{"id":1760,"date":"2026-08-15T22:24:56","date_gmt":"2026-08-15T22:24:56","guid":{"rendered":"https:\/\/lean-app.com\/lean-vs-foodvisor\/"},"modified":"2026-08-22T17:47:38","modified_gmt":"2026-08-22T17:47:38","slug":"lean-vs-foodvisor","status":"publish","type":"post","link":"https:\/\/lean-app.com\/es\/lean-vs-foodvisor\/","title":{"rendered":"Lean vs Foodvisor : scan photo IA face \u00e0 ta d\u00e9pense r\u00e9elle"},"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\" 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0}\n#lvm-shell .duo-row .mini-phone{max-width:180px}\n\n#lvm-shell .method{display:grid;grid-template-columns:1fr 1.4fr;gap:36px;align-items:center;margin:42px 0}\n#lvm-shell .method.flip{grid-template-columns:1.4fr 1fr}\n#lvm-shell .method.flip .m-phone{order:2}\n#lvm-shell .method .m-tag{font-family:var(--font-mono);font-size:11px;font-weight:600;text-transform:uppercase;letter-spacing:.08em;color:var(--pink);margin-bottom:8px}\n#lvm-shell .method h3{margin-top:0}\n#lvm-shell .method p{font-size:16px;color:var(--muted);line-height:1.7}\n\n#lvm-shell .cta-band{margin:40px 0;padding:26px 28px;background:var(--paper);border-radius:16px;display:flex;align-items:center;justify-content:space-between;gap:20px;flex-wrap:wrap;border:1px solid var(--rule-soft)}\n#lvm-shell .cta-band .l{font-family:var(--font-display);font-size:18px;line-height:1.35;font-weight:500;color:var(--ink);flex:1;min-width:240px;letter-spacing:-.01em}\n#lvm-shell .cta-band .stores{display:flex;gap:10px;align-items:center}\n#lvm-shell .cta-band .stores a{line-height:0;transition:transform .15s}\n#lvm-shell .cta-band .stores a:hover{transform:translateY(-2px)}\n#lvm-shell .cta-band .stores img{height:42px;width:auto;border-radius:9px}\n\n#lvm-shell .pyramid{margin:30px auto;max-width:440px}\n#lvm-shell .pyramid .level{margin:6px auto;padding:13px 18px;border-radius:14px;color:#fff;font-family:var(--font-display);font-weight:500;font-size:15px;letter-spacing:-.01em;display:flex;justify-content:space-between;align-items:center;box-shadow:0 6px 18px rgba(0,0,0,.06)}\n#lvm-shell .pyramid .level .k{font-family:var(--font-mono);font-size:10px;text-transform:uppercase;letter-spacing:.08em;opacity:.75}\n#lvm-shell .pyramid .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-1760 #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-1760 #lvm-shell .wrap,\nbody.postid-1760 #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-1760 #lvm-shell .wrap,\n  body.postid-1760 #lvm-shell main.wrap{padding-left:18px!important;padding-right:18px!important}\n}\nhtml, body{overflow-x:hidden!important}\nbody.postid-1760 #lvm-shell{overflow-x:hidden;max-width:100vw}\nbody.postid-1760 #lvm-shell *{max-width:100%}\nbody.postid-1760 #lvm-shell .nav-row{max-width:100vw;box-sizing:border-box}\nbody.postid-1760 #lvm-shell.force-show .rev{opacity:1!important;transform:none!important}\n\n\/* === A.1 PHONE BACKGROUND CLASSES === *\/\nbody.postid-1760 #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-1760 #lvm-shell .phone-bg.tab-depense{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp)}\nbody.postid-1760 #lvm-shell .phone-bg.tab-bilan{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp)}\nbody.postid-1760 #lvm-shell .phone-bg.tab-kcal{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp)}\nbody.postid-1760 #lvm-shell .phone-bg.tab-strategie{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp)}\nbody.postid-1760 #lvm-shell .phone-bg.sub-BMR{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp)}\nbody.postid-1760 #lvm-shell .phone-bg.sub-NEAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp)}\nbody.postid-1760 #lvm-shell .phone-bg.sub-EAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp)}\nbody.postid-1760 #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 === *\/\n@media (max-width:760px){\n  body.postid-1760 #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-1760 #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-1760 #lvm-shell .cta-band .stores{width:100%!important;justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-1760 #lvm-shell .cta-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-1760 #lvm-shell .cta-band .stores picture{width:100%!important;display:block!important}\n  body.postid-1760 #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-1760 #lvm-shell .get-band{padding:38px 22px!important}\n  body.postid-1760 #lvm-shell .get-band .stores{justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-1760 #lvm-shell .get-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-1760 #lvm-shell .get-band .stores picture{width:100%!important;display:block!important}\n  body.postid-1760 #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-1760 #lvm-shell .get-band h3{font-size:26px!important;line-height:1.2!important}\n  body.postid-1760 #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-1760 #lvm-shell .brand-banner img{width:54px!important;height:54px!important}\n  body.postid-1760 #lvm-shell .brand-banner > div{padding:16px 12px!important;gap:8px!important}\n  body.postid-1760 #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-1760 #lvm-shell .scorecard{padding:18px 16px!important;border-radius:16px!important}\n  body.postid-1760 #lvm-shell .scorecard-head{display:none!important}\n  body.postid-1760 #lvm-shell .scorecard-row{\n    display:block!important;\n    padding:14px 0!important;\n    border-bottom:1px solid #E8E2D6!important;\n  }\n  body.postid-1760 #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-1760 #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-1760 #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-1760 #lvm-shell .scorecard-row .bar.lean::before{color:#FF2D6E!important}\n  body.postid-1760 #lvm-shell .scorecard-row .bar.mfp::before{color:#6ABF6C!important}\n  body.postid-1760 #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-1760 #lvm-shell .scorecard-row .bar .b > i{\n    display:block!important;\n    height:100%!important;\n    border-radius:99px!important;\n  }\n  body.postid-1760 #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  body.postid-1760 #lvm-shell .cv-wrap{height:380px!important;min-height:360px!important;max-height:420px!important;width:100%!important}\n  body.postid-1760 #lvm-shell .cv-wrap canvas{width:100%!important;height:100%!important;display:block!important}\n  body.postid-1760 #lvm-shell .fig{padding:16px 4px 14px!important;margin:24px -4px 14px!important;overflow:visible!important}\n  body.postid-1760 #lvm-shell .fig-head{padding:0 12px!important;flex-wrap:wrap!important;gap:6px!important;margin-bottom:10px!important}\n  body.postid-1760 #lvm-shell .fig-body{padding:0 2px!important}\n  body.postid-1760 #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-1760 #lvm-shell .cv-wrap{height:360px!important;min-height:340px!important;max-height:380px!important}\n  body.postid-1760 #lvm-shell .fig{padding:14px 2px 12px!important;margin:20px -6px 12px!important;border-radius:14px!important}\n  body.postid-1760 #lvm-shell .fig-body{padding:0!important}\n}\n\n\/* === v11.2 TABLEAU MOBILE STACKED CARDS avec mini-tags Lean\/Foodvisor === *\/\n@media (max-width:760px){\n  body.postid-1760 #lvm-shell .table{border-radius:14px!important}\n  body.postid-1760 #lvm-shell .table-row.head{display:none!important}\n  body.postid-1760 #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-1760 #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 Display',sans-serif!important;text-transform:none!important;\n  }\n  body.postid-1760 #lvm-shell .table-row > .cell.lean{\n    grid-area:lean!important;border-right:1px solid #E8E2D6!important;\n    position:relative!important;background:#FFF1F5!important;padding-top:30px!important;\n  }\n  body.postid-1760 #lvm-shell .table-row > .cell:not(.lean):not(.crit){\n    grid-area:mfp!important;background:#EFF7EF!important;padding-top:30px!important;\n    position:relative!important;\n  }\n  body.postid-1760 #lvm-shell .table-row > .cell.lean::before{\n    content:\"LEAN\"!important;position:absolute!important;top:8px!important;left:12px!important;\n    right:auto!important;bottom:auto!important;width:auto!important;height:auto!important;\n    background:transparent!important;\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:10px!important;font-weight:700!important;letter-spacing:.07em!important;\n    color:#FF2D6E!important;\n  }\n  body.postid-1760 #lvm-shell .table-row > .cell:not(.lean):not(.crit)::before{\n    content:\"FOODVISOR\"!important;position:absolute!important;top:8px!important;left:12px!important;\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:10px!important;font-weight:700!important;letter-spacing:.07em!important;\n    color:#6ABF6C!important;\n  }\n  body.postid-1760 #lvm-shell .table-row > .cell{\n    padding:12px 12px!important;font-size:13px!important;line-height:1.4!important;\n    align-items:flex-start!important;gap:7px!important;\n  }\n  body.postid-1760 #lvm-shell .icn{flex-shrink:0!important;margin-top:1px!important}\n}\n\n\/* === A.5 MINI-LOGOS triplet NEAT\/EAT\/TEF === *\/\n@media (max-width:760px){\n  body.postid-1760 #lvm-shell .mini-row{gap:6px!important;margin:24px 0!important;grid-template-columns:repeat(3,1fr)!important}\n  body.postid-1760 #lvm-shell .mini-phone{max-width:100px!important;padding:2px!important;border-radius:14px!important;border-width:1px!important}\n  body.postid-1760 #lvm-shell .mini-phone.tiny{max-width:96px!important;padding:2px!important;border-radius:13px!important}\n  body.postid-1760 #lvm-shell .mini-phone .notch{width:30px!important;height:8px!important;border-radius:0 0 5px 5px!important}\n  body.postid-1760 #lvm-shell .mini-phone .scr{border-radius:11px!important}\n  body.postid-1760 #lvm-shell .mini-cap{font-size:10px!important;margin-top:8px!important}\n  body.postid-1760 #lvm-shell .mini-cap strong{font-size:12px!important;margin-top:2px!important}\n}\n\n\/* === MOCKUP TAP HINT MOBILE === *\/\n@media (max-width:760px){\n  body.postid-1760 #lvm-shell .tap-hint.mobile{position:relative!important;width:100%!important;left:auto!important;top:auto!important;text-align:center!important;margin:0 auto 14px!important;display:block!important}\n  body.postid-1760 #lvm-shell .tap-hint.desktop{display:none!important}\n  body.postid-1760 #lvm-shell .tap-hint.hidden{display:none!important;height:0!important;margin:0!important;padding:0!important}\n}\n\n\/* === A.6 BODYSCAN ILLUST partie BMR === *\/\nbody.postid-1760 #lvm-shell .bodyscan-illust{margin:40px auto 8px!important;display:flex!important;flex-direction:column!important;align-items:center!important;gap:14px!important;max-width:220px!important}\nbody.postid-1760 #lvm-shell .bodyscan-illust .mini-phone{max-width:200px!important;padding:3px!important;border-radius:22px!important;border-width:1px!important}\nbody.postid-1760 #lvm-shell .bodyscan-illust .mini-phone .notch{width:40px!important;height:11px!important;border-radius:0 0 7px 7px!important}\nbody.postid-1760 #lvm-shell .bodyscan-illust .mini-phone .scr{border-radius:18px!important}\n@media (max-width:760px){\n  body.postid-1760 #lvm-shell .bodyscan-illust{max-width:180px!important}\n  body.postid-1760 #lvm-shell .bodyscan-illust .mini-phone{max-width:160px!important;padding:3px!important;border-radius:20px!important}\n  body.postid-1760 #lvm-shell .bodyscan-illust .mini-phone .notch{width:34px!important;height:9px!important;border-radius:0 0 6px 6px!important}\n  body.postid-1760 #lvm-shell .bodyscan-illust .mini-phone .scr{border-radius:16px!important}\n}\n<\/style>\n<script data-wpmeteor-nooptimize=\"true\">\n(function(){\n  \/* Hard fallback: force .rev visible after 2s if IntersectionObserver doesn't fire *\/\n  setTimeout(function(){\n    var shell = document.getElementById('lvm-shell');\n    if(!shell) return;\n    var anyOn = shell.querySelector('.rev.on');\n    if(!anyOn){ shell.classList.add('force-show'); }\n  }, 2000);\n\n  \/* ResizeObserver fallback: ensure charts resize correctly *\/\n  if (typeof ResizeObserver !== 'undefined'){\n    var observer = new ResizeObserver(function(entries){\n      entries.forEach(function(entry){\n        var canvas = entry.target.querySelector('canvas');\n        if (!canvas || !window.Chart) return;\n        var inst = window.Chart.getChart(canvas);\n        if (inst) { try { inst.resize(); } catch(e){} }\n      });\n    });\n    document.querySelectorAll('#lvm-shell .cv-wrap').forEach(function(w){ observer.observe(w); });\n  }\n})();\n<\/script>\n<div id=\"lvm-shell\"><div class=\"progress\" aria-hidden=\"true\"><i id=\"progBar\"><\/i><\/div>\n\n<header class=\"nav\">\n  <div class=\"nav-row\">\n    <a class=\"nav-brand\" href=\"https:\/\/lean-app.com\/es\/\" aria-label=\"Inicio Lean\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/>\n      <span>Lean<\/span>\n    <\/a>\n    <span class=\"nav-spacer\"><\/span>\n    <a class=\"nav-link\" href=\"https:\/\/lean-app.com\/es\/tdee-calculator\/\">Calculadora TDEE<\/a>\n    <div class=\"nav-stores\">\n      <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=vs-foodvisor\" target=\"_blank\" rel=\"noopener\" aria-label=\"Descargar en la App Store\">\n        <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\" \/>\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=vs-foodvisor\" target=\"_blank\" rel=\"noopener\" aria-label=\"Disponible en Google Play\">\n        <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\" \/>\n      <\/a>\n    <\/div>\n  <\/div>\n<\/header>\n\n<main class=\"wrap\">\n\n<section class=\"hero\" aria-labelledby=\"title\">\n  <div class=\"crumb\"><a href=\"https:\/\/lean-app.com\/es\/\">Inicio<\/a> &nbsp;\/&nbsp; Lean vs Foodvisor<\/div>\n  <div class=\"eyebrow\">Comparatif &middot; Nutrition &amp; TDEE<\/div>\n  <h1 id=\"title\">Lean vs Foodvisor.\n    <span class=\"alt\">Le pionnier du scan photo face au seul qui recompose ton TDEE en continu.<\/span>\n  <\/h1>\n  <p class=\"dek\">Foodvisor voit ton assiette. Lean voit ta d\u00e9pense r\u00e9elle. Deux IA, deux moiti\u00e9s du probl\u00e8me.<\/p>\n  <div class=\"byline\">\n    <img class=\"by-logo\" src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/>\n    <span><strong>El equipo Lean<\/strong> &middot; Lecture 12&nbsp;min &middot; Mis \u00e0 jour 15 ao\u00fbt 2026<\/span>\n  <\/div>\n  <div class=\"hero-stores\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=vs-foodvisor\" 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=vs-foodvisor\" 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\">Descarga gratuita<\/span>\n  <\/div>\n\n  <div class=\"hero-bottom\">\n    <div class=\"hero-lead\">\n      Foodvisor est le pionnier fran\u00e7ais du scan photo de repas : tu photographies ton assiette, l&rsquo;IA reconna\u00eet les aliments et estime les portions. Sur cette moiti\u00e9 du probl\u00e8me, le cr\u00e9dit est m\u00e9rit\u00e9. Mais l&rsquo;autre moiti\u00e9, ta d\u00e9pense, y reste une formule de population (Mifflin-St Jeor 1990), plus un multiplicateur d&rsquo;activit\u00e9 statique choisi une seule fois \u00e0 l&rsquo;inscription. Sans bodyfat r\u00e9el mesur\u00e9, sans adaptation m\u00e9tabolique. Le match Lean vs Foodvisor ne se joue donc pas sur la photo : il se joue sur ce que l&rsquo;app fait du chiffre, sur 3 mois de cut s\u00e9rieux.\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>Demostraci\u00f3n interactiva<\/small>Toca la pantalla para explorar la aplicaci\u00f3n<\/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>Demostraci\u00f3n interactiva<\/small>Toca la pantalla<br>para explorar la aplicaci\u00f3n<\/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=\"Vista general de la aplicaci\u00f3n Lean con desglose del TDEE\">\n          <div class=\"notch\"><\/div>\n          <div class=\"phone-screen\">\n            <button class=\"phone-back\" id=\"phoneBack\" aria-label=\"Volver\">&#8249;<\/button>\n            <div id=\"phoneImg\" class=\"phone-bg tab-depense\" role=\"img\" aria-label=\"Vista Lean, pesta\u00f1a Gasto\"><\/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=\"Detalle BMR\"><\/div>\n              <div class=\"z\" data-sub=\"NEAT\" style=\"top:33%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalle NEAT\"><\/div>\n              <div class=\"z\" data-sub=\"EAT\"  style=\"top:50%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalle EAT\"><\/div>\n              <div class=\"z\" data-sub=\"TEF\"  style=\"top:67%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalle TEF\"><\/div>\n            <\/div>\n            <div class=\"phone-navbar\" id=\"phoneNav\" aria-hidden=\"false\">\n              <button data-tab=\"bilan\"     type=\"button\" aria-label=\"Pesta\u00f1a Balance\"><\/button>\n              <button data-tab=\"kcal\"      type=\"button\" aria-label=\"Pesta\u00f1a Calor\u00edas\"><\/button>\n              <button data-tab=\"depense\"   type=\"button\" aria-label=\"Pesta\u00f1a Gasto\"><\/button>\n              <button data-tab=\"strategie\" type=\"button\" aria-label=\"Pesta\u00f1a Estrategia\"><\/button>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div class=\"phone-tabs\" role=\"tablist\" aria-label=\"Navegar por la aplicaci\u00f3n Lean\">\n          <button data-tab=\"bilan\"     type=\"button\">Balance<\/button>\n          <button data-tab=\"kcal\"      type=\"button\">Calor\u00edas<\/button>\n          <button data-tab=\"depense\"   type=\"button\" class=\"on\">Gasto<\/button>\n          <button data-tab=\"strategie\" type=\"button\">Estrategia<\/button>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"snippet rev\">\n    <div class=\"lbl\">R\u00e9ponse rapide<\/div>\n    <p>Foodvisor a invent\u00e9 le scan photo de repas en France et reste une r\u00e9f\u00e9rence pour identifier ce que tu manges : photo, reconnaissance des aliments, estimation des portions. C\u00f4t\u00e9 d\u00e9pense en revanche, Foodvisor s&rsquo;appuie sur une formule de population (Mifflin-St Jeor 1990, sans bodyfat mesur\u00e9) et un multiplicateur d&rsquo;activit\u00e9 statique choisi \u00e0 l&rsquo;inscription. Lean prend le probl\u00e8me dans l&rsquo;autre sens : recalculer chaque composant du TDEE (<span data-term=\"BMR\">BMR<span class=\"tt\">Basal Metabolic Rate. Energ\u00eda gastada en reposo. En Lean, calculada sobre la masa magra real mediante BodyScan IA.<\/span><\/span> sur bodyfat r\u00e9el via un mod\u00e8le propri\u00e9taire brevet\u00e9, <span data-term=\"NEAT\">NEAT<span class=\"tt\">Non-Exercise Activity Thermogenesis. D\u00e9pense li\u00e9e aux pas et activit\u00e9s quotidiennes hors sport.<\/span><\/span> par pas, <span data-term=\"EAT\">EAT<span class=\"tt\">Exercise Activity Thermogenesis. D\u00e9pense li\u00e9e \u00e0 vos s\u00e9ances de sport, calcul\u00e9e via MET.<\/span><\/span> par MET, <span data-term=\"TEF\">TEF<span class=\"tt\">Thermic Effect of Food. \u00c9nergie d\u00e9pens\u00e9e par la digestion. D\u00e9pend des macros ing\u00e9r\u00e9es.<\/span><\/span> par macros) et moduler le BMR par l&rsquo;adaptation m\u00e9tabolique en continu, sans coefficient \u00e0 choisir.<\/p>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"constat\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">00 &middot; Le constat<\/span><\/div>\n  <h2 id=\"constat\">Foodvisor voit ton assiette, pas ta d\u00e9pense r\u00e9elle<\/h2>\n  <p>Si tu lis \u00e7a, tu as probablement d\u00e9j\u00e0 install\u00e9 Foodvisor. Tu l&rsquo;as choisi pr\u00e9cis\u00e9ment pour ce que personne ne faisait avant lui : photographier ton assiette et laisser l&rsquo;IA reconna\u00eetre le poulet, le riz, la sauce, et estimer les portions sans sortir la balance. Tu as renseign\u00e9 ton poids, ta taille, ton \u00e2ge, ton sexe, et choisi ton niveau d&rsquo;activit\u00e9 dans une liste statique. L&rsquo;app t&rsquo;a affich\u00e9 un objectif calorique, mettons 2&nbsp;250&nbsp;kcal pour perdre du poids.<\/p>\n  <p>Tu as jou\u00e9 le jeu. Tu as scann\u00e9 tes repas, corrig\u00e9 les portions quand l&rsquo;IA h\u00e9sitait, gard\u00e9 une entr\u00e9e propre jour apr\u00e8s jour. Les 6 premi\u00e8res semaines, \u00e7a fonctionne. Tu perds. Tu es content. Puis vers la semaine 8, la balance se fige. Tu serres la vis. Tu descends \u00e0 2&nbsp;000&nbsp;kcal. L\u00e0 encore, rien ne bouge.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">&minus;10 \u00e0 &minus;15&nbsp;%<\/div>\n    <div class=\"lbl\">de baisse mesur\u00e9e du TDEE apr\u00e8s 4 \u00e0 6 semaines de d\u00e9ficit \u00e0 &minus;500&nbsp;kcal\/jour. Foodvisor ne le d\u00e9tecte pas. Ton objectif calorique reste fig\u00e9 sur ton niveau d&rsquo;activit\u00e9 d&rsquo;il y a 100&nbsp;jours.<\/div>\n  <\/div>\n\n  <p>Imaginons que Foodvisor t&rsquo;affiche un TDEE de 2&nbsp;500&nbsp;kcal. Tu manges 2&nbsp;250 (d\u00e9ficit th\u00e9orique de 250&nbsp;kcal). Mais en r\u00e9alit\u00e9, ton <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/depense-energetique-totale-v2\/\">TDEE est descendu \u00e0 2&nbsp;200&nbsp;kcal<\/a> \u00e0 cause de l&rsquo;adaptation m\u00e9tabolique. Tu es en surplus de 50&nbsp;kcal sans le savoir. Aucune chance de continuer \u00e0 perdre, m\u00eame avec l&rsquo;entr\u00e9e la plus propre du march\u00e9.<\/p>\n  <p>La promesse Foodvisor est claire et tenue : tu sais ce qu&rsquo;il y a dans ton assiette sans rien peser. C&rsquo;est pr\u00e9cieux. Ce que Foodvisor ne fait pas, c&rsquo;est <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/comment-compter-ses-calories\/\">recalculer ta d\u00e9pense<\/a> au fil des semaines de d\u00e9ficit. Et c&rsquo;est exactement l\u00e0 o\u00f9 la promesse s&rsquo;arr\u00eate, alors que c&rsquo;est le levier qui fait perdre du poids.<\/p>\n<\/section>\n\n<section aria-labelledby=\"p1\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">01 &middot; Probl\u00e8me 1<\/span><\/div>\n  <h2 id=\"p1\">La formule BMR de 1990, sans bodyfat mesur\u00e9<\/h2>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figure 1 &middot; Homme 1m80, 120&nbsp;kg, 30&nbsp;% BF<\/span><span class=\"r\">kcal<\/span><\/div>\n    <div class=\"fig-body\"><div class=\"cv-wrap\" style=\"position:relative;width:100%;height:380px;min-height:340px\"><canvas id=\"chartBMR\" aria-label=\"Comparaison BMR Mifflin-St Jeor 2500 kcal vs mod\u00e8le propri\u00e9taire brevet\u00e9 Lean 2000 kcal, \u00e9cart de 500 kcal\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>BMR estim\u00e9.<\/strong> Le mod\u00e8le propri\u00e9taire brevet\u00e9 Lean prend en compte la masse maigre. Mifflin-St Jeor (formule de population, sans bodyfat), non. \u00c9cart de 500&nbsp;kcal, l&rsquo;\u00e9quivalent d&rsquo;un d\u00e9jeuner entier.<\/p>\n  <\/div>\n\n  <p>Pour calculer ton m\u00e9tabolisme de base (le BMR, l&rsquo;\u00e9nergie que tu br\u00fbles au repos), Foodvisor part de ton profil : poids, taille, \u00e2ge, sexe. C&rsquo;est la logique de la quasi-totalit\u00e9 des trackers caloriques grand public, h\u00e9rit\u00e9e des formules de population comme Mifflin-St Jeor. Et il faut \u00eatre honn\u00eate : c&rsquo;est mieux que Harris-Benedict 1919 que d&rsquo;autres apps utilisent encore.<\/p>\n  <p>Mifflin-St Jeor, c&rsquo;est 1990 (PubMed 2305711). L&rsquo;\u00e9chantillon est large (498 sujets), la m\u00e9thodologie de calorim\u00e9trie indirecte est s\u00e9rieuse, la formule est calibr\u00e9e sur une population moderne : 10 \u00d7 poids (kg) + 6,25 \u00d7 taille (cm) \u2212 5 \u00d7 \u00e2ge \u2212 161 (femmes) ou +5 (hommes).<\/p>\n  <p>Le probl\u00e8me n&rsquo;est pas la formule choisie. Le probl\u00e8me est ce qu&rsquo;aucune formule de ce type ne peut voir : <strong>elle ne prend en compte que le poids. Pas le bodyfat. Pas la masse maigre.<\/strong> Aucun champ de l&rsquo;onboarding Foodvisor ne te demande ton pourcentage de masse grasse, et aucune mesure n&rsquo;existe dans l&rsquo;app.<\/p>\n  <p>Or, depuis les ann\u00e9es 1980, on sait que <strong>la masse grasse d\u00e9pense tr\u00e8s peu d&rsquo;\u00e9nergie<\/strong> compar\u00e9e au reste du corps. Le foie, le cerveau, le c\u0153ur, les reins, et surtout les muscles sont les vrais postes de d\u00e9pense. La masse grasse est inerte. Une personne avec 30&nbsp;% de bodyfat ne br\u00fble pas du tout autant qu&rsquo;une personne avec 10&nbsp;% de bodyfat, m\u00eame \u00e0 poids \u00e9gal.<\/p>\n  <p>Frankenfield 2013 (PubMed 23631843) a compar\u00e9 Mifflin-St Jeor \u00e0 la calorim\u00e9trie indirecte de r\u00e9f\u00e9rence sur des cohortes ob\u00e8ses et non-ob\u00e8ses. R\u00e9sultat : pr\u00e9cision \u00e0 87&nbsp;% chez les non-ob\u00e8ses, et seulement <strong>75&nbsp;% chez les ob\u00e8ses<\/strong>. Une \u00e9tude plus r\u00e9cente (PMC11820646) montre que sur les BMI sup\u00e9rieurs \u00e0 35, Mifflin se trompe de <strong>250 \u00e0 315&nbsp;kcal par jour<\/strong>. C&rsquo;est l&rsquo;\u00e9quivalent d&rsquo;un en-cas entier dans le calcul d&rsquo;un d\u00e9ficit.<\/p>\n  <p>500&nbsp;kcal, ce n&rsquo;est pas rien. Si l&rsquo;app te dit \u00ab&nbsp;ton BMR est de 2&nbsp;500&nbsp;\u00bb et qu&rsquo;en r\u00e9alit\u00e9 il est de 2&nbsp;000, tout ce qui suit est faux : ton objectif d\u00e9ficit, ta projection de perte hebdo, ta r\u00e9partition macros calcul\u00e9e en pourcentage du TDEE. Et aucune photo d&rsquo;assiette, aussi bien reconnue soit-elle, ne corrige un objectif faux.<\/p>\n\n  <div class=\"bodyscan-illust\" style=\"margin:40px auto 8px;display:flex;flex-direction:column;align-items:center;gap:14px;max-width:200px\">\n    <div class=\"mini-phone\" style=\"max-width:200px\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-bodyscan-result.webp\" alt=\"BodyScan IA Lean : bodyfat mesur\u00e9 par photo en 5 secondes\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n    <div class=\"mini-cap\">Bodyfat r\u00e9el<strong>Photo, 5 secondes<\/strong><\/div>\n  <\/div>\n\n  <div class=\"statement\">\n    <div class=\"num\">400&nbsp;kcal<\/div>\n    <div class=\"lbl\">d&rsquo;\u00e9cart entre deux hommes de 80&nbsp;kg, l&rsquo;un \u00e0 10&nbsp;% de bodyfat (BMR 1&nbsp;900), l&rsquo;autre \u00e0 30&nbsp;% (BMR 1&nbsp;500). Une formule au poids leur affiche le m\u00eame chiffre.<\/div>\n  <\/div>\n\n  <p>Conclusion partielle : si une app calcule ton BMR uniquement \u00e0 partir de ton poids, de ta taille, de ton \u00e2ge et de ton sexe, le r\u00e9sultat ne peut pas \u00eatre individualis\u00e9. C&rsquo;est math\u00e9matiquement impossible. M\u00eame avec la meilleure reconnaissance d&rsquo;assiette en entr\u00e9e.<\/p>\n<\/section>\n\n<section aria-labelledby=\"p2\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">02 &middot; Probl\u00e8me 2<\/span><\/div>\n  <h2 id=\"p2\">Le multiplicateur d&rsquo;activit\u00e9, choisi une fois pour toutes<\/h2>\n  <p>C&rsquo;est ici que \u00e7a devient grave. Et c&rsquo;est probablement le point que personne ne t&rsquo;a expliqu\u00e9.<\/p>\n  <p>Une fois ton BMR estim\u00e9, Foodvisor doit passer au TDEE total. Le <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/depense-energetique-totale-v2\/\">TDEE, c&rsquo;est le BMR + tout le reste<\/a> : la d\u00e9pense li\u00e9e aux pas, aux activit\u00e9s quotidiennes, au sport, et \u00e0 la digestion. Tout ce qui n&rsquo;est pas du m\u00e9tabolisme de base.<\/p>\n  <p>Comment Foodvisor fait \u00e7a&nbsp;? Comme la quasi-totalit\u00e9 des trackers : il te demande, au moment de l&rsquo;inscription, de choisir ton niveau d&rsquo;activit\u00e9 dans une liste statique. Ces facteurs s&rsquo;appellent en science du sport des <strong>niveaux PAL<\/strong> (Physical Activity Level), c&rsquo;est juste un multiplicateur appliqu\u00e9 \u00e0 ton BMR :<\/p>\n  <ul>\n    <li>S\u00e9dentaire (PAL 1,2) : bureau, peu de marche<\/li>\n    <li>L\u00e9g\u00e8rement actif (PAL 1,375) : marche occasionnelle<\/li>\n    <li>Actif (PAL 1,55) : sport 3 \u00e0 5 fois par semaine<\/li>\n    <li>Tr\u00e8s actif (PAL 1,725) : sport intense quasi quotidien<\/li>\n    <li>Extr\u00eamement actif (PAL 1,9) : sport tr\u00e8s intense ou travail physique<\/li>\n  <\/ul>\n  <p>Et selon ton choix, l&rsquo;app multiplie ton BMR par le coefficient associ\u00e9. C&rsquo;est tout. C&rsquo;est tout ce qu&rsquo;il y a derri\u00e8re ton objectif calorique journalier. Une case que TU as coch\u00e9e une seule fois au moment de l&rsquo;inscription. Souvent il y a six mois. Sans bouger depuis.<\/p>\n  <p>Et l\u00e0, c&rsquo;est le pi\u00e8ge silencieux : cette approximation est <strong>hyper imparfaite<\/strong>. La diff\u00e9rence entre un jour o\u00f9 tu es scotch\u00e9 au canap\u00e9 devant Netflix et un jour o\u00f9 tu vas \u00e0 Disneyland avec tes enfants et marches 15&nbsp;km, <strong>c&rsquo;est plus de 1&nbsp;000&nbsp;kcal<\/strong>. Aucune des 5 cases ne capte \u00e7a.<\/p>\n  <p>Foodvisor sait pourtant suivre ton activit\u00e9 : l&rsquo;app peut compter tes pas et enregistrer tes s\u00e9ances. Mais ces donn\u00e9es servent surtout \u00e0 afficher ton activit\u00e9, pas \u00e0 recomposer un TDEE complet : ton objectif calorique reste assis sur le multiplicateur choisi \u00e0 l&rsquo;onboarding, et la d\u00e9pense sport vient s&rsquo;y ajouter sans que la NEAT et la EAT soient s\u00e9par\u00e9es proprement.<\/p>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figure 2 &middot; 7 jours r\u00e9els<\/span><span class=\"r\">kcal\/jour<\/span><\/div>\n    <div class=\"fig-body\"><div class=\"cv-wrap\" style=\"position:relative;width:100%;height:380px;min-height:340px\"><canvas id=\"chartNEAT\" aria-label=\"Variabilit\u00e9 quotidienne de la d\u00e9pense calorique sur 7 jours, contre 2400 kcal fixes selon Foodvisor\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>D\u00e9pense r\u00e9elle<\/strong> mesur\u00e9e sur 7&nbsp;jours pour un utilisateur Lean. La ligne verte est ce que Foodvisor affichait (2&nbsp;400&nbsp;kcal fixe, multiplicateur statique \u00d7 BMR). Les annotations roses montrent pourquoi chaque jour bouge.<\/p>\n  <\/div>\n\n  <p>Tu ne peux pas r\u00e9duire ton niveau d&rsquo;activit\u00e9 \u00e0 une case statique. Tu es peut-\u00eatre actif les semaines o\u00f9 tu t\u00e9l\u00e9travailles peu, et s\u00e9dentaire celles o\u00f9 tu ne sors pas du bureau. Tu es peut-\u00eatre actif en \u00e9t\u00e9 et s\u00e9dentaire en hiver. Tu es peut-\u00eatre actif du mardi au vendredi et s\u00e9dentaire le week-end.<\/p>\n  <p>Quelle case vas-tu cocher cette semaine ? La v\u00e9rit\u00e9, c&rsquo;est qu&rsquo;aucune des 5 ne sera correcte. Et donc Foodvisor va te donner un TDEE qui sera syst\u00e9matiquement d\u00e9corr\u00e9l\u00e9 de la r\u00e9alit\u00e9.<\/p>\n  <p>Le point cl\u00e9 de cet article : m\u00eame avec une formule BMR parfaite, le multiplicateur statique suffirait \u00e0 tout casser. Tu ne peux pas estimer une <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/neat-depense-non-sportive\/\">NEAT<\/a>, une EAT et une <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/effet-thermique-des-aliments\/\">TEF<\/a> avec un multiplicateur unique appliqu\u00e9 au BMR. C&rsquo;est conceptuellement absurde.<\/p>\n  <p>Tu l&rsquo;auras compris : <strong>une formule BMR sans bodyfat, plus une approximation statique de tout le reste, \u00e7a donne tr\u00e8s peu de chances d&rsquo;atteindre tes objectifs sur 3 \u00e0 6 mois.<\/strong> Aussi propre que soit l&rsquo;entr\u00e9e c\u00f4t\u00e9 assiette.<\/p>\n\n  <div class=\"cta-band rev\" style=\"display:flex;align-items:center;justify-content:space-between;gap:20px;flex-wrap:wrap;padding:26px 28px;margin:40px 0;background:#FAF5EE;border:1px solid #E8E2D6;border-radius:16px\">\n    <div class=\"l\" style=\"flex:1;min-width:240px;font-size:18px;line-height:1.4;font-weight:500;color:#0E0E10\">Voir votre TDEE r\u00e9elle, d\u00e9compos\u00e9e en BMR + NEAT + EAT + TEF. T\u00e9l\u00e9chargement gratuit.<\/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=vs-foodvisor\" 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=vs-foodvisor\" 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<\/section>\n\n<section aria-labelledby=\"p3\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">03 &middot; Probl\u00e8me 3<\/span><\/div>\n  <h2 id=\"p3\">L&rsquo;adaptation m\u00e9tabolique, jamais mod\u00e9lis\u00e9e<\/h2>\n  <p>C&rsquo;est le boss final. La notion la plus fine. Et probablement la plus importante.<\/p>\n  <p>Quand tu es en d\u00e9ficit calorique, ton corps comprend qu&rsquo;il re\u00e7oit moins d&rsquo;\u00e9nergie qu&rsquo;avant. Pour se prot\u00e9ger, il bascule en mode \u00e9conomie. Exactement comme le mode \u00e9conomie d&rsquo;\u00e9nergie de ton iPhone : tout continue de fonctionner, mais en utilisant moins d&rsquo;\u00e9nergie. Ton BMR baisse. Ta NEAT baisse. Ta EAT baisse.<\/p>\n  <p>C&rsquo;est ce qu&rsquo;on appelle l&rsquo;adaptation m\u00e9tabolique. La litt\u00e9rature scientifique est claire et reproductible : M\u00fcller 2015 (PubMed 26399868, revisite Minnesota), Doucet 2001 (PubMed 11430776), Nunes 2020 (PMC7484122) sur 6 semaines de d\u00e9ficit. Voici les chiffres :<\/p>\n  <ul>\n    <li>D\u00e9ficit de &minus;250&nbsp;kcal par jour, sur 2 \u00e0 8 semaines : adaptation de <strong>5 \u00e0 10&nbsp;%<\/strong> (TDEE descend \u00e0 90-95&nbsp;% du niveau initial)<\/li>\n    <li>D\u00e9ficit de &minus;500&nbsp;kcal par jour : <strong>10 \u00e0 15&nbsp;%<\/strong> d&rsquo;adaptation (TDEE descend \u00e0 85-90&nbsp;%)<\/li>\n    <li>D\u00e9ficit de &minus;750&nbsp;kcal par jour : <strong>15 \u00e0 25&nbsp;%<\/strong> d&rsquo;adaptation (TDEE descend \u00e0 75-85&nbsp;%)<\/li>\n  <\/ul>\n  <p>Convention Lean : 100&nbsp;% = optimal, 90&nbsp;% = 10&nbsp;% d&rsquo;adaptation. Et comme la NEAT, l&rsquo;EAT et la TEF d\u00e9pendent toutes directement du BMR, c&rsquo;est quasiment tout le TDEE qui est impact\u00e9.<\/p>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figure 3 &middot; 8 semaines en d\u00e9ficit<\/span><span class=\"r\">kcal\/jour<\/span><\/div>\n    <div class=\"fig-body\"><div class=\"cv-wrap\" style=\"position:relative;width:100%;height:380px;min-height:340px\"><canvas id=\"chartAdapt\" aria-label=\"TDEE qui chute de 2500 \u00e0 2150 kcal sur 8 semaines, contre 2500 fixe selon Foodvisor\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>TDEE r\u00e9el<\/strong> sur 8 semaines de d\u00e9ficit \u00e0 &minus;500&nbsp;kcal\/jour. La courbe rose descend. La ligne Foodvisor reste plate. \u00c0 semaine 6, tu es d\u00e9j\u00e0 \u00e0 la maintenance. Sans rien avoir chang\u00e9.<\/p>\n  <\/div>\n\n  <p>Concr\u00e8tement : si tu avais pr\u00e9vu un d\u00e9ficit de 10&nbsp;% sur un TDEE de 2&nbsp;500 (soit manger 2&nbsp;250 par jour), et que ton corps s&rsquo;adapte de 10&nbsp;%, ton TDEE r\u00e9el est pass\u00e9 \u00e0 2&nbsp;250. Tu es \u00e0 la maintenance. Tu ne perds plus.<\/p>\n  <p>Le pi\u00e8ge, c&rsquo;est que c&rsquo;est insidieux. Au d\u00e9but, tu perds. Tu es content. Tu continues. Mais semaine apr\u00e8s semaine, l&rsquo;adaptation se cumule. Et \u00e0 un moment, sans rien avoir chang\u00e9 \u00e0 ton tracking, <strong>tu arr\u00eates de perdre<\/strong>.<\/p>\n  <p>95&nbsp;% des gens passent par l\u00e0 sans le comprendre. Ils accusent leur volont\u00e9. Ils bl\u00e2ment leur \u00ab&nbsp;m\u00e9tabolisme cass\u00e9&nbsp;\u00bb. Ils repartent dans des di\u00e8tes plus dures, ce qui aggrave l&rsquo;adaptation. Spirale.<\/p>\n  <p>Foodvisor ne calcule jamais l&rsquo;adaptation m\u00e9tabolique. Il te donne un objectif calorique fixe tant que tu ne mets pas \u00e0 jour ton poids et ton niveau d&rsquo;activit\u00e9 manuellement. Tu peux scanner tes assiettes avec une r\u00e9gularit\u00e9 exemplaire, mais quand tu stagnes apr\u00e8s 6 semaines de cut, l&rsquo;app n&rsquo;a aucune id\u00e9e du pourquoi.<\/p>\n<\/section>\n\n<section aria-labelledby=\"solution\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">04 &middot; Solution Lean<\/span><\/div>\n  <h2 id=\"solution\">Comment Lean r\u00e9sout chacun des 3 probl\u00e8mes<\/h2>\n  <p>Foodvisor a pos\u00e9 un standard sur le scan photo d&rsquo;un plat, et sa reconnaissance de la cuisine fran\u00e7aise reste excellente. Le probl\u00e8me n&rsquo;est pas ce qu&rsquo;il voit dans ton assiette, c&rsquo;est ce qu&rsquo;il ne voit pas de ton corps&nbsp;: la d\u00e9pense reste estim\u00e9e par une formule de population multipli\u00e9e par un niveau d&rsquo;activit\u00e9. Lean fait les deux&nbsp;: scan photo IA <em>et<\/em> mesure de chaque composant du TDEE (BMR&nbsp;+&nbsp;NEAT&nbsp;+&nbsp;EAT&nbsp;+&nbsp;TEF) plus l&rsquo;adaptation m\u00e9tabolique. Voici le d\u00e9tail.<\/p>\n\n  <div class=\"method\">\n    <div class=\"m-phone\">\n      <div class=\"duo-row\">\n        <div>\n          <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-bodyscan-result.webp\" alt=\"R\u00e9sultat BodyScan IA : pourcentage de masse grasse mesur\u00e9 par photo\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\">\u00c9tape 1<strong>BodyScan IA<\/strong><\/div>\n        <\/div>\n        <div>\n          <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp\" alt=\"\u00c9cran BMR Lean : m\u00e9tabolisme de base calcul\u00e9 sur la masse maigre\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\">\u00c9tape 2<strong>BMR recalcul\u00e9<\/strong><\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n    <div>\n      <div class=\"m-tag\">Le BMR sur bodyfat r\u00e9el<\/div>\n      <h3>Mod\u00e8le propri\u00e9taire brevet\u00e9, bas\u00e9 sur la masse maigre<\/h3>\n      <p>Compter parfaitement les calories entrantes ne sert \u00e0 rien si les calories sortantes sont fausses de 300&nbsp;kcal. Lean calcule le m\u00e9tabolisme sur ta <strong>masse maigre<\/strong>, la seule qui consomme r\u00e9ellement au repos, et non sur ton poids brut.<\/p>\n      <p>El <strong>BodyScan IA<\/strong> applique \u00e0 ton corps ce que Foodvisor applique \u00e0 ton assiette&nbsp;: une photo, un mod\u00e8le entra\u00een\u00e9 sur une banque de scans DEXA, ton bodyfat en quelques secondes, \u00e0 refaire chaque semaine.<\/p>\n      <p>Pas de pince \u00e0 pli cutan\u00e9, pas de balance \u00e0 imp\u00e9dance, pas de DEXA. La m\u00eame simplicit\u00e9 qu&rsquo;un scan de repas, appliqu\u00e9e \u00e0 ta composition corporelle.<\/p>\n    <\/div>\n  <\/div>\n\n  <div class=\"method flip\">\n    <div>\n      <div class=\"m-tag\">Pas de coefficient d&rsquo;activit\u00e9<\/div>\n      <h3>NEAT, EAT, TEF calculados por separado<\/h3>\n      <p><strong>NEAT.<\/strong> Tes pas r\u00e9els arrivent via HealthKit (iOS) ou Google Fit (Android) et deviennent des calories selon ton m\u00e9tabolisme. C&rsquo;est le poste le plus variable de la journ\u00e9e, et celui qu&rsquo;un niveau d&rsquo;activit\u00e9 d\u00e9clar\u00e9 aplatit compl\u00e8tement.<\/p>\n      <p><strong>EAT.<\/strong> Chaque s\u00e9ance est chiffr\u00e9e par MET sur ton temps d&rsquo;effort r\u00e9el, temps de repos exclus. Compter une heure de musculation comme une heure de course fausse le bilan de plusieurs centaines de kcal par semaine.<\/p>\n      <p><strong>TEF.<\/strong> Foodvisor identifie tes aliments, Lean en tire le co\u00fbt de digestion&nbsp;: 20 \u00e0 30&nbsp;% des calories pour les prot\u00e9ines, 5 \u00e0 10&nbsp;% pour les glucides, 1 \u00e0 3&nbsp;% pour les lipides, au lieu du forfait de 10&nbsp;% appliqu\u00e9 partout.<\/p>\n    <\/div>\n    <div class=\"m-phone\">\n      <div class=\"mini-row\" style=\"margin:0;gap:10px\">\n        <div>\n          <div class=\"mini-phone tiny\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp\" alt=\"\u00c9cran NEAT Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\" style=\"font-size:10px\"><strong style=\"font-size:12px\">NEAT<\/strong><\/div>\n        <\/div>\n        <div>\n          <div class=\"mini-phone tiny\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp\" alt=\"\u00c9cran EAT Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\" style=\"font-size:10px\"><strong style=\"font-size:12px\">EAT<\/strong><\/div>\n        <\/div>\n        <div>\n          <div class=\"mini-phone tiny\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_TEF.webp\" alt=\"\u00c9cran TEF Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n          <div class=\"mini-cap\" style=\"font-size:10px\"><strong style=\"font-size:12px\">TEF<\/strong><\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"method flip\">\n    <div>\n      <div class=\"m-tag\">Adaptation m\u00e9tabolique automatique<\/div>\n      <h3>Une premi\u00e8re mondiale sur app grand public<\/h3>\n      <p><strong>L&rsquo;adaptation m\u00e9tabolique.<\/strong> Aucun scan de repas ne d\u00e9tecte que ton m\u00e9tabolisme a ralenti de 12&nbsp;% apr\u00e8s huit semaines. Lean l&rsquo;estime selon les fourchettes publi\u00e9es (M\u00fcller 2015, Doucet 2001) et corrige ton objectif en cons\u00e9quence.<\/p>\n      <p>Au-del\u00e0 de 10 \u00e0 15&nbsp;% d&rsquo;adaptation, l&rsquo;app peut conseiller un retour \u00e0 la maintenance pour relancer le m\u00e9tabolisme avant de repartir.<\/p>\n      <p>Aucun multiplicateur d&rsquo;activit\u00e9 \u00e0 choisir. Chaque composant est mesur\u00e9, semaine apr\u00e8s semaine.<\/p>\n    <\/div>\n    <div class=\"m-phone\">\n      <div class=\"mini-phone solo\" style=\"max-width:240px!important;width:240px;padding:6px!important;border-radius:24px!important;border-width:2px!important\"><div class=\"notch\" style=\"width:60px!important;height:14px!important;border-radius:0 0 9px 9px!important\"><\/div><div class=\"scr\" style=\"border-radius:18px!important\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp\" alt=\"\u00c9cran d\u00e9pense totale Lean avec adaptation m\u00e9tabolique\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">M\u00e9thode<strong>Adaptation m\u00e9tabolique<\/strong><\/div>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"tab\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">05 &middot; Tableau comparatif<\/span><\/div>\n  <h2 id=\"tab\">Lean face \u00e0 Foodvisor, crit\u00e8re par crit\u00e8re<\/h2>\n  <p>Lecture honn\u00eate des forces et faiblesses de chaque app. Aucun crit\u00e8re ne porte sur le prix.<\/p>\n\n  <div class=\"brand-banner\" style=\"display:grid;grid-template-columns:1fr 1fr;gap:16px;margin:24px 0 18px;padding:0\">\n  <div style=\"background:#FFF1F5;border:1.5px solid #FF2D6E;border-radius:14px;padding:20px 18px;display:flex;flex-direction:column;align-items:center;text-align:center;gap:10px\">\n    <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"Lean\" width=\"64\" height=\"64\" loading=\"lazy\" decoding=\"async\" style=\"width:64px;height:64px;border-radius:14px;display:block;object-fit:cover\" \/>\n    <div style=\"font-family:-apple-system,BlinkMacSystemFont,'SF Pro Display',sans-serif;font-size:18px;font-weight:700;color:#FF2D6E;letter-spacing:-0.2px\">Lean<\/div>\n  <\/div>\n  <div style=\"background:#EFF7EF;border:1.5px solid #6ABF6C;border-radius:14px;padding:20px 18px;display:flex;flex-direction:column;align-items:center;text-align:center;gap:10px\">\n    <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/08\/fv-logo-foodvisor.jpg\" alt=\"Foodvisor\" width=\"64\" height=\"64\" loading=\"lazy\" decoding=\"async\" style=\"width:64px;height:64px;border-radius:14px;display:block;object-fit:cover\" \/>\n    <div style=\"font-family:-apple-system,BlinkMacSystemFont,'SF Pro Display',sans-serif;font-size:18px;font-weight:700;color:#6ABF6C;letter-spacing:-0.2px\">Foodvisor<\/div>\n  <\/div>\n<\/div>\n<div class=\"table\" role=\"table\" aria-label=\"Comparatif Lean face \u00e0 Foodvisor\">\n    <div class=\"table-row head\" role=\"row\">\n      <div role=\"columnheader\">Criterio<\/div>\n      <div class=\"brand-cell lean\" role=\"columnheader\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> <span>Lean<\/span><\/div>\n      <div class=\"brand-cell\" role=\"columnheader\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/08\/fv-logo-foodvisor.jpg\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> <span>Foodvisor<\/span><\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Formule BMR<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Mod\u00e8le propri\u00e9taire brevet\u00e9 (masse maigre)<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Formule de population (Mifflin-St Jeor 1990)<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Prend en compte le bodyfat<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Oui, mesur\u00e9 dans l&rsquo;app<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Non, poids-taille-\u00e2ge-sexe uniquement<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Mesure du bodyfat dans l&rsquo;app<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> BodyScan IA via photo<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> No<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">NEAT (pas, activit\u00e9 hors sport)<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Calcul\u00e9e sur les pas r\u00e9els chaque jour<\/div>\n      <div class=\"cell\"><span class=\"icn mid\">&minus;<\/span> Pas suivis, mais objectif assis sur le multiplicateur statique<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">EAT (d\u00e9pense exercice)<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Par sport via MET, temps effectif<\/div>\n      <div class=\"cell\"><span class=\"icn mid\">&minus;<\/span> S\u00e9ances enregistr\u00e9es, ajout\u00e9es \u00e0 un objectif fig\u00e9<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">TEF (digestion)<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Calcul\u00e9e selon macros, int\u00e9gr\u00e9e au TDEE<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Non int\u00e9gr\u00e9e au calcul de la d\u00e9pense<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Adaptation m\u00e9tabolique<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Automatique, semaine par semaine<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> No<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Coefficient d&rsquo;activit\u00e9 \u00e0 choisir<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Non, calcul\u00e9 sur donn\u00e9es r\u00e9elles<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Oui, multiplicateur statique choisi \u00e0 l&rsquo;inscription<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Scan photo IA d&rsquo;un plat<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Oui, illimit\u00e9<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Oui, le pionnier historique (2018)<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Reconnaissance visuelle des aliments<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Oui, scan photo IA moderne<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> R\u00e9f\u00e9rence du march\u00e9 FR, des ann\u00e9es d&rsquo;entra\u00eenement<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Scan code-barres<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> S\u00ed<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> S\u00ed<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Base de donn\u00e9es alimentaire<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> USDA + OpenFoodFacts, cur\u00e9e<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Large base, produits fran\u00e7ais bien couverts<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Coaching par di\u00e9t\u00e9ticiens<\/div>\n      <div class=\"cell lean\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Hors scope, l&rsquo;app guide via la Pyramide de Progression<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Oui, di\u00e9t\u00e9ticiens dipl\u00f4m\u00e9s (offre d\u00e9di\u00e9e)<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Recommandation d\u00e9ficit calorique<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Adapt\u00e9e au TDEE r\u00e9el<\/div>\n      <div class=\"cell\"><span class=\"icn no\"><svg viewbox=\"0 0 12 12\"><path d=\"M3 3 L9 9 M9 3 L3 9\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\"\/><\/svg><\/span> Objectif fixe, recalcul manuel requis<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">App fran\u00e7aise<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> S\u00ed<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Oui, n\u00e9e \u00e0 Paris<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">R\u00e9putation et taille audience<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> 4.7\/5, 10&nbsp;000+ users, jeune app FR<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Pionnier reconnu du scan photo, forte notori\u00e9t\u00e9 en France<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Modelo de negocio<\/div>\n      <div class=\"cell lean\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Premium, essai gratuit 7 jours sur l&rsquo;annuel<\/div>\n      <div class=\"cell\"><span class=\"icn ok\"><svg viewbox=\"0 0 12 12\"><path d=\"M2 6.5 L5 9 L10 3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/span> Version gratuite, coaching en option<\/div>\n    <\/div>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"tracking\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">06 &middot; Tracking<\/span><\/div>\n  <h2 id=\"tracking\">3 m\u00e9thodes pour tracker un repas<\/h2>\n  <p>Foodvisor a prouv\u00e9 qu&rsquo;une photo pouvait remplacer une saisie manuelle. Lean reprend cette id\u00e9e et l&rsquo;\u00e9largit&nbsp;: trois m\u00e9thodes d&rsquo;enregistrement selon le contexte, pour tenir sur la dur\u00e9e.<\/p>\n\n  <div class=\"mini-row\">\n    <div>\n      <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-database.webp\" alt=\"Recherche dans la base de donn\u00e9es USDA + OpenFoodFacts\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">M\u00e9thode 1<strong>Base de donn\u00e9es<\/strong><\/div>\n    <\/div>\n    <div>\n      <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-codebarre.webp\" alt=\"Scan de code-barres dans Lean\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">M\u00e9thode 2<strong>Code-barres<\/strong><\/div>\n    <\/div>\n    <div>\n      <div class=\"mini-phone\"><div class=\"notch\"><\/div><div class=\"scr\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-scania.webp\" alt=\"Scan photo IA d'un plat\" width=\"1179\" height=\"2556\" loading=\"lazy\" decoding=\"async\" \/><\/div><\/div>\n      <div class=\"mini-cap\">M\u00e9thode 3<strong>Scan photo IA<\/strong><\/div>\n    <\/div>\n  <\/div>\n\n  <ol>\n    <li><strong>Recherche dans la base de donn\u00e9es.<\/strong> Base cur\u00e9e, USDA + OpenFoodFacts. Pas de bruit communautaire, pas de \u00ab&nbsp;Poulet r\u00f4ti&nbsp;\u00bb rentr\u00e9 47 fois par 47 utilisateurs diff\u00e9rents avec 47 valeurs diff\u00e9rentes.<\/li>\n    <li><strong>Scan de code-barres.<\/strong> Standard. Tu scannes ton paquet de p\u00e2tes, tu obtiens les macros.<\/li>\n    <li><strong>Scan photo IA d&rsquo;un plat.<\/strong> Tu prends en photo ton assiette, l&rsquo;IA d\u00e9tecte les aliments, tu obtiens les calories et les macros par aliment. Le r\u00e9flexe que tu as d\u00e9j\u00e0 si tu viens de Foodvisor : tu le gardes tel quel.<\/li>\n  <\/ol>\n  <p>Le scan photo IA de Lean joue le m\u00eame r\u00f4le que celui de Foodvisor pour les repas pris dehors. La diff\u00e9rence se situe ailleurs&nbsp;: ce que Lean fait ensuite de ces calories, en les confrontant \u00e0 une d\u00e9pense mesur\u00e9e et non estim\u00e9e.<\/p>\n  <p>Au-del\u00e0 du repas, Lean affiche un TDEE qui se met \u00e0 jour pendant la journ\u00e9e selon tes pas. Scanner parfaitement une assiette face \u00e0 un objectif calorique fig\u00e9 ne suffit pas.<\/p>\n  <p>Et au-dessus, la Pyramide de Progression&nbsp;:<\/p>\n\n  <div class=\"pyramid\" aria-label=\"Pyramide de Progression Lean\">\n    <div class=\"level l1\"><span>Adh\u00e9rence<\/span><span class=\"k\">Base<\/span><\/div>\n    <div class=\"level l2\"><span>Objectif calorique<\/span><span class=\"k\">\u00c9tage 2<\/span><\/div>\n    <div class=\"level l3\"><span>Pas \/ NEAT<\/span><span class=\"k\">\u00c9tage 3<\/span><\/div>\n    <div class=\"level l4\"><span>Macronutriments<\/span><span class=\"k\">Sommet<\/span><\/div>\n  <\/div>\n  <div class=\"pyramid-cap\">Ne pas br\u00fbler les \u00e9tapes. Si tu n&rsquo;es pas r\u00e9gulier dans le tracking, optimiser les macros au pourcent pr\u00e8s ne sert \u00e0 rien.<\/div>\n<\/section>\n\n<section aria-labelledby=\"foodvisor-better\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">07 &middot; Honn\u00eatet\u00e9<\/span><\/div>\n  <h2 id=\"foodvisor-better\">Ce que Foodvisor fait mieux<\/h2>\n  <p>Lean n&rsquo;est pas parfait, et Foodvisor a plusieurs vraies forces qu&rsquo;il faut reconna\u00eetre. Lecture honn\u00eate, crit\u00e8re par crit\u00e8re, sur les axes o\u00f9 le pionnier reste devant. Aucun de ces axes n&rsquo;est secondaire : ce sont des piliers r\u00e9els de la promesse Foodvisor.<\/p>\n\n  <div class=\"scorecard rev\" aria-label=\"Scorecard Foodvisor face \u00e0 Lean sur 4 axes\">\n    <div class=\"scorecard-head\">\n      <div class=\"h-crit\">Axe<\/div>\n      <div class=\"h-brand\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/08\/fv-logo-foodvisor.jpg\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> Foodvisor<\/div>\n      <div class=\"h-brand\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-logo-lean-square-scaled.webp\" alt=\"\" width=\"512\" height=\"512\" loading=\"lazy\" decoding=\"async\" \/> Lean<\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Reconnaissance visuelle d&rsquo;une assiette<\/div>\n      <div class=\"bar mfp\" data-brand=\"FOODVISOR\"><div class=\"b\"><i style=\"width:95%\"><\/i><\/div><div class=\"v\">9,5<\/div><\/div>\n      <div class=\"bar lean\" data-brand=\"LEAN\"><div class=\"b\"><i style=\"width:80%\"><\/i><\/div><div class=\"v\">8,0<\/div><\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Estimation automatique des portions<\/div>\n      <div class=\"bar mfp\" data-brand=\"FOODVISOR\"><div class=\"b\"><i style=\"width:90%\"><\/i><\/div><div class=\"v\">9,0<\/div><\/div>\n      <div class=\"bar lean\" data-brand=\"LEAN\"><div class=\"b\"><i style=\"width:75%\"><\/i><\/div><div class=\"v\">7,5<\/div><\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Coaching humain (di\u00e9t\u00e9ticiens dipl\u00f4m\u00e9s)<\/div>\n      <div class=\"bar mfp\" data-brand=\"FOODVISOR\"><div class=\"b\"><i style=\"width:92%\"><\/i><\/div><div class=\"v\">9,2<\/div><\/div>\n      <div class=\"bar lean\" data-brand=\"LEAN\"><div class=\"b\"><i style=\"width:25%\"><\/i><\/div><div class=\"v\">2,5<\/div><\/div>\n    <\/div>\n    <div class=\"scorecard-row\">\n      <div class=\"crit\">Ant\u00e9riorit\u00e9 du scan photo (march\u00e9 FR)<\/div>\n      <div class=\"bar mfp\" data-brand=\"FOODVISOR\"><div class=\"b\"><i style=\"width:95%\"><\/i><\/div><div class=\"v\">9,5<\/div><\/div>\n      <div class=\"bar lean\" data-brand=\"LEAN\"><div class=\"b\"><i style=\"width:40%\"><\/i><\/div><div class=\"v\">4,0<\/div><\/div>\n    <\/div>\n  <\/div>\n\n  <p style=\"margin-top:30px\"><strong>Lecture honn\u00eate.<\/strong> Sur la reconnaissance d&rsquo;assiette, Foodvisor a cr\u00e9\u00e9 la cat\u00e9gorie en France en 2018 et son IA a des ann\u00e9es d&rsquo;entra\u00eenement d&rsquo;avance : identification des aliments, estimation des portions sans balance, gestion des plats compos\u00e9s. C&rsquo;est son terrain de jeu historique et il y reste la r\u00e9f\u00e9rence. Sur l&rsquo;accompagnement humain, Foodvisor propose un suivi par des di\u00e9t\u00e9ticiens dipl\u00f4m\u00e9s directement dans l&rsquo;app : Lean ne le propose pas, et ne pr\u00e9tend pas le remplacer. Le scan photo IA de Lean est moderne, illimit\u00e9 et largement suffisant pour l&rsquo;usage quotidien, mais Lean ne revendique pas l&rsquo;ant\u00e9riorit\u00e9 sur ce terrain.<\/p>\n  <p>Si ton angle principal est l&rsquo;identification d&rsquo;assiette la plus rod\u00e9e possible, ou un coaching humain int\u00e9gr\u00e9 \u00e0 l&rsquo;app, Foodvisor est plus pertinent que Lean. Si ton angle est la pr\u00e9cision du <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/tdee-calculator\/\">calcul TDEE<\/a>, le bodyfat mesur\u00e9 chaque semaine via BodyScan IA, et l&rsquo;adaptation m\u00e9tabolique automatique, c&rsquo;est exactement ce qui vient d&rsquo;\u00eatre d\u00e9montr\u00e9 dans les 3 sections pr\u00e9c\u00e9dentes. Certains font tourner les deux apps en parall\u00e8le le temps de choisir, et c&rsquo;est tout \u00e0 fait d\u00e9fendable.<\/p>\n<\/section>\n\n<section aria-labelledby=\"forwho\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">08 &middot; Pour qui<\/span><\/div>\n  <h2 id=\"forwho\">Pour qui Lean est-il fait<\/h2>\n  <p>4 profils. Si vous vous reconnaissez dans au moins un, Lean est probablement fait pour vous.<\/p>\n\n  <div class=\"persona\">\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>Tu as utilis\u00e9 Foodvisor s\u00e9rieusement et tu n&rsquo;as pas perdu<\/h4>\n        <p>Tu as scann\u00e9 tes assiettes, corrig\u00e9 les portions, suivi un d\u00e9ficit honn\u00eate pendant des semaines, et tu stagnes. Ce n&rsquo;est pas la photo le coupable, c&rsquo;est l&rsquo;objectif fig\u00e9 calcul\u00e9 sans bodyfat. Lean corrige \u00e0 la racine via le BMR sur bodyfat r\u00e9el.<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>Tu stagnes apr\u00e8s plusieurs semaines de cut<\/h4>\n        <p>Plateau qui s&rsquo;\u00e9ternise apr\u00e8s 4 \u00e0 8 semaines. C&rsquo;est l&rsquo;adaptation m\u00e9tabolique. Lean la calcule automatiquement et r\u00e9ajuste ton objectif chaque semaine.<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>Tu veux comprendre ton m\u00e9tabolisme<\/h4>\n        <p>Lean affiche chaque composant (BMR, NEAT, EAT, TEF) puis explique l&rsquo;adaptation \u00e0 part, au lieu de tout cacher derri\u00e8re un chiffre unique. Tu vois d&rsquo;o\u00f9 vient chaque kcal de d\u00e9pense.<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"persona-it match\">\n      <div class=\"pic\"><svg viewbox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 12 L9 18 L21 5\"\/><\/svg><\/div>\n      <div>\n        <h4>Tu veux un tracking qui dure 12 mois<\/h4>\n        <p>Scan photo IA + base cur\u00e9e + code-barres couvrent tous les usages, de l&rsquo;aliment brut \u00e0 la pizza au restaurant. C&rsquo;est ce qui fait la diff\u00e9rence entre tenir et l\u00e2cher.<\/p>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <p style=\"margin-top:30px\"><strong>Foodvisor reste plus pertinent pour<\/strong>&nbsp;: le coaching humain par des di\u00e9t\u00e9ticiens dipl\u00f4m\u00e9s directement dans l&rsquo;app, et la reconnaissance d&rsquo;assiette la plus rod\u00e9e du march\u00e9 fran\u00e7ais. La pr\u00e9cision du calcul de d\u00e9pense et l&rsquo;adaptation m\u00e9tabolique ne font juste pas partie de sa promesse principale.<\/p>\n<\/section>\n\n<section aria-labelledby=\"migrate\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">09 &middot; Migraci\u00f3n<\/span><\/div>\n  <h2 id=\"migrate\">Passer de Foodvisor \u00e0 Lean (ou utiliser les deux) en 3 minutes<\/h2>\n\n  <div class=\"steps\">\n    <div class=\"step\"><div class=\"sn\">01<\/div><h4>T\u00e9l\u00e9charge Lean<\/h4><p>App Store ou Play Store. Inscription en 30 secondes.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">02<\/div><h4>BodyScan IA<\/h4><p>Une photo, 5 secondes. Tu obtiens ton bodyfat.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">03<\/div><h4>Poids &amp; taille<\/h4><p>Tu renseignes ton poids et ta taille. C&rsquo;est tout.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">04<\/div><h4>Lean calcule<\/h4><p>BMR sur bodyfat r\u00e9el, NEAT via HealthKit \/ Google Fit (pas r\u00e9els), EAT par MET, TEF par macros, plus l&rsquo;adaptation m\u00e9tabolique qui module le BMR. Automatique.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">05<\/div><h4>Tracke un repas<\/h4><p>Photo, code-barres ou base de donn\u00e9es. Tu connais d\u00e9j\u00e0 le geste.<\/p><\/div>\n  <\/div>\n\n  <p style=\"margin-top:24px\"><strong>Note importante.<\/strong> Lean n&rsquo;importe pas ton historique Foodvisor automatiquement, ni tes aliments favoris. Si ton suivi avec un di\u00e9t\u00e9ticien Foodvisor compte pour toi, rien ne t&#8217;emp\u00eache de garder les deux le temps de la transition : Foodvisor pour l&rsquo;accompagnement humain, Lean pour le TDEE et le tracking quotidien. La sync HealthKit \/ Google Health Connect, elle, prend le relais imm\u00e9diatement pour tes pas et ton historique d&rsquo;activit\u00e9.<\/p>\n\n  <div class=\"cta-band rev\" style=\"display:flex;align-items:center;justify-content:space-between;gap:20px;flex-wrap:wrap;padding:26px 28px;margin:40px 0;background:#FAF5EE;border:1px solid #E8E2D6;border-radius:16px\">\n    <div class=\"l\" style=\"flex:1;min-width:240px;font-size:18px;line-height:1.4;font-weight:500;color:#0E0E10\">T\u00e9l\u00e9charger Lean et commencer le BodyScan IA d\u00e8s maintenant. Inscription gratuite.<\/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=vs-foodvisor\" 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=vs-foodvisor\" 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<\/section>\n\n<section aria-labelledby=\"deblock-h\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">10 &middot; Ce que Lean d\u00e9bloque<\/span><\/div>\n  <h2 id=\"deblock-h\">Ce que Lean fait, et que Foodvisor ne fait pas (sur la d\u00e9pense)<\/h2>\n  <p>Six fonctionnalit\u00e9s centr\u00e9es sur la d\u00e9pense, introuvables chez Foodvisor. Elles d\u00e9coulent toutes du m\u00eame principe&nbsp;: calculer chaque composant du TDEE pr\u00e9cis\u00e9ment, pas l&rsquo;approximer.<\/p>\n\n  <div class=\"feat-stack\">\n    <div class=\"feat-it\"><div class=\"fn\">01<\/div><div><div class=\"ft\">BodyScan IA illimit\u00e9<\/div><p class=\"fd\">Votre bodyfat r\u00e9el, mesur\u00e9 depuis une simple photo, refait chaque semaine. C&rsquo;est la donn\u00e9e qui change tout le calcul du BMR. Aucune autre app grand public ne propose \u00e7a.<\/p><\/div><div class=\"fc\">Bodyfat<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">02<\/div><div><div class=\"ft\">Adaptation m\u00e9tabolique automatique<\/div><p class=\"fd\">Votre TDEE se r\u00e9ajuste semaine apr\u00e8s semaine selon les chiffres scientifiquement \u00e9tablis. Vous \u00e9vitez les plateaux que personne ne sait expliquer.<\/p><\/div><div class=\"fc\">Adaptaci\u00f3n<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">03<\/div><div><div class=\"ft\">TDEE d\u00e9compos\u00e9e live<\/div><p class=\"fd\">BMR + NEAT + EAT + TEF affich\u00e9s chacun, mis \u00e0 jour pendant la journ\u00e9e. Plus de chiffre fig\u00e9 \u00e0 8h du matin. Vous voyez votre balance calorique en direct.<\/p><\/div><div class=\"fc\">Live<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">04<\/div><div><div class=\"ft\">NEAT sur pas r\u00e9els, sans coefficient<\/div><p class=\"fd\">Vos pas, mesur\u00e9s par votre t\u00e9l\u00e9phone, alimentent directement le calcul du TDEE chaque jour. Aucune case s\u00e9dentaire ou actif \u00e0 cocher, jamais.<\/p><\/div><div class=\"fc\">NEAT<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">05<\/div><div><div class=\"ft\">TEF calcul\u00e9e sur vos macros<\/div><p class=\"fd\">La digestion n&rsquo;est pas un forfait de 10&nbsp;%. Prot\u00e9ines 20 \u00e0 30&nbsp;%, glucides 5 \u00e0 10&nbsp;%, lipides 1 \u00e0 3&nbsp;%. Lean fait le calcul \u00e0 chaque repas et l&rsquo;int\u00e8gre au TDEE.<\/p><\/div><div class=\"fc\">TEF<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">06<\/div><div><div class=\"ft\">Historique complet et tendances<\/div><p class=\"fd\">Suivez vos tendances poids, bodyfat, masse maigre sur des mois. Comprenez vos cycles. Rep\u00e9rez les phases o\u00f9 vous progressez et celles o\u00f9 vous stagnez.<\/p><\/div><div class=\"fc\">Historique<\/div><\/div>\n  <\/div>\n\n  <p style=\"margin-top:26px\">Vous installez l&rsquo;app gratuitement, vous testez sans engagement, vous d\u00e9cidez ensuite si l&rsquo;outil colle \u00e0 votre objectif.<\/p>\n<\/section>\n\n<section aria-labelledby=\"faq-h\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">11 &middot; FAQ<\/span><\/div>\n  <h2 id=\"faq-h\">Preguntas frecuentes<\/h2>\n  <div class=\"faq\">\n    <details><summary>Foodvisor a invent\u00e9 le scan photo de repas, pourquoi le comparer \u00e0 Lean&nbsp;?<\/summary><div class=\"ans\">Parce que la promesse d&rsquo;un tracker ne s&rsquo;arr\u00eate pas \u00e0 l&rsquo;assiette. Foodvisor est la r\u00e9f\u00e9rence historique de la reconnaissance photo en France, mais son objectif calorique repose sur une formule de population (Mifflin-St Jeor 1990, sans bodyfat mesur\u00e9) et un multiplicateur d&rsquo;activit\u00e9 statique choisi \u00e0 l&rsquo;inscription. Ce sont deux moiti\u00e9s du m\u00eame probl\u00e8me&nbsp;: Foodvisor excelle sur ce qui entre, Lean sur ce qui se d\u00e9pense.<\/div><\/details>\n    <details><summary>Pourquoi Foodvisor ne calcule pas le BMR sur le bodyfat r\u00e9el&nbsp;?<\/summary><div class=\"ans\">Parce qu&rsquo;aucune mesure de bodyfat n&rsquo;existe dans l&rsquo;app, et que les formules de population n&rsquo;utilisent que le poids, la taille, l&rsquo;\u00e2ge et le sexe. Lean int\u00e8gre le BodyScan IA pour mesurer ton bodyfat depuis une simple photo, \u00e0 refaire chaque semaine, ce qui permet un BMR bas\u00e9 sur la masse maigre r\u00e9elle via un mod\u00e8le propri\u00e9taire brevet\u00e9.<\/div><\/details>\n    <details><summary>Le scan photo de Lean vaut-il celui de Foodvisor&nbsp;?<\/summary><div class=\"ans\">Foodvisor garde l&rsquo;ant\u00e9riorit\u00e9 et des ann\u00e9es d&rsquo;entra\u00eenement sur la reconnaissance d&rsquo;assiette, en particulier l&rsquo;estimation des portions. Le scan photo IA de Lean identifie les aliments, les calories et les macros avec une pr\u00e9cision comparable pour l&rsquo;usage quotidien, et il est illimit\u00e9. Sur ce crit\u00e8re, les deux font le job. La vraie diff\u00e9rence entre les deux apps se joue sur le calcul de la d\u00e9pense.<\/div><\/details>\n    <details><summary>Foodvisor compte mes pas, \u00e7a suffit pour la NEAT&nbsp;?<\/summary><div class=\"ans\">Compter les pas et les int\u00e9grer au calcul sont deux choses diff\u00e9rentes. Chez Foodvisor, l&rsquo;objectif calorique reste assis sur le multiplicateur d&rsquo;activit\u00e9 statique choisi \u00e0 l&rsquo;inscription. Lean calcule la NEAT directement \u00e0 partir des pas r\u00e9els mesur\u00e9s chaque jour, sans coefficient \u00e0 choisir, et la s\u00e9pare proprement de la d\u00e9pense sport (EAT).<\/div><\/details>\n    <details><summary>Lean est-il gratuit ou payant&nbsp;?<\/summary><div class=\"ans\">Lean est Premium, avec un essai gratuit de 7 jours sur l&rsquo;abonnement annuel. Tu t\u00e9l\u00e9charges, tu testes BodyScan IA, scan photo IA d&rsquo;un plat, recomposition TDEE, sans engagement. Si l&rsquo;outil colle \u00e0 ton objectif, tu continues. Sinon tu d\u00e9sactives le renouvellement avant la fin de la p\u00e9riode d&rsquo;essai.<\/div><\/details>\n    <details><summary>Peut-on utiliser Lean et Foodvisor en parall\u00e8le&nbsp;?<\/summary><div class=\"ans\">Oui, surtout en transition. Certains gardent Foodvisor pour le coaching avec un di\u00e9t\u00e9ticien et utilisent Lean au quotidien pour le TDEE, la recomposition et le tracking. L&rsquo;effort de double saisie est r\u00e9el&nbsp;: \u00e0 terme, la plupart choisissent l&rsquo;app qui pilote leur objectif calorique, et c&rsquo;est pr\u00e9cis\u00e9ment le terrain o\u00f9 Lean est construit pour \u00eatre le plus pr\u00e9cis.<\/div><\/details>\n  <\/div>\n<\/section>\n\n<section aria-labelledby=\"conclu\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">12 &middot; Conclusion<\/span><\/div>\n  <h2 id=\"conclu\">L&rsquo;assiette est r\u00e9gl\u00e9e. La d\u00e9pense, non.<\/h2>\n  <p>Ce n&rsquo;est pas Foodvisor face \u00e0 Lean en marketing. C&rsquo;est l&rsquo;entr\u00e9e face \u00e0 la sortie, deux moiti\u00e9s d&rsquo;une m\u00eame \u00e9quation.<\/p>\n  <p>Foodvisor a r\u00e9solu la moiti\u00e9 gauche&nbsp;: savoir ce que tu manges, sans balance, gr\u00e2ce au scan photo le plus rod\u00e9 du march\u00e9 fran\u00e7ais. Mais pour la moiti\u00e9 droite, ta d\u00e9pense, Foodvisor s&rsquo;appuie sur une formule de population de 1990 sans bodyfat mesur\u00e9, un multiplicateur d&rsquo;activit\u00e9 fig\u00e9 que tu coches une seule fois \u00e0 l&rsquo;inscription, et aucune adaptation m\u00e9tabolique. Le combo des trois rend tout suivi calorique pr\u00e9cis impossible au-del\u00e0 de quelques semaines de cut. C&rsquo;est math\u00e9matique.<\/p>\n  <p>Lean a \u00e9t\u00e9 construit pour cette moiti\u00e9-l\u00e0&nbsp;: BMR bas\u00e9 sur le <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/depense-energetique-totale-v2\/\">bodyfat r\u00e9el<\/a> (mesur\u00e9 par BodyScan IA) via un mod\u00e8le propri\u00e9taire brevet\u00e9, <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/neat-depense-non-sportive\/\">NEAT par pas r\u00e9els<\/a>, EAT par sport et MET, <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/effet-thermique-des-aliments\/\">TEF par macros<\/a>, plus l&rsquo;adaptation m\u00e9tabolique qui module le BMR semaine apr\u00e8s semaine. Chaque composant calcul\u00e9 pr\u00e9cis\u00e9ment, sans coefficient magique. Et le r\u00e9flexe photo que tu as pris chez Foodvisor, tu le gardes&nbsp;: le scan photo IA est int\u00e9gr\u00e9, en illimit\u00e9.<\/p>\n  <p>Si tu as essay\u00e9 Foodvisor s\u00e9rieusement et que tu n&rsquo;as pas eu les r\u00e9sultats que tu esp\u00e9rais sur ton cut, le probl\u00e8me n&rsquo;est pas toi, ni la photo. Le probl\u00e8me est le TDEE fig\u00e9 sous le capot. Change le moteur, garde le r\u00e9flexe.<\/p>\n<\/section>\n\n<div class=\"get-band rev\" style=\"background:#F1E9DC;border-radius:24px;padding:48px 36px;margin:60px 0 40px;text-align:center\">\n  <div class=\"kicker\">Descarga<\/div>\n  <h3>Lean se puede descargar gratis<\/h3>\n  <p>iOS et Android. Le BodyScan IA fonctionne avec une simple photo. Pas de pince \u00e0 pli cutan\u00e9, pas de balance imp\u00e9dance, pas de DEXA.<\/p>\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=vs-foodvisor\" target=\"_blank\" rel=\"noopener\" aria-label=\"Descargar Lean en la App Store\">\n      <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\" \/>\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=vs-foodvisor\" target=\"_blank\" rel=\"noopener\" aria-label=\"Descargar Lean en Google Play\">\n      <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\" \/>\n    <\/a>\n  <\/div>\n<\/div>\n\n<section aria-labelledby=\"links\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">Para ir m\u00e1s lejos<\/span><\/div>\n  <h3 id=\"links\" style=\"margin-top:0\">Maillage interne<\/h3>\n  <ul>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/tdee-calculator\/\">Calculadora TDEE gratuita en l\u00ednea<\/a> &middot; version web, sans inscription, m\u00eame logique que l&rsquo;app (BMR + NEAT + EAT + TEF).<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/depense-energetique-totale-v2\/\">Comprendre la TDEE en d\u00e9tail (BMR, NEAT, EAT, TEF, adaptation)<\/a> &middot; article scientifique de fond.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/comment-compter-ses-calories\/\">C\u00f3mo contar tus calor\u00edas correctamente<\/a> &middot; guide pratique pour d\u00e9butants.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/neat-depense-non-sportive\/\">NEAT&nbsp;: gasto por pasos y actividad fuera del deporte<\/a>.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/effet-thermique-des-aliments\/\">TEF&nbsp;: la digesti\u00f3n quema calor\u00edas<\/a>.<\/li>\n  <\/ul>\n<\/section>\n\n<section aria-labelledby=\"src\" class=\"sources\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">Fuentes<\/span><\/div>\n  <h3 id=\"src\" style=\"margin-top:0;color:var(--ink)\">Bibliographie<\/h3>\n  <ol>\n    <li>Harris J.A., Benedict F.G. (1919). A Biometric Study of Basal Metabolism in Man. Carnegie Institution of Washington.<\/li>\n    <li>Mifflin M.D., St Jeor S.T. et al. (1990). A new predictive equation for resting energy expenditure in healthy individuals. American Journal of Clinical Nutrition. <a class=\"inline\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/2305711\/\" target=\"_blank\" rel=\"noopener\">PubMed 2305711<\/a>.<\/li>\n    <li>Frankenfield D.C. (2013). Bias and accuracy of resting metabolic rate equations in non-obese and obese adults. Clinical Nutrition. <a class=\"inline\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/23631843\/\" target=\"_blank\" rel=\"noopener\">PubMed 23631843<\/a>.<\/li>\n    <li>Westerterp K.R. (2004). Diet induced thermogenesis. Nutrition &amp; Metabolism. <a class=\"inline\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/15507147\/\" target=\"_blank\" rel=\"noopener\">PubMed 15507147<\/a>.<\/li>\n    <li>M\u00fcller M.J. et al. (2015). Metabolic adaptation to caloric restriction and subsequent refeeding: the Minnesota Starvation Experiment revisited. American Journal of Clinical Nutrition. <a class=\"inline\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/26399868\/\" target=\"_blank\" rel=\"noopener\">PubMed 26399868<\/a>.<\/li>\n    <li>Doucet E. et al. (2001). Evidence for the existence of adaptive thermogenesis during weight loss. British Journal of Nutrition. <a class=\"inline\" href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/11430776\/\" target=\"_blank\" rel=\"noopener\">PubMed 11430776<\/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>Article publi\u00e9 le 15 ao\u00fbt 2026. Mis \u00e0 jour r\u00e9guli\u00e8rement avec les retours d&rsquo;utilisateurs et les nouvelles \u00e9tudes pertinentes. Lean est disponible sur iOS et 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=vs-foodvisor\" 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=vs-foodvisor\" 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\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:    {drill:false},\n    kcal:     {drill:false},\n    depense:  {drill:true},\n    strategie:{drill:false}\n  };\n  var subMap = {BMR:1, NEAT:1, EAT:1, TEF:1};\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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data-wpmeteor-nooptimize=\"true\">\n(function shrinkAnnotationsOnMobile(){\n  if (typeof window.Chart === 'undefined') { return setTimeout(shrinkAnnotationsOnMobile, 100); }\n  var isMobile = window.matchMedia && window.matchMedia('(max-width:540px)').matches;\n  if (!isMobile) return;\n  function apply(){\n    ['chartBMR','chartNEAT','chartAdapt'].forEach(function(id){\n      var c = window.Chart.getChart(id);\n      if (!c) return;\n      var anns = c.options && c.options.plugins && c.options.plugins.annotation && c.options.plugins.annotation.annotations;\n      if (!anns) return;\n      Object.keys(anns).forEach(function(k){\n        var a = anns[k];\n        if (a.type !== 'label' || !a.font) return;\n        if (a.font.size >= 20) { a.font.size = 14; }\n        else if (a.font.size >= 14) { a.font.size = 12; }\n      });\n      try { c.update('none'); } catch(e){}\n    });\n  }\n  var tries = 0;\n  function tryApply(){\n    apply();\n    tries++;\n    if (tries < 20) setTimeout(tryApply, 300);\n  }\n  tryApply();\n})();\n<\/script>\n\n\n\n<!-- lean-mesh-v15 -->\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;\">Leer tambi\u00e9n<\/p><ul style=\"list-style:none;padding:0;margin:0;display:grid;grid-template-columns:1fr;gap:10px;\"><li><a href=\"\/es\/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;\">Metabolismo basal (BMR)&nbsp;: todo lo que hay que saber para calcularlo <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Definici\u00f3n, ecuaci\u00f3n del TDEE, 4 f\u00f3rmulas hist\u00f3ricas, por qu\u00e9 la grasa corporal lo cambia todo.<\/span><\/a><\/li><li><a href=\"\/es\/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;\">Gasto energ\u00e9tico total (TDEE)&nbsp;: la f\u00f3rmula can\u00f3nica BMR + NEAT + EAT + TEF <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Entiende las 4 piezas + la adaptaci\u00f3n metab\u00f3lica, fuentes cient\u00edficas 2025.<\/span><\/a><\/li><li><a href=\"\/es\/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&nbsp;: tu gasto deportivo real, sesi\u00f3n por sesi\u00f3n <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Los MET correctos, la trampa del doble conteo, lo que Garmin y MyFitnessPal pasan por alto.<\/span><\/a><\/li><li><a href=\"\/es\/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;\">Mejores aplicaciones para contar calor\u00edas en 2026&nbsp;: 8 aplicaciones probadas <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=\"\/es\/alternative-myfitnesspal\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Alternativa a MyFitnessPal en 2026: las 5 opciones reales probadas <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Comparatif honn\u00eate, pr\u00e9cision TDEE, ergonomie.<\/span><\/a><\/li><li><a href=\"\/es\/comparatifs\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Todas las comparativas de Lean frente a las principales aplicaciones de calor\u00edas <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Hub&nbsp;: MyFitnessPal, Yazio, Cronometer, Lifesum, FatSecret, Noom.<\/span><\/a><\/li><li><a href=\"\/es\/lean-vs-myfitnesspal\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Lean frente a MyFitnessPal&nbsp;: la f\u00f3rmula TDEE que lo cambia todo <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Por qu\u00e9 MFP se equivoca en tu gasto cal\u00f3rico real.<\/span><\/a><\/li><li><a href=\"\/es\/calculateur-tdee\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Calculateur TDEE : la formule canonique BMR + NEAT + EAT + TEF <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Calculateur bodyfat-aware avec breakdown des 4 briques m\u00e9taboliques.<\/span><\/a><\/li><\/ul><\/aside>","protected":false},"excerpt":{"rendered":"<p>Lean Calculateur TDEE Accueil &nbsp;\/&nbsp; Lean vs Foodvisor Comparatif &middot; Nutrition &amp; TDEE Lean vs Foodvisor. Le pionnier du scan photo face au seul qui recompose ton TDEE en continu. Foodvisor voit ton assiette. Lean voit ta d\u00e9pense r\u00e9elle. Deux IA, deux moiti\u00e9s du probl\u00e8me. L&rsquo;\u00e9quipe Lean &middot; Lecture 12&nbsp;min &middot; Mis \u00e0 jour 15 [&hellip;]<\/p>","protected":false},"author":1,"featured_media":1763,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-lvm-blank","format":"standard","meta":{"footnotes":""},"categories":[13],"tags":[],"class_list":["post-1760","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-comparateurs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Lean vs Foodvisor : scan photo IA face \u00e0 ta d\u00e9pense r\u00e9elle<\/title>\n<meta name=\"description\" content=\"Foodvisor a invent\u00e9 le scan photo de repas. 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