Sleep Analysis — Recovery Patterns and Intensity Interaction

Understanding Recovery Behavior

Sleep completes the behavioral loop we previously explored in Steps (volume) and METs (intensity).

Now we examine:

1. Sleep structure alone
2. Sleep × MET intensity relationship

This allows us to evaluate whether activity supports recovery — or disrupts it.

Section 1 — Sleep Data Alone

Descriptive Overview

Command used:

indexly analyze-csv sleep.csv --show-summary

Numeric Summary

Average sleep ≈ 7 hours Moderate variability observed.

Derived Metric — Sleep Efficiency

[ $SleepEfficiency = \frac{TotalMinutesAsleep}{TotalTimeInBed}$ ]

This captures recovery quality rather than just duration.

Weekday Structural Effect

Command:

indexly infer-csv sleep.csv --y TotalMinutesAsleep --group day_of_week --test kruskal

Result

Interpretation

• Weekday sleep differences are statistically significant • Effect size = very small (2.4%)

Meaning:

Sleep varies slightly across weekdays, but practical difference is limited.

This aligns with earlier Steps findings: Users are structured but not extreme.

Behavioral Shape

ASCII Pattern (Conceptual):

Sleep Duration (Week)
Mon  ███████
Tue  ████████
Wed  ████████
Thu  ███████
Fri  ███████
Sat  █████████
Sun  ████████

Mild variation. No dramatic weekend spike.

Section 2 — Sleep × MET Intensity Relationship

To compare intensity with recovery, we derived:

day_of_week = TEXT(SleepDay, "dddd")

Merge performed on day_of_week.

Merged rows: n = 7

1️⃣ METs vs Total Sleep Duration

Command:

indexly infer-csv sleepday.csv mets.csv --merge-on day_of_week --x mets_pro_mins --y TotalMinutesAsleep --test correlation --use-raw

Pearson Result

Interpretation

Strong negative correlation.

As intensity ↑ Sleep duration ↓

However:

• n = 7 (weekday aggregation) • Borderline significance • Wide confidence interval

This suggests a possible intensity–fatigue tradeoff pattern.

2️⃣ METs vs Sleep Efficiency

Command:

indexly infer-csv sleepday.csv mets.csv --merge-on day_of_week --x mets_pro_mins --y SleepEfficiency --test correlation --use-raw

Result

No statistically significant relationship.

Interpretation:

Intensity does not meaningfully predict sleep quality.

Combined Interpretation

A) Intensity vs Duration

Higher MET levels correspond to slightly shorter sleep duration.

Possible explanations:

• Later evening activity • Physiological arousal • Reduced total rest time

But evidence is fragile (n=7).

B) Intensity vs Efficiency

No meaningful effect.

Users sleep similarly efficiently regardless of activity intensity.

Integrated Behavioral Narrative

From Steps + METs + Sleep:

• Users are routine-driven • Evening-dominant in activity • Sleep structured but not extreme • Intensity does not improve recovery • High intensity may slightly reduce duration

The dominant pattern remains:

Time-of-day > Weekly variation > Intensity effects

Bellabeat Strategic Implication

This enables a full-cycle positioning:

Move → Recover → Sleep → Repeat

However, messaging should avoid:

“Train harder for better sleep”

Instead:

Promote balanced daily rhythm Encourage sustainable evening activity Support consistent recovery habits

Limitations

1. Merge reduced data to 7 weekday points
2. Aggregation hides within-person variability
3. No lagged modeling (activity today → sleep tomorrow)
4. Observational dataset — not causal

These findings are exploratory and descriptive. They should not be interpreted as medical guidance.

Readers are strongly encouraged to consult referenced statistical documentation and original dataset methodology before applying insights operationally.

Final Position

Sleep analysis confirms:

Users are balanced, not extreme performers. Behavior is habitual. Intensity does not dominate recovery.

This completes the movement–intensity–recovery triangle. If you’d like to explore the statistical methods in more depth, feel free to check the references section for further reading and background.

Next recommended analysis:

Sleep × Stress / Sleep × Variability (if available) Or lagged day-to-day modeling for deeper causality insight.