{"id":485,"date":"2025-07-13T23:54:29","date_gmt":"2025-07-13T23:54:29","guid":{"rendered":"https:\/\/lean-app.com\/?p=485"},"modified":"2026-09-05T10:40:02","modified_gmt":"2026-09-05T10:40:02","slug":"comment-compter-ses-calories","status":"publish","type":"post","link":"https:\/\/lean-app.com\/es\/comment-compter-ses-calories\/","title":{"rendered":"C\u00f3mo contar tus calor\u00edas: los 4 m\u00e9todos comparados"},"content":{"rendered":"<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" href=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp\" fetchpriority=\"low\">\n<link rel=\"preload\" as=\"image\" 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.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-485 #lvm-shell .hero{display:block!important;align-items:initial!important;justify-content:initial!important;text-align:left!important;flex-direction:initial!important;padding:54px 0 0!important}\nbody.postid-485 #lvm-shell .wrap,\nbody.postid-485 #lvm-shell main.wrap{display:block!important;max-width:760px!important;margin-left:auto!important;margin-right:auto!important;padding-left:28px!important;padding-right:28px!important}\n@media (max-width:820px){\n  body.postid-485 #lvm-shell .wrap,\n  body.postid-485 #lvm-shell main.wrap{padding-left:18px!important;padding-right:18px!important}\n}\nhtml, body{overflow-x:hidden!important}\nbody.postid-485 #lvm-shell{overflow-x:hidden;max-width:100vw}\nbody.postid-485 #lvm-shell *{max-width:100%}\nbody.postid-485 #lvm-shell .nav-row{max-width:100vw;box-sizing:border-box}\nbody.postid-485 #lvm-shell.force-show .rev{opacity:1!important;transform:none!important}\n\n\/* === A.1 PHONE BACKGROUND CLASSES === *\/\nbody.postid-485 #lvm-shell .phone-bg{position:absolute;inset:0;width:100%;height:100%;background-size:cover;background-position:center top;background-repeat:no-repeat;transition:opacity .28s ease;background-color:#FAF0E6}\nbody.postid-485 #lvm-shell .phone-bg.tab-depense{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_depense.webp)}\nbody.postid-485 #lvm-shell .phone-bg.tab-bilan{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_bilan.webp)}\nbody.postid-485 #lvm-shell .phone-bg.tab-kcal{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_kcal.webp)}\nbody.postid-485 #lvm-shell .phone-bg.tab-strategie{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_strategie.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-BMR{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-NEAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_NEAT.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-EAT{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_EAT.webp)}\nbody.postid-485 #lvm-shell .phone-bg.sub-TEF{background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_TEF.webp)}\n\n\/* === v11.3 CTA BANDS MOBILE (badges plus gros + centrage) === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .cta-band{flex-direction:column!important;align-items:center!important;text-align:center!important;padding:26px 22px!important;gap:20px!important}\n  body.postid-485 #lvm-shell .cta-band .l{min-width:0!important;width:100%!important;font-size:16px!important;line-height:1.5!important;text-align:center!important}\n  body.postid-485 #lvm-shell .cta-band .stores{width:100%!important;justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-485 #lvm-shell .cta-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-485 #lvm-shell .cta-band .stores picture{width:100%!important;display:block!important}\n  body.postid-485 #lvm-shell .cta-band .stores img{height:56px!important;width:100%!important;max-width:170px!important;object-fit:contain!important;object-position:center!important;border-radius:10px!important}\n  body.postid-485 #lvm-shell .get-band{padding:38px 22px!important}\n  body.postid-485 #lvm-shell .get-band .stores{justify-content:center!important;flex-wrap:nowrap!important;gap:10px!important}\n  body.postid-485 #lvm-shell .get-band .stores a{flex:1!important;max-width:170px!important;display:flex!important;justify-content:center!important}\n  body.postid-485 #lvm-shell .get-band .stores picture{width:100%!important;display:block!important}\n  body.postid-485 #lvm-shell .get-band .stores img{height:56px!important;width:100%!important;max-width:170px!important;object-fit:contain!important;object-position:center!important;border-radius:10px!important}\n  body.postid-485 #lvm-shell .get-band h3{font-size:26px!important;line-height:1.2!important}\n  body.postid-485 #lvm-shell .get-band p{font-size:15px!important}\n}\n\n\/* === v11.2 BRAND BANNER above table responsive === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .brand-banner img{width:54px!important;height:54px!important}\n  body.postid-485 #lvm-shell .brand-banner > div{padding:16px 12px!important;gap:8px!important}\n  body.postid-485 #lvm-shell .brand-banner > div > div{font-size:15px!important}\n}\n\n\/* === v11.4 SCORECARD partie 7: redesign mobile === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .scorecard{padding:18px 16px!important;border-radius:16px!important}\n  body.postid-485 #lvm-shell .scorecard-head{display:none!important}\n  body.postid-485 #lvm-shell .scorecard-row{\n    display:block!important;\n    padding:14px 0!important;\n    border-bottom:1px solid #E8E2D6!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .crit{\n    display:block!important;\n    font-size:13px!important;\n    font-weight:600!important;\n    color:#0E0E10!important;\n    margin-bottom:10px!important;\n    padding-right:0!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar{\n    display:grid!important;\n    grid-template-columns:54px 1fr 32px!important;\n    column-gap:8px!important;\n    align-items:center!important;\n    padding:5px 0!important;\n    flex-direction:initial!important;\n    position:relative!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar::before{\n    content:attr(data-brand)!important;\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:11px!important;\n    font-weight:600!important;\n    text-transform:uppercase!important;\n    letter-spacing:.05em!important;\n    color:#0E0E10!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar.lean::before{color:#FF2D6E!important}\n  body.postid-485 #lvm-shell .scorecard-row .bar.mfp::before{color:#5B7FFF!important}\n  body.postid-485 #lvm-shell .scorecard-row .bar .b{\n    height:10px!important;\n    width:100%!important;\n    border-radius:99px!important;\n    position:relative!important;\n    background:#EFEAE0!important;\n    overflow:hidden!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar .b > i{\n    display:block!important;\n    height:100%!important;\n    border-radius:99px!important;\n  }\n  body.postid-485 #lvm-shell .scorecard-row .bar .v{\n    font-family:-apple-system,'SF Pro Display',sans-serif!important;\n    font-size:12px!important;\n    font-weight:700!important;\n    color:#0E0E10!important;\n    min-width:0!important;\n    text-align:right!important;\n  }\n}\n\n\/* === A.2 CHARTS MOBILE === *\/\n@media (max-width:760px){\n  \/* v13: charts FULL WIDTH (less card padding) + plus hauts pour vraie respiration *\/\n  body.postid-485 #lvm-shell .cv-wrap{height:380px!important;min-height:360px!important;max-height:420px!important;width:100%!important}\n  body.postid-485 #lvm-shell .cv-wrap canvas{width:100%!important;height:100%!important;display:block!important}\n  body.postid-485 #lvm-shell .fig{padding:16px 4px 14px!important;margin:24px -4px 14px!important;overflow:visible!important}\n  body.postid-485 #lvm-shell .fig-head{padding:0 12px!important;flex-wrap:wrap!important;gap:6px!important;margin-bottom:10px!important}\n  body.postid-485 #lvm-shell .fig-body{padding:0 2px!important}\n  body.postid-485 #lvm-shell .fig-cap{padding:0 12px!important;font-size:13px!important;margin-top:10px!important}\n}\n@media (max-width:480px){\n  body.postid-485 #lvm-shell .cv-wrap{height:360px!important;min-height:340px!important;max-height:380px!important}\n  body.postid-485 #lvm-shell .fig{padding:14px 2px 12px!important;margin:20px -6px 12px!important;border-radius:14px!important}\n  body.postid-485 #lvm-shell .fig-body{padding:0!important}\n}\n\n\/* === v11.2 TABLEAU MOBILE STACKED CARDS avec mini-tags Lean\/MFP === *\/\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .table{border-radius:14px!important}\n  body.postid-485 #lvm-shell .table-row.head{display:none!important}\n  body.postid-485 #lvm-shell .table-row{\n    display:grid!important;\n    grid-template-columns:1fr 1fr!important;\n    grid-template-areas:\"crit crit\" \"lean mfp\"!important;\n    gap:0!important;\n    min-height:0!important;\n  }\n  body.postid-485 #lvm-shell .table-row > .crit{\n    grid-area:crit!important;background:#0E0E10!important;color:#fff!important;\n    padding:11px 14px!important;font-size:13px!important;font-weight:600!important;\n    letter-spacing:-0.1px!important;border-right:0!important;line-height:1.35!important;\n    font-family:-apple-system,BlinkMacSystemFont,'SF Pro 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body.postid-485 #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 (override mobile mini-phone) === *\/\nbody.postid-485 #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-485 #lvm-shell .bodyscan-illust .mini-phone{max-width:200px!important;padding:3px!important;border-radius:22px!important;border-width:1px!important}\nbody.postid-485 #lvm-shell .bodyscan-illust .mini-phone .notch{width:40px!important;height:11px!important;border-radius:0 0 7px 7px!important}\nbody.postid-485 #lvm-shell .bodyscan-illust .mini-phone .scr{border-radius:18px!important}\n@media (max-width:760px){\n  body.postid-485 #lvm-shell .bodyscan-illust{max-width:180px!important}\n  body.postid-485 #lvm-shell .bodyscan-illust 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El problema nunca fue la eficacia, es la adherencia: la introducci\u00f3n manual alimento por alimento agota a todo el mundo, y casi el 80&nbsp;% de la gente abandona en menos de 3&nbsp;meses. Esta gu\u00eda compara los 4 m\u00e9todos para contar tus calor\u00edas, del m\u00e1s r\u00e1pido (escaneo IA de foto, 5&nbsp;segundos) al m\u00e1s preciso (base de datos + b\u00e1scula), y te dice cu\u00e1l elegir seg\u00fan tu perfil.\n    <\/div>\n        <div class=\"phone-wrap rev\">\n      <div class=\"phone-stage\">\n        <div class=\"tap-hint mobile\" id=\"tapHintMobile\" aria-hidden=\"true\">\n          <span class=\"th-pill\"><small>Demostraci\u00f3n interactiva<\/small>Toca la pantalla para explorar la aplicaci\u00f3n<\/span>\n          <svg class=\"th-arrow\" viewbox=\"0 0 24 24\" fill=\"none\" aria-hidden=\"true\">\n            <path d=\"M12 4 L12 20 M5 13 L12 20 L19 13\" stroke=\"currentColor\" stroke-width=\"2.4\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n          <\/svg>\n        <\/div>\n        <div class=\"tap-hint desktop\" id=\"tapHintDesktop\" aria-hidden=\"true\">\n          <span class=\"th-pill\"><small>Demostraci\u00f3n interactiva<\/small>Toca la pantalla<br>para explorar la aplicaci\u00f3n<\/span>\n          <svg class=\"th-arrow\" viewbox=\"0 0 104 34\" fill=\"none\" aria-hidden=\"true\">\n            <path d=\"M4 9 C 34 1, 64 20, 94 27\" stroke=\"currentColor\" stroke-width=\"2.6\" fill=\"none\" stroke-linecap=\"round\"\/>\n            <path d=\"M86 20 L 94 27 L 84 30\" stroke=\"currentColor\" stroke-width=\"2.6\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n          <\/svg>\n        <\/div>\n        <div class=\"phone\" id=\"phone\" role=\"img\" aria-label=\"Vista general de la aplicaci\u00f3n Lean con desglose del TDEE\">\n          <div class=\"notch\"><\/div>\n          <div class=\"phone-screen\">\n            <button class=\"phone-back\" id=\"phoneBack\" aria-label=\"Volver\">&#8249;<\/button>\n            <div id=\"phoneImg\" class=\"phone-bg tab-depense\" role=\"img\" aria-label=\"Vista Lean, pesta\u00f1a Gasto\"><\/div>\n            <div class=\"phone-zones\" id=\"phoneZones\">\n              <div class=\"z\" data-sub=\"BMR\"  style=\"top:11%;height:21%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalle BMR\"><\/div>\n              <div class=\"z\" data-sub=\"NEAT\" style=\"top:33%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalle NEAT\"><\/div>\n              <div class=\"z\" data-sub=\"EAT\"  style=\"top:50%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalle EAT\"><\/div>\n              <div class=\"z\" data-sub=\"TEF\"  style=\"top:67%;height:16%\" role=\"button\" tabindex=\"0\" aria-label=\"Detalle TEF\"><\/div>\n            <\/div>\n            <div class=\"phone-navbar\" id=\"phoneNav\" aria-hidden=\"false\">\n              <button data-tab=\"bilan\"     type=\"button\" aria-label=\"Pesta\u00f1a Balance\"><\/button>\n              <button data-tab=\"kcal\"      type=\"button\" aria-label=\"Pesta\u00f1a Calor\u00edas\"><\/button>\n              <button data-tab=\"depense\"   type=\"button\" aria-label=\"Pesta\u00f1a Gasto\"><\/button>\n              <button data-tab=\"strategie\" type=\"button\" aria-label=\"Pesta\u00f1a Estrategia\"><\/button>\n            <\/div>\n          <\/div>\n        <\/div>\n        <div class=\"phone-tabs\" role=\"tablist\" aria-label=\"Navegar por la aplicaci\u00f3n Lean\">\n          <button data-tab=\"bilan\"     type=\"button\">Balance<\/button>\n          <button data-tab=\"kcal\"      type=\"button\">Calor\u00edas<\/button>\n          <button data-tab=\"depense\"   type=\"button\" class=\"on\">Gasto<\/button>\n          <button data-tab=\"strategie\" type=\"button\">Estrategia<\/button>\n        <\/div>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"snippet rev\">\n    <div class=\"lbl\">Respuesta r\u00e1pida<\/div>\n    <p>Cuatro m\u00e9todos para contar tus calor\u00edas: el <strong>escaneo IA de foto<\/strong> (fotograf\u00edas tu plato, la IA identifica cada alimento y su cantidad), el <strong>escaneo de c\u00f3digo de barras<\/strong> (productos envasados, datos del fabricante), la <strong>base de datos<\/strong> (b\u00fasqueda + pesaje, el modo m\u00e1s preciso) y la<strong>entrada r\u00e1pida<\/strong> (una estimaci\u00f3n global en 10&nbsp;segundos). El buen m\u00e9todo es el que mantendr\u00e1s 6 meses, no el m\u00e1s preciso sobre el papel. Y contar tus entradas solo sirve si tu gasto, el <span data-term=\"TDEE\">TDEE<span class=\"tt\">Total Daily Energy Expenditure: tu gasto cal\u00f3rico total en 24&nbsp;h. TDEE = BMR + NEAT + EAT + TEF.<\/span><\/span>, est\u00e1 bien calculado.<\/p>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"constat\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">01 &middot; La constataci\u00f3n<\/span><\/div>\n  <h2 id=\"constat\">Por qu\u00e9 el 80&nbsp;% de la gente deja de contar en 3 meses<\/h2>\n  <p>Contar calor\u00edas funciona. Ese no es el tema. El tema es que casi nadie aguanta: los estudios sobre programas de p\u00e9rdida de peso muestran una deserci\u00f3n masiva desde las primeras semanas, mucho antes del primer resultado visible en la b\u00e1scula.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">80&nbsp;%<\/div>\n    <div class=\"lbl\">de los inscritos en un programa de p\u00e9rdida de peso abandonan en los 3 primeros meses (Alexander et al., 2018). La causa dominante: la fricci\u00f3n del seguimiento, no la falta de voluntad.<\/div>\n  <\/div>\n\n  <p>La fricci\u00f3n, concretamente: buscar \u00abpechuga de pollo\u00bb en una lista, elegir la entrada correcta entre 40 duplicados, estimar 150&nbsp;g a ojo, volver a empezar con las jud\u00edas, luego con el aceite de oliva. Tres veces al d\u00eda. 90 veces al mes. Es esa contabilidad la que mata el tracking, no la idea de registrar.<\/p>\n\n  <p>Conclusi\u00f3n pr\u00e1ctica: la elecci\u00f3n de tu m\u00e9todo de registro no es un detalle de ergonom\u00eda. Es LA variable que decide si tus datos seguir\u00e1n existiendo dentro de 3 meses. Estos son los 4 m\u00e9todos, clasificados del m\u00e1s r\u00e1pido al m\u00e1s exigente.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"scan-ia\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">02 &middot; M\u00e9todo 1<\/span><\/div>\n  <h2 id=\"scan-ia\">El escaneo IA de foto: 5 segundos por comida<\/h2>\n  <p>Fotograf\u00edas tu plato. La IA de Lean hace una <strong>doble identificaci\u00f3n<\/strong> : primero reconoce cada alimento presente (pollo a la plancha, jud\u00edas verdes), luego estima la cantidad de cada uno. El resultado no es una cifra global sacada de la chistera: es una descomposici\u00f3n alimento por alimento, con calor\u00edas y macros para cada l\u00ednea.<\/p>\n\n  <figure style=\"margin:26px auto;max-width:250px;text-align:center\"><video controls playsinline preload=\"none\" poster=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/06\/poster-video-scan-ia-lean.webp\" width=\"1080\" height=\"1920\" style=\"width:100%;height:auto;border-radius:16px;display:block;background:#F7F6F2\"><source src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/07\/scan-aliments-ia-application-lean.mp4\" type=\"video\/mp4\"><\/video><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">El escaneo IA en condiciones reales: foto, an\u00e1lisis, resultado.<\/figcaption><\/figure>\n\n  <p>En im\u00e1genes, paso a paso, con una comida real:<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/scan-ia-repas-analyse-nutritionnelle-lean-app-511x1024.jpg\" alt=\"Scan IA dans Lean : photo du plat prise, champ de d\u00e9tails optionnel au clavier\" width=\"511\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Encuadras. Un detalle por teclado, si quieres.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/analyse-ia-repas-poulet-haricots-verts-lean-app-519x1024.jpg\" alt=\"Analyse IA en cours dans Lean : la photo du plat est trait\u00e9e\" width=\"519\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">La IA analiza el plato.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/resultat-scan-ia-repas-poulet-lean-app-519x1024.jpg\" alt=\"R\u00e9sultat du scan IA : chaque aliment compt\u00e9 avec calories et macros\" width=\"519\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Cada alimento contado.<\/figcaption><\/figure><\/div>\n\n  <p>En platos corrientes, el error t\u00edpico es de alrededor de \u00b110&nbsp;% por comida. Es menos preciso que una b\u00e1scula, y m\u00e1s que suficiente para pilotar una p\u00e9rdida de grasa (volvemos a ello con la ley de los grandes n\u00fameros m\u00e1s abajo). Sobre todo, es el \u00fanico m\u00e9todo que cubre las comidas sin etiqueta: casa, restaurante, comedor.<\/p>\n\n  <h3>Donde Lean va m\u00e1s lejos: t\u00fa mantienes el control<\/h3>\n  <p>Un escaneo IA solo es \u00fatil si puedes corregir lo que propone. Lean te deja tres palancas, por alimento:<\/p>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Palanca 1<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Modificar las cantidades<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Un deslizador por alimento: aqu\u00ed, el pan estimado en 101,4&nbsp;g por la IA, corregido a 70&nbsp;g. Calor\u00edas y total del plato se recalculan al instante.<\/p><div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/mise-a-jour-calories-apres-ajustement-scan-ia-519x1024.jpg\" alt=\"Repas scann\u00e9 dans Lean avant correction : pain estim\u00e9 \u00e0 101,4 g\" width=\"519\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Pan estimado en 101,4&nbsp;g por la IA.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/modifier-quantites-macros-scan-ia-lean-app-514x1024.jpg\" alt=\"Slider de quantit\u00e9 dans Lean : pain corrig\u00e9 \u00e0 70 g, recalcul instantan\u00e9\" width=\"514\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Corregido a 70&nbsp;g, todo se recalcula.<\/figcaption><\/figure><\/div><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Palanca 2<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Modificar los macros<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">\u00bfConoces tu receta mejor que la media? Aqu\u00ed, las patatas fritas: la IA propone 4,1&nbsp;g de grasas, corriges a 14&nbsp;g. Donut y total siguen.<\/p><div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/modifier-macronutriments-aliment-scan-ia-lean-app-527x1024.jpg\" alt=\"Macros des chips estim\u00e9es par l'IA dans Lean : 4,1 g de lipides\" width=\"527\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Patatas fritas seg\u00fan la IA: 4,1&nbsp;g de grasas.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/modification-quantite-chips-scan-ia-lean-app-517x1024.jpg\" alt=\"Macros des chips corrig\u00e9es \u00e0 la main dans Lean : 14 g de lipides\" width=\"517\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Corregidas a mano: 14&nbsp;g.<\/figcaption><\/figure><\/div><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Palanca 3<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Volver a escanear un solo alimento<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">\u00bfLa IA ha confundido el pavo con queso? Escribes el nombre correcto, la l\u00ednea se vuelve a analizar sola, sin rehacer todo el plato.<\/p><div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/modifier-macronutriments-chips-scan-ia-lean-app-516x1024.jpg\" alt=\"Re-scan d'un aliment dans Lean : saisie du bon nom au clavier\" width=\"516\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Precisas: jam\u00f3n de pavo.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:160px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/resultat-final-apres-correction-scan-ia-lean-app-518x1024.jpg\" alt=\"R\u00e9sultat apr\u00e8s re-scan dans Lean : jambon de dinde identifi\u00e9, total recalcul\u00e9\" width=\"518\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Jam\u00f3n de pavo, 53&nbsp;kcal. Total recalculado.<\/figcaption><\/figure><\/div><\/div><\/div>\n\n  <h3>La prueba frente a Cal AI: descomposici\u00f3n contra estimaci\u00f3n global<\/h3>\n  <p>Misma foto, dos apps. La diferencia salta a la vista:<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0\"><figure style=\"flex:1;max-width:190px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/comparatif-analyse-ia-repas-lean-vs-cal-ai-532x1024.jpg\" alt=\"Comparatif scan IA : la m\u00eame photo de repas donn\u00e9e \u00e0 Lean et \u00e0 Cal AI\" width=\"532\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:18px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">La misma foto, dada a las dos apps.<\/figcaption><\/figure><\/div>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:center;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/resultat-scan-ia-sandwich-chips-lean-app-533x1024.jpg\" alt=\"R\u00e9sultat Lean : sandwich et chips d\u00e9compos\u00e9s aliment par aliment\" width=\"533\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Lean: cada alimento, total detallado.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:200px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/07\/analyse-scan-repas-ia-application-cal-ai-869x1024.webp\" alt=\"R\u00e9sultat Cal AI : enveloppe globale du plat sans d\u00e9composition\" width=\"869\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:18px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Cal AI: una estimaci\u00f3n global por plato.<\/figcaption><\/figure><\/div>\n\n  <p>Es la diferencia estructural con la mayor\u00eda de las apps de escaneo, Cal AI a la cabeza: devuelven una <strong>estimaci\u00f3n global<\/strong> por plato, un total de calor\u00edas y macros sin descomposici\u00f3n modificable alimento por alimento. Si la estimaci\u00f3n es err\u00f3nea, la tomas o la tiras. Con una descomposici\u00f3n por alimento, el error se corrige con dos toques.<\/p>\n\n  <p><strong>El buen uso:<\/strong> platos caseros, restaurante, comedor, todas las comidas sin c\u00f3digo de barras. El m\u00e9todo por defecto del 90&nbsp;% de las comidas.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"code-barres\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">03 &middot; M\u00e9todo 2<\/span><\/div>\n  <h2 id=\"code-barres\">El escaneo de c\u00f3digo de barras: la precisi\u00f3n de la etiqueta<\/h2>\n  <p>Para todo lo que sale de un envase, el c\u00f3digo de barras es imbatible: los valores nutricionales vienen directamente de los datos del fabricante. Lean se apoya en las bases USDA y OpenFoodFacts, es decir, millones de referencias. Escaneas, ajustas la cantidad, listo.<\/p>\n\n  <figure style=\"margin:26px auto;max-width:250px;text-align:center\"><video controls playsinline preload=\"none\" poster=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/06\/poster-video-scan-code-barres-lean.webp\" width=\"1080\" height=\"1920\" style=\"width:100%;height:auto;border-radius:16px;display:block;background:#F7F6F2\"><source src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/Barcode-Scan-calorie-lean-app.mp4\" type=\"video\/mp4\"><\/video><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Escaneo del c\u00f3digo de barras: ficha exacta, entrada directa.<\/figcaption><\/figure>\n\n  <p>El l\u00edmite es evidente: tu plato de pasta casera no tiene c\u00f3digo de barras. Por eso este m\u00e9todo se combina de forma natural con el escaneo IA de foto, no lo sustituye.<\/p>\n\n  <p><strong>El buen uso:<\/strong> compra, snacks, productos procesados, batidos. Precisi\u00f3n m\u00e1xima en lo industrial, cero esfuerzo.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"base-donnees\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">04 &middot; M\u00e9todo 3<\/span><\/div>\n  <h2 id=\"base-donnees\">La base de datos + b\u00e1scula: el modo precisi\u00f3n<\/h2>\n  <p>El m\u00e9todo hist\u00f3rico: pesas tu alimento, lo buscas en la base, introduces los gramos. Con una b\u00e1scula de cocina, es el m\u00e9todo m\u00e1s preciso que existe, el de las preparaciones de competici\u00f3n y los protocolos cl\u00ednicos.<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/fonctionnalites-ajout-aliment-recherche-scan-lean-app-502x1024.jpg\" alt=\"Recherche d'un aliment dans la base de donn\u00e9es Lean avec historique et ajout rapide\" width=\"502\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">B\u00fasqueda, historial, entrada con dos toques.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/details-nutritionnels-thon-conserve-lean-app-501x1024.jpg\" alt=\"Fiche nutritionnelle exacte d'un produit dans Lean avec impact sur les budgets du jour\" width=\"501\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Ficha exacta del producto, impacto en vivo.<\/figcaption><\/figure><\/div>\n\n  <p>El panel \u00abRestante \/ Impacto\u00bb te muestra, antes incluso de validar, lo que el alimento cambia en tus presupuestos del d\u00eda: calor\u00edas, prote\u00ednas, carbohidratos, grasas, fibra. Decides con conocimiento de causa, no a posteriori.<\/p>\n\n  <p>Lean reduce la fricci\u00f3n al m\u00ednimo: historial de tus alimentos frecuentes, b\u00fasqueda en USDA + OpenFoodFacts, entrada con dos toques desde la lista. Pero seamos honestos sobre el coste real: de 2 a 3 minutos por comida, y la disciplina de pesar. Es precisamente esa fricci\u00f3n la que produce el 80&nbsp;% de abandono del m\u00e9todo cl\u00e1sico.<\/p>\n\n  <p><strong>El buen uso:<\/strong> definici\u00f3n estricta, recomposici\u00f3n avanzada, deportes con categor\u00eda de peso. Y tus 5 a 10 alimentos recurrentes del d\u00eda a d\u00eda, que el historial hace casi instant\u00e1neos.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"ajout-rapide\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">05 &middot; M\u00e9todo 4<\/span><\/div>\n  <h2 id=\"ajout-rapide\">La entrada r\u00e1pida: la estimaci\u00f3n que salva el d\u00eda<\/h2>\n  <p>Buffet de cumplea\u00f1os, d\u00eda sobrecargado, comida de pie entre dos reuniones: algunas comidas nunca se registrar\u00e1n con detalle. La entrada r\u00e1pida te deja introducir una estimaci\u00f3n global, calor\u00edas y macros estimados, en 10 segundos. Menos preciso, infinitamente mejor que un vac\u00edo de datos.<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0;flex-wrap:wrap\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/ajout-rapide-calories-macros-lean-app-501x1024.jpg\" alt=\"Formulaire d'ajout rapide dans Lean : nom, calories et macros saisis \u00e0 la main\" width=\"501\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Nombre, calor\u00edas, macros: 10 segundos.<\/figcaption><\/figure>\n    <figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/resultat-ajout-rapide-repas-journal-alimentaire-lean-app-501x1024.jpg\" alt=\"Ajout rapide enregistr\u00e9 dans le journal alimentaire Lean\" width=\"501\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Una estimaci\u00f3n, d\u00eda salvado.<\/figcaption><\/figure><\/div>\n\n  <p>El balance cal\u00f3rico en vivo hace el resto: en la captura, 2&nbsp;143&nbsp;kcal de objetivo, 1&nbsp;000 consumidas, 1&nbsp;143 restantes, recalculadas continuamente a lo largo del d\u00eda. Un d\u00eda aproximado pero registrado vale m\u00e1s que un d\u00eda perfecto abandonado: es la media semanal la que pilota el resultado, no la pureza de un d\u00eda aislado.<\/p>\n\n  <div style=\"display:flex;gap:14px;justify-content:center;align-items:flex-start;margin:24px 0\"><figure style=\"flex:1;max-width:180px;min-width:140px;margin:0;text-align:center\"><img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2025\/08\/details-nutritionnels-repas-ajout-rapide-lean-app-504x1024.jpg\" alt=\"D\u00e9tails nutritionnels d'un repas en ajout rapide dans Lean : restants et impact\" width=\"504\" height=\"1024\" loading=\"lazy\" decoding=\"async\" style=\"width:100%;height:auto;border-radius:22px;box-shadow:0 10px 28px rgba(0,0,0,.10);display:block\"><figcaption style=\"font-size:13px;color:#3A3A3C;margin-top:10px;line-height:1.45;text-transform:none;letter-spacing:0\">Restante e impacto, recalculados en vivo.<\/figcaption><\/figure><\/div>\n\n  <p><strong>El buen uso:<\/strong> d\u00edas cargados, comidas sociales, rescate antiabandono. El m\u00e9todo que impide el \u00abhe fallado una comida, adi\u00f3s a la semana\u00bb.<\/p>\n\n  <div class=\"hero-stores\" style=\"margin:36px 0 12px\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"T\u00e9l\u00e9charger sur l'App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Disponible sur Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <span class=\"or\">Descarga gratuita<\/span>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"profils\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">06 &middot; Elegir<\/span><\/div>\n  <h2 id=\"profils\">Qu\u00e9 m\u00e9todo para qu\u00e9 perfil<\/h2>\n  <p>Primero, el duelo de los 4 m\u00e9todos en los 4 criterios que cuentan: la facilidad por comida, la disponibilidad (\u00bffunciona en todas partes, todo el tiempo?), el mantenimiento a largo plazo y la precisi\u00f3n.<\/p>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><h3 style=\"margin:0 0 2px;font-size:17px\">Escaneo IA de foto<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">La foto, 5 segundos<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Facilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Disponibilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Largo plazo<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Precisi\u00f3n<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><\/div><\/div>\n    <div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #0F8F5C\"><h3 style=\"margin:0 0 2px;font-size:17px\">C\u00f3digo de barras<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">La etiqueta exacta<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Facilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Disponibilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Largo plazo<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Precisi\u00f3n<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><\/div><\/div>\n    <div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #C8A019\"><h3 style=\"margin:0 0 2px;font-size:17px\">Base de datos + b\u00e1scula<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">El modo precisi\u00f3n<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Facilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Disponibilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Largo plazo<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Precisi\u00f3n<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><\/div><\/div>\n    <div style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #6E6E73\"><h3 style=\"margin:0 0 2px;font-size:17px\">Entrada r\u00e1pida<\/h3><p style=\"margin:0 0 12px;font-size:13px;color:#6E6E73\">La estimaci\u00f3n de rescate<\/p><div style=\"display:grid;grid-template-columns:1fr 1fr;gap:8px\"><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Facilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Disponibilidad<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Largo plazo<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><div style=\"display:flex;justify-content:space-between;align-items:center;background:#fff;border-radius:8px;padding:9px 12px;gap:6px\"><span style=\"font-size:13px;color:#3A3A3C\">Precisi\u00f3n<\/span><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span><\/div><\/div><\/div><\/div>\n\n  <p style=\"font-size:13px;color:#6E6E73;margin-top:-6px\"><span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#DDF2E6;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">++<\/span> excelente &nbsp; <span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#F0F7F0;color:#0F8F5C;font-weight:800;font-size:13px;letter-spacing:0.04em\">+<\/span> bueno &nbsp; <span style=\"display:inline-block;min-width:30px;text-align:center;padding:2px 9px;border-radius:999px;background:#FBEDED;color:#D02E2E;font-weight:800;font-size:13px;letter-spacing:0.04em\">&minus;<\/span> punto d\u00e9bil. \u00abLargo plazo\u00bb = la probabilidad de que sigas usando el m\u00e9todo dentro de 6 meses.<\/p>\n\n  <p>Lectura del duelo: ning\u00fan m\u00e9todo gana en todo. El escaneo IA domina en adherencia, la base + b\u00e1scula en precisi\u00f3n, y la entrada r\u00e1pida solo existe para tapar huecos. De ah\u00ed los perfiles: los 4 m\u00e9todos no se excluyen, la buena estrategia es una mezcla, dosificada seg\u00fan tu exigencia del momento y tu agenda. Tres perfiles cubren casi todos los casos.<\/p>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Perfil 1 \u00b7 Principiante<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Cero fricci\u00f3n, aprender haciendo<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Escaneo IA de foto en todas partes, c\u00f3digo de barras en lo envasado, ning\u00fan pesaje. Objetivo \u00fanico: aguantar 3 meses y educar tu ojo en las porciones. La precisi\u00f3n vendr\u00e1 despu\u00e9s, el h\u00e1bito primero.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #0F8F5C\"><div class=\"kicker\" style=\"color:#0F8F5C;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Perfil 2 \u00b7 Rigor m\u00e1ximo<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Cada uno por ciento cuenta<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">B\u00e1scula + base de datos en las comidas caseras, c\u00f3digo de barras en lo envasado, escaneo IA en el restaurante. Para las fases cortas en las que la precisi\u00f3n realmente compensa: final de definici\u00f3n, pesaje oficial.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #C8A019\"><div class=\"kicker\" style=\"color:#C8A019;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Perfil 3 \u00b7 Con prisa o avanzado<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Datos fiables sin pensar en ello<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">C\u00f3digo de barras y escaneo IA a diario, entrada r\u00e1pida de rescate los d\u00edas imposibles. Ya conoces tus porciones, solo quieres que los datos existan.<\/p><\/div><\/div>\n\n  <p>Regla de decisi\u00f3n, v\u00e1lida para todos: en caso de duda, elige el m\u00e9todo m\u00e1s ligero que est\u00e9s seguro de mantener. Un dato perfecto que dejas de introducir al cabo de 3 semanas vale cero.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"precision\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">07 &middot; Precisi\u00f3n<\/span><\/div>\n  <h2 id=\"precision\">El error de \u00b110&nbsp;% que desaparece en un mes<\/h2>\n  <p>La objeci\u00f3n cl\u00e1sica frente al escaneo IA: \u00ab\u00b110&nbsp;% de error es demasiado\u00bb. Es razonar sobre una comida aislada. Pero t\u00fa no registras una comida: registras unas 90 al mes.<\/p>\n\n  <div role=\"img\" aria-label=\"Doce comidas escaneadas: sobreestimaciones y subestimaciones se compensan alrededor del consumo real\" style=\"margin:24px 0;padding:24px 20px 14px;background:#F7F6F2;border-radius:12px\"><div style=\"position:relative;height:150px;display:flex;align-items:flex-start;gap:6px\"><div style=\"position:absolute;left:0;right:0;top:74px;height:2px;background:#0E0E10;z-index:2\"><\/div><div style=\"flex:1;height:58px;margin-top:17px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:30px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:22px;margin-top:53px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:48px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:40px;margin-top:35px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:12px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:30px;margin-top:45px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:55px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:18px;margin-top:57px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:35px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><div style=\"flex:1;height:45px;margin-top:30px;background:#FFD2E2;border-radius:4px 4px 0 0\"><\/div><div style=\"flex:1;height:20px;margin-top:75px;background:#BFE8CE;border-radius:0 0 4px 4px\"><\/div><\/div><div style=\"display:flex;justify-content:space-between;margin-top:10px;font-size:12px;color:#6E6E73\"><span>Comida 1<\/span><span>Comida 12<\/span><\/div><p style=\"margin:10px 0 0;font-size:13px;color:#3A3A3C;line-height:1.5;text-transform:none\">Rosa: comidas sobreestimadas. Verde: comidas subestimadas. L\u00ednea negra: tu consumo real. En un mes, las desviaciones se compensan.<\/p><\/div>\n\n  <p>Es la ley de los grandes n\u00fameros (Kolmog\u00f3rov): los errores aleatorios independientes se compensan cuando el n\u00famero de observaciones aumenta. El +12&nbsp;% del lunes anula el 8&nbsp;% que falta el martes. Lo que queda en un mes es el sesgo medio del modelo, cercano a cero en un escaneo bien calibrado, no el ruido de una comida.<\/p>\n\n  <p>La introducci\u00f3n manual, en cambio, sufre un problema de naturaleza muy distinta: un <strong>sesgo sistem\u00e1tico<\/strong>. Los humanos infradeclaran lo que comen, siempre en el mismo sentido, y un sesgo nunca se suaviza.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">47&nbsp;%<\/div>\n    <div class=\"lbl\">de diferencia entre las calor\u00edas declaradas a mano y las calor\u00edas realmente consumidas, medida con agua doblemente marcada en sujetos con dietas fallidas (Lichtman, NEJM 1992). El sesgo humano no se compensa, el ruido aleatorio del escaneo, s\u00ed.<\/div>\n  <\/div>\n\n  <p>Moraleja: la pregunta no es \u00ab\u00bfel escaneo IA es perfecto?\u00bb sino \u00ab\u00bfqu\u00e9 m\u00e9todo produce los datos m\u00e1s correctos en 90 comidas?\u00bb. Y ah\u00ed, la automatizaci\u00f3n gana, porque sustituye un sesgo direccional por un ruido que se anula.<\/p>\n<\/section>\n\n\n<section aria-labelledby=\"depense\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">08 &middot; El gasto<\/span><\/div>\n  <h2 id=\"depense\">Contar no basta: falta la otra mitad de la ecuaci\u00f3n<\/h2>\n  <p>Un diario alimentario perfecto no dice nada por s\u00ed solo. Perder grasa es un balance energ\u00e9tico negativo: entradas por debajo de las salidas. Si tu gasto est\u00e1 mal calculado, tu d\u00e9ficit es una ficci\u00f3n contable, y puedes contar tus calor\u00edas al gramo sin avanzar nunca.<\/p>\n\n  <div class=\"statement\">\n    <div class=\"num\">TDEE = BMR + NEAT + EAT + TEF<\/div>\n    <div class=\"lbl\">La ecuaci\u00f3n can\u00f3nica del gasto. <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/metabolisme-de-base\/\">BMR<\/a> = metabolismo basal. <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/neat-depense-non-sportive\/\">NEAT<\/a> = actividad fuera del deporte. <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/eat\/\">EAT<\/a> = sesiones de deporte. <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/effet-thermique-des-aliments\/\">TEF<\/a> = digesti\u00f3n.<\/div>\n  <\/div>\n\n  <p>El problema: casi todas las apps estiman tu gasto con Harris-Benedict (1919) o Mifflin-St Jeor (1990), f\u00f3rmulas por peso bruto que ignoran tu composici\u00f3n corporal. A igual peso, un cuerpo al 15&nbsp;% de grasa corporal y un cuerpo al 30&nbsp;% no queman en absoluto igual. Lean calcula el gasto de otra manera, en cuatro etapas:<\/p>\n\n  <div class=\"bodyscan-illust\">\n    <div class=\"mini-phone\">\n      <div class=\"notch\"><\/div>\n      <div class=\"scr\" style=\"background-image:url(https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-screen_BMR.webp);background-size:cover;background-position:center top;background-repeat:no-repeat\"><\/div>\n    <\/div>\n    <div class=\"mini-cap\" style=\"text-align:center;font-size:13px;color:#6E6E73;max-width:200px\">Grasa corporal real. Foto, 5 segundos.<\/div>\n  <\/div>\n\n  <div class=\"solution-grid\" style=\"display:grid;grid-template-columns:1fr;gap:14px;margin:22px 0\"><div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Etapa 1<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">BodyScan IA<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Haces una foto en la app. La IA estima tu grasa corporal a partir de tu morfolog\u00eda visible, repetida cada semana. Es esa medici\u00f3n la que ancla todo el c\u00e1lculo en tu masa magra, no en tu peso bruto.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Etapa 2<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">BMR mediante modelo propietario patentado<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">El algoritmo patentado Lean calcula tu metabolismo basal a partir de tu masa magra real. M\u00e1s preciso que Harris-Benedict 1919 o Mifflin-St Jeor 1990, que solo conocen tu peso.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Etapa 3<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">NEAT, EAT y TEF medidos, no a tanto alzado<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">Tus pasos cuentan en el NEAT, tus sesiones de deporte en el EAT mediante las tablas MET de referencia, y el TEF se calcula sobre los macros que realmente has comido, precisamente gracias a tu tracking.<\/p><\/div>\n    <div class=\"solution-card\" style=\"background:#F7F6F2;padding:18px 20px;border-radius:0 12px 12px 0;border-left:4px solid #FF2D6E\"><div class=\"kicker\" style=\"color:#FF2D6E;font-size:12px;font-weight:700;text-transform:uppercase;letter-spacing:0.06em;margin-bottom:8px\">Etapa 4<\/div><h3 style=\"margin:0 0 6px;font-size:18px\">Adaptaci\u00f3n metab\u00f3lica recalculada<\/h3><p style=\"margin:0;font-size:15px;line-height:1.55\">En d\u00e9ficit prolongado, el BMR baja. Lean es la primera app en modelizar ese coeficiente y aplicarlo multiplicativamente al BMR. Convenci\u00f3n 100&nbsp;% = \u00f3ptimo, 90&nbsp;% = adaptaci\u00f3n de 10 puntos.<\/p><\/div><\/div>\n\n  <p>Resultado: el balance en vivo compara lo que comes (contado con los 4 m\u00e9todos de esta gu\u00eda) con lo que realmente gastas, recalculado continuamente. Es la combinaci\u00f3n de las dos mitades, entradas correctas y salidas correctas, la que hace previsible una p\u00e9rdida de grasa. La una sin la otra es contabilidad en el vac\u00edo.<\/p>\n\n  <div class=\"hero-stores\" style=\"margin:36px 0 12px\">\n    <a href=\"https:\/\/apps.apple.com\/fr\/app\/lean-calorie-ai-podometre\/id6738668646?utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-appstore-official.webp\" alt=\"T\u00e9l\u00e9charger sur l'App Store\" width=\"413\" height=\"122\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <a href=\"https:\/\/play.google.com\/store\/apps\/details?id=com.lean.testsqflite&#038;utm_source=seo&#038;utm_medium=blog&#038;utm_campaign=guide-compter-calories\" target=\"_blank\" rel=\"noopener\">\n      <img src=\"https:\/\/lean-app.com\/wp-content\/uploads\/2026\/05\/lvm-googleplay-official.webp\" alt=\"Disponible sur Google Play\" width=\"315\" height=\"95\" loading=\"lazy\" decoding=\"async\" \/>\n    <\/a>\n    <span class=\"or\">Descarga gratuita<\/span>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"faq\" class=\"rev\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">09 &middot; FAQ<\/span><\/div>\n  <h2 id=\"faq\">Preguntas frecuentes sobre el conteo de calor\u00edas<\/h2>\n  <div class=\"faq\">\n    <details><summary>\u00bfCu\u00e1ntas calor\u00edas al d\u00eda para perder peso?<\/summary><div class=\"ans\">No existe ninguna cifra universal: todo depende de tu gasto total (TDEE), que var\u00eda seg\u00fan tu metabolismo basal, tu actividad diaria, tu deporte y tu composici\u00f3n corporal. La regla fiable: un d\u00e9ficit de 300 a 500&nbsp;kcal por debajo de tu TDEE real, nunca una cifra gen\u00e9rica tipo \u00ab1&nbsp;500&nbsp;kcal\u00bb sacada de la nada. Puedes estimar el tuyo con la <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/calculateur-deficit-calorique\/\">calculadora de d\u00e9ficit cal\u00f3rico<\/a>.<\/div><\/details>\n    <details><summary>\u00bfEl escaneo fotogr\u00e1fico con IA es fiable para contar calor\u00edas?<\/summary><div class=\"ans\">S\u00ed, a condici\u00f3n de entender lo que se mide. En platos corrientes, el error t\u00edpico es de alrededor de \u00b110&nbsp;% por comida, con una identificaci\u00f3n alimento por alimento que puedes corregir (cantidades, macros, reescaneo). En un mes, unas 90 comidas, los errores aleatorios se compensan por la ley de los grandes n\u00fameros: lo que cuenta es el sesgo medio, no la desviaci\u00f3n de una comida aislada.<\/div><\/details>\n    <details><summary>\u00bfHay que pesar los alimentos para contar calor\u00edas?<\/summary><div class=\"ans\">Solo si est\u00e1s en una fase en la que cada uno por ciento cuenta: definici\u00f3n estricta, recomposici\u00f3n avanzada, deporte con categor\u00eda de peso. Para la gran mayor\u00eda de la gente, escaneo IA de foto y c\u00f3digo de barras bastan de sobra. La adherencia a 6 meses pesa mucho m\u00e1s en el resultado final que la precisi\u00f3n te\u00f3rica de un pesaje que abandonar\u00e1s.<\/div><\/details>\n    <details><summary>\u00bfHay que contar calor\u00edas todos los d\u00edas?<\/summary><div class=\"ans\">Es la media semanal la que pilota el resultado, no la perfecci\u00f3n diaria. Los d\u00edas cargados, una entrada r\u00e1pida de 10 segundos vale infinitamente m\u00e1s que un vac\u00edo de datos. Una comida aproximada no rompe nada; una semana sin ning\u00fan dato, s\u00ed, porque oculta la tendencia.<\/div><\/details>\n    <details><summary>\u00bfPor qu\u00e9 no pierdo peso si cuento mis calor\u00edas?<\/summary><div class=\"ans\">Dominan dos causas. Una, la infradeclaraci\u00f3n: hasta un 47&nbsp;% de diferencia medida entre calor\u00edas declaradas a mano y calor\u00edas realmente consumidas (Lichtman, 1992); el escaneo IA reduce ese sesgo midiendo el plato en lugar de tu memoria. Dos, un gasto sobreestimado: la mayor\u00eda de las apps calculan tu TDEE con f\u00f3rmulas por peso bruto que ignoran tu grasa corporal y la adaptaci\u00f3n metab\u00f3lica. Si las salidas est\u00e1n mal, el d\u00e9ficit mostrado no existe.<\/div><\/details>\n    <details><summary>\u00bfContar calor\u00edas vuelve obsesivo?<\/summary><div class=\"ans\">El objetivo es exactamente el contrario: reducir el seguimiento a unos segundos por comida para que no ocupe ning\u00fan espacio mental, donde la contabilidad manual, en cambio, puede volverse invasiva. Unos meses de tracking educan tu ojo en las porciones; despu\u00e9s puedes aligerar, incluso pasar a entrada r\u00e1pida. En caso de trastorno de la conducta alimentaria diagnosticado, habla con un profesional de la salud antes de registrar.<\/div><\/details>\n    <details><summary>\u00bfQu\u00e9 diferencia hay entre Lean y MyFitnessPal para contar calor\u00edas?<\/summary><div class=\"ans\">MyFitnessPal se basa en una base colaborativa gigante pero en una introducci\u00f3n esencialmente manual, y un TDEE estimado por peso bruto. Lean descompone cada plato por alimento mediante el escaneo IA, y sobre todo calcula tu gasto sobre tu grasa corporal real medida por BodyScan, con adaptaci\u00f3n metab\u00f3lica. Contar las entradas solo tiene sentido si las salidas son correctas. El detalle app por app est\u00e1 en nuestra <a class=\"inline\" href=\"https:\/\/lean-app.com\/es\/meilleures-applications-calories-2026\/\">comparativa de las mejores aplicaciones de calor\u00edas<\/a>.<\/div><\/details>\n  <\/div>\n<\/section>\n\n\n<section aria-labelledby=\"src\" class=\"sources\">\n  <div class=\"section-label\"><span class=\"bar\"><\/span><span class=\"num\">Fuentes<\/span><\/div>\n  <h3 id=\"src\" style=\"margin-top:0;color:var(--ink)\">Bibliograf\u00eda<\/h3>\n  <ol class=\"src-list\">\n    <li>Burke L.E., Wang J., Sevick M.A. (2011). Self-monitoring in weight loss: a systematic review of the literature. <em>J Am Diet Assoc<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/21185970\/\" target=\"_blank\" rel=\"noopener\">PubMed 21185970<\/a>.<\/li>\n    <li>Alexander E., Tseng E., Durkin N. et al. (2018). Factors associated with early dropout in an employer-based commercial weight-loss program. <em>Obes Sci Pract<\/em>. <a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC6298204\/\" target=\"_blank\" rel=\"noopener\">PMC6298204<\/a>.<\/li>\n    <li>Lichtman S.W. et al. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. <em>N Engl J Med<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/1454084\/\" target=\"_blank\" rel=\"noopener\">PubMed 1454084<\/a>.<\/li>\n    <li>Martin C.K. et al. (2009). A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method. <em>Br J Nutr<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/18616837\/\" target=\"_blank\" rel=\"noopener\">PubMed 18616837<\/a>.<\/li>\n    <li>USDA FoodData Central. Base de datos nutricional de referencia. <a href=\"https:\/\/fdc.nal.usda.gov\/\" target=\"_blank\" rel=\"noopener\">fdc.nal.usda.gov<\/a>.<\/li>\n    <li>Open Food Facts. Base colaborativa mundial de productos alimentarios envasados. <a href=\"https:\/\/world.openfoodfacts.org\/\" target=\"_blank\" rel=\"noopener\">world.openfoodfacts.org<\/a>.<\/li>\n    <li>Hall K.D. et al. (2011). Quantification of the effect of energy imbalance on bodyweight. <em>Lancet<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/21872751\/\" target=\"_blank\" rel=\"noopener\">PubMed 21872751<\/a>.<\/li>\n    <li>Mifflin M.D. et al. (1990). A new predictive equation for resting energy expenditure in healthy individuals. <em>Am J Clin Nutr<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/2305711\/\" target=\"_blank\" rel=\"noopener\">PubMed 2305711<\/a>.<\/li>\n    <li>Harris J.A., Benedict F.G. (1919). A Biometric Study of Basal Metabolism in Man. Carnegie Institution of Washington.<\/li>\n    <li>Ainsworth B.E. et al. (2011). Compendium of Physical Activities: a second update of codes and MET values. <em>Med Sci Sports Exerc<\/em>. <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/21681120\/\" target=\"_blank\" rel=\"noopener\">PubMed 21681120<\/a>.<\/li>\n  <\/ol>\n<\/section>\n\n<\/main>\n\n<footer>\n  <div class=\"wrap\">\n    <div class=\"row\">\n      <div>\n        <div class=\"kicker\">Lean &middot; lean-app.com<\/div>\n        <p>Gu\u00eda publicada el 13 de julio de 2025 por el equipo Lean. Actualizada el 11 de junio de 2026: revisi\u00f3n completa, nuevas capturas del escaneo IA y fuentes cient\u00edficas. 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