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1.
BMJ Open ; 9(2): e026401, 2019 03 01.
Article in English | MEDLINE | ID: mdl-30826800

ABSTRACT

OBJECTIVES: It remains unclear if geriatric patients with different delirium motor subtypes express different levels of motor activity. Thus, we used two accelerometer-based devices to simultaneously measure upright activity and wrist activity across delirium motor subtypes in geriatric patients. DESIGN: Cross-sectional study. SETTINGS: Geriatric ward in a university hospital in Norway. PARTICIPANTS: Sixty acutely admitted patients, ≥75 years, with DSM-5-delirium. OUTCOME MEASURES: Upright activity measured as upright time (minutes) and sit-to-stand transitions (numbers), total wrist activity (counts) and wrist activity in a sedentary position (WAS, per cent of the sedentary time) during 24 hours ongoing Delirium Motor Subtype Scalesubtyped delirium. RESULTS: Mean age was 86.7 years. 15 had hyperactive, 20 hypoactive, 17 mixed and 8 had no-subtype delirium. We found more upright time in the no-subtype group than in the hypoactive group (119.3 vs 37.8 min, p=0.042), but no differences between the hyperactive, the hypoactive and the mixed groups (79.1 vs 37.8 vs 50.1 min, all p>0.28). The no-subtype group had a higher number of transitions than the hypoactive (54.3 vs 17.4, p=0.005) and the mixed groups (54.3 vs 17.5, p=0.013). The hyperactive group had more total wrist activity than the hypoactive group (1.238×104 vs 586×104 counts, p=0.009). The hyperactive and the mixed groups had more WAS than the hypoactive group (20% vs 11%, p=0.032 and 19% vs 11%, p=0.049). CONCLUSIONS: Geriatric patients with delirium demonstrated a low level of upright activity, with no differences between the hyperactive, hypoactive and mixed groups, possibly due to poor gait function. The hyperactive and mixed groups had more WAS than the hypoactive group, indicating true differences in motor activity across delirium motor subtypes, also in geriatric patients. Wrist activity appears more suitable than an upright activity for both diagnostic purposes and activity monitoring in geriatric delirium.


Subject(s)
Delirium/classification , Delirium/diagnosis , Monitoring, Ambulatory/instrumentation , Psychomotor Agitation/classification , Psychomotor Agitation/diagnosis , Wearable Electronic Devices , Accelerometry/instrumentation , Aged , Aged, 80 and over , Cross-Sectional Studies , Female , Geriatric Assessment , Hospitalization , Hospitals, University , Humans , Male , Monitoring, Ambulatory/methods , Norway , Sedentary Behavior , Telemedicine/methods , Transducers
2.
J Sci Med Sport ; 22(5): 557-561, 2019 May.
Article in English | MEDLINE | ID: mdl-30509863

ABSTRACT

OBJECTIVES: The development of a reliable method for the identification of sedentary, light and moderate physical activities in older adults. The method consists of a validated set of definitions for the identification of the initiation and termination of physical activities performed by older adult participants, video recorded during free-living and a laboratory setting. DESIGN: Inter-rater reliability assessment in a fully crossed design. METHODS: An iterative consensus process was used to define the initiation and termination of common activities of daily living. These definitions were then tested using videos recorded in two scenarios (1) by 9 raters who annotated a video recording, of a free-living protocol in a home environment, recorded in a first person view, using a body-worn camera and (2) by 7 raters who annotated a video recording, of older adults performing a semi-structured protocol in a living-lab environment, recorded in a third person view, using wall mounted cameras. RESULTS: Inter-rater reliability was excellent for all items, with Krippendorff's alpha and Fleiss' kappa all above 0.84 and a percentage of agreement above 88%. All ICC(C,1) inter-rater values for the activity quantity and duration were all above 0.9. CONCLUSIONS: This set of physical activity initiation and termination definitions offers independent researchers a gold standard method to allow for the consistent annotation of high-frequency video footage (25fps), in both a free-living and laboratory setting. When synchronised with body-worn or ambient sensors, this annotation will allow for the development and validation of physical activity classification systems to a higher resolution than before.


Subject(s)
Activities of Daily Living , Exercise , Video Recording , Aged , Aged, 80 and over , Female , Humans , Male , Models, Theoretical , Observer Variation , Reproducibility of Results
3.
Sensors (Basel) ; 17(3)2017 Mar 10.
Article in English | MEDLINE | ID: mdl-28287449

ABSTRACT

Physical activity monitoring algorithms are often developed using conditions that do not represent real-life activities, not developed using the target population, or not labelled to a high enough resolution to capture the true detail of human movement. We have designed a semi-structured supervised laboratory-based activity protocol and an unsupervised free-living activity protocol and recorded 20 older adults performing both protocols while wearing up to 12 body-worn sensors. Subjects' movements were recorded using synchronised cameras (≥25 fps), both deployed in a laboratory environment to capture the in-lab portion of the protocol and a body-worn camera for out-of-lab activities. Video labelling of the subjects' movements was performed by five raters using 11 different category labels. The overall level of agreement was high (percentage of agreement >90.05%, and Cohen's Kappa, corrected kappa, Krippendorff's alpha and Fleiss' kappa >0.86). A total of 43.92 h of activities were recorded, including 9.52 h of in-lab and 34.41 h of out-of-lab activities. A total of 88.37% and 152.01% of planned transitions were recorded during the in-lab and out-of-lab scenarios, respectively. This study has produced the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate (≥25 fps) video labelled data recorded in a free-living environment from older adults living independently. This dataset is suitable for validation of existing activity classification systems and development of new activity classification algorithms.


Subject(s)
Exercise , Aged , Algorithms , Humans , Movement , Technology
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