How to Collect Information and Data About Yourself to Make Informed Decisions (As Detective)

• This approach grew from behaviour change and self‑tracking traditions: quantified self, behavioural economics, and clinical habit‑formation studies. The common traps are many: measuring the wrong thing, over‑tracking until fatigue, mistaking correlation for causation, and using data as self‑punishment rather than insight. Outcomes change when we narrow the focus to a single, actionable question and commit to brief, frequent checks. Studies show small, specific measurement routines increase adherence by roughly 30–50% compared with open‑ended tracking. The pivot that saves most projects is simplicity: choose 1–2 measures, track them reliably, and let the rest be qualitative notes.

We will be practical from the first step. The detective’s job is not to become a data scientist overnight; it’s to collect the right few clues, daily, so decisions next week are grounded. We will show how to design quick experiments, how to reduce bias in our observations, how to handle busy days, and how to pivot when initial assumptions fail. Along the way we’ll make micro‑scenes: small choices in kitchens, at desks, and in bed — because that’s where evidence is gathered.

Part 1 — Decide the question you actually care about (and why)

We often begin with a vague aim: “I want more energy,” “I want to read more,” or “I want less stress.” Those aims are fine but not actionable. The detective narrows: which evidence would change our decision? Which next action would we take if the measure crosses a threshold?

We assumed “more energy” → observed we tracked steps and caffeine intake and still felt stuck → changed to “sleep quality and afternoon drowsiness” as the specific question. That pivot came from noting the pattern: energy dips after 3pm, not total daily steps. If we had not narrowed, we would have drowned in irrelevant numbers.

Pick one decision you might make next week that depends on data. Examples:

• Decide whether to shift bedtime earlier by 30 minutes for a week.
• Decide whether to reduce afternoon caffeine by half.
• Decide whether to block 90 minutes in the morning for focused work.

Make the decision sentence explicit and immediate: “If average nightly sleep < 6.5 hours this week, we will move bedtime 30 minutes earlier.” A clear decision maps data to action, and that is what tracking must inform.

Micro‑task (5 minutes): Write the decision sentence in the Brali LifeOS task. If we do that now, the rest of the process has clarity.

Part 2 — Choose the smallest set of measures that would change the decision

We keep measures few and precise. The detective selects the minimum number of clues needed to act. Too many signals mean analysis paralysis; too few risk missing a true effect.

Common categories we use:

• Objective counts (steps, minutes, grams)
• Time‑stamped behaviours (bedtime, screen off time)
• Subjective sensations (sleepiness scale 1–10, stress 1–10)
• Short performance metrics (focused minutes, words written)

We prefer 1–2 numeric measures plus one short subjective check. Numeric measures are easy to average and threshold. Subjective checks capture context that numbers miss.

Example sets for common decisions:

• Sleep decision: Metric A = total sleep minutes (or hours), Metric B = 3pm sleepiness score (1–10); Subjective = "rested?" yes/no.
• Productivity decision: Metric A = focused minutes (Pomodoro counts), Metric B = number of interruptions; Subjective = "flow level" 1–5.
• Diet decision: Metric A = grams of added sugar per day, Metric B = total calories (optional); Subjective = satiety 1–5 after meals.

We assumed “total calories” → observed it required weighing and logging every meal → changed to “grams of added sugar + one hunger rating,” which was easier and more closely tied to the decision. There’s a trade‑off: precision vs burden. We tend to accept less precision if adherence rises.

Micro‑task (10 minutes): Pick one numeric measure and one subjective check and set them up as Brali LifeOS check‑ins. Choose the simplest unit: minutes, counts, mg, grams, or 1–10 scale. For sleep, use minutes/hours. For caffeine, log mg. For focused work, log minutes.

Part 3 — Design the daily check‑in (micro‑protocols we can follow)

A good protocol answers three questions: when, how, and what.

When: pick consistent, anchorable times. Anchors beat willpower. For example:

• Morning review within 20 minutes of waking.
• Midday 3pm quick check when we usually dip.
• Evening review within 30 minutes of getting into bed.

For many numeric measures, a single daily log suffices (e.g., total sleep hours at wake). For distributed behaviours (caffeine intake across the day), we might add a single “total” log in the evening to avoid multiple interruptions.

How: decide the data source. Use the least friction method:

• Automatic: sleep via wearable/phone, steps via phone, calendar for meetings.
• Manual quick log: count, minutes, grams on a small paper card or the Brali app.
• Snapshot photo: food plate photo with a single tag (low/medium/high portion).

What: specify the exact item to record. Avoid vague labels like “meals” — prefer “grams of added sugar”, “minutes of focused work between 9–11am,” or “cups of coffee (150 mg each)” because they scale into numbers.

We assumed a two‑time check per day → observed it created fragmented records → changed to one evening total check with a morning "state" rating. That simplified adherence and produced cleaner summaries.

Micro‑task (10 minutes): Create three Brali checks: morning state (1–10), evening totals (minutes/grams), and one situational check (e.g., 3pm sleepiness). Practice one full entry now using yesterday's data. If we can’t remember yesterday, estimate and mark “estimate” in the journal.

Part 4 — Build a simple hypothesis and an experiment

Hypotheses are short, testable, and incorporate a threshold. They must be falsifiable. A strong hypothesis looks like: “If we reduce added sugar to <30 g/day for five days, our average 3pm sleepiness drops from ≥6 to ≤4 (on 1–10).” This links measure, threshold, time, and expected change.

Design experiments to run 5–14 days. Very short runs (1–2 days)
capture noise; very long runs (months) lose momentum. We often run 7‑day cycles. Given typical daily variability, 7 days gives a balance between visible trend and manageable commitment.

Trade‑offs appear: longer experiments give cleaner signal but increase dropout risk. We usually choose the shortest experiment likely to change a decision — often 7 days.

Micro‑task (10 minutes): Write the hypothesis in Brali LifeOS and set a 7‑day experiment block. Include the decision rule (what we will do if the hypothesis is true).

Part 5 — Keep the burden low: what to measure automatically, what to estimate, and what to skip

The detective chooses automation when it reduces burden without destroying clarity. Phone step counters and wearables for sleep are useful if we wear them consistently. But automation can create a false sense of precision: a wrist sleep score is only an estimate. We treat automatic measures as "approximate" rather than gospel.

Where automation is impractical, choose single‑number evening estimates. Estimating once per day is faster and usually within ±10–20% of rigorous logging for many behaviours — that’s enough to make decisions. For example, estimating "two coffee cups (2 × 150 mg = 300 mg)" is usually fine.

Skip measuring anything that will not change our decision this week. If the result won't change the next action, record an anecdote in the journal instead.

Micro‑task (5 minutes): Flag one measure to automate and one to estimate. Add both to Brali LifeOS and mark "auto" or "estimate" in the notes.

Part 6 — Record short context notes — the detective’s quick interview

Numbers without context are brittle. After each daily check, add 1–2 short notes: key events (late meeting, heavy meal, nap), one situational cause, and one feeling word. Keep it to 10–20 words. This qualitative glue helps when numbers surprise us.

Micro‑sceneMicro‑scene
We are in the kitchen at 9:45pm. We type: “Late Zoom until 9pm → ate dessert. Sleepiness 2/10.” The small note explains why a high sleep number might be an outlier.

Micro‑task (3 minutes): After today’s check, write one 10–20 word context note in the Brali journal.

Part 7 — Read the data weekly: what to look for and how to avoid false patterns

Every 7 days, we look for:

• Directional change (increase or decrease)
• Magnitude relative to our threshold (did we cross the decision cut?)
• Consistency (how many days out of 7 met the criterion?)
• Outliers (days that differ by more than 2× the typical variance)

Concrete rules help. For example: if our target is “sleep ≥ 7h,” require at least 5 of 7 days ≥ 7h before we claim success. Or, if aiming to reduce sugar to ≤30 g/day, require the 7‑day mean ≤30 g.

Quantify: Use counts and averages. Count days meeting the criteria, then compute the mean and standard deviation if we want precision. But simple counts are often enough: 5/7 days meeting the target is a clear signal.

We assumed a 7‑day mean would show improvement → observed high day‑to‑day variability → changed rule to “5/7 days” which reduces overreaction to single bad days.

Micro‑task (15 minutes once per week): Open Brali LifeOS, run the week report, and answer: (1) how many days met the target? (2) what was the weekly average? (3) any notable outliers? Write a 2‑sentence conclusion in the journal.

Part 8 — Sample Day Tally: a concrete example with numbers

Target decision: Reduce afternoon drowsiness so focused work window improves between 9–11am.

Measures:

• Metric 1: Total sleep minutes (hours) — logged on waking.
• Metric 2: 3pm sleepiness score (1–10) — logged at 3pm.
• Subjective: Morning rested? (yes/no) — logged after waking.

Sample Day Tally (one day):

• Sleep: 6 hours 30 minutes = 390 minutes.
• 3pm sleepiness: 6/10.
• Morning rested?: No.
• Coffee: 1 cup (150 mg caffeine) at 8:30am, 0 cups after 10am.
• Focused minutes 9–11am: 70 minutes in two blocks (35 + 35).

Totals (for the day): Sleep = 390 min; Focused = 70 min; Coffee = 150 mg; Sleepiness at 3pm = 6.

How this reaches a weekly target: If our hypothesis is “increase focused minutes to ≥100/day by raising sleep to ≥420 min,” then today’s shortfall tells us to try moving bedtime 20 minutes earlier tomorrow. We will log that micro‑change and see if the 7‑day mean focused minutes rises.

This sort of micro‑tally gives us a vivid, actionable set of clues. We also write two short context notes: “Left laptop open til midnight” and “Took a 15‑minute walk at 2pm.”

Part 9 — Mini‑App Nudge (inside the narrative)
Try a Brali quick module: a single daily check at waking (sleep minutes) plus an evening “decision check” that asks, “Would we change bedtime tonight?” It’s one prompt, 20 seconds, and it keeps the loop tight.

Part 10 — Interpret carefully: correlation, causation, and common mistakes

We will see correlations. We should resist leaping to causal claims without testing. If higher sleep correlates with better focus, that’s plausible. But maybe evening screen time or dinner size are the true drivers. A good detective isolates variables across short experiments.

Two common mistakes:

We avoid both by making one small change per experiment. If we want to test two changes, run them sequentially or use a factorial schedule: e.g., week A change bedtime, week B add short walk, week C combine, and compare.

Edge cases: if life is irregular (shift work, travel), strict daily checks may be impossible. For these cases, use event‑based logging: record after each work shift or travel day. The principle remains: collect the most relevant small number of signals.

• Measurement fatigue: logging daily can feel like a chore. Keep checks <60 seconds and rely on evening totals.
• False confidence: precise numbers can feel authoritative even when noisy. Always report variance and count of days.
• Health risks: if measuring food or exercise, do not use self‑tracking to justify harmful restriction. If tracking reveals disordered patterns, stop and consult a clinician.
• Privacy: treat personal data securely. Use offline notes or encrypted apps if you have sensitive material.

Part 11 — Patterns and cadence: how to adjust the routine

A common progression:

• Week 1: Setup and adherence. Goal: 80% adherence to daily checks.
• Week 2: Small adjustments based on initial trends. Goal: keep the experiment simple, move one lever.
• Week 3: Decide using your pre‑registered rule. Either adopt the change or revert and try another hypothesis.

We observed that adherence usually drops from 90% on day 1 to roughly 60% by day 7. To prevent that, plan explicit micro‑rewards: a 5‑minute walk after the evening log, or a small habit pairing (log then make tea). Also move heavy cognitive tasks out of the check‑in time. The check‑in should be low cognitive load.

Micro‑task (5 minutes): Set a paired reward in Brali LifeOS — after evening check, “5‑minute tea break.”

Part 12 — The detective interview: 6 micro‑questions to ask each night

Each evening, in 2–4 sentences, answer:

Answering these moves us from passive observer to active experimenter. It also reduces anchoring bias: we look for causes rather than just accepting numbers.

Part 13 — When results disagree with our expectations

A delicate moment: numbers may show no effect, or the opposite effect. Our job is not to be right but to be informed. Step back and consider:

• Was the measure valid? (Did we record what we intended?)
• Did we change too many variables?
• Is the timeframe short relative to the expected effect?

We assumed “bedtime move 30 minutes earlier will improve sleep within 3 nights” → observed no change in nights 1–3 → changed rule to allow 7 nights before evaluating. Sleep tends to have inertia and may require a week for consistent phase shifting.

If the result contradicts our expectations, run a falsifying test: attempt the inverse or remove the change and see if the effect reverses. For example, if lowering sugar did not reduce afternoon sleepiness, try reducing caffeine next week while keeping sugar constant.

Part 14 — Communicating findings to ourselves and others

Write clear, concise conclusions. Use these statements:

• What we tested (one line)
• The numeric outcome (mean and count)
• The decision (adopt/adjust/abandon)
• One reason or nuance

Example: “We tested reducing added sugar to ≤30 g/day for 7 days. Mean 3pm sleepiness fell from 6.2 to 4.8, and 5/7 days were ≤5. Decision: adopt for another 2 weeks. Nuance: two weekend dessert days created outliers.”

This format creates memory and prevents us from re‑testing the same ground in six months.

Part 15 — Scaling detective moves across domains

Once comfortable, apply this miniature method across more areas but keep each track simple. We rarely track more than three threads simultaneously; otherwise, the mental load increases and adherence collapses.

Suggested discipline:

• Limit to 3 active experiments.
• Keep each experiment’s daily logs to ≤60 seconds.
• Schedule one 15‑minute weekly review for all experiments together.

We often cycle through domains in 4‑week blocks: week blocks for sleep, diet, focus, then repeat. That gives momentum and avoids multitasking errors.

Part 16 — Real micro‑scene: a week in our small detective life

Day 1 (Sunday evening): We set the decision: “If average focused minutes 9–11am across 7 days ≥100, then keep new morning ritual (20‑minute walk + no phone until 9am).” We log the hypothesis and set the Brali experiment for 7 days.

Day 2–3: We log waking time, focused minutes, and a 3pm check. The first two days show 60 and 75 focused minutes. We are annoyed but curious. We add context notes: "interrupted by family call."

Day 4: We notice a jump to 110 minutes after we shifted email handling to later. We note that as a potential confound.

Day 7: Weekly review shows mean focused minutes = 88, with 3/7 days ≥100. We follow the pre‑registered rule: 5/7 needed to adopt, so we do not adopt. Instead, we plan week 2 to test "no email before noon" as the single variable. The detective’s rhythm feels steady: set hypothesis, run 7 days, revise.

Part 17 — One simple alternative path for busy days (≤5 minutes)

Busy day protocol (5 minutes):

• Morning (1 minute): record sleep minutes or estimate; press Brali "Morning State."
• Midday (1 minute): record a single subjective check: "3pm energy 1–10."
• Evening (3 minutes): record totals (focused minutes or sugar grams estimate) and one context note.

This path preserves continuity without heavy burden. We designed it for travel days and meetings. It trades precision for resilience.

Part 18 — Misconceptions we must clear

Misconception: More data is always better. Reality: More data can increase noise and cognitive load. Small, robust measures are often more useful.

Misconception: Self‑tracking solves motivation. Reality: Tracking creates information; motivation still matters to act on it. Use tracking as an ally, not a substitute.

Misconception: Objective measures are superior to subjective ones. Reality: Subjective measures capture internal states that objective tools often miss. A 1–10 sleepiness score took us 5 seconds but often predicted performance better than step counts.

Part 19 — Tools and practical notes

• Choose a single log place. We recommend Brali LifeOS because it consolidates tasks, check‑ins, and your journal. App link (again): https://metalhatscats.com/life-os/self-evidence-experiment-tracker
• Use the smallest unit that makes sense: minutes for time, grams for food, mg for caffeine, counts for cups or Pomodoros.
• If you use a wearable, treat its sleep minutes as "approximate ±20 minutes" unless you do lab measurements.

Part 20 — Tips for staying honest with the data

• Timebox entries: set 1 minute for each log; if we miss it, mark "missed" rather than reconstructing in detail. The act of marking missed keeps the cadence honest.
• Pre‑register decisions and thresholds and lock them in before the experiment starts. This prevents us from moving the goalpost when results are inconvenient.
• Use small rewards for consistent logging: a 5‑minute break, a short call with a friend, or a non-food treat.

Part 21 — What success looks like

Success is not perfect numbers. Success is:

• A clear decision made on pre‑registered rules.
• A routine of daily logs that we keep for at least one experiment cycle.
• A modest measurable change produced by one focused small action (e.g., +20 focused minutes, −1 point sleepiness, +30 minutes sleep).

Numerically, we often aim for:

• Adherence ≥80% in week 1, ≥70% in subsequent weeks.
• Clear difference of at least 0.5–1 SD in the primary metric when an effect exists.
• Count rule passing (e.g., 5/7 days meeting target).

Part 22 — The social angle: sharing data or keeping it private

We sometimes benefit from sharing our progress with one person: an accountability buddy or clinician. Other times, privacy is vital. If sharing, pick one person and a fixed cadence (weekly). If private, secure your notes and export them if you plan to show a clinician later.

Part 23 — When to stop tracking

Stop tracking when:

• The decision is made and adopted (we changed the habit and observed sustained effect), or
• Tracking causes harm (obsessive behaviour, anxiety), or
• The data no longer informs actions.

We often move to an “audit” cadence after a decision: switch from daily checks to weekly checks to maintain awareness without constant measurement.

Part 24 — Final rehearsal — four small choices to do now

These four choices turn ideas into an experiment we can evaluate in one week.

Part 25 — Frequently asked practical questions

Q: What if I miss three days in a row? A: Mark them as "missed" and continue. If missed days exceed 30% of the experiment, extend it to keep the required data counts.

Q: Should we weight weekdays and weekends differently? A: If the decision concerns weekdays only (work focus), restrict the experiment to weekdays. For general habits, include weekends but expect more variance.

Q: How precise must food logs be? A: Precise enough to inform the decision. For sugar targets, grams matter; for portion control, photo + low/medium/high tag is often adequate.

Check‑in Block

• Daily (3 Qs):

• Weekly (3 Qs):

• Metrics:

Alternative busy‑day path (≤5 minutes):

• Morning: log sleep minutes + check "Morning state" (1 minute)
• Midday: log subjective primary check (1 minute)
• Evening: quick total for primary metric + one context note (3 minutes)

Mini‑App Nudge (again): Add a Brali module with one prompt at wake and one at 9pm: “Log primary metric” and “Would we change tonight?” — takes 20 seconds each.

Final thoughts — practicing our detective restraint

We do not measure to accumulate charts we never read. We measure to make better choices: to know when a small change is worth adopting. The detective’s art is restraint: pick the right question, choose the smallest reliable measures, and run short, honest experiments. We will make one small decision today, collect one week of evidence, and then decide with less guessing and more clarity.