Objectively measured arm use in daily life improves during the first 6 months poststroke: a longitudinal observational cohort study.

BACKGROUND: It is unclear how arm use in daily life changes after stroke since studies investigating the change in arm use poststroke are scarce. The aim of this study was to investigate the change in arm use during the first six months poststroke. Secondary aim was to compare arm use changes between arm recovery clusters. METHODS: Arm use was measured during week 3, 12, and 26 poststroke with accelerometers on the wrists and the nonaffected leg. Outcomes were the amount of affected and nonaffected arm use during sitting and standing per day and per sit/stand hour, and the daily ratio between arms. Arm function was measured with the Fugl-Meyer Upper Extremity Scale to identify recovery clusters (poor/moderate/excellent). Generalized estimating equations compared arm use outcomes between time points and between recovery clusters. RESULTS: Thirty-three stroke patients participated. Affected arm use per day increased between week 3 and 12 (30 %; p = 0.04) and it increased per sit/stand hour between week 3-12 (31 %; p < 0.001) and between week 3 and 26 (48 %; p = 0.02). Nonaffected arm use per day decreased between week 3 and 12 (13 %; p < 0.001) and between week 3 and 26 (22 %; p < 0.001) and it decreased per sit/stand hour between week 3 and 26 (18 %; p = 0.003). The daily ratio increased between week 3 and 12 (43 %; p < 0.001) and between week 3 and 26 (95 %; p < 0.001). Changes in arm use did not differ significantly between recovery clusters (p = 0.11-0.62). Affected arm use was higher in the excellent recovery cluster (p < 0.001). CONCLUSIONS: Affected arm use and the ratio between arms increase during the first 26 weeks poststroke especially in patients with excellent arm recovery.

Effect of different operationalizations of sedentary behavior in people with chronic stroke.

Purpose: Sedentary behavior is common in people with stroke and has devastating impact on their health. Quantifying it is important to provide people with stroke with adequate physical behavior recommendations. Sedentary behavior can be quantified in terms of posture (sitting) or intensity (low energy expenditure). We compared the effect of different operationalizations of sedentary behavior on sedentary behavior outcomes (total time; way of accumulation) in people with stroke.Methods: Sedentary behavior was analyzed in 44 people with chronic stroke with an activity monitor that measured both body postures and movement intensity. It was operationalized as: (1) combining postural and intensity data; (2) using only postural data; (3) using only intensity data. For each operationalization, we quantified a set of outcomes. Repeated measures ANOVA and Bland-Altman plots were used to compare the operationalizations.Results: All sedentary behavior outcomes differed significantly between all operationalizations (p < 0.01). Bland-Altman plots showed large limits of agreement for all outcomes, showing large individual differences between operationalizations.Conclusions: Although it was neither possible nor our aim to investigate the validity of the two-component definition of sedentary behavior, our study shows that the type of operationalization of sedentary behavior significantly influences sedentary behavior outcomes in people with stroke.Implications for RehabilitationReliable assessment of sedentary behavior after stroke is important in order to provide adequate physical behavior recommendations for people with stroke.Sedentary behavior can be operationalized in terms of body posture (sitting time) or in terms of movement intensity (time <1.5 MET) or as a combination of both criteria; this study reveals that the type of operationalization affects the different outcome measures used to quantify sedentary behavior.Comparing sedentary behavior outcomes requires caution and should only be done when sedentary behavior is operationalized in the same way.

The Accuracy of the Detection of Body Postures and Movements Using a Physical Activity Monitor in People after a Stroke.

BACKGROUND: In stroke rehabilitation not only are the levels of physical activity important, but body postures and movements performed during one’s daily-life are also important. This information is provided by a new one-sensor accelerometer that is commercially available, low-cost, and user-friendly. The present study examines the accuracy of this activity monitor (Activ8) in detecting several classes of body postures and movements in people after a stroke. METHODS: Twenty-five people after a stroke participated in an activity protocol with either basic activities or daily-life activities performed in a laboratory and/or at home. Participants wore an Activ8 on their less-affected thigh. The primary outcome was the difference in registered time for the merged class “upright position” (standing/walking/running) between the Activ8 and the video recording (the reference method). Secondary analyses focused on classes other than “upright position”. RESULTS: The Activ8 underestimated the merged class “upright position” by 3.8% (775 s). The secondary analyses showed an overestimation of “lying/sitting” (4.5% (569 s)) and of “cycling” (6.5% (206 s)). The differences were lowest for basic activities in the laboratory and highest for daily-life activities at home. CONCLUSIONS: The Activ8 is sufficiently accurate in detecting different classes of body postures and movements of people after a stroke during basic activities and daily-life activities in a laboratory and/or at home.

Development and validation of a clinically applicable arm use monitor for people after stroke.

OBJECTIVE: To develop and validate a clinically applicable and easy-to-use accelerometry-based device to measure arm use in people after stroke; the Activ8 arm use monitor (Activ8-AUM). DESIGN: Development and validation study. PATIENTS: A total of 25 people at different stages of rehabilitation after stroke were included in this study. METHODS: The Activ8-AUM consists of 3 single-sensor Activ8s: one on the unaffected thigh and one on each wrist. Arm use was calculated by combining movement intensity of the arms with data from body posture and movements on the leg sensor. Data were divided into 2 sets: one for determining situation-specific movement intensity thresholds for arm use, and the other to validate the Activ8-AUM using video recordings. RESULTS: Overall agreement between the Activ8-AUM and video recordings was 75%, sensitivity was 73% and specificity was 77%. Agreement between the different categories of arm use ranged from 42% to 93% for the affected arm and from 24% to 82% for the unaffected arm. CONCLUSION: By combining the movement intensity threshold with body posture and movements, good agreement was reached between the Activ8-AUM and video recordings. This result, together with the easy-to-use configuration, makes the Activ8-AUM a promising device to measure arm use in people after stroke.

Sedentary behavior: Different types of operationalization influence outcome measures.

INTRODUCTION: Sedentary behavior (SB) influences health status independently of physical activity. The formal definition of SB is: "any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting or reclining posture". However, measuring SB mostly does not include both the intensity and postural component. The aim of this study was to quantify the effect of type of operationalization of SB on total sedentary time and the pattern of SB. METHODS: 53 healthy subjects were measured 24h with a multi-sensor activity monitor that provides a valid one-second detection of body postures and movements and a calculated intensity measure. The SB outcome measures were: total sedentary time; number of sedentary bouts; mean bout length; fragmentation; and W-index. All outcomes were calculated for three types of operationalization of SB: 1) waking time in lying and sitting posture and below the sedentary intensity threshold (<0.016g comparable with Actigraph <150 counts, COMBI); 2) waking time in lying and sitting posture (POST); 3) waking time below the sedentary intensity threshold (<0.016g, INT). Outcome measures based on these three operationalizations were compared with repeated measures ANOVA. RESULTS: Total sedentary time was significantly different (p<0.001) between all three conditions: 505.8 (113.85)min (COMBI), 593.2 (112.09)min (POST), and 565.5 (108.54)min (INT). Significant differences were also found for other outcome measures. CONCLUSION: Our study shows that type of operationalization significantly affects SB outcome measures. Therefore, if SB is defined according to the formal definition, measurements must include both the intensity and postural component.

Screening for balance disorders in mildly affected multiple sclerosis patients.

Multiple sclerosis (MS) patients often complain about balance problems when Romberg's test and tandem gait are normal. The aim of the study was to determine if measures of trunk sway taken during a battery of stance and gait tasks could be used to detect subclinical balance disorders. We recorded trunk angular sway in the pitch and roll directions from 20 MS patients (EDSS 1.4 ± 0.5) and 20 age- and gender-matched healthy controls (HCs), during 12 stance and gait tasks. We filmed 22 subjects simultaneously. Two neurologists assessed the videos, deciding whether task performance was pathological. Sway measures were significantly different between patients and HCs in eight out of 12 balance tasks. The most significant differences between MS patients and HCs were pitch angle range standing on one leg with eyes open on a firm surface (mean 3.13° vs. 2.09°, p = 0.005), and on a foam support surface (mean 6.24° vs. 2.96°, p = 0.006), pitch velocity range walking 8 m with eyes closed (mean 75.5 vs. 50.2°/s, p < 0.001) and pitch velocity range walking 3 m on heels (mean 85.37 vs. 60.9°/s, p = 0.002). Multivariate analysis revealed a model with three tasks which detected balance disorders in 84% of the MS patients and 90% of the HCs correctly. The neurologists achieved accuracies of 30% for the MS patients and 82% for the HCs. Using trunk sway measures during stance and gait tasks is a sensitive screening method for balance problems in MS patients, and is more accurate than assessment by trained neurologists.