{"id":485,"date":"2025-07-13T23:54:29","date_gmt":"2025-07-13T23:54:29","guid":{"rendered":"https:\/\/lean-app.com\/?p=485"},"modified":"2026-06-11T15:54:50","modified_gmt":"2026-06-11T15:54:50","slug":"comment-compter-ses-calories","status":"publish","type":"post","link":"https:\/\/lean-app.com\/en\/comment-compter-ses-calories\/","title":{"rendered":"How to Count Your Calories: The 4 Methods Compared"},"content":{"rendered":"<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp\" 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.l1{background:#0E0E10;width:100%}\n#lvm-shell .pyramid .l2{background:#1D1D1F;width:84%}\n#lvm-shell .pyramid .l3{background:#3a3a3c;width:68%}\n#lvm-shell .pyramid .l4{background:var(--pink);width:52%}\n#lvm-shell .pyramid-cap{text-align:center;font-size:13px;color:var(--muted);margin-top:14px}\n\n\/* Section 7 honnetete : scorecard horizontal bars *\/\n#lvm-shell .scorecard{margin:30px 0 10px;border:1px solid var(--rule);border-radius:20px;padding:28px 26px;background:#fff}\n#lvm-shell .scorecard-head{display:grid;grid-template-columns:1.4fr 1fr 1fr;column-gap:28px;align-items:center;padding-bottom:18px;margin-bottom:8px;border-bottom:1px solid var(--rule-soft)}\n#lvm-shell .scorecard-head .h-crit{font-family:var(--font-mono);font-size:11px;font-weight:500;text-transform:uppercase;letter-spacing:.08em;color:var(--muted)}\n#lvm-shell .scorecard-head .h-brand{display:flex;align-items:center;gap:8px;font-family:var(--font-display);font-size:14px;font-weight:600;color:var(--ink)}\n#lvm-shell .scorecard-head .h-brand img{width:22px;height:22px;border-radius:5px;object-fit:cover}\n#lvm-shell .scorecard-row{display:grid;grid-template-columns:1.4fr 1fr 1fr;column-gap:28px;align-items:center;padding:14px 0;border-bottom:1px solid var(--rule-soft)}\n#lvm-shell .scorecard-row:last-child{border-bottom:0}\n#lvm-shell .scorecard-row .crit{font-size:14px;color:var(--ink);font-weight:500;padding-right:14px}\n#lvm-shell .scorecard-row .bar{display:flex;flex-direction:row-reverse;align-items:center;gap:10px}\n#lvm-shell .scorecard-row .bar .b{flex:1;height:8px;border-radius:99px;background:var(--rule-soft);overflow:hidden;position:relative}\n#lvm-shell .scorecard-row .bar .b > i{display:block;height:100%;border-radius:99px;transition:width 1s cubic-bezier(.22,.61,.36,1)}\n#lvm-shell .scorecard-row .bar.lean .b > i{background:var(--pink)}\n#lvm-shell .scorecard-row .bar.mfp .b > i{background:var(--mfp)}\n#lvm-shell .scorecard-row .bar .v{font-family:var(--font-mono);font-size:12px;font-weight:600;color:var(--ink);min-width:32px;text-align:left}\n\n\/* Section 8 pour qui : persona checklist *\/\n#lvm-shell .persona{margin:28px 0 10px;display:grid;grid-template-columns:1fr;gap:14px}\n#lvm-shell .persona-it{display:grid;grid-template-columns:54px 1fr;gap:16px;padding:22px 24px;background:#fff;border:1px solid var(--rule);border-radius:18px;align-items:center}\n#lvm-shell .persona-it.match{background:var(--pink-soft);border-color:rgba(255,45,110,.25)}\n#lvm-shell .persona-it .pic{width:54px;height:54px;border-radius:50%;display:flex;align-items:center;justify-content:center;background:var(--rule-soft);position:relative;font-family:var(--font-mono);font-size:13px;font-weight:600;color:var(--ink)}\n#lvm-shell .persona-it.match .pic{background:var(--pink);color:#fff}\n#lvm-shell .persona-it .pic svg{width:24px;height:24px}\n#lvm-shell .persona-it h4{margin:0 0 4px;font-size:17px;letter-spacing:-.01em}\n#lvm-shell .persona-it p{margin:0;font-size:14px;color:var(--muted);line-height:1.55}\n#lvm-shell .persona-it.match h4{color:var(--ink)}\n\n\/* Section 9 migration : timeline steps *\/\n#lvm-shell .steps{display:grid;grid-template-columns:repeat(5,1fr);gap:14px;margin:28px 0;position:relative}\n#lvm-shell .steps::before{content:\"\";position:absolute;top:14px;left:7px;right:calc(20% - 18px);height:1px;background:linear-gradient(90deg,var(--pink) 0%,var(--rule-soft) 100%);z-index:0}\n#lvm-shell .step{position:relative;padding-top:24px;z-index:1}\n#lvm-shell .step::before{content:\"\";position:absolute;top:8px;left:0;width:14px;height:14px;border-radius:50%;background:var(--pink);border:3px solid #fff;box-shadow:0 0 0 1px var(--rule)}\n#lvm-shell .step .sn{font-family:var(--font-mono);font-size:11px;color:var(--pink);font-weight:600;letter-spacing:.08em}\n#lvm-shell .step h4{margin:6px 0 6px;font-size:15px;letter-spacing:-.01em}\n#lvm-shell .step p{font-size:13px;color:var(--muted);line-height:1.5;margin:0}\n\n\/* Section 10 debloque : feature stack numbered XL *\/\n#lvm-shell .feat-stack{margin:30px 0 10px;border-top:1px solid var(--rule)}\n#lvm-shell .feat-it{display:grid;grid-template-columns:auto 1fr auto;gap:24px;padding:26px 0;border-bottom:1px solid var(--rule);align-items:center}\n#lvm-shell .feat-it .fn{font-family:var(--font-display);font-size:48px;font-weight:600;color:var(--pink);line-height:1;letter-spacing:-.04em;width:74px}\n#lvm-shell .feat-it .ft{font-family:var(--font-display);font-size:22px;font-weight:600;color:var(--ink);letter-spacing:-.015em;line-height:1.25;margin-bottom:6px}\n#lvm-shell .feat-it .fd{font-size:15px;color:var(--muted);line-height:1.55;margin:0}\n#lvm-shell .feat-it .fc{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;letter-spacing:.08em;color:var(--muted);font-weight:500}\n#lvm-shell .feat-it:last-child{border-bottom:0}\n\n#lvm-shell .faq{margin:22px 0}\n#lvm-shell .faq details{border-bottom:1px solid var(--rule);padding:20px 0}\n#lvm-shell .faq details:first-of-type{border-top:1px solid var(--rule)}\n#lvm-shell .faq summary{cursor:pointer;list-style:none;display:flex;justify-content:space-between;align-items:center;gap:18px;font-family:var(--font-display);font-size:20px;font-weight:500;letter-spacing:-.015em;color:var(--ink)}\n#lvm-shell .faq summary::-webkit-details-marker{display:none}\n#lvm-shell .faq summary::after{content:\"+\";font-size:24px;color:var(--muted);font-weight:300;line-height:1;transition:transform .25s, color .25s}\n#lvm-shell .faq details[open] summary::after{transform:rotate(45deg);color:var(--pink)}\n#lvm-shell .faq details[open] summary{color:var(--pink)}\n#lvm-shell .faq .ans{margin-top:14px;font-size:16px;color:var(--muted);line-height:1.65}\n\n#lvm-shell .get-band{background:var(--paper-2);border-radius:24px;padding:48px 36px;margin:60px 0 40px;text-align:center}\n#lvm-shell .get-band .kicker{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;color:var(--pink);font-weight:600;letter-spacing:.1em;margin-bottom:14px}\n#lvm-shell .get-band h3{font-size:36px;margin:0 0 14px;letter-spacing:-.025em}\n#lvm-shell .get-band p{font-size:16px;color:var(--muted);max-width:480px;margin:0 auto 26px}\n#lvm-shell .get-band .stores{display:flex;justify-content:center;gap:14px;flex-wrap:wrap}\n#lvm-shell .get-band .stores a{line-height:0;transition:transform .15s}\n#lvm-shell .get-band .stores a:hover{transform:translateY(-3px)}\n#lvm-shell .get-band .stores img{height:60px;width:auto;border-radius:11px}\n\n#lvm-shell .sources{font-size:14px;color:var(--muted);line-height:1.7}\n#lvm-shell .sources ol{padding-left:22px}\n#lvm-shell .sources li{margin-bottom:8px}\n\n#lvm-shell footer{padding:50px 0 60px;border-top:1px solid var(--rule);margin-top:40px}\n#lvm-shell footer .row{display:flex;justify-content:space-between;align-items:center;gap:18px;flex-wrap:wrap}\n#lvm-shell footer .kicker{font-family:var(--font-mono);font-size:11px;text-transform:uppercase;letter-spacing:.08em;color:var(--pink);font-weight:600}\n#lvm-shell footer p{font-size:13px;color:var(--muted);margin:8px 0 0}\n#lvm-shell footer .stores{display:flex;gap:8px}\n#lvm-shell footer .stores img{height:34px;width:auto;border-radius:6px}\n\n#lvm-shell .rev{opacity:0;transform:translateY(12px);transition:opacity .8s cubic-bezier(.22,.61,.36,1),transform .8s cubic-bezier(.22,.61,.36,1)}\n#lvm-shell .rev.on{opacity:1;transform:translateY(0)}\n@media (prefers-reduced-motion:reduce){#lvm-shell .rev{transition:none;opacity:1;transform:none}}\n\n@media (max-width:760px){\n  #lvm-shell .nav-row{padding:8px 18px;gap:8px}\n  #lvm-shell .nav-link{display:none}\n  #lvm-shell .nav-stores img{height:24px}\n  #lvm-shell .wrap{padding:0 22px}\n  #lvm-shell .hero{padding:34px 0 0}\n  #lvm-shell h1{font-size:46px;letter-spacing:-.035em}\n  #lvm-shell h1 .alt{font-size:.55em;margin-top:10px}\n  #lvm-shell .dek{font-size:20px}\n  #lvm-shell .hero-stores img{height:42px}\n  #lvm-shell .hero-bottom{grid-template-columns:1fr;gap:28px;margin:30px 0 40px;padding-top:24px;align-items:stretch}\n  #lvm-shell .phone-wrap{order:-1}\n  #lvm-shell .phone{width:240px}\n  #lvm-shell .tap-hint.desktop{display:none}\n  #lvm-shell .tap-hint.mobile{display:block;position:relative;left:auto;top:auto;text-align:center;margin:0 auto 10px;width:100%}\n  #lvm-shell .tap-hint.mobile .th-arrow{position:relative;display:block;margin:6px auto 0;width:34px;height:34px;transform:none;color:var(--pink)}\n  #lvm-shell .snippet{padding:24px 22px}\n  #lvm-shell .snippet p{font-size:18px}\n  #lvm-shell section{padding:48px 0}\n  #lvm-shell h2{font-size:34px;letter-spacing:-.03em}\n  #lvm-shell h3{font-size:24px}\n  #lvm-shell .section-label{margin-bottom:22px}\n  #lvm-shell .statement{padding:24px 0;margin:32px 0}\n  #lvm-shell .statement .num{font-size:44px}\n  #lvm-shell .statement .lbl{font-size:19px}\n  #lvm-shell .fig{padding:20px 14px 14px;border-radius:16px}\n  #lvm-shell .cv-wrap{height:310px}\n  #lvm-shell .method{grid-template-columns:1fr;gap:20px}\n  #lvm-shell .method.flip{grid-template-columns:1fr}\n  #lvm-shell .method.flip .m-phone{order:0}\n  #lvm-shell .mini-row{grid-template-columns:repeat(3,1fr);gap:10px}\n  #lvm-shell .mini-phone{padding:3px;border-radius:18px;border-width:1px;max-width:110px}\n  #lvm-shell .mini-phone .notch{width:42px;height:11px;border-radius:0 0 8px 8px}\n  #lvm-shell .mini-phone .scr{border-radius:15px}\n  #lvm-shell .mini-cap{font-size:10px}\n  #lvm-shell .mini-cap strong{font-size:13px}\n  #lvm-shell .duo-row{grid-template-columns:repeat(2,1fr);gap:12px}\n  #lvm-shell .duo-row .mini-phone{max-width:130px}\n  #lvm-shell .steps{grid-template-columns:1fr;gap:18px}\n  #lvm-shell .steps::before{display:none}\n  #lvm-shell .step{padding-top:0;padding-left:24px}\n  #lvm-shell .step::before{top:6px;left:0}\n  #lvm-shell .table-row{grid-template-columns:1.4fr .9fr .9fr}\n  #lvm-shell .table-row > .crit{padding:13px 12px;font-size:13px}\n  #lvm-shell .table-row > .cell{padding:13px 10px;font-size:12px;gap:8px}\n  #lvm-shell .table-row.head > div{padding:14px 12px;font-size:10px;gap:7px}\n  #lvm-shell .table-row.head .brand-cell img{width:20px;height:20px}\n  #lvm-shell .get-band{padding:36px 22px;border-radius:18px;margin:40px 0 30px}\n  #lvm-shell .get-band h3{font-size:28px}\n  #lvm-shell .get-band .stores img{height:50px}\n  #lvm-shell .cta-band{padding:22px;gap:14px}\n  #lvm-shell .cta-band .l{font-size:16px;min-width:0}\n  #lvm-shell .cta-band .stores img{height:38px}\n  #lvm-shell .faq summary{font-size:18px;gap:14px}\n  #lvm-shell .pyramid{max-width:100%}\n  #lvm-shell .pyramid .level{padding:11px 14px;font-size:14px}\n  #lvm-shell .scorecard{padding:20px 16px;border-radius:16px}\n  #lvm-shell .scorecard-head{grid-template-columns:1.2fr 1fr 1fr;column-gap:14px}\n  #lvm-shell .scorecard-head .h-brand{font-size:12px;gap:5px}\n  #lvm-shell .scorecard-head .h-brand img{width:18px;height:18px}\n  #lvm-shell .scorecard-row{grid-template-columns:1.2fr 1fr 1fr;column-gap:14px;padding:12px 0}\n  #lvm-shell .scorecard-row .crit{font-size:13px;padding-right:8px}\n  #lvm-shell .scorecard-row .bar{gap:6px}\n  #lvm-shell .scorecard-row .bar .v{font-size:11px;min-width:26px}\n  #lvm-shell .persona-it{grid-template-columns:44px 1fr;gap:12px;padding:16px 16px;border-radius:14px}\n  #lvm-shell .persona-it .pic{width:44px;height:44px;font-size:12px}\n  #lvm-shell .persona-it h4{font-size:15px}\n  #lvm-shell .persona-it p{font-size:13px}\n  #lvm-shell .feat-it{grid-template-columns:auto 1fr;gap:14px;padding:20px 0}\n  #lvm-shell .feat-it .fn{font-size:36px;width:54px}\n  #lvm-shell .feat-it .ft{font-size:18px}\n  #lvm-shell .feat-it .fd{font-size:13px}\n  #lvm-shell .feat-it .fc{display:none}\n}\n@media (max-width:480px){\n  #lvm-shell .phone-tabs{gap:5px}\n  #lvm-shell .phone-tabs button{padding:5px 8px;font-size:10px}\n  #lvm-shell .nav-stores{gap:4px}\n  #lvm-shell .nav-stores img{height:22px}\n  #lvm-shell .hero-stores img{height:40px}\n  #lvm-shell .crumb{font-size:12px}\n  #lvm-shell .table-row{grid-template-columns:1.3fr .85fr .85fr}\n  #lvm-shell .table-row > .crit{padding:11px 9px;font-size:12px}\n  #lvm-shell .table-row > .cell{padding:11px 8px;font-size:11px;gap:6px}\n  #lvm-shell .table-row.head > div{padding:11px 9px;font-size:9px;gap:5px}\n}<\/style>\n\n<style id=\"lvm-collision-reset\">\n\/* Hard reset for global theme styles that collide with our content *\/\nbody.postid-485 #lvm-shell .hero{display:block!important;align-items:initial!important;justify-content:initial!important;text-align:left!important;flex-direction:initial!important;padding:54px 0 0!important}\nbody.postid-485 #lvm-shell .wrap,\nbody.postid-485 #lvm-shell main.wrap{display:block!important;max-width:760px!important;margin-left:auto!important;margin-right:auto!important;padding-left:28px!important;padding-right:28px!important}\n@media (max-width:820px){\n  body.postid-485 #lvm-shell .wrap,\n  body.postid-485 #lvm-shell main.wrap{padding-left:18px!important;padding-right:18px!important}\n}\nhtml, body{overflow-x:hidden!important}\nbody.postid-485 #lvm-shell{overflow-x:hidden;max-width:100vw}\nbody.postid-485 #lvm-shell *{max-width:100%}\nbody.postid-485 #lvm-shell .nav-row{max-width:100vw;box-sizing:border-box}\nbody.postid-485 #lvm-shell.force-show .rev{opacity:1!important;transform:none!important}\n\n\/* === A.1 PHONE BACKGROUND CLASSES === *\/\nbody.postid-485 #lvm-shell .phone-bg{position:absolute;inset:0;width:100%;height:100%;background-size:cover;background-position:center top;background-repeat:no-repeat;transition:opacity .28s ease;background-color:#FAF0E6}\nbody.postid-485 #lvm-shell .phone-bg.tab-depense{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp)}\nbody.postid-485 #lvm-shell .phone-bg.tab-bilan{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp)}\nbody.postid-485 #lvm-shell .phone-bg.tab-kcal{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp)}\nbody.postid-485 #lvm-shell .phone-bg.tab-strategie{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-BMR{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-NEAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-EAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-TEF{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_TEF.webp)}\n\n\/* === v11.3 CTA BANDS MOBILE (badges plus gros + centrage) === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .cta-band{flex-direction:column!important;align-items:center!important;text-align:center!important;padding:26px 22px!important;gap:20px!important}\n  body.postid-485 #lvm-shell .cta-band .l{min-width:0!important;width:100%!important;font-size:16px!important;line-height:1.5!important;text-align:center!important}\n  body.postid-485 #lvm-shell .cta-band .stores{width:100%!important;justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-485 #lvm-shell .cta-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-485 #lvm-shell .cta-band .stores picture{width:100%!important;display:block!important}\n  body.postid-485 #lvm-shell .cta-band .stores img{height:56px!important;width:100%!important;max-width:170px!important;object-fit:contain!important;object-position:center!important;border-radius:10px!important}\n  body.postid-485 #lvm-shell .get-band{padding:38px 22px!important}\n  body.postid-485 #lvm-shell .get-band .stores{justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-485 #lvm-shell .get-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-485 #lvm-shell .get-band .stores picture{width:100%!important;display:block!important}\n  body.postid-485 #lvm-shell .get-band .stores img{height:56px!important;width:100%!important;max-width:170px!important;object-fit:contain!important;object-position:center!important;border-radius:10px!important}\n  body.postid-485 #lvm-shell .get-band h3{font-size:26px!important;line-height:1.2!important}\n  body.postid-485 #lvm-shell .get-band p{font-size:15px!important}\n}\n\n\/* === v11.2 BRAND BANNER above table responsive === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .brand-banner img{width:54px!important;height:54px!important}\n  body.postid-485 #lvm-shell .brand-banner > div{padding:16px 12px!important;gap:8px!important}\n  body.postid-485 #lvm-shell .brand-banner > div > div{font-size:15px!important}\n}\n\n\/* === v11.4 SCORECARD partie 7: redesign mobile === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .scorecard{padding:18px 16px!important;border-radius:16px!important}\n  body.postid-485 #lvm-shell .scorecard-head{display:none!important}\n  body.postid-485 #lvm-shell .scorecard-row{\n    display:block!important;\n    padding:14px 0!important;\n    border-bottom:1px solid #E8E2D6!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .crit{\n    display:block!important;\n    font-size:13px!important;\n    font-weight:600!important;\n    color:#0E0E10!important;\n    margin-bottom:10px!important;\n    padding-right:0!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar{\n    display:grid!important;\n    grid-template-columns:54px 1fr 32px!important;\n    column-gap:8px!important;\n    align-items:center!important;\n    padding:5px 0!important;\n    flex-direction:initial!important;\n    position:relative!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar::before{\n    content:attr(data-brand)!important;\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:11px!important;\n    font-weight:600!important;\n    text-transform:uppercase!important;\n    letter-spacing:.05em!important;\n    color:#0E0E10!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar.lean::before{color:#FF2D6E!important}\n  body.postid-485 #lvm-shell .scorecard-row .bar.mfp::before{color:#5B7FFF!important}\n  body.postid-485 #lvm-shell .scorecard-row .bar .b{\n    height:10px!important;\n    width:100%!important;\n    border-radius:99px!important;\n    position:relative!important;\n    background:#EFEAE0!important;\n    overflow:hidden!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar .b > i{\n    display:block!important;\n    height:100%!important;\n    border-radius:99px!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar .v{\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:12px!important;\n    font-weight:700!important;\n    color:#0E0E10!important;\n    min-width:0!important;\n    text-align:right!important;\n  }\n}\n\n\/* === A.2 CHARTS MOBILE === *\/\n@media (max-width:760px){\n  \/* v13: charts FULL WIDTH (less card padding) + plus hauts pour vraie respiration *\/\n  body.postid-485 #lvm-shell .cv-wrap{height:380px!important;min-height:360px!important;max-height:420px!important;width:100%!important}\n  body.postid-485 #lvm-shell .cv-wrap canvas{width:100%!important;height:100%!important;display:block!important}\n  body.postid-485 #lvm-shell .fig{padding:16px 4px 14px!important;margin:24px -4px 14px!important;overflow:visible!important}\n  body.postid-485 #lvm-shell .fig-head{padding:0 12px!important;flex-wrap:wrap!important;gap:6px!important;margin-bottom:10px!important}\n  body.postid-485 #lvm-shell .fig-body{padding:0 2px!important}\n  body.postid-485 #lvm-shell .fig-cap{padding:0 12px!important;font-size:13px!important;margin-top:10px!important}\n}\n@media (max-width:480px){\n  body.postid-485 #lvm-shell .cv-wrap{height:360px!important;min-height:340px!important;max-height:380px!important}\n  body.postid-485 #lvm-shell .fig{padding:14px 2px 12px!important;margin:20px -6px 12px!important;border-radius:14px!important}\n  body.postid-485 #lvm-shell .fig-body{padding:0!important}\n}\n\n\/* === v11.2 TABLEAU MOBILE STACKED CARDS avec mini-tags Lean\/MFP === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .table{border-radius:14px!important}\n  body.postid-485 #lvm-shell .table-row.head{display:none!important}\n  body.postid-485 #lvm-shell .table-row{\n    display:grid!important;\n    grid-template-columns:1fr 1fr!important;\n    grid-template-areas:\"crit crit\" \"lean mfp\"!important;\n    gap:0!important;\n    min-height:0!important;\n  }\n  body.postid-485 #lvm-shell .table-row > .crit{\n    grid-area:crit!important;background:#0E0E10!important;color:#fff!important;\n    padding:11px 14px!important;font-size:13px!important;font-weight:600!important;\n    letter-spacing:-0.1px!important;border-right:0!important;line-height:1.35!important;\n    font-family:-apple-system,BlinkMacSystemFont,'SF Pro 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The problem has never been effectiveness, it is adherence: manual food-by-food entry wears everyone down, and nearly 80% of people quit within 3 months. This guide compares the 4 methods to count your calories, from the fastest (AI photo scan, 5 seconds) to the most precise (database + scale), and tells you which one to pick for your profile.\n    <\/div>\n        <div class=\"phone-wrap rev\">\n      <div class=\"phone-stage\">\n        <div class=\"tap-hint mobile\" id=\"tapHintMobile\" aria-hidden=\"true\">\n          <span class=\"th-pill\"><small>Interactive demo<\/small>Tap the screen to explore the app<\/span>\n          <svg class=\"th-arrow\" viewbox=\"0 0 24 24\" fill=\"none\" aria-hidden=\"true\">\n            <path d=\"M12 4 L12 20 M5 13 L12 20 L19 13\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n          <\/svg>\n        <\/div>\n        <div class=\"tap-hint desktop\" id=\"tapHintDesktop\" aria-hidden=\"true\">\n          <span class=\"th-pill\"><small>Interactive demo<\/small>Tap the screen<br>to explore the app<\/span>\n          <svg class=\"th-arrow\" viewbox=\"0 0 104 34\" fill=\"none\" aria-hidden=\"true\">\n            <path d=\"M4 9 C 34 1, 64 20, 94 27\" stroke=\"currentColor\" stroke-width=\"2.6\" fill=\"none\" stroke-linecap=\"round\"\/>\n            <path d=\"M86 20 L 94 27 L 84 30\" stroke=\"currentColor\" stroke-width=\"2.6\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n          <\/svg>\n        <\/div>\n        <div class=\"phone\" id=\"phone\" role=\"img\" aria-label=\"Preview of the Lean app with TDEE drill-down\">\n          <div class=\"notch\"><\/div>\n          <div class=\"phone-screen\">\n            <button class=\"phone-back\" id=\"phoneBack\" aria-label=\"Back\">&#8249;<\/button>\n            <div id=\"phoneImg\" class=\"phone-bg tab-depense\" role=\"img\" aria-label=\"Lean preview, Expenditure tab\"><\/div>\n            <div class=\"phone-zones\" id=\"phoneZones\">\n              <div class=\"z\" data-sub=\"BMR\"  style=\"top:11%;height:21%\" role=\"button\" tabindex=\"0\" aria-label=\"BMR detail\"><\/div>\n              <div class=\"z\" data-sub=\"NEAT\" style=\"top:33%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"NEAT detail\"><\/div>\n              <div class=\"z\" data-sub=\"EAT\"  style=\"top:50%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"EAT detail\"><\/div>\n              <div class=\"z\" data-sub=\"TEF\"  style=\"top:67%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"TEF detail\"><\/div>\n            <\/div>\n            <div class=\"phone-navbar\" id=\"phoneNav\" aria-hidden=\"false\">\n              <button data-tab=\"bilan\"     type=\"button\" aria-label=\"Balance tab\"><\/button>\n              <button data-tab=\"kcal\"      type=\"button\" aria-label=\"Calories tab\"><\/button>\n              <button data-tab=\"depense\"   type=\"button\" aria-label=\"Expenditure tab\"><\/button>\n              <button data-tab=\"strategie\" type=\"button\" aria-label=\"Strategy tab\"><\/button>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div class=\"phone-tabs\" role=\"tablist\" aria-label=\"Navigate the Lean app\">\n          <button data-tab=\"bilan\"     type=\"button\">Balance<\/button>\n          <button data-tab=\"kcal\"      type=\"button\">Calories<\/button>\n          <button data-tab=\"depense\"   type=\"button\" class=\"on\">Expenditure<\/button>\n          <button data-tab=\"strategie\" type=\"button\">Strategy<\/button>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"snippet rev\">\n    <div class=\"lbl\">Quick answer<\/div>\n    <p>Four methods to count your calories: the <strong>AI photo scan<\/strong> (you photograph your plate, the AI identifies every food and its quantity), the <strong>barcode scan<\/strong> (packaged products, manufacturer data), the <strong>database<\/strong> (search + weighing, the most precise mode) and<strong>quick add<\/strong> (a global envelope in 10 seconds). The right method is the one you will stick with for 6 months, not the most precise on paper. And counting what goes in only helps if your expenditure, your <span data-term=\"TDEE\">TDEE<span class=\"tt\">Total Daily Energy Expenditure: your total calorie expenditure over 24 h. TDEE = BMR + NEAT + EAT + TEF.<\/span><\/span>, is calculated right.<\/p>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"constat\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">01 &middot; The finding<\/span><\/div>\n  <h2 id=\"constat\">Why 80% of people quit counting within 3 months<\/h2>\n  <p>Counting calories works. That is not the issue. The issue is that almost nobody sticks with it: studies on weight-loss programs show massive attrition within the first weeks, well before the first visible result on the scale.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">80%<\/div>\n    <div class=\"lbl\">of people enrolled in a weight-loss program drop out within the first 3 months (Alexander et al., 2018). The dominant cause: tracking friction, not lack of willpower.<\/div>\n  <\/div>\n\n  <p>The friction, concretely: searching &ldquo;chicken breast&rdquo; in a list, picking the right entry among 40 duplicates, eyeballing 150 g, starting over for the beans, then for the olive oil. Three times a day. 90 times a month. That bookkeeping is what kills tracking, not the idea of tracking.<\/p>\n\n  <p>Practical conclusion: the choice of your logging method is not an ergonomic detail. It is THE variable that decides whether your data will still exist in 3 months. Here are the 4 methods, ranked from fastest to most demanding.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"scan-ia\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">02 &middot; Method 1<\/span><\/div>\n  <h2 id=\"scan-ia\">The AI photo scan: 5 seconds per meal<\/h2>\n  <p>You take a photo of your plate. Lean&rsquo;s AI runs a <strong>double identification<\/strong> : first it recognizes every food present (grilled chicken, green beans), then it estimates the quantity of each. The result is not one global number out of a hat: it is a food-by-food breakdown, with calories and macros for every line.<\/p>\n\n  <figure style=\"margin:26px auto;max-width:250px;text-align:center\"><video controls playsinline preload=\"none\" poster=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/06\/poster-video-scan-ia-lean.webp\" width=\"1080\" height=\"1920\" style=\"width:100%;height:auto;border-radius:16px;display:block;background:#F7F6F2\"><source src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/07\/ai-food-scanner-lean-app.mp4\" type=\"video\/mp4\"><\/video><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">The AI scan in real conditions: photo, analysis, result.<\/figcaption><\/figure>\n\n  <p>In pictures, step by step, on a real meal:<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/ai-food-scan-nutrition-analysis-lean-app.jpg\" alt=\"Scan IA dans Lean : photo du plat prise, champ de d\u00e9tails optionnel au clavier\" width=\"511\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">You frame the shot. A keyboard detail, if you want.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/ai-analysis-chicken-green-beans-meal-lean-app.jpg\" alt=\"Analyse IA en cours dans Lean : la photo du plat est trait\u00e9e\" width=\"519\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">The AI analyzes the plate.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/ai-food-scan-result-chicken-meal-lean-app.jpg\" alt=\"R\u00e9sultat du scan IA : chaque aliment compt\u00e9 avec calories et macros\" width=\"519\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Every food counted.<\/figcaption><\/figure><\/div>\n\n  <p>On everyday dishes, the typical error is around &plusmn;10% per meal. Less precise than a scale, and largely enough to drive fat loss (we come back to this with the law of large numbers below). Above all, it is the only method that covers meals without a label: home, restaurant, cafeteria.<\/p>\n\n  <h3>Where Lean goes further: you stay in control<\/h3>\n  <p>An AI scan is only useful if you can correct what it suggests. Lean gives you three levers, per food:<\/p>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Lever 1<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Edit the quantities<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">One slider per food: here, bread estimated at 101.4 g by the AI, corrected to 70 g. Calories and dish total recalculate instantly.<\/p><div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/updated-total-calories-after-ai-scan-adjustment.jpg\" alt=\"Repas scann\u00e9 dans Lean avant correction : pain estim\u00e9 \u00e0 101,4 g\" width=\"519\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Bread estimated at 101.4 g by the AI.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/edit-ai-scan-result-quantities-macros-lean-app.jpg\" alt=\"Slider de quantit\u00e9 dans Lean : pain corrig\u00e9 \u00e0 70 g, recalcul instantan\u00e9\" width=\"514\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Corrected to 70 g, everything recalculates.<\/figcaption><\/figure><\/div><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Lever 2<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Edit the macros<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">You know your recipe better than the average? Here, the chips: the AI suggests 4.1 g of fat, you correct to 14 g. Donut and total follow.<\/p><div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/editing-macronutrients-ai-scanned-food-lean-app.jpg\" alt=\"Macros des chips estim\u00e9es par l'IA dans Lean : 4,1 g de lipides\" width=\"527\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Chips according to the AI: 4.1 g of fat.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/editing-chips-quantity-ai-scan-result-lean-app.jpg\" alt=\"Macros des chips corrig\u00e9es \u00e0 la main dans Lean : 14 g de lipides\" width=\"517\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Hand-corrected: 14 g.<\/figcaption><\/figure><\/div><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Lever 3<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Re-scan a single food<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">The AI mistook turkey for cheese? You type the right name, the line re-analyzes itself, without redoing the whole dish.<\/p><div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/editing-macronutrients-chips-ai-scan-lean-app.jpg\" alt=\"Re-scan d'un aliment dans Lean : saisie du bon nom au clavier\" width=\"516\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">You type: turkey ham.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/final-result-after-ai-scan-correction-lean-app.jpg\" alt=\"R\u00e9sultat apr\u00e8s re-scan dans Lean : jambon de dinde identifi\u00e9, total recalcul\u00e9\" width=\"518\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Turkey ham, 53 kcal. Total recalculated.<\/figcaption><\/figure><\/div><\/div><\/div>\n\n  <h3>The test against Cal AI: breakdown versus envelope<\/h3>\n  <p>Same photo, two apps. The difference is plain to see:<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0\"><figure style=\"flex:1;max-width:190px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/lean-app-vs-cal-ai-meal-scan-comparison.jpg\" alt=\"Comparatif scan IA : la m\u00eame photo de repas donn\u00e9e \u00e0 Lean et \u00e0 Cal AI\" width=\"532\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:18px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">The same photo, given to both apps.<\/figcaption><\/figure><\/div>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:center;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/lean-app-ai-scan-result-sandwich-chips.jpg\" alt=\"R\u00e9sultat Lean : sandwich et chips d\u00e9compos\u00e9s aliment par aliment\" width=\"533\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Lean: every food, detailed total.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:200px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/07\/cal-ai-app-ai-meal-scan-analysis.webp\" alt=\"R\u00e9sultat Cal AI : enveloppe globale du plat sans d\u00e9composition\" width=\"869\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:18px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Cal AI: one global envelope per dish.<\/figcaption><\/figure><\/div>\n\n  <p>That is the structural difference with most scanning apps, Cal AI first among them: they return one <strong>single overall value,<\/strong> per dish, one total of calories and macros with no editable food-by-food breakdown. If the estimate is wrong, you take it or you toss it. With a food-by-food breakdown, the error gets fixed in two taps.<\/p>\n\n  <p><strong>The right use:<\/strong> home-cooked meals, restaurants, cafeterias, every meal without a barcode. The default method for 90% of meals.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"code-barres\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">03 &middot; Method 2<\/span><\/div>\n  <h2 id=\"code-barres\">Barcode scan: label-grade precision<\/h2>\n  <p>For anything that comes out of a package, the barcode is unbeatable: nutrition values come straight from manufacturer data. Lean relies on the USDA and OpenFoodFacts databases, millions of references. You scan, you adjust the quantity, done.<\/p>\n\n  <figure style=\"margin:26px auto;max-width:250px;text-align:center\"><video controls playsinline preload=\"none\" poster=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/06\/poster-video-scan-code-barres-lean.webp\" width=\"1080\" height=\"1920\" style=\"width:100%;height:auto;border-radius:16px;display:block;background:#F7F6F2\"><source src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/Barcode-Scan-calorie-lean-app.mp4\" type=\"video\/mp4\"><\/video><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Barcode scan: exact product sheet, direct add.<\/figcaption><\/figure>\n\n  <p>The limit is obvious: your homemade plate of pasta has no barcode. That is why this method naturally combines with the AI photo scan; it does not replace it.<\/p>\n\n  <p><strong>The right use:<\/strong> groceries, snacks, processed products, shakers. Maximum precision on packaged food, zero effort.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"base-donnees\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">04 &middot; Method 3<\/span><\/div>\n  <h2 id=\"base-donnees\">Database + scale: precision mode<\/h2>\n  <p>The historical method: you weigh your food, look it up in the database, enter the grams. With a kitchen scale, it is the most precise method there is, the one used for competition prep and clinical protocols.<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/food-logging-features-search-scan-lean-app.jpg\" alt=\"Recherche d'un aliment dans la base de donn\u00e9es Lean avec historique et ajout rapide\" width=\"502\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Search, history, two-tap add.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/nutritional-details-canned-tuna-lean-app.jpg\" alt=\"Fiche nutritionnelle exacte d'un produit dans Lean avec impact sur les budgets du jour\" width=\"501\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Exact product sheet, live impact.<\/figcaption><\/figure><\/div>\n\n  <p>The &ldquo;Remaining \/ Impact&rdquo; panel shows you, before you even confirm, what the food changes on your daily budgets: calories, protein, carbs, fat, fiber. You decide with full knowledge, not after the fact.<\/p>\n\n  <p>Lean cuts the friction to a minimum: history of your frequent foods, search across USDA + OpenFoodFacts, two-tap add from the list. But let&rsquo;s be honest about the real cost: 2 to 3 minutes per meal, plus the discipline of weighing. That friction is precisely what produces the 80% dropout of the classic method.<\/p>\n\n  <p><strong>The right use:<\/strong> strict cutting, advanced recomposition, weight-class sports. And your 5 to 10 recurring everyday foods, which history makes near-instant.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"ajout-rapide\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">05 &middot; Method 4<\/span><\/div>\n  <h2 id=\"ajout-rapide\">Quick add: the envelope that saves the day<\/h2>\n  <p>Birthday buffet, overloaded day, standing meal between two meetings: some meals will never be tracked in detail. Quick add lets you log a global envelope, estimated calories and macros, in 10 seconds. Less precise, infinitely better than a data gap.<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/quick-add-calories-macros-lean-app.jpg\" alt=\"Formulaire d'ajout rapide dans Lean : nom, calories et macros saisis \u00e0 la main\" width=\"501\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Name, calories, macros: 10 seconds.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/quick-add-meal-logged-in-food-diary-lean-app.jpg\" alt=\"Ajout rapide enregistr\u00e9 dans le journal alimentaire Lean\" width=\"501\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">One envelope, day saved.<\/figcaption><\/figure><\/div>\n\n  <p>The live calorie balance does the rest: in the capture, a 2,143 kcal target, 1,000 consumed, 1,143 remaining, recalculated continuously throughout the day. An approximate but tracked day beats a perfect abandoned day: the weekly average drives the result, not the purity of one isolated day.<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/nutritional-details-quick-add-meal-lean-app.jpg\" alt=\"D\u00e9tails nutritionnels d'un repas en ajout rapide dans Lean : restants et impact\" width=\"504\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Remaining and impact, recalculated live.<\/figcaption><\/figure><\/div>\n\n  <p><strong>The right use:<\/strong> busy days, social meals, anti-quitting backup. The method that prevents &ldquo;I missed one meal, the week is ruined&rdquo;.<\/p>\n\n  <div class=\"hero-stores\" style=\"margin:36px 0 12px\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"T\u00e9l\u00e9charger sur l'App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Disponible sur Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <span class=\"or\">Free download<\/span>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"profils\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">06 &middot; Choosing<\/span><\/div>\n  <h2 id=\"profils\">Which method for which profile<\/h2>\n  <p>First, the match between the 4 methods on the 4 criteria that matter: ease per meal, availability (does it work everywhere, all the time), long-term staying power and accuracy.<\/p>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><h3 style=\"margin:0 0 2px;font-size:17px\">AI photo scan<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">One photo, 5 seconds<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Ease<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Availability<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Long term<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Accuracy<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><\/div><\/div>\n    <div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #0F8F5C\"><h3 style=\"margin:0 0 2px;font-size:17px\">Barcode<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">The exact label<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Ease<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Availability<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Long term<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Accuracy<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><\/div><\/div>\n    <div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #C8A019\"><h3 style=\"margin:0 0 2px;font-size:17px\">Database + scale<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">Precision mode<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Ease<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Availability<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Long term<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Accuracy<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><\/div><\/div>\n    <div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #6E6E73\"><h3 style=\"margin:0 0 2px;font-size:17px\">Quick add<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">The backup envelope<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Ease<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Availability<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Long term<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Accuracy<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><\/div><\/div><\/div>\n\n  <p style=\"font-size:13px;color:#6E6E73;margin-top:-6px\"><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span> excellent &nbsp; <span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span> good &nbsp; <span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span> weak spot. &ldquo;Long term&rdquo; = the probability you will still be using the method in 6 months.<\/p>\n\n  <p>Reading the match: no method wins everywhere. The AI scan dominates on adherence, database + scale on precision, and quick add only exists to plug the gaps. Hence the profiles: the 4 methods are not mutually exclusive, the right strategy is a mix, dosed to your current standards and your schedule. Three profiles cover nearly every case.<\/p>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Profile 1 \u00b7 Beginner<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Zero friction, learn by doing<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">AI photo scan everywhere, barcode for packaged food, no weighing. Single goal: last 3 months and train your eye on portions. Precision comes later, habit comes first.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #0F8F5C\"><div class=\"kicker\" style=\"color:#0F8F5C;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Profile 2 \u00b7 Maximum rigor<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Every percent counts<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Scale + database for home meals, barcode for packaged food, AI scan at restaurants. For short phases where precision really pays: end of a cut, official weigh-in.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #C8A019\"><div class=\"kicker\" style=\"color:#C8A019;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Profile 3 \u00b7 Busy or advanced<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Reliable data without thinking about it<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Barcode and AI scan day to day, quick add as a backup on impossible days. You already know your portions, you just want the data to exist.<\/p><\/div><\/div>\n\n  <p>Tie-breaking rule, valid for everyone: when in doubt, pick the lightest method you are certain to stick with. Perfect data you stop logging after 3 weeks is worth zero.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"precision\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">07 &middot; Accuracy<\/span><\/div>\n  <h2 id=\"precision\">The &plusmn;10% error that vanishes over a month<\/h2>\n  <p>The classic objection to the AI scan: &ldquo;&plusmn;10% error is too much&rdquo;. That is reasoning on one isolated meal. But you are not tracking one meal: you track about 90 per month.<\/p>\n\n  <div role=\"img\" aria-label=\"Twelve scanned meals: overestimates and underestimates cancel out around your actual intake\" style=\"margin:24px 0;padding:24px 20px 14px;background:#F7F6F2;border-radius:12px\"><div style=\"position:relative;height:150px;display:flex;align-items:flex-start;gap:6px\"><div style=\"position:absolute;left:0;right:0;top:74px;height:2px;background:#0E0E10;z-index:2\"><\/div><div style=\"flex:1;height:58px;margin-top:17px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:30px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:22px;margin-top:53px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:48px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:40px;margin-top:35px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:12px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:30px;margin-top:45px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:55px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:18px;margin-top:57px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:35px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:45px;margin-top:30px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:20px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><\/div><div style=\"display:flex;justify-content:space-between;margin-top:10px;font-size:12px;color:#6E6E73\"><span>Meal 1<\/span><span>Meal 12<\/span><\/div><p style=\"margin:10px 0 0;font-size:13px;color:#3A3A3C;line-height:1.5;text-transform:none\">Pink: overestimated meals. Green: underestimated meals. Black line: your actual intake. Over a month, the gaps cancel out.<\/p><\/div>\n\n  <p>That is the law of large numbers (Kolmogorov): independent random errors cancel out as the number of observations grows. Monday&rsquo;s +12% cancels Tuesday&rsquo;s missing 8%. What remains over a month is the model&rsquo;s average bias, close to zero on a well-calibrated scan, not the noise of one meal.<\/p>\n\n  <p>Manual entry suffers from a problem of an entirely different nature: a <strong>systematic bias<\/strong>. Humans under-report what they eat, always in the same direction, and a bias never smooths out.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">47%<\/div>\n    <div class=\"lbl\">gap between hand-reported calories and calories actually consumed, measured with doubly labeled water in subjects failing their diet (Lichtman, NEJM 1992). Human bias never averages out; the random noise of a scan does.<\/div>\n  <\/div>\n\n  <p>The takeaway: the question is not &ldquo;is the AI scan perfect?&rdquo; but &ldquo;which method produces the most accurate data over 90 meals?&rdquo;. And there, automation wins, because it replaces a directional bias with noise that cancels itself out.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"depense\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">08 &middot; Expenditure<\/span><\/div>\n  <h2 id=\"depense\">Counting is not enough: the other half of the equation is missing<\/h2>\n  <p>A perfect food diary says nothing on its own. Losing fat is a negative energy balance: intake below expenditure. If your expenditure is miscalculated, your deficit is an accounting fiction, and you can count your calories to the gram without ever moving forward.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">TDEE = BMR + NEAT + EAT + TEF<\/div>\n    <div class=\"lbl\">The canonical equation of energy expenditure. <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/metabolisme-de-base\/\">BMR<\/a> = basal metabolic rate. <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/neat-depense-non-sportive\/\">NEAT<\/a> = non-exercise activity. <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/eat\/\">EAT<\/a> = workouts. <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/effet-thermique-des-aliments\/\">TEF<\/a> = digestion.<\/div>\n  <\/div>\n\n  <p>The problem: almost every app estimates your expenditure with Harris-Benedict (1919) or Mifflin-St Jeor (1990), raw-weight formulas that ignore your body composition. At equal weight, a body at 15% bodyfat and a body at 30% do not burn anywhere near the same. Lean calculates expenditure differently, in four steps:<\/p>\n\n  <div class=\"bodyscan-illust\">\n    <div class=\"mini-phone\">\n      <div class=\"notch\"><\/div>\n      <div class=\"scr\" style=\"background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp);background-size:cover;background-position:center top;background-repeat:no-repeat\"><\/div>\n    <\/div>\n    <div class=\"mini-cap\" style=\"text-align:center;font-size:13px;color:#6E6E73;max-width:200px\">Real bodyfat. One photo, 5 seconds.<\/div>\n  <\/div>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Step 1<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">AI BodyScan<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">You take a photo in the app. The AI estimates your bodyfat from your visible morphology, redone every week. That measurement is what anchors the whole calculation on your lean mass, not your raw weight.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Step 2<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">BMR via patented proprietary model<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Lean&rsquo;s patented algorithm calculates your basal metabolic rate from your real lean mass. More precise than Harris-Benedict 1919 or Mifflin-St Jeor 1990, which only know your weight.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Step 3<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">NEAT, EAT and TEF measured, not flat-rated<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Your steps count in NEAT, your workouts in EAT via the reference MET tables, and TEF is calculated from the macros you actually ate, precisely thanks to your tracking.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Step 4<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Metabolic adaptation recalculated<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">In a prolonged deficit, BMR drops. Lean is the first app to model this coefficient and apply it multiplicatively to BMR. Convention: 100% = optimal, 90% = 10 points of adaptation.<\/p><\/div><\/div>\n\n  <p>Result: the live calorie balance compares what you eat (counted with the 4 methods in this guide) to what you actually burn, recalculated continuously. It is the combination of both halves, accurate input and accurate output, that makes fat loss predictable. One without the other is bookkeeping in a vacuum.<\/p>\n\n  <div class=\"hero-stores\" style=\"margin:36px 0 12px\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"T\u00e9l\u00e9charger sur l'App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Disponible sur Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <span class=\"or\">Free download<\/span>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"faq\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">09 &middot; FAQ<\/span><\/div>\n  <h2 id=\"faq\">Frequently asked questions about calorie counting<\/h2>\n  <div class=\"faq\">\n    <details><summary>How many calories per day to lose weight?<\/summary><div class=\"ans\">There is no universal number: everything depends on your total expenditure (TDEE), which varies with your basal metabolism, daily activity, training and body composition. The reliable rule: a deficit of 300 to 500 kcal below your real TDEE, never a generic number like &ldquo;1,500 kcal&rdquo; pulled out of thin air. You can estimate yours with the <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/calculateur-deficit-calorique\/\">calorie deficit calculator<\/a>.<\/div><\/details>\n    <details><summary>Is the AI photo scan reliable for counting calories?<\/summary><div class=\"ans\">Yes, as long as you understand what is being measured. On everyday dishes, the typical error is around &plusmn;10% per meal, with a food-by-food identification you can correct (quantities, macros, re-scan). Over a month, roughly 90 meals, random errors cancel out through the law of large numbers: what matters is the average bias, not the error of one isolated meal.<\/div><\/details>\n    <details><summary>Do you need to weigh your food to count calories?<\/summary><div class=\"ans\">Only if you are in a phase where every percent counts: strict cutting, advanced recomposition, weight-class sports. For the vast majority of people, AI photo scan and barcodes are more than enough. Adherence over 6 months weighs far more in the final result than the theoretical precision of a weighing routine you will abandon.<\/div><\/details>\n    <details><summary>Do you need to count calories every day?<\/summary><div class=\"ans\">The weekly average drives the result, not daily perfection. On busy days, a 10-second quick add beats a data gap by a mile. One approximate meal breaks nothing; a week with no data at all does, because it hides the trend.<\/div><\/details>\n    <details><summary>Why am I not losing weight even though I count my calories?<\/summary><div class=\"ans\">Two causes dominate. One, under-reporting: up to a 47% measured gap between hand-logged calories and calories actually consumed (Lichtman, 1992); the AI scan reduces that bias by measuring the plate instead of your memory. Two, overestimated expenditure: most apps calculate your TDEE with raw-weight formulas that ignore your bodyfat and metabolic adaptation. If the output side is wrong, the deficit on screen does not exist.<\/div><\/details>\n    <details><summary>Does counting calories make you obsessive?<\/summary><div class=\"ans\">The goal is exactly the opposite: bring tracking down to a few seconds per meal so it takes up zero mental space, whereas manual bookkeeping can become invasive. A few months of tracking train your eye on portions; then you can lighten up, or switch to quick add. If you have a diagnosed eating disorder, talk to a healthcare professional before tracking.<\/div><\/details>\n    <details><summary>What is the difference between Lean and MyFitnessPal for counting calories?<\/summary><div class=\"ans\">MyFitnessPal relies on a giant collaborative database but mostly manual entry, and a TDEE estimated from raw body weight. Lean breaks every dish down food by food with the AI scan, and above all calculates your expenditure from your real bodyfat measured by BodyScan, with metabolic adaptation. Counting what goes in only makes sense if what goes out is right. The app-by-app breakdown is in our <a class=\"inline\" href=\"https:\/\/lean-app.com\/en\/meilleures-applications-calories-2026\/\">comparison of the best calorie apps<\/a>.<\/div><\/details>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"src\" class=\"sources\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">Sources<\/span><\/div>\n  <h3 id=\"src\" style=\"margin-top:0;color:var(--ink)\">References<\/h3>\n  <ol class=\"src-list\">\n    <li>Burke L.E., Wang J., Sevick M.A. (2011). Self-monitoring in weight loss: a systematic review of the literature. <em>J Am Diet Assoc<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/21185970\/\" target=\"_blank\" rel=\"noopener\">PubMed 21185970<\/a>.<\/li>\n    <li>Alexander E., Tseng E., Durkin N. et al. (2018). Factors associated with early dropout in an employer-based commercial weight-loss program. <em>Obes Sci Pract<\/em>. <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC6298204\/\" target=\"_blank\" rel=\"noopener\">PMC6298204<\/a>.<\/li>\n    <li>Lichtman S.W. et al. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. <em>N Engl J Med<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/1454084\/\" target=\"_blank\" rel=\"noopener\">PubMed 1454084<\/a>.<\/li>\n    <li>Martin C.K. et al. (2009). A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. <em>Br J Nutr<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/18616837\/\" target=\"_blank\" rel=\"noopener\">PubMed 18616837<\/a>.<\/li>\n    <li>USDA FoodData Central. The reference nutrition database. <a href=\"https:\/\/fdc.nal.usda.gov\/\" target=\"_blank\" rel=\"noopener\">fdc.nal.usda.gov<\/a>.<\/li>\n    <li>Open Food Facts. The global collaborative database of packaged food products. <a href=\"https:\/\/world.openfoodfacts.org\/\" target=\"_blank\" rel=\"noopener\">world.openfoodfacts.org<\/a>.<\/li>\n    <li>Hall K.D. et al. (2011). Quantification of the effect of energy imbalance on bodyweight. <em>Lancet<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/21872751\/\" target=\"_blank\" rel=\"noopener\">PubMed 21872751<\/a>.<\/li>\n    <li>Mifflin M.D. et al. (1990). A new predictive equation for resting energy expenditure in healthy individuals. <em>Am J Clin Nutr<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/2305711\/\" target=\"_blank\" rel=\"noopener\">PubMed 2305711<\/a>.<\/li>\n    <li>Harris J.A., Benedict F.G. (1919). A Biometric Study of Basal Metabolism in Man. Carnegie Institution of Washington.<\/li>\n    <li>Ainsworth B.E. et al. (2011). Compendium of Physical Activities: a second update of codes and MET values. <em>Med Sci Sports Exerc<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/21681120\/\" target=\"_blank\" rel=\"noopener\">PubMed 21681120<\/a>.<\/li>\n  <\/ol>\n<\/section>\n\n<\/main>\n\n<footer>\n  <div class=\"wrap\">\n    <div class=\"row\">\n      <div>\n        <div class=\"kicker\">Lean &middot; lean-app.com<\/div>\n        <p>Guide published July 13, 2025 by The Lean Team. Updated June 11, 2026: full redesign, new AI scan captures and scientific sources. Lean is available on iOS and Android.<\/p>\n      <\/div>\n      <div class=\"stores\" style=\"display:flex;gap:10px;align-items:center;flex-wrap:wrap\">\n        <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" style=\"height:48px;width:auto;border-radius:9px;display:block\" \/><\/a>\n        <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" style=\"height:48px;width:auto;border-radius:9px;display:block\" \/><\/a>\n      <\/div>\n    <\/div>\n  <\/div>\n<\/footer>\n\n<script data-wpmeteor-nooptimize=\"true\">\n(function(){\n  var bar = document.getElementById('progBar');\n  function up(){\n    var h = document.documentElement;\n    var sc = (h.scrollTop)\/Math.max(1,(h.scrollHeight - h.clientHeight));\n    bar.style.transform = 'scaleX(' + Math.max(0,Math.min(1,sc)) + ')';\n  }\n  document.addEventListener('scroll', up, {passive:true});\n  up();\n})();\n\n(function(){\n  if (!('IntersectionObserver' in window)) {\n    document.querySelectorAll('.rev').forEach(function(n){n.classList.add('on')});\n    return;\n  }\n  var obs = new IntersectionObserver(function(entries){\n    entries.forEach(function(e){\n      if (e.isIntersecting) { e.target.classList.add('on'); obs.unobserve(e.target); }\n    });\n  }, {threshold:0.12});\n  document.querySelectorAll('.rev').forEach(function(n){ obs.observe(n); });\n})();\n\nsetTimeout(function(){\n  var shell = document.getElementById('lvm-shell');\n  if (shell) shell.classList.add('force-show');\n}, 2000);\n\n(function(){\n  var phoneImg = document.getElementById('phoneImg');\n  var phoneBack = document.getElementById('phoneBack');\n  var zones = document.getElementById('phoneZones');\n  var topTabs = document.querySelectorAll('.phone-tabs button');\n  var navTaps = document.querySelectorAll('.phone-navbar button');\n\n  var tabMap = {\n    bilan:    {src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp',     drill:false},\n    kcal:     {src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp',      drill:false},\n    depense:  {src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp',   drill:true},\n    strategie:{src:'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp', drill:false}\n  };\n  var subMap = {\n    BMR:  'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp',\n    NEAT: 'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp',\n    EAT:  'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp',\n    TEF:  'https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_TEF.webp'\n  };\n  var currentTab = 'depense';\n\n  function setActive(tab){\n    topTabs.forEach(function(b){ b.classList.toggle('on', b.dataset.tab===tab); });\n  }\n  function showTab(tab){\n    var t = tabMap[tab]; if(!t) return;\n    currentTab = tab;\n    phoneImg.style.opacity = 0;\n    setTimeout(function(){\n      phoneImg.className = 'phone-bg tab-' + tab;\n      phoneImg.style.opacity = 1;\n      zones.style.display = t.drill ? 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showSub(z.dataset.sub); });\n    z.addEventListener('keydown', function(e){\n      if (e.key==='Enter' || e.key===' ') { e.preventDefault(); hideHints(); showSub(z.dataset.sub); }\n    });\n  });\n  phoneBack.addEventListener('click', function(){ hideHints(); showTab(currentTab); });\n})();\n<\/script>\n<\/div>\n\n\n\n<!-- lean-mesh-v11 -->\n<aside class=\"lean-mesh\" style=\"margin:48px auto;max-width:760px;padding:24px 28px;background:#ffffff;border-left:4px solid #FF2D6E;border-radius:0 12px 12px 0;box-shadow:0 6px 24px rgba(20,20,40,0.06);font-family:-apple-system,'SF Pro Text','Segoe UI',Roboto,Arial,sans-serif;color:#1a1a2e;\"><p style=\"margin:0 0 14px;font-size:13px;font-weight:700;letter-spacing:0.06em;text-transform:uppercase;color:#FF2D6E;\">Read also<\/p><ul style=\"list-style:none;padding:0;margin:0;display:grid;grid-template-columns:1fr;gap:10px;\"><li><a href=\"\/en\/depense-energetique-totale-v2\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Total Daily Energy Expenditure (TDEE): the canonical formula BMR + NEAT + EAT + TEF <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">The other half of the equation: understanding your expenditure, component by component.<\/span><\/a><\/li><li><a href=\"\/en\/metabolisme-de-base\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Basal Metabolic Rate (BMR): everything you need to know to calculate it accurately <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Why your bodyfat changes everything, and why the 1919 formulas get it wrong.<\/span><\/a><\/li><li><a href=\"\/en\/meilleures-applications-calories-2026\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Best calorie counting apps in 2026: 8 apps tested <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Lean, MFP, Cronometer, Yazio, Lifesum, FatSecret, Noom, Foodvisor.<\/span><\/a><\/li><li><a href=\"\/en\/calculateur-deficit-calorique\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Calorie Deficit Calculator: the right approach for a cut <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Deficit on your real bodyfat-aware TDEE. 3 paces: slow \/ moderate \/ aggressive.<\/span><\/a><\/li><li><a href=\"\/en\/eat\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">EAT: the calorie expenditure of your workout sessions <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Why your watch overestimates, and how Lean calculates your workouts.<\/span><\/a><\/li><li><a href=\"\/en\/effet-thermique-des-aliments\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">TEF: the thermic effect of food based on your macros <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">The expenditure component your food tracking feeds directly.<\/span><\/a><\/li><\/ul><\/aside>","protected":false},"excerpt":{"rendered":"<p>Scan IA photo, code-barres, base de donn\u00e9es, ajout rapide : la pr\u00e9cision r\u00e9elle de chaque m\u00e9thode pour compter ses calories, et celle qui tient 6 mois.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-lvm-blank","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-485","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Count Your Calories: The 4 Methods Compared - Lean<\/title>\n<meta name=\"description\" content=\"AI photo scan, barcode, food database, quick add: what each method is really worth, and the one you will still be using in 6 months.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/lean-app.com\/en\/comment-compter-ses-calories\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta 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