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  "path": "/2026-so-far-life-tracking-update/",
  "publishedAt": "2026-07-03T07:30:00.000Z",
  "site": "https://www.jamesleighton.com",
  "tags": [
    "100DaysToOffload",
    "WebMentions"
  ],
  "textContent": "Here are my stats for January to June 2026. This was generated using my data from Exist, and I found it highlights some key facts I hadn't considered as much as I should.\n\n### Key Themes\n\n  * I should make sure I hit at least 5,500 steps in a day before I retire to play games or watch TV in the evening.\n  * I should keep up my reading habit, and try and read at least every other day.\n  * I should continue with my diet plan (Huel!), as it seems to be working!\n  * I really need to think about what makes the difference between a 5/9 day and one that is 7+\n\n\n\nOverview Steps Mood Sleep Weight Activity Leisure Insights\n\nAvg daily steps\n\n5,260\n\nAvg mood\n\n5.8 / 10\n\nAvg sleep\n\n7h 26m\n\nWeight change\n\n−7.4 kg\n\nAvg active min\n\n26m\n\nAvg gaming (when played)\n\n1h 20m\n\nAvg reading (when read)\n\n39m\n\nAvg board game (when played)\n\n1h 34m\n\nMonthly snapshot\n\n__Steps (÷1000) __Mood __Sleep (hours)\n\nBest month\n\nFeb\n\n6,971 avg/day\n\nLowest month\n\nJan\n\n3,618 avg/day\n\nPeak single day\n\n17,770\n\n6 Jun\n\nDays over 10k\n\n17\n\nAverage daily steps by month\n\nDaily steps — full timeline\n\nHighs and lows\n\nTop 3 step days\n\n  1. 6 Jun17,770\n  2. 14 Apr15,877\n  3. 16 Apr13,854\n\n\n\nBottom 3 step days\n\n  1. 18 Jun1,441\n  2. 13 May1,513\n  3. 14 Jan1,519\n\n\n\nNational Trust & date nights\n\nBoth tags strongly associated with high step days. National Trust visits averaged 9,642 steps; date nights averaged 9,715 — nearly double the overall daily average of 5,260.\n\nNational Trust visits — steps (avg 9,642)\n\n  1. 16 Mar11,728\n  2. 15 Feb11,491\n  3. 14 Feb10,098\n  4. 26 Apr10,822\n  5. 12 Apr9,001\n\n\n\nDate nights — steps (avg 9,715)\n\n  1. 6 Jun17,770\n  2. 5 Jun13,655\n  3. 21 Jun10,046\n  4. 26 Apr10,822\n  5. 21 Mar7,565\n\n\n\nBest month\n\nApr\n\n6.3 avg\n\nToughest month\n\nJun\n\n5.1 avg\n\nDays rated 9+\n\n22\n\nDays rated ≤3\n\n18\n\nAverage mood by month (1–10 scale)\n\nMood score distribution\n\nHighs and lows\n\n15 days scored a 9; 4 days scored ≤ 2. Listed below are the standout examples.\n\nBest mood days (9 / 10)\n\n  1. 14 Apr9 · 15,877 steps\n  2. 15 Feb9 · 11,491 steps\n  3. 11 Apr9 · 11,447 steps\n\n\n\nWorst mood days\n\n  1. 24 Jun1 · 2,694 steps\n  2. 31 Jan2 · 1,784 steps\n  3. 9 Jun2 · 2,222 steps\n\n\n\nBest month\n\nFeb\n\n7h 36m avg\n\nLowest month\n\nMay\n\n7h 21m avg\n\nNights ≥8 hours\n\n31\n\nNights under 6h\n\n2\n\nAverage daily sleep by month\n\nDaily sleep — full timeline\n\nHighs and lows\n\nLongest nights\n\n  1. 17 Mar8h 51m\n  2. 20 Feb8h 48m\n  3. 7 Mar8h 45m\n\n\n\nShortest nights\n\n  1. 28 Jun5h 26m\n  2. 4 Apr5h 30m\n  3. 4 Feb5h 41m\n\n\n\nStart (1 Jan)\n\nBaseline\n\n0 kg change\n\nLatest (28 Jun)\n\n−7.4 kg\n\nTotal change\n\n−7.4 kg\n\nAvg per month\n\n−1.2 kg\n\nCumulative weight change since 1 Jan\n\nNet weight change per month\n\nHighs and lows\n\nLargest day-over-day changes — likely a mix of real change and weigh-in noise (clothes, hydration, time of day).\n\nBiggest single-day drops\n\n  1. 14 Jun−2.14 kg\n  2. 4 Feb−2.01 kg\n  3. 20 May−1.33 kg\n\n\n\nBiggest single-day gains\n\n  1. 18 May+1.77 kg\n  2. 16 Mar+1.68 kg\n  3. 8 Jun+1.32 kg\n\n\n\nBest month\n\nFeb\n\n44m avg\n\nLowest month\n\nMay\n\n15m avg\n\nPeak day\n\n2h 09m\n\n26 Apr\n\nDays under 10 min\n\n66\n\nAvg on NT visits\n\n52m\n\n10 visits\n\nAvg on date nights\n\n34m\n\n9 dates\n\nAvg on sick days\n\n11m\n\n6 sick days\n\nAverage active minutes by month\n\nSteps vs active minutes\n\n__Avg steps ÷100 __Avg active minutes\n\nHighs and lows\n\nMost active days\n\n  1. 26 Apr2h 09m\n  2. 14 Apr2h 07m\n  3. 1 Mar1h 48m\n\n\n\nLeast active days\n\n  1. 21 Jan2m\n  2. 20 Jun2m\n  3. 14 Jan3m\n\n\n\nNational Trust visits\n\n10 visits — every single one scored mood 8 or 9. Average active time was 52m, nearly double the first-half daily average.\n\nSteps & activity on NT days\n\n  1. 13–16 Feb9, 9, 9, 9 mood · 38–75m active\n  2. 16 Mar8 mood · 11,728 steps · 53m active\n  3. 26 Apr9 mood · 10,822 steps · 2h 09m active\n  4. 2 May8 mood · 9,852 steps · 16m active\n  5. 24 May8 mood · 8,015 steps · 20m active\n\n\n\nDate nights — steps & mood\n\n  1. 5–6 Jun9, 8 mood · 13,655 / 17,770 steps\n  2. 26 Apr9 mood · 10,822 steps\n  3. 21 Jun9 mood · 10,046 steps\n  4. 21 Mar7 mood · 7,565 steps\n  5. 4 Jan9 mood · 4,359 steps\n\n\n\nGaming — days played\n\n85\n\nof 180 (47%)\n\nGaming — total time\n\n113h 52m\n\nReading — days read\n\n96\n\nof 180 (53%)\n\nReading — total time\n\n62h 29m\n\nBoard games — sessions\n\n5\n\nBoard games — total time\n\n7h 51m\n\nAverage gaming time by month (when played)\n\nAverage across days when gaming actually happened — not diluted by zero days.\n\nAverage reading time by month (when read)\n\nTotal monthly leisure time\n\n__Gaming __Reading __Board games\n\nHighs and lows\n\nLongest gaming sessions\n\n  1. 15 Apr6h 22m\n  2. 13 Jun5h 00m\n  3. 13 May4h 11m\n\n\n\nLongest reading sessions\n\n  1. 17 Jan1h 51m\n  2. 24 Feb1h 48m\n  3. 24 May1h 47m\n\n\n\nTop 3 correlations\n\nPearson correlation across all 180 days. Values closer to ±1 mean a stronger relationship; 0 means none.\n\nSteps × Mood +0.54 Strong positive (n = 161) __ The clearest signal in the dataset. Days with 7,500+ steps averaged mood **7.6 / 10** ; days under 3,000 steps averaged **5.2 / 10** — a +2.4 point swing. Walking more was the single most reliable mood lever in the data. |  Gaming × Steps −1,182 Substitution effect __ On days with 60+ minutes of gaming, average steps dropped to **4,668** vs **5,850** on days with no gaming — a ~1,200 step deficit. May, the heaviest gaming month (21 days, 1h 47m avg), had the lowest active minutes of the first half. |  Sleep × Mood +0.09 Effectively none (n = 159) __ Counterintuitive but consistent: a longer night didn't predict a better day. Sleep was so stable (7h 21m – 7h 36m every month) that there wasn't enough variation for it to drive mood. A good baseline, but not a useful lever.\n---|---|---\n\nOther observations\n\n**The weekend effect is huge**\n\nSaturday and Sunday averaged mood 7.0 vs 5.4 on weekdays — a +1.6 point gap, bigger than any single correlation. Sunday was the peak day (mood 7.3, ~6,900 steps); Tuesday was the slump (mood 4.8). Weekday mood is likely the biggest opportunity area for the second half of the year.\n\n**Steps → mood is dose-dependent**\n\nNot just correlated — the relationship climbs in clear steps. Days under 3k steps averaged mood 5.2; days at 5k+ averaged 6.7; 7.5k+ averaged 7.6; 10k+ averaged 8.2. Roughly +1 mood point per 2,500 extra steps.\n\n**The \"good stack\" days**\n\nOn days that hit 7,500+ steps, included reading, AND had 7+ hours of sleep, mood averaged **7.9** vs **5.7** on every other day — a +2.2 point swing. Only 13 of 180 days hit all three; the compound effect is the strongest single signal in the data.\n\n**Steady weight loss**\n\n7.4 kg shed over 6 months — an average of 1.2 kg per month — with March showing the biggest single-month drop (−4.5 kg). The trend held even during quieter movement months, suggesting diet was likely the primary driver.\n\n**April had the best week**\n\n10–17 April: 8 consecutive days of 5,000+ steps, including a 5-day run of 7,500+. Every day in that stretch scored mood 7–9. Concrete proof of what a sustained high-movement week can look like.\n\n**Reading is the most consistent habit**\n\nRead on 96 of 180 days (53%) at a steady ~39 minutes per session. Unlike gaming, the monthly average barely moves — a quiet, dependable habit that survived even the toughest months.\n\n**Being ill killed activity more than steps**\n\n6 sick days across the first half of the year (a 5-day cluster in early April, one isolated day in May). On those days active minutes collapsed to just 11m on average — less than half the overall mean — while steps held up surprisingly well at 5,256. Mood averaged 4.67, with the final sick day (7 Apr) hitting a 3. Notably, 3 of the April sick days overlapped with holiday periods — so the Easter mood boost was partly undermined by illness.\n\nGoals for the second half of 2026\n\n📌 Three concrete, data-grounded goals I'm setting for the next six months — to revisit in January and see how I did against each baseline from the first half of the year.\n\n01\n\nMove the daily step median up\n\nMedian daily steps: 4,517 → 5,500+\n\nHalf my days in the first half of the year were below 4,500 steps. Pulling the median up to 5,500 means more \"good enough\" days and fewer slump days. Based on the dose-response gradient, each extra 2,500 steps was worth roughly +1 mood point — moving the median is more impactful than chasing peak days.\n\n02\n\nWalk more on gaming days\n\nAvg steps on 60+ min gaming days: 4,668 → 5,500+\n\nHeavy gaming days came with a ~1,200 step deficit vs non-gaming days. Closing that gap removes the substitution effect — a deliberate walk before or after a gaming session is the smallest possible behaviour change with the biggest payoff.\n\n03\n\nStack 30 \"good days\"\n\nDays hitting 7.5k+ steps, reading, and 7h+ sleep: 13 → 30 in the second half\n\nThe single most powerful pattern in the data — these days averaged mood 7.9, more than 2 points above the rest. Roughly one stack day a week, with a few extras around weekends and holidays, gets there.\n\n* * *\n\nThis is post 47 of #100DaysToOffload.\n\n* * *\n\nTo respond on your own website, write a post which contains a link to this post - then enter the URl of your page here. Learn more about WebMentions.\n\n* * *",
  "title": "2026 So Far - Life Tracking Update",
  "updatedAt": "2026-07-03T07:10:16.039Z"
}