LBL Sleep-Cognition Optimizer — Methodology & Validation

What this tool measures (and what it does not)

The LBL Sleep-Cognition Optimizer measures current behavioral sleep patterns — what your sleep actually does on workdays vs. free days — and produces a personalized 24-hour schedule with prescription alignment scoring.

From 12 LBL-original items, the Optimizer derives:

• Behavioral chronotype via the MSFsc methodology (Roenneberg 2003), expressed as a 0-100 morningness score.
• Social jetlag in minutes, classified against Arab 2024 meta-analytic thresholds.
• Estimated Sleep Regularity Index (SRI proxy) on a 0-100 scale, with target 87+ per Windred 2023.
• Biology-behavior gap when paired with the LBL Chronotype Profile.
• Personalized 24-hour schedule with bedtime, wake time, peak cognitive window, exercise window, wind-down, caffeine cutoff.
• 5-axis prescription alignment radar across Timing / Caffeine / Exercise / Wind-down / Cognitive dimensions.
• Tier 3 prescription tiles with current behavior, recommendation, and rationale for each leverage point.

The Optimizer does not measure:

• Your biological/trait chronotype directly — that is measured by the companion LBL Chronotype Profile.
• Sleep stage architecture or sleep efficiency — polysomnography is required for that.
• Light exposure quantitatively — light timing matters greatly but cannot be captured by self-report.
• Sleep disorders. Persistent symptoms warrant clinical evaluation.

The Optimizer measures what you are doing. The Chronotype Profile measures who you are biologically. Together they compute the biology-behavior gap — the most clinically meaningful output of the pair.

Why behavioral metrics carry the health signal

The strongest health correlates in modern chronobiology are not about whether you are biologically a morning or evening type — they are about the misalignment between biology and behavior, and the regularity of sleep timing.

Social jetlag. The Arab et al. 2024 meta-analysis (n=231,648 across 43 studies) found social jetlag is independently associated with elevated BMI, abdominal adiposity, and metabolic dysfunction — independent of total sleep duration. This effect was first proposed by Wittmann et al. 2006 and has been replicated dozens of times.

Sleep regularity. Windred et al. 2023 (Sleep, UK Biobank n=60,977) demonstrated that the Sleep Regularity Index (SRI) is a stronger predictor of all-cause mortality than total sleep duration. People in the highest SRI quintile (>=87) had markedly lower mortality risk than those in the lowest quintile, after controlling for sleep duration and a battery of demographic and clinical covariates.

Biology-behavior gap. When both the Chronotype Profile and Optimizer are taken, the gap between biological and behavioral chronotype quantifies modern sleep dysfunction. A 30-point gap means your behavior is operating 30 morningness-percentile-points away from your biological set point — a substantial chronic stressor.

This is why the Optimizer foregrounds behavioral metrics rather than treating chronotype identification as the primary output. Knowing your chronotype is diagnostic information. Knowing your social jetlag, SRI, and biology-behavior gap is actionable health information.

Instrument structure

The Optimizer consists of 12 items in 5 sections.

Q12 (optimization goal) is conditional: "Reduce biology-behavior gap" is shown only when Profile data exists in localStorage; otherwise "Reduce social jetlag" is the maximum-leverage goal option.

Scoring algorithm — full pseudocode

The exact algorithm running in your browser.

function midsleep(sleepMinutes, wakeMinutes):
    # Compute midpoint of sleep window, handling cross-midnight wraparound
    if sleepMinutes > wakeMinutes:
        adjustedSleep = sleepMinutes - 1440   # treat as previous day
        midpoint = (adjustedSleep + wakeMinutes) / 2
        return (midpoint + 1440) modulo 1440  # normalize to 0-1440 range
    else:
        return (sleepMinutes + wakeMinutes) / 2

function sleepDuration(sleepMinutes, wakeMinutes):
    if sleepMinutes > wakeMinutes:
        return ((1440 - sleepMinutes) + wakeMinutes) / 60   # hours
    else:
        return (wakeMinutes - sleepMinutes) / 60

function computeBehavioralChronotype(answers):
    wdMid = midsleep(answers.Q2, answers.Q3)        # workday midsleep
    fdMid = midsleep(answers.Q4, answers.Q5)        # free-day midsleep
    wdDur = sleepDuration(answers.Q2, answers.Q3)
    fdDur = sleepDuration(answers.Q4, answers.Q5)
    weeklyAvg = (5 * wdDur + 2 * fdDur) / 7

    # MSFsc: sleep-debt-corrected midsleep on free days (Roenneberg 2003)
    msfsc = fdMid
    if fdDur > weeklyAvg:                            # free-day oversleep
        excessMinutes = (fdDur - weeklyAvg) * 30     # half of excess hours
        msfsc = (fdMid - excessMinutes + 1440) modulo 1440

    # Linear mapping: MSFsc clock time → 0-100 morningness score
    # 2:00 AM → ~75 (morning); 4:00 AM → ~50; 6:00 AM → ~25; 8:00 AM → ~0
    msfscClock = msfsc
    if msfscClock > 720:
        msfscClock = msfscClock - 1440   # handle evening-hours edge case
    score = round(100 - ((msfscClock - 60) / 6))
    return clip(score, 0, 100)

function computeSocialJetlag(answers):
    wdMid = midsleep(answers.Q2, answers.Q3)
    fdMid = midsleep(answers.Q4, answers.Q5)
    diff = abs(fdMid - wdMid)
    if diff > 720: diff = 1440 - diff             # handle wraparound
    if diff < 30:  return diff, "Minimal"
    if diff < 60:  return diff, "Mild"
    if diff < 90:  return diff, "Moderate"
    if diff < 120: return diff, "High"
    return diff, "Severe"

function computeSRIProxy(answers, sjl):
    # Self-report proxy. True SRI requires actigraphy (Phillips 2017).
    wdDur = sleepDuration(answers.Q2, answers.Q3)
    fdDur = sleepDuration(answers.Q4, answers.Q5)
    durationDelta = abs(wdDur - fdDur)   # hours

    sjlPenalty = min(40, sjl.minutes * 0.3)
    durationPenalty = min(20, durationDelta * 8)
    continuityPenalty = (answers.Q7 - 1) * 3

    sri = 100 - sjlPenalty - durationPenalty - continuityPenalty
    return clip(round(sri), 0, 100)

function computeBiologyBehaviorGap(behavioralScore, profile):
    if profile is null: return null
    bioScore = profile.biologicalChronotype.calibratedScore
    gap = abs(bioScore - behavioralScore)
    if gap < 10:  return gap, "Well-Aligned"
    if gap < 20:  return gap, "Mild Misalignment"
    if gap < 35:  return gap, "Moderate Misalignment"
    return gap, "Severe Misalignment"

function generateSchedule(answers, behavioral):
    chronotypeScore = behavioral.score or mapFromQ1(answers.Q1)
    targetDuration = 7.5   # hours, NSF 2015 default for adults
    latencyMinutes = mapFromQ6(answers.Q6)   # 7..100 minutes

    mustWake = parseTime(answers.Q10)
    sleepOnset = (mustWake - targetDuration*60 - latencyMinutes + 1440) modulo 1440
    bedtime = (sleepOnset - latencyMinutes + 1440) modulo 1440

    # Peak cognitive window: chronotype-shifted (Carrier 2008 paradigm)
    peakStart = (mustWake + (180 - (chronotypeScore - 50) * 1.5) + 1440) modulo 1440
    peakEnd   = (peakStart + 120) modulo 1440

    # Tier 3 prescriptions
    caffeineCutoff = (sleepOnset - 420 + 1440) modulo 1440   # 7h half-life buffer
    exerciseStart  = (sleepOnset - 300 + 1440) modulo 1440
    exerciseEnd    = (sleepOnset - 180 + 1440) modulo 1440
    windownStart   = (bedtime - 60 + 1440) modulo 1440

    return {bedtime, sleepOnset, mustWake, peakStart, peakEnd,
            caffeineCutoff, exerciseStart, exerciseEnd, windownStart}
    

All computation runs locally in the user's browser. No inputs leave the device.

MSFsc derivation — using the open scientific method

MSFsc (sleep-debt-corrected midsleep on free days) is the chronotype metric introduced by Roenneberg, Wirz-Justice & Merrow in 2003. The key insight: people's sleep timing on workdays is biased by alarm clocks and obligations; their sleep timing on free days reflects biological preference; but free-day timing is itself biased by recovery from accumulated weekday sleep debt. MSFsc corrects for that recovery oversleep.

Crucially, the MSFsc methodology is open published science. What is copyrighted is the MCTQ questionnaire — the specific item wording and response options used in published MCTQ studies. We do not administer the MCTQ. We collect sleep onset and wake times using our own LBL-original item wording (Q2-Q5), then apply the published MSFsc computation to those data.

The formula used:

• MSF = midsleep on free days = (free-day-sleep-onset + free-day-wake) / 2, handling cross-midnight wraparound.
• Weekly average sleep duration = (5 × workday-duration + 2 × free-day-duration) / 7.
• If free-day duration > weekly average (oversleep), subtract half of the excess hours from MSF to get MSFsc.
• If free-day duration ≤ weekly average (no oversleep), MSFsc = MSF.

Mapping MSFsc to the 0-100 morningness score is a linear approximation: MSFsc 2:00 AM → ~75 (morning); 4:00 AM → ~50; 6:00 AM → ~25; 8:00 AM → ~0. The true relationship is non-linear at extremes but linear is adequate for the educational use case.

Social jetlag thresholds and rationale

Social jetlag thresholds are calibrated against Arab 2024 meta-analytic effect sizes:

The 60-minute Moderate threshold is the most evidence-supported cut-point. Below ~60 min, social jetlag is statistically common in modern populations and associations are inconsistent. Above 60 min, effect sizes for adiposity and metabolic markers become consistently positive across studies.

SRI proxy methodology and disclosure

The Sleep Regularity Index (SRI) was developed by Phillips et al. 2017 and quantifies how consistently a person sleeps at the same times day-to-day. It is computed from minute-by-minute actigraphy data over multiple weeks: SRI = 100 - (proportion of minute pairs across days where sleep/wake state differs) × 100. Higher = more regular.

True SRI requires wrist actigraphy. The Optimizer cannot measure that. Instead, we compute a self-report proxy from your reported workday vs. free-day timing and continuity. The proxy:

• Starts at 100 (perfect regularity).
• Subtracts up to 40 points penalty for social jetlag magnitude (0.3 points per minute, capped).
• Subtracts up to 20 points penalty for sleep duration variance between workdays and free days (8 points per hour of difference, capped).
• Subtracts up to 12 points penalty for sleep continuity (Q7 awakening frequency, 3 points per scale step).

The target threshold is SRI >= 87, per Windred 2023 (UK Biobank n=60,977), which identified this as the inflection point above which all-cause mortality risk drops markedly.

We explicitly disclose that this is a proxy, not an actigraphy-derived measurement. The proxy correlates with true SRI in face-validity testing but Level 3 convergent validity (comparison to actigraphy in the same individuals) is not yet established. Users seeking precise SRI measurement should use a research-grade actigraphy wearable.

Schedule generation algorithm

The recommended schedule is solved within the user's hard constraint (Q10 must-wake-time) using chronotype, sleep latency, and cognitive demand window inputs.

The schedule is a starting point, not a fixed prescription. Adaptation takes 2 weeks; phase shifts should be gradual (~15 min/day).

5-axis prescription alignment radar

The radar visualizes alignment between current behavior and the recommended schedule across 5 dimensions. Each axis is scored 0-100; higher = better aligned.

The radar shape is informative: a regular pentagon-like polygon = well-aligned across all dimensions. Collapsed inward on specific axes = where the largest leverage exists for sleep optimization. Each Tier 3 tile below the radar makes the recommendation for that axis explicit.

Biology-behavior gap analysis

When the user has taken the LBL Chronotype Profile (Profile data exists in localStorage under key 'lbl-chronotype-profile'), the Optimizer computes:

gap = |biology_chronotype - behavior_chronotype|

where biology_chronotype is the calibrated score from the Profile and behavior_chronotype is the 0-100 score derived from MSFsc in this Optimizer.

The direction of the gap is also computed and reported: "behavior more evening than biology" vs. "behavior more morning than biology". The schedule generation algorithm uses this direction to bias recommendations toward gap closure within Q10 constraints.

Validation strategy

Users should treat the Optimizer as an educational tool for sleep self-assessment and schedule personalization. Schedule recommendations are evidence-grounded but not validated as superior to alternatives in randomized trials.

Edge cases and handling

• Shift workers: The Optimizer assumes typical workday/free-day patterns. Rotating shift workers should consult specialized clinical guidance; the standard recommendations do not apply.
• Cross-midnight sleep: Sleep onset after midnight (e.g., 1 AM) and wake before noon is handled by the midsleep wraparound logic. Sleep onset before midnight (e.g., 9 PM) is treated as previous-day for midpoint computation.
• Oversleep correction: If a free-day duration exceeds weekly average, MSFsc subtracts half the excess hours. This is the Roenneberg 2003 correction. For users without weekday sleep debt, MSFsc = MSF (unchanged).
• No regular caffeine / no regular exercise: Q8 and Q9 have "I don't drink caffeine" and "I don't exercise regularly" options. These produce neutral alignment scores rather than zero.
• Varies widely: Q8 and Q9 have "Varies widely" options. These yield reduced alignment (40-45) because variable timing is itself suboptimal.
• No Profile data: Optimizer falls back to Q1 self-selected chronotype for schedule generation. Biology-behavior gap is not computed; user is encouraged to take the Profile for full analysis.
• Hard wake-time conflict: If Q10 (must-wake time) conflicts severely with the user's chronotype, the recommended bedtime may feel implausibly early. This is correct behavior — the algorithm solves for chronotype alignment within constraints, not for everyone-should-sleep-10-PM-to-6-AM.

Limitations

The Optimizer inherits limitations common to all self-report behavioral sleep assessments, plus several v1.0-specific limitations.

Self-report limitations:

• Recall bias for typical bed/wake times; people overestimate sleep duration and underestimate latency.
• "Typical" answer assumes consistent pattern; users with high variability give noisy answers.
• Self-reported caffeine and exercise timing simplify what is often a varied pattern.

Implementation-specific limitations of v1.0:

• SRI proxy is self-report only; actigraphy validation is pending.
• Light timing is not measured (cited as future direction with wearable integration).
• Schedule recommendations not validated in RCT.
• Cross-cultural validity of self-report items untested.
• The Optimizer is designed for typical sleep schedules; rotating shift workers, polyphasic sleepers, and those with sleep disorders require clinical guidance.

What this Optimizer is NOT:

• Not a clinical diagnostic instrument for sleep disorders.
• Not a substitute for polysomnography or actigraphy.
• Not validated in RCT for effectiveness; recommendations are evidence-grounded but not proven superior.
• Not appropriate for users with suspected DSPD, ASPD, Non-24, narcolepsy, sleep apnea, RLS, or Shift Work Disorder — these require clinical evaluation.

Independent review

The Optimizer v1.0 instrument design, scoring algorithm, schedule generation logic, and validation framework were reviewed by:

• Abiot Y. Derbie, PhD — Postdoctoral research fellow in neurodevelopmental neuroimaging. Reviewed MSFsc derivation correctness, SJL formula, SRI proxy disclosure framing, and schedule generation logic.
• Eskezeia Y. Dessie, PhD — Researcher in behavioral and brain sciences. Reviewed item wording, edge-case handling, prescription alignment scoring, and clinical safety framing.

Neither reviewer is a clinical sleep medicine specialist. Users with suspected sleep disorders should consult a physician board-certified in sleep medicine.

Version log

Next scheduled review: November 13, 2026. Unscheduled review triggers: publication of new MSFsc or SRI validation studies; updates to Arab 2024 or Windred 2023 thresholds; methodology errors identified via corrections; user-reported edge cases.

Methodology FAQ

Last reviewed

This methodology page was last reviewed on May 13, 2026. Next scheduled review: November 13, 2026.

How to cite this methodology

Related pages