{"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":"2026-09-08T18:18:33","modified_gmt":"2026-09-08T18:18:33","slug":"lean-vs-foodvisor","status":"publish","type":"post","link":"https:\/\/lean-app.com\/pt\/lean-vs-foodvisor\/","title":{"rendered":"Lean vs Foodvisor: scan de foto por IA diante do seu gasto real"},"content":{"rendered":"<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp\" 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.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 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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=\"Baixar na 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=\"Dispon\u00edvel no 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\/pt\/\">In\u00edcio<\/a> &nbsp;\/&nbsp; Lean vs Foodvisor<\/div>\n  <div class=\"eyebrow\">Comparativo &middot; Nutri\u00e7\u00e3o &amp; TDEE<\/div>\n  <h1 id=\"title\">Lean vs Foodvisor.\n    <span class=\"alt\">O pioneiro do scan de foto diante do \u00fanico que recomp\u00f5e seu TDEE continuamente.<\/span>\n  <\/h1>\n  <p class=\"dek\">O Foodvisor v\u00ea seu prato. O Lean v\u00ea seu gasto real. Duas IAs, duas metades do problema.<\/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>A equipe Lean<\/strong> &middot; Leitura 12&nbsp;min &middot; Atualizado em 15 de agosto de 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\">Download gratuito<\/span>\n  <\/div>\n\n  <div class=\"hero-bottom\">\n    <div class=\"hero-lead\">\n      O Foodvisor \u00e9 o pioneiro franc\u00eas do scan de foto de refei\u00e7\u00f5es: voc\u00ea fotografa seu prato, a IA reconhece os alimentos e estima as por\u00e7\u00f5es. Nessa metade do problema, o cr\u00e9dito \u00e9 merecido. Mas a outra metade, seu gasto, continua sendo uma f\u00f3rmula de popula\u00e7\u00e3o (Mifflin-St Jeor 1990), mais um multiplicador de atividade est\u00e1tico escolhido uma \u00fanica vez no cadastro. Sem gordura corporal real medida, sem adapta\u00e7\u00e3o metab\u00f3lica. O confronto Lean vs Foodvisor n\u00e3o se joga portanto na foto: joga-se no que o app faz com o n\u00famero, em 3 meses de cutting s\u00e9rio.\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>Demonstra\u00e7\u00e3o interativa<\/small>Toque na tela para explorar o aplicativo<\/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>Demonstra\u00e7\u00e3o interativa<\/small>Toque na tela<br>para explorar o aplicativo<\/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=\"Vis\u00e3o geral do app Lean com detalhamento do TDEE\">\n          <div class=\"notch\"><\/div>\n          <div class=\"phone-screen\">\n            <button class=\"phone-back\" id=\"phoneBack\" aria-label=\"Voltar\">&#8249;<\/button>\n            <div id=\"phoneImg\" class=\"phone-bg tab-depense\" role=\"img\" aria-label=\"Vis\u00e3o geral Lean, aba 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=\"Detalhe BMR\"><\/div>\n              <div class=\"z\" data-sub=\"NEAT\" style=\"top:33%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalhe NEAT\"><\/div>\n              <div class=\"z\" data-sub=\"EAT\"  style=\"top:50%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalhe EAT\"><\/div>\n              <div class=\"z\" data-sub=\"TEF\"  style=\"top:67%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalhe TEF\"><\/div>\n            <\/div>\n            <div class=\"phone-navbar\" id=\"phoneNav\" aria-hidden=\"false\">\n              <button data-tab=\"bilan\"     type=\"button\" aria-label=\"Aba Balan\u00e7o\"><\/button>\n              <button data-tab=\"kcal\"      type=\"button\" aria-label=\"Aba Calorias\"><\/button>\n              <button data-tab=\"depense\"   type=\"button\" aria-label=\"Aba Gasto\"><\/button>\n              <button data-tab=\"strategie\" type=\"button\" aria-label=\"Aba Estrat\u00e9gia\"><\/button>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div class=\"phone-tabs\" role=\"tablist\" aria-label=\"Navegar no app Lean\">\n          <button data-tab=\"bilan\"     type=\"button\">Balan\u00e7o<\/button>\n          <button data-tab=\"kcal\"      type=\"button\">Calorias<\/button>\n          <button data-tab=\"depense\"   type=\"button\" class=\"on\">Gasto<\/button>\n          <button data-tab=\"strategie\" type=\"button\">Estrat\u00e9gia<\/button>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"snippet rev\">\n    <div class=\"lbl\">Resposta r\u00e1pida<\/div>\n    <p>O Foodvisor inventou o scan de foto de refei\u00e7\u00f5es na Fran\u00e7a e continua sendo uma refer\u00eancia para identificar o que voc\u00ea come: foto, reconhecimento dos alimentos, estimativa das por\u00e7\u00f5es. Do lado do gasto, em contrapartida, o Foodvisor se apoia em uma f\u00f3rmula de popula\u00e7\u00e3o (Mifflin-St Jeor 1990, sem gordura corporal medida) e um multiplicador de atividade est\u00e1tico escolhido no cadastro. O Lean pega o problema no sentido inverso: recalcular cada componente do TDEE (<span data-term=\"BMR\">BMR<span class=\"tt\">Basal Metabolic Rate. Energia gasta em repouso. Na Lean, calculada sobre a massa magra real via BodyScan IA.<\/span><\/span> sobre gordura corporal real via um modelo propriet\u00e1rio patenteado, <span data-term=\"NEAT\">NEAT<span class=\"tt\">Non-Exercise Activity Thermogenesis. Gasto ligado aos passos e \u00e0s atividades cotidianas fora do esporte.<\/span><\/span> por passos, <span data-term=\"EAT\">EAT<span class=\"tt\">Exercise Activity Thermogenesis. Gasto ligado aos seus treinos, calculado via MET.<\/span><\/span> por MET, <span data-term=\"TEF\">TEF<span class=\"tt\">Thermic Effect of Food. Energia gasta pela digest\u00e3o. Depende dos macros ingeridos.<\/span><\/span> por macros) e modular o BMR pela adapta\u00e7\u00e3o metab\u00f3lica continuamente, sem coeficiente a escolher.<\/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; A constata\u00e7\u00e3o<\/span><\/div>\n  <h2 id=\"constat\">O Foodvisor v\u00ea seu prato, n\u00e3o seu gasto real<\/h2>\n  <p>Se voc\u00ea est\u00e1 lendo isso, provavelmente j\u00e1 instalou o Foodvisor. Voc\u00ea o escolheu precisamente pelo que ningu\u00e9m fazia antes dele: fotografar seu prato e deixar a IA reconhecer o frango, o arroz, o molho, e estimar as por\u00e7\u00f5es sem tirar a balan\u00e7a. Voc\u00ea informou seu peso, sua altura, sua idade, seu sexo, e escolheu seu n\u00edvel de atividade em uma lista est\u00e1tica. O app mostrou um objetivo cal\u00f3rico, digamos 2&nbsp;250&nbsp;kcal para perder peso.<\/p>\n  <p>Voc\u00ea entrou no jogo. Escaneou suas refei\u00e7\u00f5es, corrigiu as por\u00e7\u00f5es quando a IA hesitava, manteve um registro limpo dia ap\u00f3s dia. As primeiras 6 semanas, funciona. Voc\u00ea perde. Fica contente. Depois, por volta da semana 8, a balan\u00e7a congela. Voc\u00ea aperta o cinto. Desce para 2&nbsp;000&nbsp;kcal. De novo, nada se mexe.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">&minus;10 a &minus;15&nbsp;%<\/div>\n    <div class=\"lbl\">de queda medida do TDEE ap\u00f3s 4 a 6 semanas de d\u00e9ficit a &minus;500&nbsp;kcal\/dia. O Foodvisor n\u00e3o detecta isso. Seu objetivo cal\u00f3rico fica congelado no seu n\u00edvel de atividade de 100&nbsp;dias atr\u00e1s.<\/div>\n  <\/div>\n\n  <p>Imagine que o Foodvisor mostra um TDEE de 2&nbsp;500&nbsp;kcal. Voc\u00ea come 2&nbsp;250 (d\u00e9ficit te\u00f3rico de 250&nbsp;kcal). Mas na realidade, seu <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/depense-energetique-totale-v2\/\">TDEE caiu para 2&nbsp;200&nbsp;kcal<\/a> por causa da adapta\u00e7\u00e3o metab\u00f3lica. Voc\u00ea est\u00e1 em super\u00e1vit de 50&nbsp;kcal sem saber. Nenhuma chance de continuar perdendo, mesmo com o registro mais limpo do mercado.<\/p>\n  <p>A promessa do Foodvisor \u00e9 clara e cumprida: voc\u00ea sabe o que h\u00e1 no seu prato sem pesar nada. \u00c9 precioso. O que o Foodvisor n\u00e3o faz \u00e9 <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/comment-compter-ses-calories\/\">recalcular seu gasto<\/a> ao longo das semanas de d\u00e9ficit. E \u00e9 exatamente a\u00ed que a promessa para, quando \u00e9 a alavanca que faz perder peso.<\/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; Problema 1<\/span><\/div>\n  <h2 id=\"p1\">A f\u00f3rmula BMR de 1990, sem gordura corporal medida<\/h2>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figura 1 &middot; Homem 1,80 m, 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=\"Compara\u00e7\u00e3o BMR Mifflin-St Jeor 2500 kcal vs modelo propriet\u00e1rio patenteado Lean 2000 kcal, diferen\u00e7a de 500 kcal\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>BMR estimado.<\/strong> O modelo propriet\u00e1rio patenteado Lean leva em conta a massa magra. Mifflin-St Jeor (f\u00f3rmula de popula\u00e7\u00e3o, sem gordura corporal), n\u00e3o. Diferen\u00e7a de 500&nbsp;kcal, o equivalente a um almo\u00e7o inteiro.<\/p>\n  <\/div>\n\n  <p>Para calcular seu metabolismo basal (o BMR, a energia que voc\u00ea queima em repouso), o Foodvisor parte do seu perfil: peso, altura, idade, sexo. \u00c9 a l\u00f3gica da quase totalidade dos trackers de calorias de consumo, herdada das f\u00f3rmulas de popula\u00e7\u00e3o como Mifflin-St Jeor. E \u00e9 preciso ser honesto: \u00e9 melhor do que Harris-Benedict 1919, que outros apps ainda usam.<\/p>\n  <p>Mifflin-St Jeor \u00e9 de 1990 (PubMed 2305711). A amostra \u00e9 ampla (498 sujeitos), a metodologia de calorimetria indireta \u00e9 s\u00e9ria, a f\u00f3rmula \u00e9 calibrada sobre uma popula\u00e7\u00e3o moderna: 10 \u00d7 peso (kg) + 6,25 \u00d7 altura (cm) \u2212 5 \u00d7 idade \u2212 161 (mulheres) ou +5 (homens).<\/p>\n  <p>O problema n\u00e3o \u00e9 a f\u00f3rmula escolhida. O problema \u00e9 o que nenhuma f\u00f3rmula desse tipo pode ver: <strong>ela s\u00f3 leva em conta o peso. Nem a gordura corporal. Nem a massa magra.<\/strong> Nenhum campo do onboarding do Foodvisor pede seu percentual de massa gorda, e nenhuma medi\u00e7\u00e3o existe no app.<\/p>\n  <p>Mas desde os anos 80 se sabe que <strong>a massa gorda gasta muito pouca energia<\/strong> comparada ao resto do corpo. O f\u00edgado, o c\u00e9rebro, o cora\u00e7\u00e3o, os rins e sobretudo os m\u00fasculos s\u00e3o os verdadeiros consumidores. A massa gorda \u00e9 inerte. Uma pessoa com 30&nbsp;% de gordura corporal n\u00e3o queima nem de longe o mesmo que uma pessoa com 10&nbsp;%, mesmo com o mesmo peso.<\/p>\n  <p>Frankenfield 2013 (PubMed 23631843) comparou Mifflin-St Jeor com a calorimetria indireta de refer\u00eancia em coortes obesas e n\u00e3o obesas. Resultado: precis\u00e3o de 87&nbsp;% nos n\u00e3o obesos, e apenas <strong>75&nbsp;% nos obesos<\/strong>. Um estudo mais recente (PMC11820646) mostra que nos IMC acima de 35, Mifflin erra em <strong>250 a 315&nbsp;kcal por dia<\/strong>. \u00c9 o equivalente a um lanche inteiro no c\u00e1lculo de um d\u00e9ficit.<\/p>\n  <p>500&nbsp;kcal n\u00e3o \u00e9 pouca coisa. Se o app diz \u00ab&nbsp;seu BMR \u00e9 de 2&nbsp;500&nbsp;\u00bb e na realidade \u00e9 de 2&nbsp;000, tudo o que vem depois est\u00e1 errado: seu objetivo de d\u00e9ficit, sua proje\u00e7\u00e3o de perda semanal, sua distribui\u00e7\u00e3o de macros calculada em porcentagem do TDEE. E nenhuma foto de prato, por melhor reconhecida que seja, corrige um objetivo errado.<\/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\">Gordura corporal real<strong>Foto, 5 segundos<\/strong><\/div>\n  <\/div>\n\n  <div class=\"statement\">\n    <div class=\"num\">400&nbsp;kcal<\/div>\n    <div class=\"lbl\">de diferen\u00e7a entre dois homens de 80&nbsp;kg, um com 10&nbsp;% de gordura corporal (BMR 1&nbsp;900), o outro com 30&nbsp;% (BMR 1&nbsp;500). Uma f\u00f3rmula pelo peso mostra a eles o mesmo n\u00famero.<\/div>\n  <\/div>\n\n  <p>Conclus\u00e3o parcial: se um app calcula seu BMR unicamente a partir do seu peso, da sua altura, da sua idade e do seu sexo, o resultado n\u00e3o pode ser individualizado. \u00c9 matematicamente imposs\u00edvel. Mesmo com o melhor reconhecimento de prato na entrada.<\/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; Problema 2<\/span><\/div>\n  <h2 id=\"p2\">O multiplicador de atividade, escolhido de uma vez por todas<\/h2>\n  <p>\u00c9 aqui que a coisa fica grave. E provavelmente \u00e9 o ponto que ningu\u00e9m te explicou.<\/p>\n  <p>Uma vez estimado seu BMR, o Foodvisor precisa passar ao TDEE total. O <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/depense-energetique-totale-v2\/\">TDEE, \u00e9 o BMR + todo o resto<\/a> : o gasto ligado aos passos, \u00e0s atividades cotidianas, ao esporte e \u00e0 digest\u00e3o. Tudo o que n\u00e3o \u00e9 metabolismo basal.<\/p>\n  <p>Como o Foodvisor faz isso? Como a quase totalidade dos trackers: ele pede, no momento do cadastro, para escolher seu n\u00edvel de atividade em uma lista est\u00e1tica. Esses fatores se chamam em ci\u00eancia do esporte <strong>n\u00edveis PAL<\/strong> (Physical Activity Level), \u00e9 s\u00f3 um multiplicador aplicado ao seu BMR:<\/p>\n  <ul>\n    <li>Sedent\u00e1rio (PAL 1,2): escrit\u00f3rio, pouca caminhada<\/li>\n    <li>Levemente ativo (PAL 1,375): caminhada ocasional<\/li>\n    <li>Ativo (PAL 1,55): esporte 3 a 5 vezes por semana<\/li>\n    <li>Muito ativo (PAL 1,725): esporte intenso quase di\u00e1rio<\/li>\n    <li>Extremamente ativo (PAL 1,9): esporte muito intenso ou trabalho f\u00edsico<\/li>\n  <\/ul>\n  <p>E segundo sua escolha, o app multiplica seu BMR pelo coeficiente associado. \u00c9 s\u00f3 isso. \u00c9 tudo o que h\u00e1 por tr\u00e1s do seu objetivo cal\u00f3rico di\u00e1rio. Uma caixa que VOC\u00ca marcou uma \u00fanica vez no momento do cadastro. Muitas vezes seis meses atr\u00e1s. Sem mudar desde ent\u00e3o.<\/p>\n  <p>E a\u00ed est\u00e1 a armadilha silenciosa: essa aproxima\u00e7\u00e3o \u00e9 <strong>hiperimperfeita<\/strong>. A diferen\u00e7a entre um dia em que voc\u00ea fica grudado no sof\u00e1 vendo Netflix e um dia em que vai \u00e0 Disneyland com seus filhos e anda 15&nbsp;km, <strong>s\u00e3o mais de 1&nbsp;000&nbsp;kcal<\/strong>. Nenhuma das 5 caixas capta isso.<\/p>\n  <p>O Foodvisor sabe, no entanto, acompanhar sua atividade: o app pode contar seus passos e registrar seus treinos. Mas esses dados servem sobretudo para mostrar sua atividade, n\u00e3o para recompor um TDEE completo: seu objetivo cal\u00f3rico continua assentado no multiplicador escolhido no onboarding, e o gasto do esporte se soma a ele sem que o NEAT e o EAT sejam separados direito.<\/p>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figura 2 &middot; 7 dias reais<\/span><span class=\"r\">kcal\/dia<\/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=\"Variabilidade di\u00e1ria do gasto cal\u00f3rico em 7 dias, contra 2400 kcal fixas segundo o Foodvisor\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>Gasto real<\/strong> medido durante 7&nbsp;dias em um usu\u00e1rio do Lean. A linha verde \u00e9 o que o Foodvisor mostrava (2&nbsp;400&nbsp;kcal fixas, multiplicador est\u00e1tico \u00d7 BMR). As anota\u00e7\u00f5es rosa mostram por que cada dia se mexe.<\/p>\n  <\/div>\n\n  <p>Voc\u00ea n\u00e3o pode reduzir seu n\u00edvel de atividade a uma caixa est\u00e1tica. Talvez voc\u00ea seja ativo nas semanas em que faz pouco home office, e sedent\u00e1rio nas que n\u00e3o sai do escrit\u00f3rio. Talvez seja ativo no ver\u00e3o e sedent\u00e1rio no inverno. Talvez seja ativo de ter\u00e7a a sexta e sedent\u00e1rio no fim de semana.<\/p>\n  <p>Qual caixa voc\u00ea vai marcar esta semana? A verdade \u00e9 que nenhuma das 5 ser\u00e1 correta. E portanto o Foodvisor vai te dar um TDEE sistematicamente desconectado da realidade.<\/p>\n  <p>O ponto-chave deste artigo: mesmo com uma f\u00f3rmula BMR perfeita, o multiplicador est\u00e1tico bastaria para quebrar tudo. Voc\u00ea n\u00e3o pode estimar um <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/neat-depense-non-sportive\/\">NEAT<\/a>, um EAT e um <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/effet-thermique-des-aliments\/\">TEF<\/a> com um multiplicador \u00fanico aplicado ao BMR. \u00c9 conceitualmente absurdo.<\/p>\n  <p>Voc\u00ea j\u00e1 entendeu: <strong>uma f\u00f3rmula BMR sem gordura corporal, mais uma aproxima\u00e7\u00e3o est\u00e1tica de todo o resto, d\u00e1 muito poucas chances de atingir seus objetivos em 3 a 6 meses.<\/strong> Por mais limpo que seja o registro do lado do prato.<\/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\">Ver seu TDEE real, decomposto em BMR + NEAT + EAT + TEF. Download gratuito.<\/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; Problema 3<\/span><\/div>\n  <h2 id=\"p3\">A adapta\u00e7\u00e3o metab\u00f3lica, nunca modelada<\/h2>\n  <p>\u00c9 o chef\u00e3o final. A no\u00e7\u00e3o mais fina. E provavelmente a mais importante.<\/p>\n  <p>Quando voc\u00ea est\u00e1 em d\u00e9ficit cal\u00f3rico, seu corpo entende que recebe menos energia do que antes. Para se proteger, ele passa para o modo economia. Exatamente como o modo de economia de energia do seu iPhone: tudo continua funcionando, mas usando menos energia. Seu BMR cai. Seu NEAT cai. Seu EAT cai.<\/p>\n  <p>\u00c9 o que se chama adapta\u00e7\u00e3o metab\u00f3lica. A literatura cient\u00edfica \u00e9 clara e reproduz\u00edvel: M\u00fcller 2015 (PubMed 26399868, revis\u00e3o de Minnesota), Doucet 2001 (PubMed 11430776), Nunes 2020 (PMC7484122) em 6 semanas de d\u00e9ficit. Estes s\u00e3o os n\u00fameros:<\/p>\n  <ul>\n    <li>D\u00e9ficit de &minus;250&nbsp;kcal por dia, durante 2 a 8 semanas: adapta\u00e7\u00e3o de <strong>de 5 a 10&nbsp;%<\/strong> (o TDEE cai para 90-95&nbsp;% do n\u00edvel inicial)<\/li>\n    <li>D\u00e9ficit de &minus;500&nbsp;kcal por dia: <strong>10 a 15&nbsp;%<\/strong> de adapta\u00e7\u00e3o (o TDEE cai para 85-90&nbsp;%)<\/li>\n    <li>D\u00e9ficit de &minus;750&nbsp;kcal por dia: <strong>15 a 25&nbsp;%<\/strong> de adapta\u00e7\u00e3o (o TDEE cai para 75-85&nbsp;%)<\/li>\n  <\/ul>\n  <p>Conven\u00e7\u00e3o Lean: 100&nbsp;% = ideal, 90&nbsp;% = 10&nbsp;% de adapta\u00e7\u00e3o. E como o NEAT, o EAT e o TEF dependem todos diretamente do BMR, \u00e9 quase todo o TDEE que \u00e9 afetado.<\/p>\n\n  <div class=\"fig\">\n    <div class=\"fig-head\"><span class=\"l\">Figura 3 &middot; 8 semanas em d\u00e9ficit<\/span><span class=\"r\">kcal\/dia<\/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 que cai de 2500 para 2150 kcal em 8 semanas, contra 2500 fixas segundo o Foodvisor\"><\/canvas><\/div><\/div>\n    <p class=\"fig-cap\"><strong>TDEE real<\/strong> em 8 semanas de d\u00e9ficit a &minus;500&nbsp;kcal\/dia. A curva rosa desce. A linha do Foodvisor fica plana. Na semana 6, voc\u00ea j\u00e1 est\u00e1 na manuten\u00e7\u00e3o. Sem ter mudado nada.<\/p>\n  <\/div>\n\n  <p>Concretamente: se voc\u00ea previu um d\u00e9ficit de 10&nbsp;% sobre um TDEE de 2&nbsp;500 (ou seja, comer 2&nbsp;250 por dia), e seu corpo se adapta em 10&nbsp;%, seu TDEE real passou a 2&nbsp;250. Voc\u00ea est\u00e1 na manuten\u00e7\u00e3o. N\u00e3o perde mais.<\/p>\n  <p>A armadilha \u00e9 que \u00e9 insidioso. No in\u00edcio, voc\u00ea perde. Fica contente. Continua. Mas semana ap\u00f3s semana, a adapta\u00e7\u00e3o se acumula. E em algum momento, sem ter mudado nada no seu tracking, <strong>voc\u00ea para de perder<\/strong>.<\/p>\n  <p>95&nbsp;% das pessoas passam por isso sem entender. Culpam a for\u00e7a de vontade. Culpam o \u00ab&nbsp;metabolismo quebrado&nbsp;\u00bb. Voltam para dietas mais duras, o que agrava a adapta\u00e7\u00e3o. Espiral.<\/p>\n  <p>O Foodvisor nunca calcula a adapta\u00e7\u00e3o metab\u00f3lica. Ele te d\u00e1 um objetivo cal\u00f3rico fixo enquanto voc\u00ea n\u00e3o atualiza seu peso e seu n\u00edvel de atividade manualmente. Voc\u00ea pode escanear seus pratos com uma regularidade exemplar, mas quando estagna ap\u00f3s 6 semanas de cutting, o app n\u00e3o tem a menor ideia do porqu\u00ea.<\/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; Solu\u00e7\u00e3o Lean<\/span><\/div>\n  <h2 id=\"solution\">Como o Lean resolve cada um dos 3 problemas<\/h2>\n  <p>O Foodvisor estabeleceu um padr\u00e3o no scan de foto de um prato, e seu reconhecimento da cozinha francesa continua excelente. O problema n\u00e3o \u00e9 o que ele v\u00ea no seu prato, \u00e9 o que ele n\u00e3o v\u00ea do seu corpo: o gasto continua estimado por uma f\u00f3rmula de popula\u00e7\u00e3o multiplicada por um n\u00edvel de atividade. O Lean faz os dois: scan de foto por IA <em>e<\/em> medi\u00e7\u00e3o de cada componente do TDEE (BMR&nbsp;+&nbsp;NEAT&nbsp;+&nbsp;EAT&nbsp;+&nbsp;TEF) mais a adapta\u00e7\u00e3o metab\u00f3lica. Veja o detalhe.<\/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\">Passo 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\">Passo 2<strong>BMR recalculado<\/strong><\/div>\n        <\/div>\n      <\/div>\n    <\/div>\n    <div>\n      <div class=\"m-tag\">O BMR sobre gordura corporal real<\/div>\n      <h3>Modelo propriet\u00e1rio patenteado, baseado na massa magra<\/h3>\n      <p>Contar perfeitamente as calorias que entram n\u00e3o serve para nada se as calorias que saem est\u00e3o erradas em 300&nbsp;kcal. O Lean calcula o metabolismo sobre sua <strong>massa magra<\/strong>, a \u00fanica que consome realmente em repouso, e n\u00e3o sobre seu peso bruto.<\/p>\n      <p>O <strong>BodyScan IA<\/strong> aplica ao seu corpo o que o Foodvisor aplica ao seu prato: uma foto, um modelo treinado em um banco de scans DEXA, sua gordura corporal em alguns segundos, a refazer toda semana.<\/p>\n      <p>Sem adip\u00f4metro, sem balan\u00e7a de bioimped\u00e2ncia, sem DEXA. A mesma simplicidade de um scan de refei\u00e7\u00e3o, aplicada \u00e0 sua composi\u00e7\u00e3o corporal.<\/p>\n    <\/div>\n  <\/div>\n\n  <div class=\"method flip\">\n    <div>\n      <div class=\"m-tag\">Sem coeficiente de atividade<\/div>\n      <h3>NEAT, EAT, TEF calculados separadamente<\/h3>\n      <p><strong>NEAT.<\/strong> Seus passos reais chegam via HealthKit (iOS) ou Google Fit (Android) e viram calorias segundo seu metabolismo. \u00c9 a rubrica mais vari\u00e1vel do dia, e a que um n\u00edvel de atividade declarado achata completamente.<\/p>\n      <p><strong>EAT.<\/strong> Cada treino \u00e9 calculado por MET sobre seu tempo de esfor\u00e7o real, tempos de descanso exclu\u00eddos. Contar uma hora de muscula\u00e7\u00e3o como uma hora de corrida falseia o balan\u00e7o em v\u00e1rias centenas de kcal por semana.<\/p>\n      <p><strong>TEF.<\/strong> O Foodvisor identifica seus alimentos, o Lean extrai deles o custo de digest\u00e3o: de 20 a 30&nbsp;% das calorias para as prote\u00ednas, de 5 a 10&nbsp;% para os carboidratos, de 1 a 3&nbsp;% para as gorduras, em vez da taxa fixa de 10&nbsp;% aplicada em todo lugar.<\/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\">Adapta\u00e7\u00e3o metab\u00f3lica autom\u00e1tica<\/div>\n      <h3>Uma primeira mundial em um app de consumo<\/h3>\n      <p><strong>A adapta\u00e7\u00e3o metab\u00f3lica.<\/strong> Nenhum scan de refei\u00e7\u00e3o detecta que seu metabolismo desacelerou 12&nbsp;% ap\u00f3s oito semanas. O Lean o estima segundo as faixas publicadas (M\u00fcller 2015, Doucet 2001) e corrige seu objetivo em consequ\u00eancia.<\/p>\n      <p>Passados 10 a 15&nbsp;% de adapta\u00e7\u00e3o, o app pode aconselhar um retorno \u00e0 manuten\u00e7\u00e3o para relan\u00e7ar o metabolismo antes de recome\u00e7ar.<\/p>\n      <p>Nenhum multiplicador de atividade a escolher. Cada componente \u00e9 medido, semana ap\u00f3s semana.<\/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\u00e9todo<strong>Adapta\u00e7\u00e3o metab\u00f3lica<\/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; Tabela comparativa<\/span><\/div>\n  <h2 id=\"tab\">Lean diante do Foodvisor, crit\u00e9rio por crit\u00e9rio<\/h2>\n  <p>Leitura honesta dos pontos fortes e fracos de cada app. Nenhum crit\u00e9rio se refere ao pre\u00e7o.<\/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=\"Comparativo Lean diante do Foodvisor\">\n    <div class=\"table-row head\" role=\"row\">\n      <div role=\"columnheader\">Crit\u00e9rio<\/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\">F\u00f3rmula 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> Modelo propriet\u00e1rio patenteado (massa magra)<\/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> F\u00f3rmula de popula\u00e7\u00e3o (Mifflin-St Jeor 1990)<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Leva em conta a gordura corporal<\/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> Sim, medido no 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> N\u00e3o, apenas peso-altura-idade-sexo<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Medi\u00e7\u00e3o da gordura corporal no 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 foto<\/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> N\u00e3o<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">NEAT (passos, atividade fora do esporte)<\/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> Calculado sobre os passos reais todo dia<\/div>\n      <div class=\"cell\"><span class=\"icn mid\">&minus;<\/span> Passos acompanhados, mas objetivo assentado no multiplicador est\u00e1tico<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">EAT (gasto do exerc\u00edcio)<\/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> Por esporte via MET, tempo efetivo<\/div>\n      <div class=\"cell\"><span class=\"icn mid\">&minus;<\/span> Treinos registrados, adicionados a um objetivo congelado<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">TEF (digest\u00e3o)<\/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> Calculado segundo macros, integrado no 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> N\u00e3o integrado ao c\u00e1lculo do gasto<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Adapta\u00e7\u00e3o metab\u00f3lica<\/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> Autom\u00e1tica, semana a semana<\/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> N\u00e3o<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Coeficiente de atividade a escolher<\/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> N\u00e3o, calculado sobre dados reais<\/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> Sim, multiplicador est\u00e1tico escolhido no cadastro<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Scan de foto por IA de um prato<\/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> Sim, ilimitado<\/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> Sim, o pioneiro hist\u00f3rico (2018)<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Reconhecimento visual dos alimentos<\/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> Sim, scan de foto por IA moderno<\/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> Refer\u00eancia do mercado FR, anos de treinamento<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Scan de c\u00f3digo de barras<\/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> Sim<\/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> Sim<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Base de dados de alimentos<\/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, curada<\/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> Base ampla, produtos franceses bem cobertos<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Coaching por nutricionistas<\/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> Fora do escopo, o app guia via a Pir\u00e2mide de Progress\u00e3o<\/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> Sim, nutricionistas diplomados (oferta dedicada)<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Recomenda\u00e7\u00e3o de d\u00e9ficit cal\u00f3rico<\/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> Adaptada ao TDEE real<\/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> Objetivo fixo, rec\u00e1lculo manual necess\u00e1rio<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">App franc\u00eas<\/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> Sim<\/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> Sim, nascido em Paris<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Reputa\u00e7\u00e3o e tamanho da audi\u00eancia<\/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, mais de 10&nbsp;000 usu\u00e1rios, app jovem<\/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> Pioneiro reconhecido do scan de foto, forte notoriedade na Fran\u00e7a<\/div>\n    <\/div>\n    <div class=\"table-row\" role=\"row\">\n      <div class=\"crit\">Modelo de neg\u00f3cio<\/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, teste gratuito de 7 dias no plano anual<\/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> Vers\u00e3o gratuita, coaching opcional<\/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\u00e9todos para registrar uma refei\u00e7\u00e3o<\/h2>\n  <p>O Foodvisor provou que uma foto podia substituir uma entrada manual. O Lean retoma essa ideia e a amplia: tr\u00eas m\u00e9todos de registro segundo o contexto, para aguentar ao longo do tempo.<\/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\u00e9todo 1<strong>Base de dados<\/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\u00e9todo 2<strong>C\u00f3digo de barras<\/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\u00e9todo 3<strong>Scan de foto por IA<\/strong><\/div>\n    <\/div>\n  <\/div>\n\n  <ol>\n    <li><strong>Busca na base de dados.<\/strong> Base curada, USDA + OpenFoodFacts. Sem ru\u00eddo comunit\u00e1rio, sem \u00ab&nbsp;Frango assado&nbsp;\u00bb inserido 47 vezes por 47 usu\u00e1rios diferentes com 47 valores diferentes.<\/li>\n    <li><strong>Scan de c\u00f3digo de barras.<\/strong> Padr\u00e3o. Voc\u00ea escaneia seu pacote de macarr\u00e3o, obt\u00e9m os macros.<\/li>\n    <li><strong>Scan de foto por IA de um prato.<\/strong> Voc\u00ea fotografa seu prato, a IA detecta os alimentos, voc\u00ea obt\u00e9m as calorias e os macros por alimento. O reflexo que voc\u00ea j\u00e1 tem se vem do Foodvisor: voc\u00ea o mant\u00e9m tal qual.<\/li>\n  <\/ol>\n  <p>O scan de foto por IA do Lean desempenha o mesmo papel que o do Foodvisor para as refei\u00e7\u00f5es feitas fora. A diferen\u00e7a est\u00e1 em outro lugar: o que o Lean faz em seguida com essas calorias, confrontando-as com um gasto medido e n\u00e3o estimado.<\/p>\n  <p>Al\u00e9m da refei\u00e7\u00e3o, o Lean mostra um TDEE que se atualiza durante o dia segundo seus passos. Escanear perfeitamente um prato diante de um objetivo cal\u00f3rico congelado n\u00e3o basta.<\/p>\n  <p>E acima, a Pir\u00e2mide de Progress\u00e3o:<\/p>\n\n  <div class=\"pyramid\" aria-label=\"Pir\u00e2mide de Progress\u00e3o Lean\">\n    <div class=\"level l1\"><span>Ader\u00eancia<\/span><span class=\"k\">Base<\/span><\/div>\n    <div class=\"level l2\"><span>Objetivo cal\u00f3rico<\/span><span class=\"k\">N\u00edvel 2<\/span><\/div>\n    <div class=\"level l3\"><span>Passos \/ NEAT<\/span><span class=\"k\">N\u00edvel 3<\/span><\/div>\n    <div class=\"level l4\"><span>Macronutrientes<\/span><span class=\"k\">Topo<\/span><\/div>\n  <\/div>\n  <div class=\"pyramid-cap\">N\u00e3o queimar etapas. Se voc\u00ea n\u00e3o \u00e9 regular no tracking, otimizar os macros no um por cento n\u00e3o serve para nada.<\/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; Honestidade<\/span><\/div>\n  <h2 id=\"foodvisor-better\">O que o Foodvisor faz melhor<\/h2>\n  <p>O Lean n\u00e3o \u00e9 perfeito, e o Foodvisor tem v\u00e1rios pontos fortes reais que \u00e9 preciso reconhecer. Leitura honesta, crit\u00e9rio por crit\u00e9rio, nos eixos em que o pioneiro continua na frente. Nenhum desses eixos \u00e9 secund\u00e1rio: s\u00e3o pilares reais da promessa do Foodvisor.<\/p>\n\n  <div class=\"scorecard rev\" aria-label=\"Scorecard Foodvisor diante do Lean em 4 eixos\">\n    <div class=\"scorecard-head\">\n      <div class=\"h-crit\">Eixo<\/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\">Reconhecimento visual de um prato<\/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\">Estimativa autom\u00e1tica das por\u00e7\u00f5es<\/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 humano (nutricionistas diplomados)<\/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\">Anterioridade do scan de foto (mercado 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>Leitura honesta.<\/strong> No reconhecimento de prato, o Foodvisor criou a categoria na Fran\u00e7a em 2018 e sua IA tem anos de treinamento de vantagem: identifica\u00e7\u00e3o dos alimentos, estimativa das por\u00e7\u00f5es sem balan\u00e7a, gest\u00e3o dos pratos compostos. \u00c9 seu terreno hist\u00f3rico e nele continua sendo a refer\u00eancia. No acompanhamento humano, o Foodvisor oferece um acompanhamento por nutricionistas diplomados diretamente no app: o Lean n\u00e3o oferece isso, e n\u00e3o pretende substitu\u00ed-lo. O scan de foto por IA do Lean \u00e9 moderno, ilimitado e amplamente suficiente para o uso di\u00e1rio, mas o Lean n\u00e3o reivindica a anterioridade nesse terreno.<\/p>\n  <p>Se seu foco principal \u00e9 a identifica\u00e7\u00e3o de prato mais rodada poss\u00edvel, ou um coaching humano integrado ao app, o Foodvisor \u00e9 mais relevante do que o Lean. Se seu foco \u00e9 a precis\u00e3o do <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/tdee-calculator\/\">c\u00e1lculo do TDEE<\/a>, a gordura corporal medida toda semana via BodyScan IA, e a adapta\u00e7\u00e3o metab\u00f3lica autom\u00e1tica, \u00e9 exatamente o que acaba de ser demonstrado nas 3 se\u00e7\u00f5es anteriores. Alguns rodam os dois apps em paralelo enquanto escolhem, e \u00e9 totalmente defens\u00e1vel.<\/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; Para quem<\/span><\/div>\n  <h2 id=\"forwho\">Para quem o Lean foi feito<\/h2>\n  <p>4 perfis. Se voc\u00ea se reconhece em pelo menos um, o Lean provavelmente foi feito para voc\u00ea.<\/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>Voc\u00ea usou o Foodvisor a s\u00e9rio e n\u00e3o perdeu<\/h4>\n        <p>Voc\u00ea escaneou seus pratos, corrigiu as por\u00e7\u00f5es, seguiu um d\u00e9ficit honesto durante semanas, e estagna. O culpado n\u00e3o \u00e9 a foto, \u00e9 o objetivo congelado calculado sem gordura corporal. O Lean corrige na raiz via o BMR sobre gordura corporal real.<\/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>Voc\u00ea estagna ap\u00f3s v\u00e1rias semanas de cutting<\/h4>\n        <p>Plat\u00f4 que se eterniza ap\u00f3s 4 a 8 semanas. \u00c9 a adapta\u00e7\u00e3o metab\u00f3lica. O Lean a calcula automaticamente e reajusta seu objetivo toda semana.<\/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>Voc\u00ea quer entender seu metabolismo<\/h4>\n        <p>O Lean mostra cada componente (BMR, NEAT, EAT, TEF) e depois explica a adapta\u00e7\u00e3o \u00e0 parte, em vez de esconder tudo atr\u00e1s de um n\u00famero \u00fanico. Voc\u00ea v\u00ea de onde vem cada kcal de gasto.<\/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>Voc\u00ea quer um tracking que dure 12 meses<\/h4>\n        <p>Scan de foto por IA + base curada + c\u00f3digo de barras cobrem todos os usos, do alimento cru \u00e0 pizza no restaurante. \u00c9 o que faz a diferen\u00e7a entre aguentar e desistir.<\/p>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <p style=\"margin-top:30px\"><strong>O Foodvisor continua mais relevante para<\/strong>&nbsp;: o coaching humano por nutricionistas diplomados diretamente no app, e o reconhecimento de prato mais rodado do mercado franc\u00eas. A precis\u00e3o do c\u00e1lculo do gasto e a adapta\u00e7\u00e3o metab\u00f3lica simplesmente n\u00e3o fazem parte da promessa principal dele.<\/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; Migra\u00e7\u00e3o<\/span><\/div>\n  <h2 id=\"migrate\">Passar do Foodvisor para o Lean (ou usar os dois) em 3 minutos<\/h2>\n\n  <div class=\"steps\">\n    <div class=\"step\"><div class=\"sn\">01<\/div><h4>Baixe o Lean<\/h4><p>App Store ou Play Store. Cadastro em 30 segundos.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">02<\/div><h4>BodyScan IA<\/h4><p>Uma foto, 5 segundos. Voc\u00ea obt\u00e9m sua gordura corporal.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">03<\/div><h4>Peso &amp; altura<\/h4><p>Voc\u00ea informa seu peso e sua altura. \u00c9 s\u00f3 isso.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">04<\/div><h4>O Lean calcula<\/h4><p>BMR sobre gordura corporal real, NEAT via HealthKit \/ Google Fit (passos reais), EAT por MET, TEF por macros, mais a adapta\u00e7\u00e3o metab\u00f3lica que modula o BMR. Autom\u00e1tico.<\/p><\/div>\n    <div class=\"step\"><div class=\"sn\">05<\/div><h4>Registre uma refei\u00e7\u00e3o<\/h4><p>Foto, c\u00f3digo de barras ou base de dados. Voc\u00ea j\u00e1 conhece o gesto.<\/p><\/div>\n  <\/div>\n\n  <p style=\"margin-top:24px\"><strong>Nota importante.<\/strong> O Lean n\u00e3o importa seu hist\u00f3rico do Foodvisor automaticamente, nem seus alimentos favoritos. Se seu acompanhamento com um nutricionista do Foodvisor conta para voc\u00ea, nada te impede de manter os dois durante a transi\u00e7\u00e3o: o Foodvisor para o acompanhamento humano, o Lean para o TDEE e o tracking di\u00e1rio. A sincroniza\u00e7\u00e3o HealthKit \/ Google Health Connect, por sua vez, assume imediatamente seus passos e seu hist\u00f3rico de atividade.<\/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\">Baixe o Lean e comece o BodyScan IA agora mesmo. Cadastro gratuito.<\/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; O que o Lean desbloqueia<\/span><\/div>\n  <h2 id=\"deblock-h\">O que o Lean faz, e que o Foodvisor n\u00e3o faz (no gasto)<\/h2>\n  <p>Seis funcionalidades centradas no gasto, inexistentes no Foodvisor. Todas derivam do mesmo princ\u00edpio: calcular cada componente do TDEE com precis\u00e3o, n\u00e3o aproxim\u00e1-lo.<\/p>\n\n  <div class=\"feat-stack\">\n    <div class=\"feat-it\"><div class=\"fn\">01<\/div><div><div class=\"ft\">BodyScan IA ilimitado<\/div><p class=\"fd\">Sua gordura corporal real, medida a partir de uma simples foto, refeita toda semana. \u00c9 o dado que muda todo o c\u00e1lculo do BMR. Nenhum outro app de consumo oferece isso.<\/p><\/div><div class=\"fc\">Gordura corporal<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">02<\/div><div><div class=\"ft\">Adapta\u00e7\u00e3o metab\u00f3lica autom\u00e1tica<\/div><p class=\"fd\">Seu TDEE se reajusta semana a semana segundo os n\u00fameros cientificamente estabelecidos. Voc\u00ea evita os plat\u00f4s que ningu\u00e9m sabe explicar.<\/p><\/div><div class=\"fc\">Adapta\u00e7\u00e3o<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">03<\/div><div><div class=\"ft\">TDEE decomposto ao vivo<\/div><p class=\"fd\">BMR + NEAT + EAT + TEF mostrados cada um, atualizados durante o dia. Chega de n\u00famero congelado \u00e0s 8 da manh\u00e3. Voc\u00ea v\u00ea seu balan\u00e7o cal\u00f3rico ao vivo.<\/p><\/div><div class=\"fc\">Ao vivo<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">04<\/div><div><div class=\"ft\">NEAT sobre passos reais, sem coeficiente<\/div><p class=\"fd\">Seus passos, medidos pelo seu telefone, alimentam diretamente o c\u00e1lculo do TDEE todo dia. Nenhuma caixa sedent\u00e1rio ou ativo a marcar, nunca.<\/p><\/div><div class=\"fc\">NEAT<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">05<\/div><div><div class=\"ft\">TEF calculado sobre seus macros<\/div><p class=\"fd\">A digest\u00e3o n\u00e3o \u00e9 uma taxa fixa de 10&nbsp;%. Prote\u00ednas de 20 a 30&nbsp;%, carboidratos de 5 a 10&nbsp;%, gorduras de 1 a 3&nbsp;%. O Lean faz o c\u00e1lculo a cada refei\u00e7\u00e3o e o integra ao TDEE.<\/p><\/div><div class=\"fc\">TEF<\/div><\/div>\n    <div class=\"feat-it\"><div class=\"fn\">06<\/div><div><div class=\"ft\">Hist\u00f3rico completo e tend\u00eancias<\/div><p class=\"fd\">Acompanhe suas tend\u00eancias de peso, gordura corporal, massa magra ao longo de meses. Entenda seus ciclos. Identifique as fases em que voc\u00ea progride e aquelas em que estagna.<\/p><\/div><div class=\"fc\">Hist\u00f3rico<\/div><\/div>\n  <\/div>\n\n  <p style=\"margin-top:26px\">Voc\u00ea instala o app gratuitamente, testa sem compromisso e depois decide se a ferramenta combina com seu objetivo.<\/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\">Perguntas frequentes<\/h2>\n  <div class=\"faq\">\n    <details><summary>O Foodvisor inventou o scan de foto de refei\u00e7\u00f5es, por que compar\u00e1-lo ao Lean?<\/summary><div class=\"ans\">Porque a promessa de um tracker n\u00e3o para no prato. O Foodvisor \u00e9 a refer\u00eancia hist\u00f3rica do reconhecimento por foto na Fran\u00e7a, mas seu objetivo cal\u00f3rico repousa em uma f\u00f3rmula de popula\u00e7\u00e3o (Mifflin-St Jeor 1990, sem gordura corporal medida) e um multiplicador de atividade est\u00e1tico escolhido no cadastro. S\u00e3o duas metades do mesmo problema: o Foodvisor se destaca no que entra, o Lean no que se gasta.<\/div><\/details>\n    <details><summary>Por que o Foodvisor n\u00e3o calcula o BMR sobre a gordura corporal real?<\/summary><div class=\"ans\">Porque nenhuma medi\u00e7\u00e3o de gordura corporal existe no app, e as f\u00f3rmulas de popula\u00e7\u00e3o s\u00f3 usam o peso, a altura, a idade e o sexo. O Lean integra o BodyScan IA para medir sua gordura corporal a partir de uma simples foto, a refazer toda semana, o que permite um BMR baseado na massa magra real via um modelo propriet\u00e1rio patenteado.<\/div><\/details>\n    <details><summary>O scan de foto do Lean vale o do Foodvisor?<\/summary><div class=\"ans\">O Foodvisor mant\u00e9m a anterioridade e anos de treinamento no reconhecimento de prato, em particular na estimativa das por\u00e7\u00f5es. O scan de foto por IA do Lean identifica os alimentos, as calorias e os macros com uma precis\u00e3o compar\u00e1vel para o uso di\u00e1rio, e \u00e9 ilimitado. Nesse crit\u00e9rio, os dois d\u00e3o conta. A diferen\u00e7a real entre os dois apps se joga no c\u00e1lculo do gasto.<\/div><\/details>\n    <details><summary>O Foodvisor conta meus passos, basta para o NEAT?<\/summary><div class=\"ans\">Contar os passos e integr\u00e1-los ao c\u00e1lculo s\u00e3o duas coisas diferentes. No Foodvisor, o objetivo cal\u00f3rico continua assentado no multiplicador de atividade est\u00e1tico escolhido no cadastro. O Lean calcula o NEAT diretamente a partir dos passos reais medidos todo dia, sem coeficiente a escolher, e o separa direito do gasto do esporte (EAT).<\/div><\/details>\n    <details><summary>O Lean \u00e9 gratuito ou pago?<\/summary><div class=\"ans\">O Lean \u00e9 Premium, com um teste gratuito de 7 dias na assinatura anual. Voc\u00ea baixa, testa o BodyScan IA, o scan de foto por IA de um prato, a recomposi\u00e7\u00e3o do TDEE, sem compromisso. Se a ferramenta combina com seu objetivo, voc\u00ea continua. Sen\u00e3o, desativa a renova\u00e7\u00e3o antes do fim do per\u00edodo de teste.<\/div><\/details>\n    <details><summary>\u00c9 poss\u00edvel usar o Lean e o Foodvisor em paralelo?<\/summary><div class=\"ans\">Sim, sobretudo na transi\u00e7\u00e3o. Alguns mant\u00eam o Foodvisor para o coaching com um nutricionista e usam o Lean no dia a dia para o TDEE, a recomposi\u00e7\u00e3o e o tracking. O esfor\u00e7o de entrada dupla \u00e9 real: com o tempo, a maioria escolhe o app que pilota seu objetivo cal\u00f3rico, e \u00e9 precisamente o terreno em que o Lean foi constru\u00eddo para ser o mais preciso.<\/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; Conclus\u00e3o<\/span><\/div>\n  <h2 id=\"conclu\">O prato est\u00e1 resolvido. O gasto, n\u00e3o.<\/h2>\n  <p>N\u00e3o \u00e9 Foodvisor contra Lean em marketing. \u00c9 a entrada diante da sa\u00edda, duas metades de uma mesma equa\u00e7\u00e3o.<\/p>\n  <p>O Foodvisor resolveu a metade esquerda: saber o que voc\u00ea come, sem balan\u00e7a, gra\u00e7as ao scan de foto mais rodado do mercado franc\u00eas. Mas para a metade direita, seu gasto, o Foodvisor se apoia em uma f\u00f3rmula de popula\u00e7\u00e3o de 1990 sem gordura corporal medida, um multiplicador de atividade congelado que voc\u00ea marca uma \u00fanica vez no cadastro, e nenhuma adapta\u00e7\u00e3o metab\u00f3lica. A combina\u00e7\u00e3o dos tr\u00eas torna qualquer acompanhamento cal\u00f3rico preciso imposs\u00edvel al\u00e9m de algumas semanas de cutting. \u00c9 matem\u00e1tico.<\/p>\n  <p>O Lean foi constru\u00eddo para essa metade: BMR baseado na <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/depense-energetique-totale-v2\/\">gordura corporal real<\/a> (medido por BodyScan IA) via um modelo propriet\u00e1rio patenteado, <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/neat-depense-non-sportive\/\">NEAT por passos reais<\/a>, EAT por esporte e MET, <a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/effet-thermique-des-aliments\/\">TEF por macros<\/a>, mais a adapta\u00e7\u00e3o metab\u00f3lica que modula o BMR semana ap\u00f3s semana. Cada componente calculado com precis\u00e3o, sem coeficiente m\u00e1gico. E o reflexo da foto que voc\u00ea pegou no Foodvisor, voc\u00ea mant\u00e9m: o scan de foto por IA est\u00e1 integrado, ilimitado.<\/p>\n  <p>Se voc\u00ea tentou o Foodvisor a s\u00e9rio e n\u00e3o teve os resultados que esperava no seu cutting, o problema n\u00e3o \u00e9 voc\u00ea, nem a foto. O problema \u00e9 o TDEE congelado debaixo do cap\u00f4. Mude o motor, mantenha o reflexo.<\/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\">Download<\/div>\n  <h3>A Lean pode ser baixada gratuitamente<\/h3>\n  <p>iOS e Android. O BodyScan IA funciona com uma simples foto. Sem adip\u00f4metro, sem balan\u00e7a de bioimped\u00e2ncia, sem 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=\"Baixar o Lean na 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=\"Baixar o Lean no 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 al\u00e9m<\/span><\/div>\n  <h3 id=\"links\" style=\"margin-top:0\">Links internos<\/h3>\n  <ul>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/tdee-calculator\/\">Calculadora TDEE gratuita online<\/a> &middot; vers\u00e3o web, sem cadastro, mesma l\u00f3gica do app (BMR + NEAT + EAT + TEF).<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/depense-energetique-totale-v2\/\">Entender o TDEE em detalhe (BMR, NEAT, EAT, TEF, adapta\u00e7\u00e3o)<\/a> &middot; artigo cient\u00edfico de fundo.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/comment-compter-ses-calories\/\">Como contar suas calorias corretamente<\/a> &middot; guia pr\u00e1tico para iniciantes.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/neat-depense-non-sportive\/\">NEAT&nbsp;: gasto por passos e atividade fora do esporte<\/a>.<\/li>\n    <li><a class=\"inline\" href=\"https:\/\/lean-app.com\/pt\/effet-thermique-des-aliments\/\">TEF&nbsp;: a digest\u00e3o queima calorias<\/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\">Fontes<\/span><\/div>\n  <h3 id=\"src\" style=\"margin-top:0;color:var(--ink)\">Bibliografia<\/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>Artigo publicado em 15 de agosto de 2026. Atualizado regularmente com o feedback dos usu\u00e1rios e os novos estudos relevantes. O Lean est\u00e1 dispon\u00edvel para iOS e 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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setTimeout(tryApply, 300);\n  }\n  tryApply();\n})();\n<\/script>\n\n\n\n<!-- lean-mesh-v19 -->\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;\">Leia tamb\u00e9m<\/p><ul style=\"list-style:none;padding:0;margin:0;display:grid;grid-template-columns:1fr;gap:10px;\"><li><a href=\"https:\/\/lean-app.com\/pt\/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): tudo o que \u00e9 preciso saber para calcul\u00e1-lo <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Defini\u00e7\u00e3o, equa\u00e7\u00e3o TDEE, 4 f\u00f3rmulas hist\u00f3ricas, por que a gordura corporal muda tudo.<\/span><\/a><\/li><li><a href=\"https:\/\/lean-app.com\/pt\/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): a f\u00f3rmula can\u00f4nica BMR + NEAT + EAT + TEF <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Entenda os 4 blocos + a adapta\u00e7\u00e3o metab\u00f3lica, fontes cient\u00edficas 2025.<\/span><\/a><\/li><li><a href=\"https:\/\/lean-app.com\/pt\/comparatifs-croises\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">MyFitnessPal ou Yazio? 12 duelos de apps de calorias comparados <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">O veredito de cada duelo de relance: quem ganha em qu\u00ea, e o que nenhum dos dois calcula.<\/span><\/a><\/li><li><a href=\"https:\/\/lean-app.com\/pt\/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;\">Melhores aplicativos para contar calorias em 2026: 8 apps testados <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=\"https:\/\/lean-app.com\/pt\/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;\">Qual alternativa ao MyFitnessPal em 2026? 5 apps testados <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Comparativo honesto, precis\u00e3o do TDEE, ergonomia.<\/span><\/a><\/li><li><a href=\"https:\/\/lean-app.com\/pt\/comparatifs\/\" style=\"display:block;padding:14px 16px;background:#FAF7F2;border-radius:8px;color:#1a1a2e;text-decoration:none;font-weight:600;line-height:1.4;\">Todos os comparativos do Lean frente aos grandes apps de calorias <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Hub: MyFitnessPal, Yazio, Cronometer, Lifesum, FatSecret, Noom.<\/span><\/a><\/li><li><a href=\"https:\/\/lean-app.com\/pt\/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 ao MyFitnessPal: a f\u00f3rmula TDEE que muda tudo <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Por que o MFP erra no seu gasto cal\u00f3rico real.<\/span><\/a><\/li><li><a href=\"https:\/\/lean-app.com\/pt\/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;\">Calculadora TDEE: a f\u00f3rmula can\u00f4nica BMR + NEAT + EAT + TEF <span style=\"color:#4D4D52;font-weight:400;display:block;font-size:14px;margin-top:4px;\">Calculadora bodyfat-aware com detalhamento dos 4 blocos metab\u00f3licos.<\/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 de foto por IA diante do seu gasto real<\/title>\n<meta name=\"description\" content=\"O Foodvisor inventou o scan de refei\u00e7\u00f5es por foto. 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