Brainard, G. C., Hanifin, J. P., Greeson, J. M., et al. “Action spectrum for melatonin regulation in humans: Evidence for a novel circadian photoreceptor,” Journal of Neuroscience 21(16). 2001. https://pubmed.ncbi.nlm.nih.gov/11487664/, pp. 6405–6412.
Küller, R., Mikellides, B., & Janssens, J. “Color, arousal, and performance: A comparison of three experiments,” Color Research & Application 34(2). 2009. https://doi.org/10.1002/col.20476, pp. 141–152.
Schloss, K. B., Witzel, C., & Lai, L. Y. “Blue hues don’t bring the blues: Questioning conventional notions of color-emotion associations,” Journal of the Optical Society of America A 37(5). 2020. https://doi.org/10.1364/JOSAA.383588, pp. 813–824.
Heiderscheit, A., & Madson, A. “Use of the iso principle as a central method in mood management: A music psychotherapy clinical case study,” Music Therapy Perspectives 33(1). 2015. https://doi.org/10.1093/mtp/miu042, pp. 45–52.
Lehrer, P. M., & Gevirtz, R. “Heart rate variability biofeedback: How and why does it work?” Frontiers in Psychology 5. 2014. https://doi.org/10.3389/fpsyg.2014.00756, article 756.
Leslie, G., Ghandeharioun, A., Zhou, D., et al. “Engineering music to slow breathing and invite relaxed physiology,” Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII), IEEE; arXiv:1907.08844. 2019. https://doi.org/10.1109/ACII.2019.8925531, pp. 1–7.
Kandinsky, W. (trans. M. T. H. Sadler) “Concerning the spiritual in art,” German edition 1911; the 1914 English translation first appeared as The Art of Spiritual Harmony; Project Gutenberg eBook 5321. 1911. https://www.gutenberg.org/ebooks/5321, Part II, chapters V and VI.
de Witte, M., Pinho, A. da S., Stams, G.-J., et al. “Music therapy for stress reduction: A systematic review and meta-analysis,” Health Psychology Review 16(1). 2022. https://doi.org/10.1080/17437199.2020.1846580, pp. 134–159.
Bradt, J., Dileo, C., & Shim, M. “Music interventions for preoperative anxiety,” Cochrane Database of Systematic Reviews 2013(6). 2013. https://doi.org/10.1002/14651858.CD006908.pub2, article CD006908.
Thoma, M. V., La Marca, R., Brönnimann, R., et al. “The effect of music on the human stress response,” PLOS ONE 8(8). 2013. https://doi.org/10.1371/journal.pone.0070156, article e70156.
Graff, V., Cai, L., Badiola, I., et al. “Music versus midazolam during preoperative nerve block placements: A prospective randomized controlled study,” Regional Anesthesia & Pain Medicine 44(8). 2019. https://doi.org/10.1136/rapm-2018-100251, pp. 796–799.
Yang, C., Mousavi, S., Dash, A., et al. “Studying behavioral addiction by combining surveys and digital traces: A case study of TikTok,” ICWSM 2025, arXiv:2501.15539. 2025. https://arxiv.org/pdf/2501.15539, Sections 4 and 5.2; Appendix A.10.
Masood, M., Kannan, S., Liu, Z., et al. “Counting how the seconds count: Understanding algorithm-user interplay in TikTok via ML-driven analysis of video content,” Proceedings of CHI ’26. 2026. https://doi.org/10.1145/3772318.3790311, Tables 1 and 2.
Zannettou, S., Nemes-Nemeth, O., Ayalon, O., et al. “Leveraging rights of data subjects for social media analysis: Studying TikTok via data donations,” arXiv:2301.04945, version 1 (earlier version of the CHI 2024 paper). 2023. https://ar5iv.labs.arxiv.org/html/2301.04945, Introduction (median 90 videos and 27 minutes per day).
Zannettou, S., Nemes-Nemeth, O., Ayalon, O., et al. “Analyzing user engagement with TikTok’s short format video recommendations using data donations,” Proceedings of the CHI Conference on Human Factors in Computing Systems. 2024. https://doi.org/10.1145/3613904.3642433, article 731, pp. 1–16.
Masood, M., Kannan, S., Chabra, O., et al. “SILC: Lookahead caching for short-form video delivery systems,” arXiv:2605.05188, preprint. 2026. https://arxiv.org/abs/2605.05188, Section 4.1.
Li, Z., Xie, Y., Netravali, R., et al. “Dashlet: Taming swipe uncertainty for robust short video streaming,” 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI ’23). 2023. https://www.usenix.org/conference/nsdi23/presentation/li-zhuqi, pp. 1583–1599.
Yi, X., Hong, L., Zhong, E., et al. “Beyond clicks: Dwell time for personalization,” Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14). 2014. https://recsys.acm.org/recsys14/session-metrics-evaluation/, session listing with abstract.
Zhan, R., Pei, C., Su, Q., et al. “Deconfounding duration bias in watch-time prediction for video recommendation,” Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22). 2022. https://doi.org/10.1145/3534678.3539092, pp. 4472–4481.
Vombatkere, K., Mousavi, S., Zannettou, S., et al. “TikTok and the art of personalization: Investigating exploration and exploitation on social media feeds,” Proceedings of the ACM Web Conference 2024 (WWW ’24). 2024. https://doi.org/10.1145/3589334.3645600, 9 pp.
Zaccaro, A., Piarulli, A., Laurino, M., et al. “How breath-control can change your life: A systematic review on psycho-physiological correlates of slow breathing,” Frontiers in Human Neuroscience 12. 2018. https://doi.org/10.3389/fnhum.2018.00353, article 353.
Shaffer, F., & Meehan, Z. M. “A practical guide to resonance frequency assessment for heart rate variability biofeedback,” Frontiers in Neuroscience 14. 2020. https://doi.org/10.3389/fnins.2020.570400, article 570400.
Van Diest, I., Verstappen, K., Aubert, A. E., et al. “Inhalation/exhalation ratio modulates the effect of slow breathing on heart rate variability and relaxation,” Applied Psychophysiology and Biofeedback 39(3–4). 2014. https://doi.org/10.1007/s10484-014-9253-x, pp. 171–180.
Elliott, W. J., & Izzo, J. L., Jr. “Device-guided breathing to lower blood pressure: Case report and clinical overview,” Medscape General Medicine 8(3). 2006. https://pmc.ncbi.nlm.nih.gov/articles/PMC1781326/, article 23.
Tsai, H. J., Kuo, T. B. J., Lee, G. S., et al. “Efficacy of paced breathing for insomnia: Enhances vagal activity and improves sleep quality,” Psychophysiology 52(3). 2015. https://doi.org/10.1111/psyp.12333, pp. 388–396.
Scarratt, R. J., Heggli, O. A., Vuust, P., et al. “The audio features of sleep music: Universal and subgroup characteristics,” PLOS ONE 18(1). 2023. https://doi.org/10.1371/journal.pone.0278813, article e0278813.
Rankin, C. H., Abrams, T., Barry, R. J., et al. “Habituation revisited: An updated and revised description of the behavioral characteristics of habituation,” Neurobiology of Learning and Memory 92(2). 2009. https://doi.org/10.1016/j.nlm.2008.09.012, pp. 135–138.
Gingras, B., Pohler, G., & Fitch, W. T. “Exploring shamanic journeying: Repetitive drumming with shamanic instructions induces specific subjective experiences but no larger cortisol decrease than instrumental meditation music,” PLOS ONE 9(7). 2014. https://doi.org/10.1371/journal.pone.0102103, article e102103.
Kirsch, I., & Braffman, W. “Imaginative suggestibility and hypnotizability,” Current Directions in Psychological Science 10(2). 2001. https://doi.org/10.1111/1467-8721.00115, abstract.
American Lung Association, Editorial Staff. “Understanding vital signs: The importance of your respiratory rate,” Lung.org blog. 2025. https://www.lung.org/blog/respiratory-rate-vital-signs, web page. Patient-education source, not peer reviewed.