The Science
Evidence-led methods, transparent maths
MaxPet turns everyday owner-logged data into objective, reproducible figures a clinician can scrutinise. Every calculation below is either owner-entered or grounded in a peer-reviewed method or a published nutritional standard — and it is documented plainly so you can judge it for yourself.
1 — Body condition & energy
Morphometric body-fat, ideal weight and feeding portions
MaxPet estimates canine body-fat from a few tape measurements, derives an ideal lean weight, and converts that into a personalised daily calorie target and grams-to-feed for the pet's current food.
Witzel et al. (2014) morphometrics
Body-fat percentage is estimated from the peer-reviewed Witzel et al. (2014) morphometric equations, using owner-taken tape measurements — chest and pelvic circumference, head circumference and hind-limb length — together with the pet's age. This is the validated morphometric approach, not the older Mawby method.
From body-fat to portions
From estimated body-fat, MaxPet derives an ideal (lean) body weight, then applies FEDIAF / NRC Maintenance Energy Requirement (MER) factors, adjusted for life stage and activity, to produce a daily calorie target — and divides that by the current food's energy density to give grams-to-feed.
How is this calculated?
- Owner-taken tape measurements (chest and pelvic circumference, head circumference, hind-limb length) and the pet's age feed the Witzel et al. (2014) equations to estimate body-fat %.
- Body-fat % is used to derive an ideal (lean) body weight.
- FEDIAF / NRC MER factors are applied to that lean weight to give a daily energy requirement in kilocalories.
- The daily calorie target is divided by the current food's energy density (kcal per gram) to give grams-to-feed.
Conditions the owner records can flag an advisory lean-vs-preserve direction (for example, joint or airway disease vs. conditions where restriction is contraindicated). This is advisory context only — MaxPet never auto-restricts calories.
2 — Food & feeding
The owner's food menu, tracked against a computed allowance
Each pet has a food menu the owner builds. One food is marked the current diet and carries an energy density in kilocalories per gram. MaxPet combines that with the daily calorie target from the body-condition method above to show grams-to-feed and to track daily intake against the allowance.
Energy density comes from the label
The kcal-per-gram figure for a food is entered by the owner from the manufacturer's own label or data sheet. MaxPet does not re-derive a food's energy or maintain a shared food database — the number you see is the maker's stated figure, recorded by the owner.
Intake against allowance
Meals and water are logged with amounts. The day's grams fed are shown against the computed daily allowance, so over- or under-feeding is visible at a glance rather than estimated.
3 — Health Score
A 0–100 wellness composite, structured like Life's Essential 8
The Health Score is a server-computed 0–100 wellness composite, structured like the American Heart Association's "Life's Essential 8" cardiovascular-health score. It is weighted across six domains. Missing data lowers the confidence of the score, not the score itself.
Body condition
Uses the most conservative of weight-versus-ideal deviation, body condition score and body-fat %.
Activity
Draws on the pet's logged activity minutes.
Nutrition
Draws on the pet's logged food, water and current diet.
Digestion
Draws on the pet's logged toilet events.
Symptoms
Draws on logged symptoms, their severity and whether they have been resolved.
Preventive care
Draws on the pet's logged preventive care.
Weighted domains combine into a single 0–100 score.
Confidence, not penalty
Where data is missing, the score's confidence is lowered rather than the score itself, so gaps in logging do not artificially depress a pet's result.
A wellness indicator, not a diagnosis
The Health Score is a wellness indicator, explicitly not a diagnosis. MaxPet is a decision-support tool: it structures and surfaces owner-logged data and statistical associations to support clinical judgement. It does not diagnose, detect disease, or replace a veterinary examination, and it is always shown with a note to that effect.
4 — See Trends
Rank correlations between a pet's data and its environment
"See Trends" looks for statistical associations between a pet's logged metrics — behaviour and mood, sleep, activity, food, water, toilet counts, symptoms, weight and medication doses — and environmental factors, dietary changes and medications. Results are presented as associations, never as causation.
Tie-corrected Spearman correlations
It uses tie-corrected Spearman rank correlations across metric pairs, including grass, tree and weed pollen, air quality (including PM2.5 and PM10), temperature, humidity and rainfall, alongside diet changes and medications.
Delayed reactions and false discovery
It scans a 0–3 day lag to catch delayed reactions — pollen today, symptoms tomorrow — requires a minimum of around ten aligned days of data, and applies Benjamini–Hochberg false-discovery-rate correction across all pairs.
How is this calculated?
- Tie-corrected Spearman rank correlation is computed between each pair of logged and environmental metrics.
- A 0–3 day lag window is scanned to catch delayed responses.
- A minimum of roughly ten aligned days is required before a correlation is reported.
- Benjamini–Hochberg FDR correction is applied across all pairs to control false positives.
- Findings are shown as statistical associations to investigate — never causation, and never a diagnosis.
5 — Environmental data
Licensed, nationwide, snapshotted daily
The environmental inputs behind "See Trends" come from licensed WeatherAPI.com data covering UK settlements, snapshotted daily. Each pet is mapped to its nearest place, so pollen, air-quality and weather figures reflect local conditions.
UK settlements covered, snapshotted daily.
Nearest-place mapping
Every pet is matched to its nearest covered settlement, keeping the environmental figures used in a correlation local to where the pet actually lives.
In the consult room
Decision support, not diagnosis
Used well, these methods can save time and add objectivity to a consultation — while leaving every clinical decision with the veterinary surgeon.
Structured longitudinal history
Begin a consult with a dated, owner-logged history — weight trend, diet, symptoms with severity, medication adherence via dose logs, activity, sleep and behaviour — instead of relying on recall.
Objective body-condition figures
Validated Witzel morphometrics and FEDIAF / NRC energy figures can support weight-management and feeding-plan conversations.
Hypothesis-generating trends
Trend correlations may surface possible environmental, dietary or medication associations worth investigating — hypothesis-generating, not diagnostic.
Shareable, exportable summary
An exportable summary of weight, medications, conditions and clinical history can cut time spent gathering history, and gives visibility of whether doses were actually given.
Bring structured, evidence-led data into the consult room
Join a pet's care team as its clinic and start from a longitudinal, owner-logged history rather than recall.