mySymptoms Food Diary App Review: The Evidence

mySymptoms is a manual food and symptom diary with a correlation engine bolted on. You log what you eat, drink and take, you log symptoms with an intensity slider, and the app produces a ranked list it calls Top Suspects for each symptom. That ranking is the whole product. Everything else is data entry.

It is also, unusually for a consumer gut app, a product with peer-reviewed research built on its own users' diaries. Two papers in gastroenterology journals have been written using data collected through this app. That is more published evidence than almost any competitor can point to, and it is the fairest place to start a review.

But the research also exposes the app's limits, and so does the company's own documentation. The analysis does not use portion size. It does not look at nutritional content. It needs symptom-free days to work, which means it works worst for the people with daily symptoms who are most likely to buy it. And the answer it gives you changes depending on five settings that most people never open.

This review works through all of that: what it does, what the studies found, what the algorithm can and cannot see, how to read the output without fooling yourself, and who should not bother.

What is mySymptoms, and what does it actually do?

mySymptoms is a smartphone food and symptom diary made by SkyGazer Labs, a small company based in Cambridge in the UK. On its about page the company says the idea began in 2001 when one of the founders was struggling with IBS and wanted to connect his meals to how he felt, and that the app was released publicly in 2010. It runs on iOS and Android.

Reading the company's own user guide, here is what the app lets you record:

• Food and drink, either as single items or as meals and recipes, with the option to break a meal down into individual ingredients
• Medication, supplements, exercise and environmental factors, each as its own event type
• Stress, as a category with its own entries
• Symptoms, from a default list you can edit, each with an intensity slider, a duration field and free text notes
• Energy and sleep quality, each as a single intensity slider
• Bowel movements, recorded on the Bristol stool scale, the seven-point scale originally validated as a marker of intestinal transit time (Lewis 1997)

Items can be added on the fly if the food database does not have them, and an Organizer screen lets you edit or delete items across your whole diary at once. There is a barcode scanner. There is a diary report you can export as a PDF or a CSV, and a per-user password so someone else picking up your phone cannot read it.

The single most important instruction in the user guide is easy to skip past: log food even on days when nothing goes wrong. The company states plainly that for the app to be effective you must also record what you eat and drink when you have no symptoms, and elsewhere that these "good days" are essential so the analysis can identify trigger foods. Most people do the opposite. They log carefully during a bad week and stop when things settle, which is precisely the data pattern the algorithm cannot use.

Is mySymptoms backed by research? Two studies, honestly read

Yes, and this is the most interesting thing about the app. Two peer-reviewed papers have been built on diaries collected through mySymptoms, both involving well-known academic gastroenterology groups.

The 2019 diary study. Researchers took diaries from 163 mySymptoms users who logged for a median of five weeks, and looked for associations at the level of the individual person rather than the group. They found demonstrable food-symptom associations for heartburn in 73% of people, discomfort in 67%, diarrhoea in 57%, bloating in 53% and gas in 48%. Abdominal pain was the outlier at 33%, and those associations clustered heavily in people whose pain came in the morning, where 68% showed a food link, compared with 27% for evening pain and 10% for night-time pain. Stress-symptom relations also turned up, though only 18% of people in the sample recorded any psychological distress at all (Clevers 2019).

Read that carefully, because it cuts both ways. It is genuine evidence that personal food-symptom patterns can be detected from this kind of diary. It also means that for the single symptom people most want answers about, abdominal pain, two thirds of users in that sample had no detectable food association at all.

The 2024 study. A much larger analysis collected 9,710 anonymised diaries that were at least three weeks long, and ran them through what the paper describes as a proprietary algorithm. Because the diaries were anonymous, the researchers ran a separate app-wide survey to work out who the users were, and 70% of respondents met Rome IV criteria for IBS (Clevers 2024). Rome IV is the diagnostic framework that reclassified these conditions as disorders of gut-brain interaction (Drossman 2016).

The associations that came out were:

• Caffeinated coffee with diarrhoea, one to two hours after the drink
• Alcoholic drinks with multiple symptoms, across a wide window of four to 72 hours
• Artificial sweeteners with multiple symptoms, at 24 to 72 hours
• Histamine-rich foods with abdominal pain and diarrhoea

Now the honest caveats, because a review that reports only the headline is not a review:

1. Author affiliations include the company. Three of the authors on the 2024 paper, including the first author, are listed with SkyGazer Labs Ltd as an affiliation. The other authors are independent academics at Gothenburg, Nottingham and KU Leuven. That is a real conflict of interest and the paper discloses the affiliations openly, but it should temper how you read a positive result about the company's own algorithm.
2. The algorithm is proprietary and unpublished. Nobody outside the company can audit how a suspect score is calculated, replicate it, or test it against a different method. For a tool whose entire value is the ranking, that is a meaningful gap.
3. It is observational. The paper's own conclusion says clinical trials must test the causality of the demonstrated food-symptom associations. Nothing here is a randomised trial of the app.
4. The users chose themselves. People who download a food diary app and log for three weeks or more are not a random sample of anyone.
5. IBS status was inferred at the population level. The diaries were anonymous, so the 70% figure comes from a separate survey of app users, not from a diagnosis attached to each diary.

None of that makes the research worthless. Compared with most gut apps, which have no published evidence whatsoever, having two papers in real journals is a genuine differentiator. It just is not the same thing as a trial showing the app helps people.

The FODMAP result is the most revealing finding in this app's own data

The 2024 paper contains a line that most coverage of this app skips. Alongside the coffee and alcohol findings, the authors note that the absence of an enriched FODMAP-symptom association contrasts with current knowledge (Clevers 2024).

In other words: in nearly ten thousand diaries from a mostly IBS population, foods high in fermentable carbohydrates did not stand out as a group.

That is worth sitting with, because the evidence that FODMAPs matter in IBS is strong. In a double-blinded, dose-graded rechallenge trial, 70% of participants given fructose and 77% given fructans reported inadequately controlled symptoms, against 14% on glucose, and the response was dose dependent (Shepherd 2008). A controlled feeding crossover trial found substantially lower symptom scores on a low FODMAP diet than on a typical Australian diet (Halmos 2014). A meta-analysis of randomised trials of exclusion diets in IBS found the low FODMAP diet the best supported of them (Dionne 2018). More recently, a randomised trial comparing a low FODMAP diet plus traditional advice against a low-carbohydrate diet and against optimised medication found meaningful symptom reduction in all three arms, with the dietary arms performing well (Nybacka 2024).

So why did the app's own data not see it? The most likely explanation is in the app's design rather than in the biology, and the company's own documentation points straight at it:

• The analysis does not use quantity. The company's FAQ states that quantities logged are for your information and for the diary reports to share with clinicians, and that they intend to include quantities in the analysis in a future update. FODMAP cutoff values were derived by looking at the FODMAP content of typical serving sizes, which is exactly why the same food can sit on either side of the line depending on how much of it you eat (Varney 2017). An engine blind to portion size cannot see a dose-dependent effect.
• The analysis does not look at composition. Asked directly whether the app tracks fibre intake, the company answered that it does not analyse based on the nutritional content of the food, only the food itself and the listed ingredients. FODMAP load is a property of composition. If "onion" is not typed as an ingredient, the fructans in the sauce are invisible.
• FODMAPs stack across a day. Several modest servings can add up to a reaction where no single item would. A ranked list of individual items is a poor instrument for spotting that. Our guide to FODMAP stacking with worked examples walks through how quickly that adds up.

This is not a reason to distrust the app. It is a reason to understand what question it is answering. It is asking "which named item appears disproportionately often before your symptoms", not "what were you exposed to, in what dose, and how did it combine".

How the Top Suspects analysis works, and the settings that decide your answer

Here is the mechanism, taken from the company's user guide.

Every time a symptom appears in your diary, the algorithm looks back over a fixed period called the analysis window and treats every event inside it as a candidate. Each item then gets a score relative to the other items in your diary, and the results are presented as a ranked list.

Tapping into a single result opens a detail screen with four things worth understanding:

• Score. How strongly this item correlates with the outcome, relative to everything else you log. Higher means a stronger correlation, nothing more.
• Confidence. Based on the number of events the item appears in. A high score on three occurrences is not the same finding as a high score on forty.
• Suspect ratio. The ratio of suspect events to clear events, where a suspect event is one containing the item that falls inside the analysis window before the outcome. This is the number that tells you whether the food is genuinely lopsided or whether you simply eat it constantly.
• Onset delay. The average time between events containing the item and the outcome, plus a chart showing how often the symptom occurred at each delay. A clear peak in that chart is more persuasive than a flat spread.

There is also a trends chart comparing how often the item and the symptom occur over weeks or months, and a list of the underlying events so you can open the actual diary days behind a result.

Five settings change the answer. This is the part almost nobody adjusts, and it is where most of the value sits.

The implication is uncomfortable but important: your Top Suspects list is not a fact about your gut. It is the output of a function, and you control five of its inputs. Two people with identical diaries and different settings will get different lists. Any review that reports "the app said X" without saying which window it used is not telling you much.

The right response is not to distrust it. It is to run the same symptom two or three ways. If tomato sauce is in your top five at a 6-hour window, at 24 hours and at 72 hours, that is a far more interesting result than one that appears only at a single setting.

Five things the analysis cannot see

Every tool has a shape, and knowing the shape of this one stops you over-reading the output.

1. How much you ate. Covered above, and it is the largest gap. The company confirms quantities are not used in the calculation.

2. What the food is made of. No nutritional analysis, so fat content, fibre type and FODMAP load are all invisible unless you name them as ingredients. Fatty and fried foods are among the most commonly reported symptom triggers in IBS, reported by 52% of patients in one survey (Böhn 2013), but "fat" is not something the engine can measure.

3. Relationships that run the other way. Asked whether the app could detect a food whose absence precedes symptoms, the company answered plainly that it looks for correlations between events in your diary and would not pick that up. So a protective pattern, such as symptoms appearing on days you skip your usual breakfast, will not surface.

4. Duration and your notes. The user guide states that symptom duration and the free text notes are not used by the analysis. They appear in the PDF report, which is useful for a clinician, but they contribute nothing to the ranking. A two-hour cramp and a nine-hour one are the same event to the algorithm unless you rate them at different intensities.

5. Combinations, as combinations. The output is a ranked list of individual items. When people describe their own triggers, they usually describe a stack: a big meal, plus alcohol, plus a bad night's sleep, plus stress. In one r/ibs thread about building a food tracker in a spreadsheet, the most useful reply argued exactly that, that the trigger is usually a stack rather than a single ingredient, and that the lag between food and symptom is the thing spreadsheets handle worst (r/ibs thread). A ranked list of singles will show a stack as several mid-ranked items rather than one clear answer, which is easy to misread as "no result".

Why it works worst for the people who need it most

This is the structural problem with correlation-based food diaries generally, and mySymptoms is honest about it in its own documentation. The company states that analysis performance depends on three things: how varied your diet is, with more variety being better; how often you experience symptoms, with less frequent being better, because symptom-free periods reveal important information; and whether your diary actually describes the factors affecting your symptoms.

Think about who that describes.

Someone with occasional symptoms and a broad diet gives the algorithm plenty of contrast: days with the food and no symptom, days with the symptom and no food. Someone with symptoms most days who has already narrowed down to a handful of safe foods gives it almost none. Every item appears before every symptom, because everything appears before everything.

And that second person is the one most likely to buy a symptom tracker. The evidence backs up the pattern: in a survey of 197 people with IBS, 84% reported symptoms from at least one food, and as IBS severity increased, people reported more foods responsible for their symptoms (Böhn 2013). An earlier study of 330 patients found 63% considered their symptoms meal-related (Simrén 2001). The worse your IBS, the longer your suspect list, and the harder it is for any correlation engine to separate signal from a diet that has already collapsed to rice and chicken.

One r/ibs poster described years of diaries, including this app, without ever landing on a clear answer: bread they baked themselves was fine while shop bread was not, some cheeses were tolerated while whipped cream was not, one pizzeria's pizza was fine and another's was not (r/ibs thread). That is not a failure of diligence. It is what a diary looks like when the real variables, portion size, fat content, total fermentable load, are ones the diary is not measuring.

If that is you, the more useful move is usually not a better app. UK dietetic guidelines set out a two-line structure: first-line healthy eating and lifestyle advice, which any healthcare professional can give, and second-line low FODMAP advice delivered by a dietitian (McKenzie 2016). The ACG guideline recommends a limited trial of a low FODMAP diet to improve global symptoms, alongside a positive diagnostic strategy rather than endless exclusion (Lacy 2021). And gut-brain therapies have their own evidence: in a network meta-analysis of 41 randomised trials with 4,072 participants, self-administered or minimal-contact CBT, face-to-face CBT and gut-directed hypnotherapy all showed efficacy (Black 2020), with digital versions of those therapies now an active area (Brenner 2024). We look at one such programme in our Cara Care app review.

A diary is a good way to answer a question. It is a poor substitute for treatment when the diary keeps coming back empty.

Data quality: the real cost of this app is time, not money

mySymptoms is entirely manual. Everything the analysis knows, you typed. Which makes logging quality the ceiling on everything else.

Ingredients are where the signal lives. The company says this repeatedly: record as many ingredients of a meal as possible, because it is typically individual ingredients that cause symptoms. In the interface, an item with a red dot has no ingredients attached and a red dot with a circle has ingredients. The difference between logging "chicken curry" and logging its onion, garlic, cream, chilli and stock cube is the difference between an analysis that can find something and one that cannot. It is also the difference between a 20-second log and a three-minute one, three times a day, for six weeks.

Log at the time, not at bedtime. In the classic study of diary compliance, participants with chronic pain submitted paper diary cards for 90% of assigned times, but electronic records showed actual compliance was only 11%, with the binder never opened on 32% of study days (Stone 2003). Back-filled entries are not memories, they are reconstructions. Our guide on how to keep a food diary for IBS covers the mechanics of in-the-moment logging and why informal trigger hunting throws up so many false positives.

The food database has gaps, and they are regional. This is the most consistent complaint in the community. One r/ibs poster starting a low FODMAP elimination asked specifically for an app with a large barcode library because the mySymptoms product list was, in their words, very limited (r/ibs thread). Another, in Germany, said the scanner simply did not work for them and guessed the database had nothing relevant for their local products (r/ibs thread). This is not unique to this app. An evaluation of 16 manual food-logging apps found that energy estimates were overestimated for Western diets and underestimated for Asian diets, and the authors concluded that expanding food databases is needed to improve comparative validity (Li 2024). Food databases inherit the product catalogue they were built from.

The practical workaround is dull but it works: when a scan returns nothing, add the item by hand and type the ingredients straight off the label. It takes a minute per new product and you only do it once, because the item is then in your own database.

Give it four to six weeks. That is the company's own answer when people ask how soon they will get results, and it matches the research: the published diary study collected a median of about five weeks per user (Clevers 2019). Checking the analysis after ten days will show you noise.

Reading your Top Suspects list without fooling yourself

The most common complaint about this app is not that it fails, it is that people cannot interpret what it shows them. One r/ibs poster on the paid version said plainly that they found the results difficult to understand and asked for help reading them (r/ibs thread). That is a design problem, not a user failure, and it deserves a straight answer.

Here is a reading order that works.

Step 1: check confidence before score. A top-ranked item built on four events is a rumour. Look for items that appear often enough that the confidence bar is not tiny.

Step 2: check the suspect ratio, not just the score. If you drink tea eight times a day, tea will appear before almost every symptom you have. The suspect ratio is what separates "genuinely lopsided" from "always present". A food you eat once a week that appears before symptoms most of those times is far more interesting than a staple at the top of the list.

Step 3: look at the onset delay chart. A clear peak at a specific delay is a real pattern. A flat spread across the whole window is usually not. The app's own research is a good reference for what plausible peaks look like: roughly one to two hours for coffee, much later for alcohol and sweeteners (Clevers 2024). If you want more on why a reaction can land far later than you expect, we cover how long after eating IBS symptoms start separately.

Step 4: re-run at a different window. Anything that survives 6 hours, 24 hours and 72 hours is worth testing. Anything that appears at only one setting probably is not.

Step 5: remember what the app itself says. The user guide carries an explicit warning on the Top Suspects screen: just because an item is on the list does not mean it causes your symptom, the algorithm can identify correlation only, and sometimes coincidence puts unrelated items on the list. That is an honest disclosure, and it is the single most important sentence in the product.

Step 6: convert a suspect into a trigger with a test, not with more logging. This is the step people skip. Correlation from a diary generates a hypothesis; a planned reintroduction tests it. The gold-standard design is the one used in the fructan and fructose rechallenge trial: a stable background diet, one substance introduced at a time, a washout between challenges, and symptoms recorded daily (Shepherd 2008). You can approximate that at home, and our guide on how to find out what triggers your IBS sets out the challenge and rechallenge structure in detail.

For a sense of what a realistic result looks like rather than a fantasy one, here is a worked reading of a six-week diary.

The point of that table is that the top of the list is usually the least interesting part of it. Staples float upwards because they are everywhere. The useful finding is normally mid-list: something you eat occasionally, with a lopsided ratio and a clean peak.

This is the same problem any trigger-hunting tool has to solve, and it is why Clairop shows a delay window and the number of meals behind every pattern rather than a single ranked list, so you can see how much a result is actually resting on.

Who mySymptoms suits, and who it does not

On the IBD point: an evaluation of 51 commercially available IBD management apps scored them on the Mobile App Rating Scale, a validated 23-item instrument for health app quality (Stoyanov 2015), and found quality ranging from 2.03 to 4.62 out of 5, with most including at least some evidence-based behaviour change techniques (Noser 2023). A general food diary can sit alongside an IBD tracker, but it will not tell you whether you are inflamed. If you have IBD and want a tracker built around the food question specifically, we go through the requirements in our guide to food diary apps for Crohn's disease, and the broader checklist of what any gut tracker needs to capture is in the best app for tracking IBS symptoms.

On cost: it is a paid app with a trial, and that is a live complaint. The thread that prompted this review was someone in r/ibs asking whether anyone had built their own spreadsheet because they were tired of paying for apps like this one (r/ibs thread), and another asked for free alternatives after the trial ended (r/ibs thread). Pricing changes by region and over time, so check the store listing rather than trusting any number in an article. The honest comparison is this: a spreadsheet, or a form that writes to a spreadsheet, captures the same fields for nothing, and people in those threads report doing exactly that successfully. What you lose is the delay-window analysis and the onset chart. What you gain is control, portability and no subscription. Which trade is right depends on whether you actually want to do the pattern hunting yourself.

The risk nobody puts in the marketing: diet narrowing

An app that hands you a ranked list of food suspects every week creates a quiet pressure to cut things out. That pressure has a documented downside.

In a chart review of 495 people referred to neurogastroenterology clinics, 39% had a history of following an exclusion diet, and those people were more than three times as likely to have symptoms of avoidant/restrictive food intake disorder (Atkins 2023). Notably, two thirds of those exclusion diets were self-initiated rather than clinician-recommended. A large population survey of 4,002 adults in the UK and US then found that 34.6% of people with a disorder of gut-brain interaction screened positive for ARFID, against 19.4% of those without, and the rate climbed with the number of gut regions affected (Flack 2026).

This does not mean tracking is harmful. It means that the output of a correlation engine should widen your options over time, not narrow them. The low FODMAP diet itself is now framed as a structured programme with restriction, then reintroduction, then personalisation, delivered ideally by a trained dietitian, precisely so people are not left stranded in the elimination phase (Halmos 2019). Prolonged blanket restriction also reduces total bacterial abundance in the gut, which is another reason the maintenance phase matters (Gibson 2020). And when a low FODMAP trial does not work, the answer is a reassessment of what is driving symptoms, not a longer exclusion list (Halmos 2017).

If you notice your list of safe foods shrinking month on month, or eating out starting to feel impossible, that is a reason to talk to a dietitian or your GP rather than to log harder. Our piece on whether the low FODMAP diet can cause an eating disorder covers the warning signs.

Privacy and what happens to your data

The company's site states that accounts can be anonymous without a name or email, that data is encrypted and protected using industry-standard security measures, and that it is compliant with HIPAA and GDPR health data regulations. The app also supports a per-user password so a shared phone does not expose your diary.

Two honest notes. First, I was not able to retrieve the text of the privacy policy page directly, so those statements come from the company's public marketing pages rather than from the policy itself. If data handling matters to you, read the current policy in the app store listing before you start logging. Second, the app's own user data, anonymised, has been used for published research, which the 2024 paper describes openly (Clevers 2024). That is reasonable and it produced useful findings, but it is worth knowing that the diaries feed a research pipeline.

More broadly, health app data sharing is not a hypothetical worry. An analysis of 24 top-rated medicines-related Android apps found 79% shared user data, with 55 unique entities receiving or processing it (Grundy 2019). Reviews of digital health for gut disorders make the same point, listing data privacy alongside accuracy and reliability as the open questions in the field (Pathipati 2023).

Myths about mySymptoms and apps like it

Myth: "The app tells you your trigger foods." It ranks correlations. The app's own results screen says the algorithm cannot identify cause, and that coincidence sometimes puts unrelated items on the list.

Myth: "No results means food isn't the problem." It might mean that, but it more often means too few symptom-free days, meals logged without ingredients, too short a period, a window set wrong, or a driver such as stress, sleep or a medication that you are not logging.

Myth: "A 24-hour window covers it." The default is 24 hours, but the app's own research found associations at 24 to 72 hours for artificial sweeteners and 4 to 72 hours for alcohol (Clevers 2024). If you only ever look at yesterday, you will only ever find yesterday's foods.

Myth: "Paying for the analysis makes it more accurate." The accuracy ceiling is set by your logging detail and your settings, not by the tier. An incomplete diary analysed beautifully is still an incomplete diary.

Myth: "If it's in the food database, the entry is right." Food databases are regional and incomplete, which is exactly what people describe when a scanner returns nothing for their local products, and what shows up in formal evaluations of nutrition apps across different cuisines (Li 2024).

Myth: "A tracker can replace dietary advice." UK guidelines place the low FODMAP diet as second-line advice delivered by a dietitian, not as something to reverse-engineer from a suspect list (McKenzie 2016).

Myth: "More logging always gives a better answer." Past a point, more logging of the same narrow diet adds nothing, because the algorithm needs variety and clear days, not volume.

When to stop logging and see a doctor

A food diary is a tool for managing symptoms that already have a diagnosis behind them. It is not a diagnostic test, and no app is.

See a doctor promptly if you have any of these, whatever your app says:

• Blood in your stool, or black or tarry stools
• Unexplained weight loss
• Fever
• Symptoms that wake you at night
• Feeling unusually tired or breathless, which can suggest anaemia
• New bowel symptoms starting after the age of 50
• A family history of bowel cancer, coeliac disease or inflammatory bowel disease
• Difficulty swallowing, persistent vomiting, or a lump in your abdomen

The ACG guideline supports a positive diagnostic strategy for IBS rather than diagnosis by exclusion, and suggests coeliac serology in people with IBS and diarrhoea, plus faecal calprotectin to rule out inflammatory bowel disease (Lacy 2021). Those are tests, not app features.

Two other reasons to put the app down. If your safe-food list is getting shorter every month, or meals have started to feel frightening, speak to your GP or a dietitian (Atkins 2023). And if you have logged carefully for two months and the analysis still shows nothing usable, that is information too: it is a reason to look at the non-food side, including stress, sleep and the gut-brain therapies with randomised evidence behind them (Black 2020), rather than a reason to log for a third month.

An eight-week way to use it that gives you a real answer

If you are going to do this, do it as an experiment with an end date rather than an open-ended habit.

Weeks 1 to 4: log properly. Every meal, including days when you feel fine, with ingredients broken out. Record symptoms at onset, with an honest average intensity for the whole episode rather than several separate entries, which the guide warns confuses the algorithm. Record bowel movements on the Bristol scale, plus medication, alcohol, stress and sleep. Do not look at the analysis.

Week 5: read the analysis three ways. Run your main symptom at a 6-hour window, a 24-hour window and a 72-hour window. Write down the items that appear in all three. Ignore everything else for now.

Week 5: sanity-check the shortlist. For each survivor, check the confidence bar, the suspect ratio and the onset delay chart. Cross off staples with even ratios and flat charts. You should end up with one to three candidates, not ten.

Weeks 6 to 8: test one of them. Keep everything else as stable as you can, remove or add the single candidate, and give it long enough to see a delayed effect. Then put it back and watch again. A suspect that survives that is worth acting on. One that does not has just saved you from cutting out a food for no reason.

Then decide whether to keep going. If the process produced something useful, keep a lighter diary. If it produced nothing after two months of good logging, take the export to your GP or a dietitian instead of buying another app. The CSV and PDF reports are there for exactly that, and a clinician can often read more into a pattern of bowel habit, timing and medication than a correlation score can. If you want to see how we approach the same problem, our method page sets out the thresholds we use before calling anything a pattern.

A ranked list of suspects is a good place to start a conversation. It was never going to be the end of one.