BACKGROUND: Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. OBJECTIVE: In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA). METHODS: Community-living elderly (n = 239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n = 15, 55-75 years), during 12-weeks period following THA. MAIN RESULTS: In leave-one-out-cross-validation (LOOCV), a strong correlation (ρ = 0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC = 0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7 ± 3.9 s versus 8.0 ± 1.8 s, p < 0.001), with no further change at 12-weeks (8.1 ± 3.9 s, p = 0.37). SIGNIFICANCE: Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.
BACKGROUND: Mobility impairment is common in older adults and negatively influences the quality of life. Mobility level may change rapidly following surgery or hospitalization in the elderly. The timed up and go (TUG) is a simple, frequently used clinical test for functional mobility; however, TUG requires supervision from a trained clinician, resulting in infrequent assessments. Additionally, assessment by TUG in clinic settings may not be completely representative of the individual's mobility in their home environment. OBJECTIVE: In this paper, we introduce a method to estimate TUG from activities detected in free-living, enabling continuous remote mobility monitoring without expert supervision. The method is used to monitor changes in mobility following total hip arthroplasty (THA). METHODS: Community-living elderly (n = 239, 65-91 years) performed a standardized TUG in a laboratory and wore a wearable pendant device that recorded accelerometer and barometric sensor data for at least three days. Activities of daily living (ADLs), including walks and sit-to-stand transitions, and their related mobility features were extracted and used to develop a regularized linear model for remote TUG test estimation. Changes in the remote TUG were evaluated in orthopaedic patients (n = 15, 55-75 years), during 12-weeks period following THA. MAIN RESULTS: In leave-one-out-cross-validation (LOOCV), a strong correlation (ρ = 0.70) was observed between the new remote TUG and standardized TUG times. Test-retest reliability of 3-days estimates was high (ICC = 0.94). Compared to week 2 post-THA, remote TUG was significantly improved at week 6 (11.7 ± 3.9 s versus 8.0 ± 1.8 s, p < 0.001), with no further change at 12-weeks (8.1 ± 3.9 s, p = 0.37). SIGNIFICANCE: Remote TUG can be estimated in older adults using 3-days of ADLs data recorded using a wearable pendant. Remote TUG has discriminatory potential for identifying frail elderly and may provide a convenient way to monitor changes in mobility in unsupervised settings.
Authors: Mariano Bernaldo de Quirós; E H Douma; Inge van den Akker-Scheek; Claudine J C Lamoth; Natasha M Maurits Journal: Sensors (Basel) Date: 2022-01-28 Impact factor: 3.576
Authors: D-Yin Lin; Brigid Brown; Craig Morrison; Nikolai S Fraser; Cheryl S L Chooi; Matthew G Cehic; David H McLeod; Michael D Henningsen; Nikolina Sladojevic; Hidde M Kroon; Ruurd L Jaarsma Journal: BMC Anesthesiol Date: 2022-08-06 Impact factor: 2.376
Authors: Juan Luis Leon-Llamas; Alvaro Murillo-Garcia; Santos Villafaina; Francisco Javier Domínguez-Muñoz; Jesús Morenas; Narcis Gusi Journal: Int J Environ Res Public Health Date: 2022-07-06 Impact factor: 4.614
Authors: Joseph T Patterson; Hao-Hua Wu; Christopher C Chung; Ilya Bendich; Jeffrey J Barry; Stefano A Bini Journal: Arthroplast Today Date: 2020-03-06
Authors: Sietse M Rispens; Lieke G E Cox; Andreas Ejupi; Kim Delbaere; Janneke Annegarn; Alberto G Bonomi Journal: Sensors (Basel) Date: 2021-03-06 Impact factor: 3.576