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Does Eating the Same Meals Help You Lose Weight?

In a 12-week program, adults whose food logs showed more repeated foods and steadier calories lost somewhat more weight — 5.9% vs 4.3%, an association, not proof of cause.

Food logs showed more repeated foods = steadier daily calories and slightly more weight loss in a correlational, self-reported study.

In a 12-week behavioural weight-loss program, 112 adults whose food logs showed more repeated foods and steadier daily calories lost somewhat more weight on average than those who ate more varied diets — 5.9% versus 4.3% in a descriptive split of the group. The study analyses food logs rather than assigning anyone to a routine, so it shows an association, not cause and effect.

What this means

In one 12-week behavioural weight-loss program, adults whose food logs showed more repeated foods and steadier daily calories lost somewhat more weight on average — 5.9% versus 4.3% between mostly-repeat and mostly-varied loggers. But that is a correlational analysis of self-reported logs in a motivated sample: it cannot show cause.

What the study found

The analysis drew on the first 12 weeks of a behavioural weight-loss program; participants logged food in an app and used a study-provided scale. Of 145 enrolled, 112 (84.8% women; mean age 52.6; mean BMI 34.5) tracked food on at least 75% of days; the group lost 5.6% of body weight on average.

More repetition went with somewhat more loss: a smaller share of unique foods tracked with more weight loss (standardized beta = −.26; p = .004), as did logging a food more than 10 times (beta = .20; p = .031). Steadier daily calories went with more loss too — each 100-kcal rise in average daily calorie deviation tracked with about 0.6% less loss (beta = −.21; p = .025). Effect sizes are modest; no confidence intervals are reported.

The clearest number is descriptive: the 91 people whose logs were more than half repeat entries lost 5.9% of body weight on average, against 4.3% for the 21 whose logs were mostly different foods.

A result that cut the other way

The authors also asked whether eating more at weekends than on weekdays went with less loss. It went the other way: a wider weekend-versus-weekday calorie gap tracked with slightly more loss (beta = .20; p = .025). The authors suspect the gap mostly reflects tracking — weekend logging tends to be less complete — and offer higher conscientiousness as another possible reading; it is not advice to eat more at weekends.

What it can’t tell you

This is a secondary, observational analysis of food logs. No one was assigned to eat the same meals or to vary them, so it describes how log patterns and weight loss moved together in one cohort. The authors state that causality cannot be assumed; they call pre-existing differences between people — the paper’s examples are better baseline self-regulation skills and lower hedonic hunger — a highly likely alternative explanation.

The logs are self-reported and often incomplete: people may log most faithfully on the days they stick to plan, which would inflate how steady and repetitive their diets look. Requiring tracking on 75% of days left a motivated sample — mostly middle-aged women — that may not represent people starting a program. Repetition was counted by exact food-name matches, so it misses variety a food’s name does not capture.

Prior human work points the same way without settling it: in the National Weight Control Registry, maintainers who kept a consistent regimen across the week were less likely to regain weight, self-reported and observational. What is new here is the view from the logs themselves; whether routine eating helps, and for whom, remains untested — the authors call for experiments to confirm it.

Terms explained

  • Correlational (observational) analysis: a study that measures things as they already are instead of assigning people to different diets. It can show that two things move together; it cannot show that one caused the other.
  • Dietary repetition: how often the same foods reappear in a person’s food log. This study measured it two ways — as the share of unique foods (a smaller share means more repetition) and as the share of foods logged more than 10 times.
  • Average daily calorie deviation: how far a person’s daily calorie intake tends to stray from their own average. A smaller deviation means steadier intake.
  • Standardized beta: a regression effect size that puts different measures on a common scale; values nearer zero mean a weaker relationship.

Sources

  1. Primary source: Hagerman CJ, Hong AE, Crane NT, Butryn ML, Forman EM. “Do routinized eating behaviors support weight loss? An examination of food logs from behavioral weight loss participants.” Health Psychology 2026;45(7):806-813, published online 2026-03-26. DOI: 10.1037/hea0001591 · PMID: 41885884 · PMCID: PMC13335809. Peer-reviewed secondary observational analysis of food-log data from a randomized behavioural weight-loss trial; full text read via the NIH author manuscript in PMC (not an open-access licence). Parent trial at Drexel University (IRB #2102008368); funded by NIH NIDDK R01 DK125641; the authors declare no potential conflicts of interest.
  2. Press signal (discovery/framing only, not evidence): “Want to lose weight? Try eating the same meals on repeat.” American Psychological Association press release, 2026-03-26. apa.org/news/press/releases/2026/03/lose-weight-same-meals (read via an archived copy; the live page was not reachable to us).
  3. Landscape — prior consistency evidence: Gorin AA, et al. “Promoting long-term weight control: does dieting consistency matter?” Int J Obes 2004. DOI: 10.1038/sj.ijo.0802550 · PMID: 14647183 (abstract-level access; National Weight Control Registry, n=1,429, one-year follow-up).
  4. Landscape — maintenance mechanics: “The role of appetite-related hormones, adaptive thermogenesis, perceived hunger and stress in long-term weight-loss maintenance.” Eur J Clin Nutr 2020. DOI: 10.1038/s41430-020-0568-9 · PMID: 32020057 (abstract-level access; mixed-methods, n=15; publisher correction 2021, DOI 10.1038/s41430-021-00881-x, unrelated to the use here).
  5. Landscape — maintenance predictors: “Evaluating appetite/satiety hormones and eating behaviours as predictors of weight loss maintenance.” Pediatr Obes 2024. DOI: 10.1111/ijpo.13105 · PMID: 38339799 · PMCID: PMC11006569 (abstract-level access).
  6. Parent-trial protocol: Forman EM, et al. “Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.” Contemporary Clinical Trials 2022;124:107029. DOI: 10.1016/j.cct.2022.107029 · PMID: 36435427 · PMCID: PMC9839592.

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