Blue Light Before Bed and Teen Sleep

ISEF Category: Behavioral and Social Sciences

Subcategory: Behavioral Neuroscience  ·  Difficulty: Intermediate  ·  Setup: Home Setup  ·  Time: 1 to 2 Months

The Hook

One bright screen at night can nudge your sleep clock more than you expect. That makes blue light before bed a real testable question, not just a wellness slogan. You can study it with a simple schedule, Fitbit sleep exports, and a careful compare of amber-filter nights versus normal screen nights.

What Is It?

Blue light is the short-wavelength part of visible light. Your brain reads it as a daytime cue, like a sunrise signal. That cue can make it harder for your body to feel sleepy on time. Teens may feel this effect more because puberty already shifts sleep timing later.

A within-subject study means the same student serves as their own comparison. On some nights, that student uses an amber filter. On other nights, the student uses the same screen setup without the filter. Fitbit sleep exports then give you data such as sleep onset, total sleep time, and sleep efficiency. Mixed-effects models help you compare the two conditions while accounting for repeated nights from the same person.

Why This Is a Good Topic

This topic works well for a science fair because you can measure it, repeat it, and compare clear conditions. It connects to a real problem, since many teens use screens right before bed. You can learn how to design a fair comparison, clean up messy sleep data, and analyze repeated measures without needing a lab bench.

Research Questions

• Does using an amber filter on screen evenings change Fitbit sleep onset time compared with unfiltered evenings?
• What is the effect of amber-filter nights on total sleep time for the same student?
• How does screen condition change sleep efficiency across repeated nights?
• To what extent does the effect differ between school nights and weekend nights?
• Which sleep outcome shifts the most, sleep onset, wake after sleep onset, or total sleep time?
• How does next-morning sleepiness change across amber-filter and unfiltered nights?

Basic Materials

• Fitbit with sleep export access.
• Smartphone or tablet with a blue-light filter setting or amber overlay app.
• Unfiltered screen access for comparison nights.
• Nightly sleep log sheet or shared spreadsheet.
• Parent consent and student assent forms.
• Calendar app for assigning and tracking night conditions.
• Excel, Google Sheets, or Numbers for organizing exports.

Advanced Materials

• Actigraphy watch with minute-level exports.
• Light meter or lux meter for checking room and screen brightness.
• Saliva collection supplies for melatonin sampling.
• Standardized amber filter lenses or calibrated screen filters.
• Quiet controlled-light room for evening comparison sessions.
• Laptop with software for mixed-effects analysis and data cleaning.

Software & Tools

• R: Fits mixed-effects models and makes repeated-measures plots.
• RStudio: Gives you a clean workspace for coding, checking data, and running models.
• Python: Cleans Fitbit exports and merges them with nightly logs.
• Google Sheets: Tracks nightly conditions, compliance, and missing entries.
• jamovi: Lets you test basic mixed-effects models with a point-and-click interface.

Experiment Steps

1. Define one primary sleep outcome, such as sleep onset time or sleep efficiency.
2. Set up a within-subject schedule that balances amber-filter and unfiltered evenings across similar school nights.
3. Write the rule set for compliance, covariates, and excluded nights before you start collecting data.
4. Build one spreadsheet row per night so you can link Fitbit exports, screen condition, and self-reports cleanly.
5. Choose a mixed-effects model that matches repeated nights nested within each student.
6. Pre-plan how you will compare effect sizes, confidence intervals, and subgroup patterns.

Common Pitfalls

• Letting students choose their filter nights themselves, which mixes the light effect with schedule bias.
• Changing bedtime on amber nights, which makes it hard to tell whether light or routine caused the sleep shift.
• Relying on the Fitbit sleep score alone, which can hide which sleep measure actually changed.
• Ignoring nights when the filter was off or another bright screen got used, which blurs the treatment.
• Treating all nights as independent data points, which understates uncertainty in repeated-measures data.

What Makes This Competitive

A stronger version goes beyond a simple before-and-after comparison. You control for school night, bedtime, caffeine, and screen time length, then model each student as their own baseline. You can also compare more than one sleep outcome, not just total sleep. That kind of design shows you understand both the behavior and the statistics behind it.

Project Variations

• Compare amber filters with blue-light blocking glasses on the same sleep outcomes.
• Test whether the effect changes on school nights versus weekend nights.
• Add next-day sleepiness ratings or reaction time as a second outcome.

Learn More

• PubMed: Search review articles on blue light, circadian rhythm, and adolescent sleep.
• NIH PubMed Central: Read free full-text studies on screen exposure and sleep timing.
• CDC: Look for teen sleep guidance and school health resources.
• Fitbit Help Center: Find export instructions for sleep logs and activity data.
• lme4 Documentation: Read examples for mixed-effects models in R.