{"id":608,"date":"2021-04-20T12:52:17","date_gmt":"2021-04-20T11:52:17","guid":{"rendered":"https:\/\/igormatias.com\/?p=608"},"modified":"2026-02-02T13:11:02","modified_gmt":"2026-02-02T13:11:02","slug":"know-you-self-better-n-of-1-based-self-examination-using-wearables-and-self-reported-behaviors-for-an-improved-well-being","status":"publish","type":"post","link":"https:\/\/igormatias.com\/pt\/2021\/04\/20\/know-you-self-better-n-of-1-based-self-examination-using-wearables-and-self-reported-behaviors-for-an-improved-well-being\/","title":{"rendered":"\u201cKnow You (Self) Better\u201d: N-of-1 Based Self-Examination Using Wearables And Self-Reported Behaviors For An Improved Well-Being"},"content":{"rendered":"

Abstract: <\/span>N-of-1 randomized trials help better understand the effects of an intervention, e.g., behavioral changes in a specific person, allowing for the correlation and causation between, for example, duration of sleep and the level of physical exercise the following day.
By leveraging the data originating from affordable wearables like Fitbit and smartphones, it is now possible to view the individual\u2019s daily behaviors and vital signs in a minimally intrusive way. Collecting and combining this data with an N-of-1 approach makes it possible to understand better the interventions that benefit or detriment the individual\u2019s well-being on an hourly, daily, or monthly level.
We introduce an N-of-1 method leveraging wearable collected behavioral data from three participants for up to 4 years to assess the correlations between sleep duration, physical activity, walking performance, resting heart rate, self-reported stress levels, traveling, relationship status, and many more intervention variables.
The study also aims to exemplify how wearable and self-reported data can help understand the changes most relevant to a specific individual, providing an alternative to the understanding based on an \u201cin the lab\u201d experiment, average population assessments; so far failing to provide personal insights into one’s well-being.
Finally, in this study, we also reflect on the corroboration of personal beliefs driving individual behaviors. Furthermore, we discuss the N-of-1 approach in our upcoming population study, focusing on monitoring the decline of memory and cognitive performance in older adults.<\/p>\n

On Data Science Clinics, Universit\u00e9 de Gen\u00e8ve, 12 April 2021.<\/span><\/p>\n

https:\/\/datascience.unige.ch\/recherche\/data-science-clinics\/12-april-2021<\/a> (Recording available)<\/p>\n\n\n

<\/p>","protected":false},"excerpt":{"rendered":"

Abstract: N-of-1 randomized trials help better understand the effects of an intervention, e.g., behavioral changes in a specific person, allowing for the correlation and causation between, for example, duration of sleep and the level of physical exercise the following day.By leveraging the data originating from affordable wearables like Fitbit and smartphones, it is now possible […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[14],"tags":[],"class_list":["post-608","post","type-post","status-publish","format-standard","hentry","category-research-talks"],"gutentor_comment":0,"yoast_head":"\n\u201cKnow You (Self) Better\u201d: N-of-1 Based Self-Examination Using Wearables And Self-Reported Behaviors For An Improved Well-Being<\/title>\n<meta name=\"description\" content=\"Abstract: N-of-1 randomized trials 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