The numbers below are specific. The person isn’t.
This case study is a composite — built from the patterns that appear consistently in time-diary research, in Laura Vanderkam’s collected data, and in the experiences people report after running their first systematic time audit. The details are illustrative; the findings are characteristic.
Call the subject Maya. Senior product manager at a mid-size software company. Thirty-seven years old. No kids. Partner who also works full-time. Feels perpetually behind on the things she considers most important. Believes she works about 55 hours a week and has almost no free time.
She decides to run a 168-hour audit.
The Setup
Maya builds her tracking template on a Sunday evening. She uses a Google Sheet — 48 rows per day, seven columns, with a fifth column for energy rating (1, 2, or 3) and a sixth for brief notes.
Her categories: Deep Work, Shallow Work, Meetings, Commute (she takes the train), Sleep, Exercise, Meals, Social, Personal Care, Household, Leisure — Active, Leisure — Passive, Unaccounted.
She decides to track with end-of-day reconstruction rather than real-time logging. Each night before bed, she’ll spend 10 minutes filling in the day’s blocks. She sets a 9:45pm reminder titled “Log the day.”
One additional rule she sets for herself: she will not change her behavior during the tracking week. The temptation is to run a “good week” for the audit. She recognizes this as self-defeating.
The Tracking Week
Monday through Friday look, from the outside, like a demanding work week. Two all-hands meetings, three product reviews, a hiring debrief, several one-on-ones. Wednesday evening she has dinner with a friend. Friday she leaves the office at 6:30pm.
Saturday she sleeps in, does grocery shopping, goes for a run, and spends the afternoon doing what she describes in her log as “various — phone, reading, TV, some work email.”
Sunday is laundry, meal prep, some reading, a family video call, and two hours of what she logs as “catching up on work stuff.”
She misses logging Tuesday evening and reconstructs it Wednesday morning from memory. It’s a little fuzzy, but she fills it in.
The Analysis: What the AI Found
At the end of the week, Maya pastes her full log — 336 entries, some crisp and some vague — into Beyond Time and runs the categorization and analysis.
The AI takes about 90 seconds to categorize every entry, calculate category totals, and flag 14 entries it’s uncertain about. Maya reviews the flagged entries and corrects five of them. Then she reads the summary.
Her time breakdown for the week:
| Category | Hours |
|---|---|
| Sleep | 49.5 |
| Shallow Work | 22.0 |
| Meetings | 19.5 |
| Deep Work | 8.5 |
| Leisure — Passive | 14.0 |
| Meals | 10.5 |
| Household | 9.5 |
| Personal Care | 7.0 |
| Commute | 6.5 |
| Exercise | 3.0 |
| Social | 3.5 |
| Leisure — Active | 2.5 |
| Unaccounted | 12.0 |
| Total | 168 |
She stares at the screen for a while.
The Three Findings That Changed Her Thinking
Finding 1: She didn’t work 55 hours
Her combined Meetings + Shallow Work + Deep Work total: 50 hours. That’s already lower than her estimate of 55. But the more important number is her Deep Work total: 8.5 hours.
Eight and a half hours of focused, high-leverage work in a 50-hour work week. The other 41.5 hours were meetings, coordination, email, Slack, reviewing documents others had written, and administrative work.
She knew the balance was skewed. She didn’t know the skew was this extreme.
The AI notes: “Your deep work hours represent 17% of your total work time. Research on knowledge work suggests that output quality is disproportionately driven by deep work hours. Your high-leverage deliverables — the ones you flagged as your top priority — likely require at least 15–20 hours of deep work per week to make meaningful progress.”
Finding 2: She has 14 hours of passive leisure she doesn’t value
Fourteen hours. An average of two hours per day.
When Maya reads this number, her first reaction is disbelief. Her second reaction is recognition. The hour before bed, half-watching TV while scrolling her phone. The Saturday afternoon she logged as “various.” The Sunday morning drift. Add them up and they’re there.
The AI adds context: “Your passive leisure hours are predominantly occurring between 9pm and midnight on weekdays, and in extended afternoon blocks on weekends. Your energy ratings during these periods average 1.2 out of 3. You’ve noted ‘unrestful’ or ‘mindless’ in the notes column on 8 of these 14 hours.”
Fourteen hours of time that isn’t providing rest, satisfaction, or recovery. It’s just occupying space.
Finding 3: Twelve hours of unaccounted time
The Unaccounted category totals 12 hours — time that doesn’t clearly belong to any named activity. Transitions. The drift between finishing an email and starting the next task. The wandering after lunch. The minutes spent opening and closing apps without doing anything.
Twelve hours. Almost a full workday and a half, accumulated in fragments too small to register but large enough to matter.
The AI flags this: “Your Unaccounted hours are concentrated in two patterns: post-meeting drift (typically 15–30 minutes after each major meeting) and morning slow-starts (typically 30–45 minutes before your first deep work session of the day). Addressing either pattern could recover approximately 3–5 hours per week.”
The Priority Alignment Check
Maya asks the AI to run the alignment analysis.
She lists her three stated top priorities: advancing a major product initiative she’s leading, getting back into regular exercise (she ran three times last week, but her goal is five), and investing in her relationship (she and her partner eat dinner together only two nights last week, both times with phones out).
My top three priorities:
1. The Horizon product initiative — my highest-leverage work project
2. Exercise — goal is 5 sessions per week
3. Quality time with my partner
Based on my log, how much time went to each of these last week?
What's the most significant misalignment between these priorities and my allocation?The AI’s response:
- Horizon project work: approximately 6 hours of her 8.5 deep work hours went directly to this initiative. Reasonable, but below the 12–15 hours she estimates the initiative requires to hit Q4 milestones.
- Exercise: 3 sessions totaling 3 hours, against a goal of 5.
- Quality time with partner: difficult to isolate precisely from the log, but shared evening time where neither person was on a device appears to total roughly 4 hours across the week, heavily concentrated on one evening.
The most significant misalignment: her highest-priority project is receiving about 6 hours of deep work per week when it needs roughly twice that to meet her own milestones.
The One Change She Makes
Maya identifies the structural cause quickly. Her deep work is being crowded by the meeting load (19.5 hours) and the post-meeting drift (estimated at 4–5 of the 12 unaccounted hours).
She considers three possible changes:
- Consolidate meetings to two days per week, protecting the other three for deep work
- Implement a strict 90-minute daily deep work block before her first meeting each day
- Reduce passive leisure hours and redirect to either sleep or focused evening work
She chooses option 2. It’s the most structurally achievable given that she doesn’t fully control her meeting schedule. She blocks 7:30–9:00am as deep work on her calendar, marks it as private (no external bookings), and tells her team she won’t respond to Slack before 9am.
She runs a second audit three months later.
What Changed (And What Didn’t)
The second audit shows her deep work hours at 13.5 for the week — a 59% increase from the baseline. The morning block held for 11 of the 13 possible sessions; two were displaced by an early customer call and one unavoidable Monday morning crisis.
Her passive leisure hours: down from 14 to 10.5. Not intentionally reduced — the morning deep work block improved her sense of accomplishment enough that she found evenings less prone to low-grade avoidance scrolling.
Sleep average: unchanged at 7 hours per night.
The Horizon initiative: she describes it as “materially further along” compared to where it was three months prior.
The exercise goal (5 sessions per week) didn’t improve. It requires a different structural intervention — probably protecting a mid-day or early evening slot, which the audit correctly shows is still unprotected.
That’s the next audit’s problem to solve.
For the Beyond Time walkthrough that Maya used for her analysis, see Beyond Time 168-Hour Audit Walkthrough. For the research behind these audit findings, see The Science of the 168-Hour Week.
Your action for today: Before you start tracking, identify your own three stated top priorities — the things you’d say matter most if asked. Write them down. The comparison between stated priorities and actual time allocation is where the audit delivers its sharpest insight.
Frequently Asked Questions
-
Is this case study based on a real person?
This case study is a composite drawn from patterns that appear consistently in 168-hour audit research and practice — particularly in Laura Vanderkam's collected time diaries and in the typical findings reported by knowledge workers who conduct systematic time audits. The specifics are illustrative, designed to make the common findings concrete and recognizable.
-
What's the most common outcome after a first 168-hour audit?
Most people make one significant structural change to their schedule and then run the next audit three months later to measure whether it held. The most common changes are: protecting a recurring deep work block, reducing or consolidating meetings, and reducing passive leisure in favor of either sleep or active leisure. Wholesale schedule redesigns rarely stick; targeted structural changes usually do.