{"id":31227,"date":"2022-08-24T09:11:43","date_gmt":"2022-08-24T14:11:43","guid":{"rendered":"https:\/\/saluddigital.com\/?p=31227"},"modified":"2025-10-20T11:16:02","modified_gmt":"2025-10-20T17:16:02","slug":"prueba-basada-en-telefonos-moviles-capaz-de-detectar-riesgo-de-caida-en-pacientes-con-amputaciones","status":"publish","type":"post","link":"https:\/\/saluddigital.com\/en\/noticias\/prueba-basada-en-telefonos-moviles-capaz-de-detectar-riesgo-de-caida-en-pacientes-con-amputaciones\/","title":{"rendered":"Mobile phone-based test capable of detecting fall risk in amputee patients"},"content":{"rendered":"
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A study published in PLOS Digital Health explored a smartphone-based test and a six-minute walk to classify fall risk in people with lower-limb amputees.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Fall prediction models are a very useful tool to intervene early and prevent major accidents. However, these types of models often overlook people living with a lower extremity amputation. The study recently published in PLOS Digital Health details how steps and footfalls with automated detection in a six-minute walk are key to the calculation of the characteristics of the steps of people with lower amputations. In this way, the researchers were able to classify the risk of falls in these patients.<\/p>

That is to say, thanks to the automated detection of footsteps through a smartphone connected to the body in a six-minute walk (6MWT, in English), it is possible to know characteristics based on steps such as the risk of falling in patients with limb amputations. lower.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The study included the participation of 80 people with lower extremity amputations, 27 with falls and 53 without falls. In addition, during the 6MWT, a smartphone placed in the posterior pelvis was used, which managed to collect information through the Walk Test mobile application developed by the Ottawa Hospital Rehabilitation Center (TOHRC).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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The study inclusion criteria were patients with: a below-the-knee or above-knee amputation; ability to walk with a cane or a pair of suitcases without assistance; at least six months after amputation; functional prosthesis; and no injuries to the stump.<\/p>

The mobile phone automatically detected footfalls using a long-term memory approach, and the characteristics of the steps were calculated using manual or automated labels. \u201cManually labeled footfalls correctly classified fall risk for 64 of 80 participants (80 % accuracy, 55.6 % sensitivity, 92.5 % specificity). Automated foot taps correctly classified 58 of 80 participants (precision 72.5 %, sensitivity 55.6 %, specificity 81.1 %)," the study details.<\/p>

The two approaches were ranked equivalently but the foot strikes had six more false positives. That is to say, the research demonstrated that automated foot strikes on a 6MWT are useful for calculation of step-based characteristics and for classification of fall risk.<\/p>

Learn more about this study at the following link:<\/p>

https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000088<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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PLOS <\/strong><\/p>

https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000088<\/a><\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Estudio publicado en PLOS Digital Health explor\u00f3 una prueba basada en tel\u00e9fonos inteligentes y una caminata de seis minutos para clasificar el riesgo de ca\u00edda en personas con amputaciones de miembros inferiores. Los modelos de predicci\u00f3n de ca\u00eddas son una herramienta muy \u00fatil para intervenir tempranamente y evitar accidentes mayores. No obstante, este tipo de modelos suele pasar por alto a personas que viven con una amputaci\u00f3n en extremidades inferiores. El estudio publicado recientemente en PLOS Digital Health, detalla c\u00f3mo los pasos y pisadas con detecci\u00f3n automatizada en una caminata de seis minutos, son clave para el c\u00e1lculo de caracter\u00edsticas de los pasos de personas con amputaciones inferiores. De esta forma los investigadores lograron clasificar el riesgo de ca\u00eddas en estos pacientes. Es decir, gracias a la detecci\u00f3n automatizada de pisadas mediante un tel\u00e9fono inteligente conectado al cuerpo en una caminata de seis minutos (6MWT, en ingl\u00e9s), es posible conocer caracter\u00edsticas basados en pasos como lo es el riesgo de ca\u00edda en pacientes con amputaciones de extremidades inferiores. El estudio incluy\u00f3 la participaci\u00f3n de 80 personas con amputaciones en extremidades inferiores, 27 con ca\u00eddas y 53 sin ca\u00eddas. Adem\u00e1s, durante la 6MWT se utiliz\u00f3 un tel\u00e9fono inteligente colocado en la pelvis posterior, el cual logr\u00f3 recopilar informaci\u00f3n por medio de la aplicaci\u00f3n m\u00f3vil Walk Test desarrollada por el Centro de Rehabilitaci\u00f3n del Hospital de Ottawa (TOHRC). Los criterios de inclusi\u00f3n del estudio fueron pacientes con: una amputaci\u00f3n por debajo de la rodilla o superior: capacidad para caminar con bast\u00f3n o un par de maletas sin ayuda; seis meses despu\u00e9s de la amputaci\u00f3n como m\u00ednimo; pr\u00f3tesis funcional; y sin heridas en el mu\u00f1\u00f3n. El tel\u00e9fono m\u00f3vil detect\u00f3 autom\u00e1ticamente las pisadas gracias al uso de un enfoque de memoria a largo plazo, y las caracter\u00edsticas de los pasos se calcularon gracias a etiquetas manuales o automatizadas. \u201cLas pisadas etiquetadas manualmente clasificaron correctamente el riesgo de ca\u00eddas para 64 de 80 participantes (80 % de precisi\u00f3n, 55,6 % de sensibilidad, 92,5 % de especificidad). Los golpes de pie automatizados clasificaron correctamente a 58 de 80 participantes (precisi\u00f3n 72,5 %, sensibilidad 55,6 %, especificidad 81,1 %)\u201d, detalla el estudio. Los dos enfoques tuvieron una clasificaci\u00f3n equivalente pero los golpes de pie tuvieron seis falsos positivos m\u00e1s. Es decir que la investigaci\u00f3n demostr\u00f3 que los golpes de pie automatizados en una 6MWT son \u00fatiles para el c\u00e1lculo de caracter\u00edsticas basadas en pasos y para la clasificaci\u00f3n del riesgo de ca\u00edda. Conoce m\u00e1s sobre este estudio en el siguiente enlace: https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000088 BIBLIOGRAF\u00cdA PLOS https:\/\/journals.plos.org\/digitalhealth\/article?id=10.1371\/journal.pdig.0000088<\/p>","protected":false},"author":1,"featured_media":31229,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3395,3401,160],"tags":[145],"class_list":["post-31227","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-apps-moviles-e-internet-de-las-cosas","category-iotapps","category-noticias","tag-noticias"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/31227","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/comments?post=31227"}],"version-history":[{"count":0,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/posts\/31227\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media\/31229"}],"wp:attachment":[{"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/media?parent=31227"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/categories?post=31227"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saluddigital.com\/en\/wp-json\/wp\/v2\/tags?post=31227"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}