BACKGROUND: Smartphone and wearable-based activity data provide an opportunity to remotely monitor functional capacity in patients. In this study, we assessed the ability of a home-based 6-minute walk test (6MWT) as well as passively collected activity data to supplement or even replace the in-clinic 6MWTs in patients with cardiovascular disease. METHODS: We enrolled 110 participants who were scheduled for vascular or cardiac procedures. Each participant was supplied with an iPhone and an Apple Watch running the VascTrac research app and was followed for 6 months. Supervised 6MWTs were performed during clinic visits at scheduled intervals. Weekly at-home 6MWTs were performed via the VascTrac app. The app passively collected activity data such as daily step counts. Logistic regression with forward feature selection was used to assess at-home 6MWT and passive data as predictors for "frailty" as measured by the gold-standard supervised 6MWT. Frailty was defined as walking <300m on an in-clinic 6MWT. RESULTS: Under a supervised in-clinic setting, the smartphone and Apple Watch with the VascTrac app were able to accurately assess 'frailty' with sensitivity of 90% and specificity of 85%. Outside the clinic in an unsupervised setting, the home-based 6MWT is 83% sensitive and 60% specific in assessing "frailty." Passive data collected at home were nearly as accurate at predicting frailty on a clinic-based 6MWT as was a home-based 6MWT, with area under curve (AUC) of 0.643 and 0.704, respectively. CONCLUSIONS: In this longitudinal observational study, passive activity data acquired by an iPhone and Apple Watch were an accurate predictor of in-clinic 6MWT performance. This finding suggests that frailty and functional capacity could be monitored and evaluated remotely in patients with cardiovascular disease, enabling safer and higher resolution monitoring of patients.
BACKGROUND: Smartphone and wearable-based activity data provide an opportunity to remotely monitor functional capacity in patients. In this study, we assessed the ability of a home-based 6-minute walk test (6MWT) as well as passively collected activity data to supplement or even replace the in-clinic 6MWTs in patients with cardiovascular disease. METHODS: We enrolled 110 participants who were scheduled for vascular or cardiac procedures. Each participant was supplied with an iPhone and an Apple Watch running the VascTrac research app and was followed for 6 months. Supervised 6MWTs were performed during clinic visits at scheduled intervals. Weekly at-home 6MWTs were performed via the VascTrac app. The app passively collected activity data such as daily step counts. Logistic regression with forward feature selection was used to assess at-home 6MWT and passive data as predictors for "frailty" as measured by the gold-standard supervised 6MWT. Frailty was defined as walking <300m on an in-clinic 6MWT. RESULTS: Under a supervised in-clinic setting, the smartphone and Apple Watch with the VascTrac app were able to accurately assess 'frailty' with sensitivity of 90% and specificity of 85%. Outside the clinic in an unsupervised setting, the home-based 6MWT is 83% sensitive and 60% specific in assessing "frailty." Passive data collected at home were nearly as accurate at predicting frailty on a clinic-based 6MWT as was a home-based 6MWT, with area under curve (AUC) of 0.643 and 0.704, respectively. CONCLUSIONS: In this longitudinal observational study, passive activity data acquired by an iPhone and Apple Watch were an accurate predictor of in-clinic 6MWT performance. This finding suggests that frailty and functional capacity could be monitored and evaluated remotely in patients with cardiovascular disease, enabling safer and higher resolution monitoring of patients.
Authors: Evelien E S van Riet; Arno W Hoes; Kim P Wagenaar; Alexander Limburg; Marcel A J Landman; Frans H Rutten Journal: Eur J Heart Fail Date: 2016-01-04 Impact factor: 15.534
Authors: Salim S Virani; Alvaro Alonso; Emelia J Benjamin; Marcio S Bittencourt; Clifton W Callaway; April P Carson; Alanna M Chamberlain; Alexander R Chang; Susan Cheng; Francesca N Delling; Luc Djousse; Mitchell S V Elkind; Jane F Ferguson; Myriam Fornage; Sadiya S Khan; Brett M Kissela; Kristen L Knutson; Tak W Kwan; Daniel T Lackland; Tené T Lewis; Judith H Lichtman; Chris T Longenecker; Matthew Shane Loop; Pamela L Lutsey; Seth S Martin; Kunihiro Matsushita; Andrew E Moran; Michael E Mussolino; Amanda Marma Perak; Wayne D Rosamond; Gregory A Roth; Uchechukwu K A Sampson; Gary M Satou; Emily B Schroeder; Svati H Shah; Christina M Shay; Nicole L Spartano; Andrew Stokes; David L Tirschwell; Lisa B VanWagner; Connie W Tsao Journal: Circulation Date: 2020-01-29 Impact factor: 29.690
Authors: V Bittner; D H Weiner; S Yusuf; W J Rogers; K M McIntyre; S I Bangdiwala; M W Kronenberg; J B Kostis; R M Kohn; M Guillotte Journal: JAMA Date: 1993-10-13 Impact factor: 56.272
Authors: Mary M McDermott; Bonnie Spring; Jeffrey S Berger; Diane Treat-Jacobson; Michael S Conte; Mark A Creager; Michael H Criqui; Luigi Ferrucci; Heather L Gornik; Jack M Guralnik; Elizabeth A Hahn; Peter Henke; Melina R Kibbe; Debra Kohlman-Trighoff; Lingyu Li; Donald Lloyd-Jones; Walter McCarthy; Tamar S Polonsky; Christopher Skelly; Lu Tian; Lihui Zhao; Dongxue Zhang; W Jack Rejeski Journal: JAMA Date: 2018-04-24 Impact factor: 56.272
Authors: Kayleigh Ruberto; Hossein Ehsani; Saman Parvaneh; Jane Mohler; Mindy Fain; Nancy K Sweitzer; Nima Toosizadeh Journal: PLoS One Date: 2022-02-16 Impact factor: 3.240