ORCID Profile
0000-0003-4260-7399
Current Organisations
Halmstad University
,
Bilecik Şeyh Edebali Üniversitesi
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 11-11-2020
DOI: 10.1097/J.PAIN.0000000000002137
Abstract: Our knowledge of the prevalence, impact, and outcomes of chronic pain in the general population is predominantly based on studies over relatively short periods of time. The aim of this study was to identify and describe trajectories of the chronic pain status over a period of 21 years. Self-reported population data (n = 1858) from 5 timepoints were analyzed. Pain was categorized by: no chronic pain (NCP), chronic regional pain (CRP), and chronic widespread pain (CWP). Latent class growth analysis was performed for identification of trajectories and logistic regression analysis for identification of predictors for pain prognosis. Five trajectories were identified: (1) persistent NCP (57%), (2) migrating from NCP to CRP or CWP (5%), (3) persistent CRP or migration between CRP and NCP (22%), (4) migration from CRP to CWP (10%), and (5) persistent CWP (6%). Age, sleeping problems, poor vitality, and physical function at baseline were associated with pain progression from NCP. Female gender, seeking care for pain, lack of social support, poor physical function, vitality, and mental health predicted poor pain prognosis among those with CRP. In conclusion, chronic pain was common in the population including 6% reporting persistent CWP, although the majority persistently reported NCP. Most people had stable pain status, but some had ongoing change in pain status over time including people who improved from chronic pain. It was possible to identify clinically relevant factors, characterizing trajectories of chronic pain development, that can be useful for identifying in iduals at risk and potential targets for intervention.
Publisher: MDPI AG
Date: 29-01-2021
DOI: 10.3390/S21030904
Abstract: Body postural allocation during daily life is important for health, and can be assessed with thigh-worn accelerometers. An algorithm based on sedentary bouts from the proprietary ActivePAL software can detect lying down from a single thigh-worn accelerometer using rotations of the thigh. However, it is not usable across brands of accelerometers. This algorithm has the potential to be refined. Aim: To refine and assess the validity of an algorithm to detect lying down from raw data of thigh-worn accelerometers. Axivity-AX3 accelerometers were placed on the thigh and upper back (reference) on adults in a development dataset (n = 50) and a validation dataset (n = 47) for 7 days. Sedentary time from the open Acti4-algorithm was used as input to the algorithm. In addition to the thigh-rotation criterion in the existing algorithm, two criteria based on standard deviation of acceleration and a time duration criterion of sedentary bouts were added. The mean difference (95% agreement-limits) between the total identified lying time/day, between the refined algorithm and the reference was +2.9 (−135,141) min in the development dataset and +6.5 (−145,159) min in the validation dataset. The refined algorithm can be used to estimate lying time in studies using different accelerometer brands.
Publisher: Springer Science and Business Media LLC
Date: 08-11-2019
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Katarina Aili.