Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults
Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/10/4605 |
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| author | Khalid Abdul Jabbar Javad Sarvestan Rana Zia Ur Rehman Sue Lord Ngaire Kerse Ruth Teh Silvia Del Din |
| author_facet | Khalid Abdul Jabbar Javad Sarvestan Rana Zia Ur Rehman Sue Lord Ngaire Kerse Ruth Teh Silvia Del Din |
| author_sort | Khalid Abdul Jabbar |
| collection | DOAJ |
| description | Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm’s sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults. |
| first_indexed | 2024-03-11T03:21:33Z |
| format | Article |
| id | doaj.art-8e6dabe127f24d6993693018e86b556a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:21:33Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8e6dabe127f24d6993693018e86b556a2023-11-18T03:09:48ZengMDPI AGSensors1424-82202023-05-012310460510.3390/s23104605Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older AdultsKhalid Abdul Jabbar0Javad Sarvestan1Rana Zia Ur Rehman2Sue Lord3Ngaire Kerse4Ruth Teh5Silvia Del Din6School of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New ZealandTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UKTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UKSchool of Clinical Sciences, Auckland University of Technology, Auckland 1010, New ZealandSchool of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New ZealandSchool of Population Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New ZealandTranslational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UKAccurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm’s sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.https://www.mdpi.com/1424-8220/23/10/4605real-worldsedentary behaviourvalidationolder adultswearable devicedigital health |
| spellingShingle | Khalid Abdul Jabbar Javad Sarvestan Rana Zia Ur Rehman Sue Lord Ngaire Kerse Ruth Teh Silvia Del Din Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults Sensors real-world sedentary behaviour validation older adults wearable device digital health |
| title | Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults |
| title_full | Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults |
| title_fullStr | Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults |
| title_full_unstemmed | Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults |
| title_short | Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults |
| title_sort | validation of an algorithm for measurement of sedentary behaviour in community dwelling older adults |
| topic | real-world sedentary behaviour validation older adults wearable device digital health |
| url | https://www.mdpi.com/1424-8220/23/10/4605 |
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