Calorie Counting and Tracking Apps

Diet Reference Archive: an approach to eating

Calorie Counting and Tracking Apps

Calorie counting means recording what you eat and drink and adding up its energy, usually against a daily target, on paper or in a phone app. It is used mainly to manage body weight, and it is the record-keeping habit at the center of most behavioral weight-loss programs.

Family: Approaches to eating that are not diets Promoted by: app companies, commercial weight programs and behavioral weight-loss research First appeared: paper food records in behavioral weight programs; digital tracking in trials from 2009 GRADE: Low, for the difference in measured body weight at 6 months between a free calorie-counting app introduced in primary care and usual care, in adults with overweight or obesity who wanted to lose weight; the interval includes a measured weight difference larger than this page’s 1-kilogram convention as well as little difference; this page’s own judgment

ON THIS PAGE

1. In brief
2. Overview
3. For the coach
4. The evidence (opens on request)
5. Key terms
6. References and Resources
7. Evidence rating and review record

This is a short archive page. The summary and sections 1 and 2 are open when the page loads. Section 3 holds the research detail and opens when you select its heading. The commercial programs built around food tracking have their own pages, WeightWatchers and Noom, and their trials are not repeated here. Back to the Diet Reference Archive index.

In brief

What is it? Recording each food and drink and adding up its calories, usually against a daily target, on paper or in a phone app such as MyFitnessPal, Lose It! or Cronometer. It names no foods. It is a way of keeping track, and what it does depends on the target a person sets and on what they do with the numbers.

Does it work? Inside structured behavioral weight programs, people who log their food more often tend to lose more weight (Burke et al., 2011; Patel et al., 2021), but that is an association: the people who log most may differ in other ways, and one review judged the level of evidence weak (Burke et al., 2011). Handing someone an app on its own is a different matter. In a trial of 212 primary care patients, introducing MyFitnessPal did not establish a clear between-group difference in measured weight at 6 months compared with usual care, and use of the app dropped sharply after the first month (Laing et al., 2014). A 12-month trial that added MyFitnessPal logging to diet and exercise advice also did not establish a clear between-group difference in measured weight (Jospe et al., 2017).

The numbers themselves are estimates: figures from app databases and restaurants can be close on average but well off for a single food (Lin et al., 2022; Urban et al., 2011). Every rating on this page is moderate, low or very low.

Is it safe? One 30-day trial in undergraduate women at low risk excluded an increase as large as this page’s symptom-score threshold (Hahn et al., 2021). A 12-month trial in adults seeking weight loss was less precise and could not exclude important increases or decreases (Jospe et al., 2018). These trials do not establish safety for people at higher risk or for longer use. In surveys, people who track report more disordered eating than people who do not, and those studies cannot tell which came first (Moody et al., 2025). In a clinic sample of people with an eating disorder, most who had used MyFitnessPal felt it had contributed to their eating disorder (Levinson et al., 2017). The caution that matters most is for anyone with a current or past eating disorder.

Who should not do it without talking to a doctor or dietitian first? These precautions are used on this page; they are not findings of the trials. A person with a current or past eating disorder; anyone under 18, since MyFitnessPal and Lose It! say their services are not for anyone under 18, and Cronometer says it is not for children under 13 and needs a parent or guardian to accept its terms for anyone under the age of majority; a person who is pregnant or breastfeeding; a person taking insulin or a sulfonylurea who plans to eat much less; a person with kidney disease; and an athlete whose energy needs are high. Each of them brings in their clinician or a registered dietitian, or finds one.

Related pages: WeightWatchers and Noom, programs built on tracking; Intuitive Eating and Mindful Eating, approaches with nothing to count; Volumetrics Diet, which asks people to think about calories without counting them. Macro Counting is a related topic. Full list on the archive index.

1. Overview

What you do

A person records each food and drink with an amount, and an app or a reference table converts it to calories and usually to protein, carbohydrate and fat. Most apps begin by asking for height, weight, age, sex, activity level and a goal, then show a daily calorie target and how much of it is left. In the version of MyFitnessPal tested in a 2012 to 2013 trial, users entered their current weight, goal weight and how fast they wanted to lose, limited to 0.5 to 2 pounds a week, and the app displayed a daily target against what they had logged (Laing et al., 2014). Entries come from searching a food database, from scanning a package barcode, and in newer versions from a photo or a spoken description; in MyFitnessPal, barcode and voice logging are now paid features (MyFitnessPal, n.d.).

Apps also log exercise and body weight and can sync with fitness trackers and scales (Lose It!, n.d.). Research calls any of these records self-monitoring; this page is about the food side.

What you do not do

Counting does not by itself forbid or require any food. The rules come from the target, from whatever eating plan the person follows, and from features an app adds, such as macronutrient goals or an eating window. Because it names no foods, its environmental footprint depends on what each person chooses to eat.

Where it came from

Keeping food records is a long-standing part of behavioral weight-loss treatment, where self-monitoring of diet, exercise and weight has been called the centerpiece of the program (Burke et al., 2011). Research moved from paper diaries to websites, handheld computers and then smartphone apps; a 2021 review found randomized trials of digital self-monitoring published from 2009 to 2019 (Patel et al., 2021). Commercial programs build on the same habit: WeightWatchers tracks foods as Points and Noom logs foods by color, and their trials are reported on their own pages.

What it claims to do

The app companies make claims about results on their own pages. MyFitnessPal’s Premium page says “8/10 Premium members have made progress toward their goals” and that a year of the app “costs about the same as 1 hour with a nutrition expert” (MyFitnessPal, n.d.). Lose It! says its “Premium members lose an average of three times as much weight as free members” (Lose It!, n.d.). As opened on November 8, 2026, MyFitnessPal’s page footnotes its figure only as “Based on a user survey,” and the Lose It! page does not say how its figure was measured. A comparison between people who chose to pay and people who did not cannot show that the paid features caused the difference, because the two groups differ in other ways. Section 3 reports what independent research shows.

What it gets right

Paying attention to what one eats can answer a question a person cares about, such as where most of their calories come from or how often they eat late at night. Across 138 experimental studies of many kinds of goals, interventions that increased how often people monitored their progress also improved goal attainment on average, and effects were larger when progress was physically recorded or reported to others (Harkin et al., 2016).

Cost, products, and who profits

The three apps opened for this page all have a free version and a paid one. On November 8, 2026, MyFitnessPal’s Premium page listed $79.99 a year or $19.99 a month, with a 7-day free trial for anyone who has never had Premium or a Premium trial, renewing automatically until cancelled (MyFitnessPal, n.d.). Cronometer’s Gold page listed $59.99 a year, which the company displays as $4.99 a month billed annually, or $10.99 billed monthly (Cronometer, n.d.). The Lose It! pages opened that day described Premium features but showed no price (Lose It!, n.d.). These are the amounts the companies’ pages showed that day, and prices change. Someone with limited money can count calories for free with a basic app, a package label or a notebook; what costs extra is mostly convenience, such as barcode scanning.

The companies earn money from subscriptions and from advertising. MyFitnessPal, Inc. says it shares information “with marketing and advertising partners to show you relevant content and advertising,” and that a user’s food diary may include sensitive information that allows someone to infer a health condition (MyFitnessPal, 2025). FitNow, Inc., which does business as Lose It! and describes itself as a Ziff Davis company, says it shares personal data with “advertisers and business partners” and may sell or transfer certain online data to advertisers (Lose It!, 2026). Cronometer Software Inc. says it does not sell personal health information or give it to third parties without consent (Cronometer, 2025b). These are the companies’ own statements, read on November 8, 2026, and the policies change.

Legal protection for this information is limited. The Federal Trade Commission notes that many companies collecting health information, including “a diet app,” “aren’t covered by HIPAA,” the federal health privacy law that governs most hospitals and doctors’ offices (Federal Trade Commission, 2024). The FTC’s Health Breach Notification Rule, amended in 2024, can cover certain health apps outside HIPAA. Covered personal-health-record businesses must notify affected users after a breach of unsecured identifiable health information; the rule does not cover every app or every disclosure (Federal Trade Commission, 2024). Whether to use an app, and which one, is the client’s decision.

How clients describe it, and what it gets confused with

A client may say they are logging, tracking, “on MyFitnessPal,” “doing Lose It!,” or that they have “300 calories left.” Tracking can mean very different things: a short look at a few ordinary days, a daily habit inside a weight program, or rules that have become hard to stop. It is confused with Macro Counting, a related topic in which people track grams of protein, carbohydrate and fat, often called “if it fits your macros”; with commercial programs such as WeightWatchers, Noom and the Mayo Clinic Diet, whose apps include a food tracker; and with the Volumetrics Diet, which asks people to think about calories without counting them. Carbohydrate counting for insulin dosing in diabetes is part of a clinical diabetes plan and is a different practice, set by the diabetes care team.

2. For the coach

SCOPE OF PRACTICE

A health and wellness coach does not prescribe calorie counting, set a calorie or nutrient target, read a client’s food log to judge their diet, or use tracking to steer a client toward eating less or weighing less. With the client’s permission, the coach can share general information about what counting is and what the research shows, and can support a client who chooses to track, or chooses not to, on their own terms. An individual calorie or nutrient target belongs with a registered dietitian or physician, and so does any tracking that is part of care for diabetes, kidney disease, pregnancy, an eating disorder, or a child’s or teenager’s eating. On this page body weight is something studies measured, never a goal.

What a coach does with it

The coaching move is the same one used for any self-monitoring. The coach asks what question the client wants the information to answer, what experience they have had with tracking before, and whether it has ever made worries about food or their body louder. Self-monitoring is the client’s choice and the client’s record: the coach sees a food log only if the client chooses to share it, and no tracking is a legitimate answer. If the client chooses to track, the arrangement they design answers their own question, uses the smallest useful amount of tracking, and includes a point at which they decide whether to keep it, change it or stop.

The numbers are not the coach’s to interpret. An app’s daily target comes from an equation and the goal the person typed in; it is not a care plan, and deciding whether a person’s intake is adequate for their health, activity or medical condition is a registered dietitian’s work. A coach who notices that a client’s target looks very low, or that the client is eating well under it, does not supply a different number; the coach asks how the client is feeling and doing, and where the conversation raises a concern, follows the referral guidance below.

Most apps are built around a goal weight. This archive treats weight as something a study measured, not as the aim of eating, and a coach keeps the conversation on what the client says matters to them. The one exception is weight a person loses without meaning to, which can be a sign of a health problem and is a reason to involve a clinician. The coach can describe the evidence honestly: logging goes with greater weight loss inside structured programs; when an app was introduced on its own, the trial did not establish a clear between-group difference in measured weight; and the calorie figures are estimates.

What to ask

• “What would you like tracking to tell you that you don’t already know?”
• “How has keeping track of food gone for you in the past, and how did it feel?”
• “If you decide to try it, how will you know whether it is helping, and when would you like to look at that together?”

When to refer

Some physical signs call for the client’s care plan or a clinician rather than a coaching conversation: dizziness or fainting, feeling cold or exhausted much of the time, hair loss, missed menstrual periods, or weight loss the client did not intend. For a client with kidney disease, what to eat depends on the stage of the disease, lab results and whether they are on dialysis, so any tracking targets come from the kidney care team.

Low blood sugar can be a side effect of insulin and of sulfonylureas, and eating less carbohydrate or skipping or delaying meals makes it more likely (National Institute of Diabetes and Digestive and Kidney Diseases, n.d.). A client on either medicine who plans to eat substantially less tells the prescriber before making the change. A client who reports shakiness, sweating or confusion may be having a low blood sugar episode now; the first step is the one in their own diabetes plan, with emergency help if symptoms are severe or the episode is one they cannot manage, and telling the prescriber is the separate step that follows.

Eating behaviors are a separate matter, and many have ordinary reasons. A client who logs every bite may be learning portion sizes, following a dietitian’s plan, or simply curious, and a busy schedule or limited food choices can explain a great deal. On its own a behavior tells a coach little, and none of this is a diagnosis.

Some patterns call for a closer look: distress after going over the number, logging long after it stopped answering any question, eating well below the app’s target to speed things up, skipping meals with others because they cannot be logged, or exercising to earn back calories. The coach asks what the behavior is for, whether it causes distress, whether it is narrowing what the client eats, and whether it gets in the way of daily life. Where the answers raise a real concern, the coach follows their own referral process, or the client takes it to the clinician who already oversees their care.

Special populations

Pregnancy and breastfeeding

In a randomized trial of 305 pregnant women in Sweden, a 6-month app that included self-monitoring and feedback on weight gain, diet and activity did not show a statistically significant effect on gestational weight gain overall compared with standard maternity care (Sandborg et al., 2021). That app was a broader program, not calorie counting alone. The searches for this page, run October 21, 2026, did not identify a trial of calorie counting itself in pregnancy or breastfeeding. Energy needs in pregnancy and breastfeeding, and any food rules, come from the prenatal care team.

Children and adolescents

In a pilot trial of 60 adolescents aged 13 to 17 with obesity, all on an energy-restricted diet with monthly clinic visits, logging with the FatSecret app did not detect a clear between-group difference in BMI z-score compared with a 3-day paper food record in this small, clinic-supervised trial; 34 completed 6 months (Audi et al., 2026). That was clinic-supervised treatment, not self-directed use. MyFitnessPal and Lose It! say their services are not intended for anyone under 18. Cronometer’s terms say it is not intended for children under 13 and that a parent or guardian must accept the terms for a user under the age of majority, and its privacy notice asks for a parent’s or guardian’s consent for children under 16 (Cronometer, 2025a, 2025b; Lose It!, 2026; MyFitnessPal, 2025). These are the companies’ own terms, read on November 8, 2026. A young person’s eating concerns go to a parent or guardian and the young person’s clinician, through the coach’s own referral process.

Older adults

In a pilot trial of 90 adults aged 60 and over, group programs supported by a tablet app, an activity monitor and, in the calorie-restriction groups, a wireless scale were feasible and well accepted, with technology use on 96.4 percent of days (Fanning et al., 2025). That report concerns feasibility, not whether counting changes health. Whether a calorie target suits an older adult’s health and medicines is a question for their clinician or dietitian.

Athletes and people with high activity

In a survey of resistance-trained women, 64.6 percent reported tracking calories, more often among competitive than recreational athletes (SantaBarbara et al., 2024). In 43 food records from Canadian endurance athletes, MyFitnessPal’s energy totals agreed poorly with a reference database, while Cronometer’s agreed well (Morello et al., 2025). Concerns about an athlete’s energy intake, menstrual periods or bone health go to the athlete’s clinician or a sports dietitian.

People taking medication for diabetes, blood pressure, or blood thinning

The prescriber is told before any substantial change in eating, as described under When to refer. The searches for this page did not identify a trial that examined the safety of calorie counting for people taking these medicines.

People with a history of disordered eating

In a sample of 105 people diagnosed with an eating disorder, about 75 percent had used MyFitnessPal, and 73 percent of those users felt it had contributed to their eating disorder (Levinson et al., 2017). The trial in undergraduate women excluded anyone with a current or past eating disorder diagnosis or a high symptom score, so it does not speak to this group (Hahn et al., 2021). For someone with this history, whether to count at all is a question for their clinician or treatment team first.

🤝 COACHING CONNECTION

Dana tells her coach she has logged every meal in an app for 2 years and feels uneasy whenever she eats out and cannot find the dish in the database. The coach reflects that logging has been part of her routine for a long time and that the restaurant meals are where it starts to feel hard, and asks what logging gives her now. Dana says it taught her a lot at first and now feels mostly like a habit she is not sure she still needs. The coach asks what she would like to find out. Dana decides to notice, over the next few restaurant meals, how she feels when she does not log, and to bring what she notices to their next session. She will decide afterward what to keep.

3. The evidence

(select the heading to open or close this section)

The research falls into 4 groups that this page keeps apart: self-monitoring inside behavioral weight programs, apps used on their own, the accuracy of the calorie figures, and the relationship between tracking and disordered eating. Body weight is reported as the studies measured it. The certainty ratings for each outcome are in the Evidence rating and review record at the end of the page.

Self-monitoring inside behavioral weight programs

A 2011 systematic review covered 22 studies published from 1993 to 2009, 15 of them on dietary self-monitoring, most often with a paper diary. All 15 found that more complete or more frequent food records went with greater weight loss, and the review found the association consistent across studies, but it judged the level of evidence weak because of the studies’ methods. Most were descriptive or secondary analyses, samples were mostly White and mostly women, and the studies relied heavily on self-report (Burke et al., 2011).

A 2021 systematic review of digital self-monitoring examined 39 randomized trials published from 2009 to 2019, with 8,232 adults with overweight or obesity, 68 percent of them women. Of 20 reported associations between how often people tracked their diet and how much weight they lost, 16 were positive; across all kinds of tracking, 50 of 68 were. The link was weaker in programs lasting 12 months or longer, where 9 of 19 associations were statistically significant, and few programs kept people tracking on 75 percent or more of days. Only 9 trials isolated self-monitoring in any way, for example by comparing ways of tracking (Patel et al., 2021).

A third review, of 59 behavioral programs that used dietary self-monitoring, found that most programs produced statistically significant weight loss compared with control groups whether they asked people to record everything they ate or only certain parts of their diet; differences in how adherence was measured kept the authors from comparing the two approaches (Raber et al., 2021). None of these associations can show how much of the weight change came from logging itself, because people who log more may differ in other ways, such as how engaged they are with the rest of the program.

Apps used on their own

One test of an app given on its own is a 2014 trial at 2 primary care clinics in Los Angeles. It randomly assigned 212 adults with a body mass index of 25 or more who said they wanted to lose weight to usual care or to usual care plus help downloading MyFitnessPal, an instructional video and a follow-up phone call. Participants were 73 percent women, with an average age of 43. At 6 months the difference in measured weight change, app group minus usual care, was -0.67 pounds, about -0.30 kilograms, with a 95 percent confidence interval from -3.3 to 2.1 pounds (about -1.50 to 0.95 kilograms); the trial did not establish a clear between-group difference. The interval excludes the 5.5-pound difference the trial was designed to detect, but it includes a measured difference larger than this page’s 1-kilogram convention as well as little difference (Laing et al., 2014).

Use fell quickly. Of the 105 people given the app, 94 used it in the first month and 34 in the sixth, and the median number of logins fell from 8 in the first month to 0 in the sixth. Of those who stopped and answered the question, 84 percent said logging was tedious. Losses to follow-up were 32 percent in the app group and 19 percent in usual care, and 14 of 107 people in usual care used MyFitnessPal anyway. A sensitivity analysis for informative dropout and one excluding the control participant who used the app most gave similar results. App group members did report using a personal calorie goal on about 2 more days a week (Laing et al., 2014).

A 12-month trial in New Zealand randomly assigned 250 adults with a body mass index of 27 or more to diet and exercise advice alone or with one of 4 monitoring strategies, one of which was logging food in MyFitnessPal; 171 remained at 12 months. Fifty people were assigned to MyFitnessPal and 48 to advice alone, and the app group was also given personalized energy and nutrient goals, so this was not a test of logging alone. Logging was asked for daily in the first month and for one week a month after that. The median MyFitnessPal participant logged on 57 of the 98 recommended days (58.2 percent), and logging fell over time; at one year, 12 percent of participants in the other groups reported monitoring their diet with an app or website.

At 12 months, the adjusted difference in measured weight, MyFitnessPal minus advice alone, was +2.2 kilograms, with a 95 percent confidence interval from -1.0 to +5.3, among the 36 people in each group with a 12-month measurement; the trial did not establish a clear between-group difference. Randomization was done off site with sealed opaque envelopes, the staff who measured outcomes did not know group assignment, and the mixed-model analysis included everyone with at least one follow-up measurement, with further analyses for missing data (Jospe et al., 2017).

Counting calories can also be taught rather than handed over as an app. In a trial of 261 low-income adults, 86 percent of them Latine and 95 percent women, community health workers delivered 11 sessions over 6 months teaching either calorie counting or MyPlate, the federal food guide at the time of the trial (see MyPlate). At 12 months, measured weight had changed by 0.75 kilograms in the calorie counting group and 0.34 kilograms in the MyPlate group, neither change statistically significant, and the report gives no interval for the difference between them; waist size fell in both (McCarthy et al., 2023).

On ways of tracking, the 2021 review found tracking more frequent with digital than with paper methods in 21 of 34 comparisons (Patel et al., 2021).

How accurate the numbers are

An app’s calorie total is only as good as its database and the person’s entries. In a Belgian study of 50 people, MyFitnessPal’s energy totals for 4-day food records were strongly correlated with a national food composition database (correlation 0.96) after the researchers removed 2.8 percent of entries as implausible (Evenepoel et al., 2020). A comparison of the 50 foods most often reported in a Chicago weight-loss study found, in its Bland-Altman analysis of calories, the poorest agreement with a research database for MyFitnessPal among 4 commercial apps, with a standard deviation of 133 calories across items, while CalorieKing and Lose It! had mostly excellent agreement (Lin et al., 2022). That study compared the apps’ database entries for single foods in the versions of the time; it does not show that every current version is off by the same amount.

In food records from endurance athletes, MyFitnessPal’s energy totals agreed poorly with a reference database; the authors pointed to the many user-entered and unverified food entries (Morello et al., 2025).

When the foods are known, the app can come close. In a crossover trial, 44 adults ate only foods that had been weighed in advance and logged them in MyFitnessPal for 3 days; the app’s average energy estimate was within 3.7 percent of the weighed amount, inside the trial’s preset margin of 18 percent (Diktas et al., 2025). That is a group average in one validation sample, judged against that trial’s margin; it can hide large errors for an individual meal, day or user.

Labels and restaurant figures have their own margins. Under federal food labeling rules, a packaged food is considered misbranded if its calorie content is more than 20 percent above the value on the label (Food Labeling, 2026). In 269 restaurant foods from 42 restaurants, stated calories matched measured calories on average, a difference of 10 calories per portion, but 50 items, 19 percent, contained at least 100 calories more than stated (Urban et al., 2011). An earlier study of reduced-calorie foods measured restaurant items at an average of 18 percent and frozen supermarket meals at 8 percent above their stated values, differences that were not statistically significant because results varied so much (Urban et al., 2010).

The person’s record is another source of error. A calorie total depends both on the database and on what and how much is recorded. In a small 1992 study, 10 adults who reported being unable to lose weight on very few calories had underreported their food intake by an average of 47 percent and overreported their physical activity by 51 percent when their intake and energy expenditure were measured over 14 days (Lichtman et al., 1992). The study was small and its participants were selected because they reported this problem, so its percentages do not describe clients in general.

Tracking and disordered eating

A 2025 systematic review identified 27 studies of fitness and diet tracking and disordered eating, 17 of them cross-sectional surveys, mostly of women aged 19 to 25. The surveys found reasonably consistent associations between tracker use and disordered eating, especially dietary restraint, excessive exercise and overall symptom scores, though the differences tended not to be large; the association was not replicated in experimental studies. The authors concluded that it is not possible to say whether trackers increase disordered eating or which comes first (Moody et al., 2025).

The single surveys behind that conclusion cover different groups. Among 493 college students, calorie tracker users reported more eating concern and dietary restraint after accounting for body mass index (Simpson & Mazzeo, 2017). Among 122 men recruited mainly through fitness social media, the 56 percent who had used MyFitnessPal reported more eating disorder symptoms, with large differences; the abstract says nearly 40 percent of users felt the app had contributed to disordered eating at least to some extent, and its conclusion gives nearly a third (Linardon & Messer, 2019). Both accounts rest on the studies’ abstracts. In 105 people with a diagnosed eating disorder, most of whom had used MyFitnessPal, 73 percent of users felt it had contributed (Levinson et al., 2017). These are associations and perceptions, not evidence that tracking caused the problems.

Studies that follow people over time are few. In 138 young adults from a population-based cohort in Minneapolis and St. Paul, those who used tracking apps for weight management reported an increase in disordered weight control behaviors over about 4 years, after accounting for their earlier behaviors (Hahn et al., 2024).

A separate 2024 meta-analysis of weight-related self-monitoring of all kinds in general adult samples found a pooled correlation of 0.13 with global eating disorder symptom scores, with a 95 percent interval from -0.02 to 0.28 across six studies. For diet monitoring, it found a correlation of 0.08 with global eating disorder symptoms, with a 95 percent interval from -0.08 to 0.24 across five studies. This interval includes no association and a positive association above the conventional 0.1 correlation threshold. The studies and monitoring definitions varied, with substantial heterogeneity between studies, and the result does not establish that tracking is either harmless or a cause of symptoms (Roth et al., 2024). The two reviews share source studies, so they are not independent evidence.

Two randomized trials tested whether logging changes eating disorder symptoms. In 200 undergraduate women who had not tracked food in the past year and were at low risk of an eating disorder, about a month of daily logging in MyFitnessPal showed no clear difference in scores on a 12-item eating disorder questionnaire compared with no app: the difference was -0.04 on a 0 to 3 scale, from -0.11 to 0.02. Participants logged on 89 percent of days, and 8 of 100 in the app group were lost to follow-up (Hahn et al., 2021).

In the New Zealand trial, the MyFitnessPal group’s global eating disorder score at 12 months differed from advice alone by -0.03 on a 0 to 6 scale, from -0.40 to 0.34, among 71 participants analyzed (Jospe et al., 2018). The companion trial report records declining logging adherence, with a median 57 of 98 recommended days (Jospe et al., 2017).

The review that found these trials notes that experimental studies have been short and have tended to exclude people at risk (Moody et al., 2025). Inside supervised weight programs, a meta-analysis of 49 trials in 6,337 adults found that eating disorder scores and binge eating fell on average from before to after the programs, while 4 studies reported new eating disorder symptoms in 0 to 6.5 percent of participants; the authors concluded that monitoring for new symptoms is important (Jebeile et al., 2023). Those programs included much more than logging, and the result describes change within groups, not a comparison with no program.

Where the evidence is absent

Two trials tested an app introduced on its own or added to advice, for 6 and 12 months; neither established a clear between-group difference in measured weight, and neither interval rules out a measured difference of more than 1 kilogram. The searches for this page, run October 21, 2026 in Europe PMC, which indexes PubMed, did not identify a trial that measured heart attacks, strokes, new cases of diabetes or death; a published trial of calorie counting alone in pregnancy, breastfeeding or children outside a clinic program; or a trial examining safety for people taking diabetes, blood pressure or blood-thinning medicines (see Special populations in section 2).

The randomized evidence on disordered eating comes from 2 trials, neither in people at high risk or in adolescents. Participants in the reviews were mostly women and mostly White (Burke et al., 2011; Moody et al., 2025; Patel et al., 2021), and apps change often, so a trial describes the version that was tested.

4. Key terms

Calorie

The unit used on food labels and in apps for the energy in food and drink; strictly, a kilocalorie.

Calorie target

The daily number an app or a plan sets for a person to aim at, usually calculated from an equation and the goal the person enters.

Self-monitoring

Information a person chooses to notice or record about their own behavior or experience, such as foods eaten, activity or body weight.

Behavioral weight program

A structured program, often with group or individual sessions, that teaches skills such as goal setting, self-monitoring and problem solving to change eating and activity.

Food composition database

A table of foods and their nutrient content that an app or researcher uses to turn a recorded food into calories and nutrients.

Association

A pattern in which two things tend to occur together; it does not by itself show that one causes the other.

Confidence interval

The range of values the data are compatible with; for the differences on this page, an interval that includes no difference means the study could not tell whether there was one.

Contamination

In a trial, people in the comparison group receiving some of the intervention anyway, which tends to shrink the difference between groups.

Underreporting

Recording less food than was actually eaten.

Disordered eating

Eating behaviors and attitudes, such as rigid restriction, binge eating or compensating for food by exercise, that can cause distress or harm whether or not they meet the criteria for an eating disorder.

Eating disorder symptom score

A questionnaire total, such as the Eating Disorder Examination Questionnaire, in which a higher score means more eating disorder thoughts and behaviors; it is not a diagnosis.

HIPAA

The Health Insurance Portability and Accountability Act, the federal law that protects health records held by most hospitals, doctors’ offices and insurers; many consumer apps are not covered by it.

5. References and Resources

Audi, A. S., Fernandes, A. E., Coelho, G. S. M. A., Moura, A. M. D. S. H., Pepe, R. B., Cercato, C., Melo, M. E., & Mancini, M. C. (2026). Effect of a mobile phone application for dietary self-monitoring on obesity in adolescents: A pilot randomized controlled trial. European Journal of Clinical Nutrition, 80(1), 96-103. https://doi.org/10.1038/s41430-025-01676-0

Burke, L. E., Wang, J., & Sevick, M. A. (2011). Self-monitoring in weight loss: A systematic review of the literature. Journal of the American Dietetic Association, 111(1), 92-102. https://doi.org/10.1016/j.jada.2010.10.008

Cronometer. (n.d.). Cronometer Gold. Retrieved November 8, 2026, from cronometer.com/gold

Cronometer. (2025a, February 12). Terms of service. cronometer.com/terms

Cronometer. (2025b, June 23). Privacy policy. cronometer.com/privacy

Diktas, H. E., Lozano, C. P., Saha, S., Broyles, S. T., Martin, C. K., & Apolzan, J. W. (2025). Validity of the PortionSize and MyFitnessPal smartphone applications for estimating dietary intake: A randomized crossover clinical trial. The Journal of Nutrition, 155(12), 4296-4306. https://doi.org/10.1016/j.tjnut.2025.09.027

Evenepoel, C., Clevers, E., Deroover, L., Van Loo, W., Matthys, C., & Verbeke, K. (2020). Accuracy of nutrient calculations using the consumer-focused online app MyFitnessPal: Validation study. Journal of Medical Internet Research, 22(10), Article e18237. https://doi.org/10.2196/18237

Fanning, J., Rejeski, W. J., Nicklas, B. J., Kritchevsky, S. B., Miller, M. E., Houston, D. K., Walkup, M. P., Kennedy, K., Stowe, C. L., Ford, S., Yow, D., & HALLO-P Investigators. (2025). Intervening on calorie intake or eating timing in older adults: Lessons learned in the Healthy Aging and Late-Life Outcomes randomized controlled pilot trial. Clinical Interventions in Aging, 20, 685-700. https://doi.org/10.2147/CIA.S520273

Federal Trade Commission. (2024, July). Complying with FTC’s Health Breach Notification Rule. ftc.gov/business-guidance/resources/complying-ftcs-health-breach-notification-rule-0

Food Labeling, 21 C.F.R. § 101.9 (2026). ecfr.gov/current/title-21/chapter-I/subchapter-B/part-101/subpart-A/section-101.9

Hahn, S. L., Bornstein, C., Burnette, C. B., Loth, K. A., & Neumark-Sztainer, D. (2024). A mixed-methods longitudinal examination of weight-related self-monitoring and disordered eating among a population-based sample of emerging adults. Journal of Eating Disorders, 12, Article 112. https://doi.org/10.1186/s40337-024-01069-x

Hahn, S. L., Kaciroti, N., Eisenberg, D., Weeks, H. M., Bauer, K. W., & Sonneville, K. R. (2021). Introducing dietary self-monitoring to undergraduate women via a calorie counting app has no effect on mental health or health behaviors: Results from a randomized controlled trial. Journal of the Academy of Nutrition and Dietetics, 121(12), 2377-2388. https://doi.org/10.1016/j.jand.2021.06.311

Harkin, B., Webb, T. L., Chang, B. P. I., Prestwich, A., Conner, M., Kellar, I., Benn, Y., & Sheeran, P. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental evidence. Psychological Bulletin, 142(2), 198-229. https://doi.org/10.1037/bul0000025

Jebeile, H., Libesman, S., Melville, H., Low-Wah, T., Dammery, G., Seidler, A. L., Jones, R. A., McMaster, C. M., Paxton, S. J., Hill, A. J., Ahern, A. L., Garnett, S. P., Braet, C., Wilfley, D. E., Baur, L. A., & Lister, N. B. (2023). Eating disorder risk during behavioral weight management in adults with overweight or obesity: A systematic review with meta-analysis. Obesity Reviews, 24(6), Article e13561. https://doi.org/10.1111/obr.13561

Jospe, M. R., Brown, R. C., Williams, S. M., Roy, M., Meredith-Jones, K. A., & Taylor, R. W. (2018). Self-monitoring has no adverse effect on disordered eating in adults seeking treatment for obesity. Obesity Science & Practice, 4(3), 283-288. https://doi.org/10.1002/osp4.168

Jospe, M. R., Roy, M., Brown, R. C., Williams, S. M., Osborne, H. R., Meredith-Jones, K. A., McArthur, J. R., Fleming, E. A., & Taylor, R. W. (2017). The effect of different types of monitoring strategies on weight loss: A randomized controlled trial. Obesity, 25(9), 1490-1498. https://doi.org/10.1002/oby.21898

Laing, B. Y., Mangione, C. M., Tseng, C.-H., Leng, M., Vaisberg, E., Mahida, M., Bholat, M., Glazier, E., Morisky, D. E., & Bell, D. S. (2014). Effectiveness of a smartphone application for weight loss compared with usual care in overweight primary care patients: A randomized, controlled trial. Annals of Internal Medicine, 161(10 Suppl.), S5-S12. https://doi.org/10.7326/M13-3005

Levinson, C. A., Fewell, L., & Brosof, L. C. (2017). My Fitness Pal calorie tracker usage in the eating disorders. Eating Behaviors, 27, 14-16. https://doi.org/10.1016/j.eatbeh.2017.08.003

Lichtman, S. W., Pisarska, K., Berman, E. R., Pestone, M., Dowling, H., Offenbacher, E., Weisel, H., Heshka, S., Matthews, D. E., & Heymsfield, S. B. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. The New England Journal of Medicine, 327(27), 1893-1898. https://doi.org/10.1056/NEJM199212313272701

Lin, A. W., Morgan, N., Ward, D., Tangney, C., Alshurafa, N., Van Horn, L., & Spring, B. (2022). Comparative validity of mostly unprocessed and minimally processed food items differs among popular commercial nutrition apps compared with a research food database. Journal of the Academy of Nutrition and Dietetics, 122(4), 825-832.e1. https://doi.org/10.1016/j.jand.2021.10.015

Linardon, J., & Messer, M. (2019). My fitness pal usage in men: Associations with eating disorder symptoms and psychosocial impairment. Eating Behaviors, 33, 13-17. https://doi.org/10.1016/j.eatbeh.2019.02.003

Lose It! (n.d.). How it works. Retrieved November 8, 2026, from loseit.com/how-it-works

Lose It! (2026, June 24). Privacy. loseit.com/privacy

McCarthy, W. J., Rico, M., Chandler, M., Herman, D. R., Chang, C., Belin, T. R., Love, S., Ramirez, E., & Gelberg, L. (2023). Randomized comparative effectiveness trial of 2 federally recommended strategies to reduce excess body fat in overweight, low-income patients: MyPlate.gov vs calorie counting. Annals of Family Medicine, 21(3), 213-219. https://doi.org/10.1370/afm.2964

Moody, S., Ross, L., Opitz, M.-C., Hemmings, A., İnce, B., Bryson, C., Kuehne, C., Douglas, D., Phillips, M., Langhorne, V., Schmidt, U., & Sharpe, H. (2025). Associations between the use of fitness and diet tracking technology and disordered eating behaviour: A systematic review. European Eating Disorders Review, 33(6), 1288-1313. https://doi.org/10.1002/erv.70006

Morello, O., McPhee, L., Kucab, M., Bellissimo, N., & Totosy de Zepetnek, J. O. (2025). Reliability and validity of nutrient assessment applications for Canadian endurance athletes: MyFitnessPal and Cronometer. Journal of Human Nutrition and Dietetics, 38(5), Article e70148. https://doi.org/10.1111/jhn.70148

MyFitnessPal. (n.d.). Reach your goals your way with Premium. Retrieved November 8, 2026, from myfitnesspal.com/premium

MyFitnessPal. (2025, March 18). MyFitnessPal privacy policy. myfitnesspal.com/privacy-policy

National Institute of Diabetes and Digestive and Kidney Diseases. (n.d.). Low blood glucose (hypoglycemia). niddk.nih.gov/health-information/diabetes/overview/preventing-problems/low-blood-glucose-hypoglycemia

Patel, M. L., Wakayama, L. N., & Bennett, G. G. (2021). Self-monitoring via digital health in weight loss interventions: A systematic review among adults with overweight or obesity. Obesity, 29(3), 478-499. https://doi.org/10.1002/oby.23088

Raber, M., Liao, Y., Rara, A., Schembre, S. M., Krause, K. J., Strong, L., Daniel-MacDougall, C., & Basen-Engquist, K. (2021). A systematic review of the use of dietary self-monitoring in behavioural weight loss interventions: Delivery, intensity and effectiveness. Public Health Nutrition, 24(17), 5885-5913. https://doi.org/10.1017/S136898002100358X

Roth, A. M., Gruber, J. R., Reif, A., Schultze, M., & Matura, S. (2024). Exploring weight-related self-monitoring as a potential risk factor for eating disorder symptoms in adults: A systematic review and meta-analysis. Appetite, 202, Article 107610. https://doi.org/10.1016/j.appet.2024.107610

Sandborg, J., Söderström, E., Henriksson, P., Bendtsen, M., Henström, M., Leppänen, M. H., Maddison, R., Migueles, J. H., Blomberg, M., & Löf, M. (2021). Effectiveness of a smartphone app to promote healthy weight gain, diet, and physical activity during pregnancy (HealthyMoms): Randomized controlled trial. JMIR mHealth and uHealth, 9(3), Article e26091. https://doi.org/10.2196/26091

SantaBarbara, K., Helms, E., & Harris, N. (2024). The associations between calorie tracking, body image dissatisfaction, eating disorders, and menstrual cycle characteristics in resistance-trained athletes. Journal of the International Society of Sports Nutrition, 21(1), Article 2433743. https://doi.org/10.1080/15502783.2024.2433743

Simpson, C. C., & Mazzeo, S. E. (2017). Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology. Eating Behaviors, 26, 89-92. https://doi.org/10.1016/j.eatbeh.2017.02.002

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Evidence rating and review record

Each row identifies the method, source, outcome or recommendation, and comparison being rated. Ratings from different tools are not interchangeable. The source is either a named published review or this page’s own judgment. The rating in the banner is for measured body weight with an app used on its own, and the banner names the tool that produced it. A high rating means the research is solid; it does not mean calorie counting is right for everyone, and it does not mean a coach should recommend it.

Every rating on this page uses GRADE, the same 4-level scale used in the clinical guidelines you will meet in practice. High means further research is very unlikely to change the conclusion. Moderate means further research may change it. Low means further research is likely to change it. Very low means the conclusion is uncertain. GRADE rates each outcome separately, so an approach can be moderate for one outcome and very low for another. Not rated means the searches for this page did not find an adequate body of evidence for that question; it does not mean there is no effect or that something is safe.

The rated rows below are this page’s own assessments, assigned from the evidence under the archive’s September 28, 2026 decision; no published GRADE rating of a calorie counting or tracking app outcome was found in the sources opened for this page. Each row names its evidence and exactly which comparison it rates, and its reasons say where the rating starts and lower it 1 level for each serious concern. For measured weight, this page uses a difference of 1 kilogram in either direction as an analytical convention for a material measured difference, the convention other archive pages use; it is not a client goal or evidence of better health. For questionnaire scores, this page uses 0.2 of a standard deviation of the starting scores, a conventional small difference.

Studies of how accurate apps and labels are measure a tool, not an effect on health, so they are described in section 3 and not rated.

On a narrow screen, scroll the table sideways to see every column.

Evidence ratings, their sources and reasons

Outcome	Certainty	Who rated it, and for which comparison	Why it is rated this way
Body weight, as measured, at 6 months, with an app used on its own (the banner rating)	Low; the interval includes a measured weight difference larger than this page’s 1-kilogram convention as well as little difference	This page’s own judgment, using the GRADE approach, from Laing et al. (2014); the trial came with no published quality rating. Comparison: usual primary care plus help downloading MyFitnessPal, as offered in 2012 to 2013, against usual care alone, in 212 adults with a body mass index of 25 or more who wanted to lose weight; difference in measured weight change, app group minus usual care, -0.67 pounds (95% confidence interval -3.3 to 2.1), about -0.30 kilograms (-1.50 to 0.95).	Randomized trials start at high. Lowered 1 level for risk of bias: 32 percent of the app group and 19 percent of the usual care group were lost to follow-up, and 14 of 107 people in usual care used the app anyway, which tends to pull the groups together; the trial’s analysis for informative dropout and its analysis without the heaviest control user gave similar results, which limits but does not remove the concern. Weight was measured by staff, so knowing one’s group is not counted as a route to bias for this outcome. Inconsistency could not be judged: one trial reported this comparison. Not lowered for indirectness: the question names this population and this way of introducing the app, although apps change. Lowered 1 level for imprecision: the interval, -1.50 to 0.95 kilograms, includes a measured difference larger than the 1-kilogram convention, with the app group up to 1.5 kilograms lower, as well as little difference; it excludes the trial’s planning difference of 5.5 pounds, about 2.5 kilograms. Publication bias was not assessed: there is too little evidence to judge it. High minus 2 is Low.
Body weight, as measured, at 12 months, with MyFitnessPal logging added to diet and exercise advice	Low; the interval includes no difference and measured weight differences larger than this page’s 1-kilogram convention	This page’s own judgment, using the GRADE approach, from Jospe et al. (2017); the trial came with no published quality rating. Comparison: diet and exercise advice plus logging food in MyFitnessPal, with personalized energy and nutrient goals, against advice alone, in adults with a body mass index of 27 or more; 50 and 48 randomized, 36 in each group with a 12-month measurement. Adjusted difference in measured weight, MyFitnessPal minus advice alone, +2.2 kilograms (95% confidence interval -1.0 to 5.3).	Randomized trials start at high. Allocation was concealed (off-site block randomization with sealed opaque envelopes), the staff who measured outcomes did not know group assignment, and the mixed-model analysis included everyone with at least one follow-up measurement, with further analyses for missing data. Lowered 1 level for risk of bias: 14 of 50 and 12 of 48 had no 12-month weight, logging fell to a median of 57 of 98 recommended days, and 12 percent of participants in the other groups reported monitoring their diet with an app or website, so the assigned contrast was incompletely delivered and partly shared; weight was measured by staff, so knowing one’s group is not counted as a route to bias for this outcome. Inconsistency could not be judged: one trial reported this comparison. Not lowered for indirectness: the question names this offered program and this population; because the app came with personalized goals and advice, it is not a test of logging alone. Lowered 1 level for imprecision: the interval, -1.0 to 5.3 kilograms, includes no difference and measured differences larger than the 1-kilogram convention. Publication bias was not assessed: there is too little evidence to judge it. High minus 2 is Low. The interval concerns measured weight, not a judgment of whether either approach succeeded.
Weight change by how often people log their food inside behavioral weight programs	Very low	This page’s own judgment, using the GRADE approach, from the 15 dietary self-monitoring studies in Burke et al. (2011) and the 20 diet associations in the 39 trials of Patel et al. (2021), which rated trial quality with an adapted Cochrane tool. Comparison: people who logged more often or more completely against those who logged less, within the same programs, in adults with overweight or obesity; no pooled estimate.	These are associations within programs, so they are rated as observational evidence and start at low, even where the program itself was tested in a randomized trial. Lowered 1 level for risk of bias: people who log more may differ in engagement and motivation, adherence was measured in many ways and in older studies was often self-reported, and few analyses accounted for other differences between people. That gives very low. Not lowered further for inconsistency: all 15 studies in the 2011 review and 16 of 20 associations in the 2021 review were positive, but counts of positive findings do not show that the sizes of the associations agreed, and the link was weaker in programs lasting 12 months or longer; the rating is already at the lowest level. Not lowered for indirectness for this association question; samples were mostly White and mostly women. Imprecision could not be judged: the studies used no common measure, so precision is unresolved, not established. Publication bias was not assessed. The evidence does not show how much of the weight change logging caused.
Body weight, as measured, at 12 months, calorie counting taught by community health workers against MyPlate taught the same way	Low (provisional)	This page’s own judgment, using the GRADE approach, from McCarthy et al. (2023); the trial came with no published quality rating. Comparison: 11 sessions over 6 months teaching calorie counting, against the same number teaching MyPlate, the federal food guide at the time, in 261 low-income adults, 86 percent Latine and 95 percent women; weight change -0.75 kilograms (-1.72 to 0.23) and -0.34 kilograms (-1.27 to 0.59). The report gives no interval for the difference between groups. An illustrative interval that treats the two group estimates as independent gives -0.41 kilograms for calorie counting minus MyPlate (about -1.76 to 0.94); it does not reproduce the published model’s covariance or the effect of the workers who delivered each approach.	Randomized trials start at high. Lowered 1 level for risk of bias: about a quarter of each group missed the 12-month visit, and 2 community health workers delivered each approach, so the approach and the workers delivering it cannot be separated. Inconsistency could not be judged from one trial. Not lowered for indirectness: the question names these taught programs and this population. Lowered 1 level for imprecision: the actual uncertainty of the between-group contrast is unresolved, and the illustrated interval includes a measured difference of more than 1 kilogram, with the calorie counting group lower, as well as little difference. Publication bias was not assessed. High minus 2 is Low. Provisional because covariance and provider effects for the between-group contrast have not been resolved; the illustrated interval assumes independent group estimates.
Eating disorder symptom score after about 1 month of logging, undergraduate women at low risk	Moderate, for no increase as large as the threshold	This page’s own judgment, using the GRADE approach, from Hahn et al. (2021); the trial came with no published quality rating. Comparison: daily logging in MyFitnessPal for about 30 days, against no app, in 200 undergraduate women who had not tracked food in the past year, had no eating disorder diagnosis and had low symptom scores; 192 analyzed. Difference on the 12-item questionnaire (scored 0 to 3) -0.04 (-0.11 to 0.02).	Randomized trials start at high. Lowered 1 level for risk of bias: the score was self-reported by women who knew whether they had been logging; they were told only that the study concerned apps and well-being, which lowers but does not remove the chance that this shaped their answers. Allocation used envelopes prepared in advance and drawn at random by research assistants, and the 8 losses, all in the app group, did not change the result in a sensitivity analysis. Inconsistency could not be judged from one trial. Not lowered for indirectness: the question names this population and this length of use. Not lowered for imprecision: with starting standard deviations of about 0.4, the threshold is an increase of about 0.08 points, and the upper limit, 0.02, excludes it. Publication bias was not assessed. High minus 1 is Moderate. This says nothing about people at higher risk or about longer use.
Eating disorder symptom score at 12 months, adults seeking weight loss	Low; important increases and decreases in symptom scores remain possible under this page’s threshold	This page’s own judgment, using the GRADE approach, from Jospe et al. (2018), a secondary analysis of the trial in the second row; no published quality rating. Comparison: diet and exercise advice plus logging food in MyFitnessPal, against advice alone, in adults with a body mass index of 27 or more; 36 of 50 and 35 of 48 analyzed. Difference in the global questionnaire score (scored 0 to 6) -0.03 (-0.40 to 0.34).	Randomized trials start at high. Lowered 1 level for risk of bias: about 3 in 10 participants in each group were not analyzed, and the questionnaire was self-completed by participants who knew their group, though the staff who collected outcomes did not know group assignment. Being a secondary analysis is not counted as a reason by itself. The companion trial report records declining logging adherence, with a median 57 of 98 recommended days. Inconsistency could not be judged from one trial. Not lowered for indirectness: the question names this population. Lowered 1 level for imprecision: with starting standard deviations of about 0.9, the threshold is a change of about 0.18 points, and the interval, -0.40 to 0.34, extends beyond it in both directions. Publication bias was not assessed. High minus 2 is Low.
Disordered eating among people who use fitness and diet trackers, in single surveys	Very low	This page’s own judgment, using the GRADE approach, from the 17 cross-sectional studies in Moody et al. (2025), which appraised studies with National Heart, Lung, and Blood Institute quality tools and pooled no results; the diet-monitoring result of Roth et al. (2024) is used as context. Comparison: users against non-users of trackers, mostly women aged 19 to 25 in student and community samples, measured at one point in time.	Observational studies start at low. Lowered 1 level for risk of bias: people choose whether to track, both tracking and symptoms were self-reported at the same moment, and earlier concern about weight or shape could explain both, so the direction cannot be known. That gives very low. Inconsistency is a concern but cannot lower the rating further: the 2025 review found the survey associations reasonably consistent, while definitions of tracking varied and the 2024 meta-analysis found substantial heterogeneity between studies; the experimental studies that did not replicate the associations answer a different question and are rated in their own rows. Not lowered for indirectness for this association question. Imprecision could not be judged for the surveys in the 2025 review, which pooled none; in the 2024 meta-analysis, the diet-monitoring correlation of 0.08 had an interval from -0.08 to 0.24, which includes no association and a positive association above 0.1. The two reviews share source studies and are not counted as independent evidence. Publication bias was not assessed; the 2024 meta-analysis had too few studies to test for it. An association does not show that tracking causes disordered eating.
Heart attacks, strokes, new cases of diabetes, and death	Not rated	No comparison: the searches for this page did not identify a trial or a long-term comparative cohort study of calorie counting or tracking apps with these outcomes.	With no studies identified, there is no body of evidence to rate. This is a statement about the searches, not a finding of no effect.
New eating disorders caused by tracking	Not rated	No comparison: no trial opened for this page counted new eating disorder diagnoses.	The trials measured symptom scores, not new diagnoses, and the long-term studies are associations. The referral guidance in section 2 applies regardless of the rating.

How this archive talks about weight. This page reports what studies measured, including body weight, and does not treat weight change as a goal or as a measure of an approach’s success. Weight a person loses without meaning to can be a sign of a health problem, and where it is, the page names it as a reason to see a clinician. People are described the way the study described them.

Last reviewed: Reviewed and approved by James Peth, MS, MPH, PhD, on November 14, 2026, after a combined final review of the sixth batch of 5 archive pages, the repairs it asked for and a targeted recheck. Written October 21, 2026, and revised November 8, 2026. What changed: the banner and the weight and symptom-score summaries now say what each interval leaves open; the 12-month app comparison is now rated from the full trial report; the 2024 meta-analysis is reported from its full text, and the disordered eating survey row is no longer provisional; the accuracy and privacy statements are narrowed; company prices, age limits and privacy statements were rechecked on November 8, 2026. The calorie counting and MyPlate row stays provisional because its between-group interval is an illustration.

Next scheduled review: each year. App prices, features and privacy policies change often and are checked first.

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Diet Reference Archive | One James

Prepared by James Peth, MS, MPH, PhD

© 2026–2027 One James. All rights reserved.

This page is for education and coaching practice. It is not medical or dietary advice.