What an Executive Discovered Running a Full 168-Hour Audit

Note: This case study is composite. The person described represents a pattern drawn from published practitioner accounts, Vanderkam’s research with readers, and reported experiences shared across productivity communities. No individual is identified.

The Baseline: A Vice President Who Felt Fully Allocated

David runs strategy for a 400-person technology company. He manages a team of eight, sits on the company’s leadership council, and regularly travels for client meetings and industry events. He is 44, has two children in secondary school, and has been in senior roles for seven years.

His prior sense of his time: approximately 55–60 hours per week of work, 6 hours of sleep per night (he has held this as a point of mild pride), minimal but adequate leisure, and “not enough” family time. He had read Vanderkam’s 168 Hours but had not run the audit.

What prompted the audit: a performance coach noted that David’s self-reports during sessions were internally inconsistent — he claimed to be strategic but described nearly all of his time in reactive terms. The coach suggested a three-week audit before their next session.

Pre-Audit Estimates

David wrote his estimates on the Sunday before Week 1. He predicted:

• Work hours: 58 per week
• Sleep: 42 hours (6 hours per night)
• Exercise: 3–4 hours
• Family time: 6–8 hours
• Discretionary personal time: 4 hours
• Household and logistics: 3 hours
• Transitions and other: 8 hours

Total: approximately 124–127 hours. This left roughly 41–44 hours unaccounted for in his pre-audit model. He noted this in writing, labeled it “probably transitions and things I’m not thinking of,” and proceeded.

Version 1: Week One Data

David used a spreadsheet with 30-minute blocks and logged transitions in real time using his phone’s notes app when he was away from his desk.

The Week 1 totals:

• Work (paid, job-adjacent): 47.5 hours
• Sleep: 49 hours (7 hours per night average)
• Exercise: 2 hours
• Family time (as primary activity): 5.5 hours
• Screen-based leisure (phone, streaming): 9.5 hours
• Household logistics: 5.5 hours
• Transitions and uncategorized: 13.5 hours
• Commute (he had not listed this separately in estimates): 6 hours

Total: 139 hours, leaving 29 hours in a category he labeled “genuinely can’t account for.”

He contacted his coach after three days to say the audit was “deeply annoying.” He completed it anyway.

What the First Week Revealed

The gaps between estimates and actuals were substantial in several categories.

Work hours: 47.5 actual versus 58 estimated — a 10.5-hour gap. Within those 47.5 hours, roughly 22 hours were meetings, 8 hours were email and messaging administration, and approximately 17 hours were what David categorized as “substantive work” (writing, analysis, strategic thinking, one-on-one coaching of his team). His self-narrative of himself as a strategic executive was supported by only about 36% of his actual working hours.

Sleep: 49 actual versus 42 estimated — 7 hours higher than claimed. This was one of the audit’s most immediate surprises. David had identified as a six-hour sleeper for years. His diary showed he was averaging seven hours, with Saturday and Sunday both over eight hours. The five-hour nights he remembered were real; they were also counterbalanced by longer nights he had not registered as “real sleep.”

Discretionary screen time: 9.5 actual versus roughly 4 estimated — more than double. This category was the most fragmented: no single session was very long, but accumulated across the week it was substantial. The entries were almost entirely phone-based (news apps, social media, messaging threads that did not require responses) during commute, lunch, and evening transition times.

Family time: 5.5 actual versus 6–8 estimated. Closer to estimate than other categories, but David noted that the sessions he logged were often low-quality — parallel activity or mealtime with devices present. His annotation: “present but not really there.”

Version 2: The Redesign

After reviewing Week 1 with his coach, David identified two structural changes to test in Week 2.

Change 1: Protect two 90-minute morning focus blocks per week. Scheduled before any meetings on Tuesdays and Thursdays, starting at 7:30 am. These were dedicated to strategic work — the category that constituted only 36% of his work hours in Week 1.

Change 2: Name the phone-free family periods explicitly. Not “more family time,” but specific labeled blocks: dinner (6–7:30 pm, phone face-down), and Saturday morning until noon (no work, no news apps). These required no additional hours — they were time he was technically present for already, restructured to increase engagement quality.

He also identified that the 13.5 hours of transitions and 29 hours “unaccounted for” represented approximately 42 hours of the week he could not describe. He committed to more granular logging in Week 2 — not more categories, just fewer “uncategorized” entries.

Week 2 and Week 3: The Stable State

Week 2 showed the changes holding with effort. The two focus blocks produced approximately 3 additional hours of substantive strategic work. The family time quality annotation shifted — David noted both dinner periods as “engaged” versus the Week 1 “parallel activity” label.

Week 3 was more revealing. The focus blocks survived one of the two scheduled slots (a leadership call displaced the Thursday block). The family periods held more consistently than the work changes. The screen-leisure category dropped from 9.5 to 6.5 hours — not through deliberate restriction but because David had started noticing the habit loop more clearly, which naturally reduced some of the automatic phone-reaching.

Three-week averages:

• Work hours: 48.5 (substantially below the 58-hour self-estimate)
• Sleep: 49.5 (substantially above the 42-hour self-estimate)
• Substantive strategic work: 19.5 hours per week (up from 17 in Week 1)
• Discretionary screen time: 7.5 hours per week (down from 9.5)
• Family time: 6 hours per week (slight increase, significant quality annotation improvement)

Lessons from the Three Weeks

The work-hours gap was the audit’s most consequential finding. David had been operating on a 58-hour self-narrative. His actual hours were closer to 48. This gap had several downstream effects: it meant his exhaustion complaints were at least partly explained by factors other than overwork (sleep debt, poor recovery from transitions), and it meant that his “I have no time for strategic work” position was not primarily a capacity problem — it was an allocation problem.

The sleep finding changed his self-narrative in a useful direction. David stopped presenting himself as a six-hour sleeper and started treating his actual sleep patterns with less cognitive dissonance. Whether this produced a measurable performance benefit is unclear; what is clear is that the narrative shift removed a source of unnecessary friction in his self-assessment.

Quality annotations matter more than hours in some categories. The family time finding was instructive: the audit’s quantitative data showed adequate hours, but the qualitative annotations revealed a quality problem. Vanderkam’s framework does not prevent this misread — the audit captures time, not attention — but maintaining quality notes alongside the category labels surfaces it.

Structural changes are easier to make at the margins than in core work time. The focus blocks were difficult to protect against a busy meeting culture. The family periods, which required only behavioral changes rather than calendar negotiation, held more reliably. This asymmetry is worth anticipating: most of the immediately actionable findings from a 168-hour audit will be in your discretionary and transition hours, not in your core scheduled commitments.

Using AI for the Three-Week Analysis

David used an AI assistant to run the three-week comparison after Week 3 concluded. He pasted his weekly totals and category summaries and asked for a variance analysis.

Here are my 168-hour audit totals for three consecutive weeks:

[Week 1 data]
[Week 2 data]
[Week 3 data]

My pre-audit estimates were: [estimates]

My stated change goals were: protect two 90-minute focus blocks, improve family engagement quality.

Please: (1) calculate week-over-week trends for each category, (2) assess whether my change goals appear to be taking hold in the data, and (3) identify which category shows the largest persistent gap from my pre-audit estimates.

The AI identified that the work-hours gap (estimates versus actuals) remained the largest persistent discrepancy across all three weeks, and that the focus-block goal was partially holding. It flagged that the transition/uncategorized category was still substantial at 10–14 hours per week, and noted this as a potential area for further investigation.

For structured multi-week tracking and automated variance analysis of this type, Beyond Time provides a built-in framework that eliminates the manual compilation step — David’s coach noted that most clients who track across multiple weeks lose the thread during the compilation phase.

What Stayed Hard

Three changes David had expected to make did not materialize in the three-week window:

The 22-hours-per-week of meeting time was essentially unchanged. Reducing it requires organizational negotiation, not personal scheduling — a constraint the audit surfaced clearly but could not solve.

The email and messaging administration (8 hours per week) dropped slightly but remained the second-largest work-hours category. Structural interventions here (batching, auto-responders, delegation) were planned for a fourth-week experiment.

The 29 “unaccounted for” hours in Week 1 came down to about 18–20 hours per week across Weeks 2 and 3 — still substantial, still imprecisely categorized. Understanding these hours fully would have required more granular logging than David found sustainable.

The audit’s value was not the changes David made in three weeks. It was the accurate model he built of his own time — a model that would inform decisions for months afterward.

After completing your next week of tracking, run the comparison against your pre-audit estimates and ask: which category’s gap most surprised me, and what does that gap tell me about the self-narrative I was operating on?

Related:

• The Complete Guide to the 168-Hour Audit
• The Deep Framework Behind the 168-Hour Audit
• How to Do the 168-Hour Audit Properly
• Why People Avoid the 168-Hour Audit

Tags: 168-hour audit, time audit case study, executive productivity, Laura Vanderkam, time management