Alison Keogh1,2, Niladri Sett1,3, Seamas Donnelly4, Ronan Mullan4, Diana Gheta5, Martina Maher-Donnelly4, Vittorio Illiano6, Francesc Calvo6, Jonas F Dorn6, Brian Mac Namee1,3, Brian Caulfield1,2. 1. Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland. 2. School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland. 3. School of Computer Science, University College Dublin, Dublin, Ireland. 4. School of Medicine, Trinity College Dublin, Dublin, Ireland. 5. Department of Rheumatology, Tallaght University Hospital, Dublin, Ireland. 6. Data and Digital, Novartis, Basel, Switzerland.
Abstract
BACKGROUND: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. OBJECTIVE: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). METHODS: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. RESULTS: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = -4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. CONCLUSION: This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.
BACKGROUND: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. OBJECTIVE: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). METHODS: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. RESULTS: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = -4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. CONCLUSION: This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.
Authors: Monique A M Gignac; Aileen M Davis; Gillian Hawker; James G Wright; Nizar Mahomed; Paul R Fortin; Elizabeth M Badley Journal: Arthritis Rheum Date: 2006-12-15
Authors: Alison Keogh; William Johnston; Mitchell Ashton; Niladri Sett; Ronan Mullan; Seamas Donnelly; Jonas F Dorn; Francesc Calvo; Brian Mac Namee; Brian Caulfield Journal: Digit Biomark Date: 2020-11-26
Authors: Julio Vega; Meng Li; Kwesi Aguillera; Nikunj Goel; Echhit Joshi; Kirtiraj Khandekar; Krina C Durica; Abhineeth R Kunta; Carissa A Low Journal: Front Digit Health Date: 2021-11-18