OBJECTIVE: To investigate the level of agreement of the behavioural mapping method with an accelerometer to measure physical activity of hospitalized patients. DESIGN: A prospective single-centre observational study. SETTING: A university medical centre in the Netherlands. SUBJECTS: Patients admitted to the hospital. MAIN MEASURES: Physical activity of participants was measured for one day from 9 AM to 4 PM with the behavioural mapping method and an accelerometer simultaneously. The level of agreement between the percentages spent lying, sitting and moving from both measures was evaluated using the Bland-Altman method and by calculating Intraclass Correlation Coefficients. RESULTS: In total, 30 patients were included. Mean (±SD) age was 63.0 (16.8) years and the majority of patients were men (n = 18). The mean percentage of time (SD) spent lying was 47.2 (23.3) and 49.7 (29.8); sitting 42.6 (20.5) and 40.0 (26.2); and active 10.2 (6.1) and 10.3 (8.3) according to the accelerometer and observations, respectively. The Intraclass Correlation Coefficient and mean difference (SD) between the two measures were 0.852 and -2.56 (19.33) for lying; 0.836 and 2.60 (17.72) for sitting; and 0.782 and -0.065 (6.23) for moving. The mean difference between the two measures is small (⩽2.6%) for all three physical activity levels. On patient level, the variation between both measures is large with differences above and below the mean of ⩾20% being common. CONCLUSION: The overall level of agreement between the behavioural mapping method and an accelerometer to identify the physical activity levels 'lying', 'sitting' and 'moving' of hospitalized patients is reasonable.
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Uit de publicatie: "Dit artikel beschrijft het ontwikkelproces van een telemetriesysteem om de loopactiviteiten van mensen na een beroerte betrouwbaar te meten en hierover feedback te geven aan de patiënt en de fysiotherapeut op afstand. Het FESTA (FEedback to STimulate Activity)-systeem bestaat uit een accelerometer en een intelligent docking station. De patiënt moet overdag de accelerometer op de onderrug dragen en ’s avonds in het docking station plaatsen. Het docking station berekent uit de meetgegevens een aantal loopparameters en vergelijkt deze met het door de fysiotherapeut gestelde doel. De informatie wordt per e-mail naar de fysiotherapeut gestuurd en de patiënt ontvangt motiverende feedback op een display. De eerste reacties van de gebruikers op het prototype zijn positief, ook al valt er nog wel wat te verbeteren."
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Purpose: Elucidating the complex interactions between physical activity (PA), a multidimensional concept, and physical capacity (PC) may reveal ways to improve rehabilitation interventions. This cross-sectional study aimed to explore which PA dimensions are related to PC in people after minor stroke. Materials and methods: Community dwelling individuals >6 months after minor stroke were evaluated with a 10-Meter-Walking-Test (10MWT), Timed-Up & Go, and the Mini Balance Evaluation System Test. The following PA outcomes were measured with an Activ8 accelerometer: counts per minute during walking (CPMwalking; a measure of intensity), number of active bouts (frequency), mean length of active bouts (distribution), and percentage of waking hours in upright positions (duration). Multivariable linear regression models, adjusted for age, sex and BMI, were used to assess the relationships between PC and PA outcomes. Results: Sixty-nine participants [62.2 ± 9.8 years, 61% male, 20 months post onset (IQR 13.0–53.5)] were included in the analysis. CPMwalking was significantly associated to PC in the 10MWT (std. ß ¼ 0.409, p ¼ 0.002), whereas other associations between PA and PC were not significant. Conclusions: The PA dimension intensity of walking is significantly associated with PC, and appears to be an important tool for future interventions in rehabilitation after minor stroke
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BACKGROUND: Hospital stays are associated with high levels of sedentary behavior and physical inactivity. To objectively investigate physical behavior of hospitalized patients, these is a need for valid measurement instruments. The aim of this study was to assess the criterion validity of three accelerometers to measure lying, sitting, standing and walking. METHODS: This cross-sectional study was performed in a university hospital. Participants carried out several mobility tasks according to a structured protocol while wearing three accelerometers (ActiGraph GT9X Link, Activ8 Professional and Dynaport MoveMonitor). The participants were guided through the protocol by a test leader and were recorded on video to serve as reference. Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were determined for the categories lying, sitting, standing and walking. RESULTS: In total 12 subjects were included with a mean age of 49.5 (SD 21.5) years and a mean body mass index of 23.8 kg/m2 (SD 2.4). The ActiGraph GT9X Link showed an excellent sensitivity (90%) and PPV (98%) for walking, but a poor sensitivity for sitting and standing (57% and 53%), and a poor PPV (43%) for sitting. The Activ8 Professional showed an excellent sensitivity for sitting and walking (95% and 93%), excellent PPV (98%) for walking, but no sensitivity (0%) and PPV (0%) for lying. The Dynaport MoveMonitor showed an excellent sensitivity for sitting (94%), excellent PPV for lying and walking (100% and 99%), but a poor sensitivity (13%) and PPV (19%) for standing. CONCLUSIONS: The validity outcomes for the categories lying, sitting, standing and walking vary between the investigated accelerometers. All three accelerometers scored good to excellent in identifying walking. None of the accelerometers were able to identify all categories validly.
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Purpose: The primary aim of this study was to investigate the concurrent validity of the PAM AM400 accelerometer for measuring physical activity in usual care in hospitalized patients by comparing it with the ActiGraph wGT3X-BT accelerometer. Materials and methods: This was a prospective single centre observational study performed at the University Medical Centre Utrecht in The Netherlands. Patients admitted to different clinical wards were included. Intraclass Correlation Coefficients (ICCs) were computed using a two-way mixed model with random subjects. Additionally, Bland-Altman plots were made to visualize the level of agreement of the PAM with the ActiGraph. To test for proportional bias, a regression analysis was performed. Results: In total 17 patients from different clinical wards were included in the analyses. The level of agreement between the PAM and ActiGraph was found strong with an ICC of 0.955. The Bland-Altman analyses showed a mean difference of 1.12min between the two accelerometers and no proportional bias (p¼0.511). Conclusions: The PAM is a suitable movement sensor to validly measure the active minutes of hospitalized patients. Implementation of this device in daily care might be helpful to change the immobility culture in hospitals.
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This paper explores a method for deducing the affective state of runners using his/her movements. The movements are measured on the arm using a smartphone’s built-in accelerometer. Multiple features are derived from the measured data. We studied which features are most predictive for the affective state by looking at the correlations between the features and the reported affect. We found that changes in runners’ movement can be used to predict change in affective state.
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The aim of this study is to examine the inter-device reliability of an activity tracker on three different levels of aggregation: minute, hour and day.
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Objective: To evaluate the preliminary effectiveness of a goal-directed movement intervention using a movement sensor on physical activity of hospitalized patients. Design: Prospective, pre-post study. Setting: A university medical center. Participants: Patients admitted to the pulmonology and nephrology/gastro-enterology wards. Intervention: The movement intervention consisted of (1) self-monitoring of patients' physical activity, (2) setting daily movement goals and (3) posters with exercises and walking routes. Physical activity was measured with a movement sensor (PAM AM400) which measures active minutes per day. Main measures: Primary outcome was the mean difference in active minutes per day pre- and post-implementation. Secondary outcomes were length of stay, discharge destination, immobility-related complications, physical functioning, perceived difficulty to move, 30-day readmission, 30-day mortality and the adoption of the intervention. Results: A total of 61 patients was included pre-implementation, and a total of 56 patients was included post-implementation. Pre-implementation, patients were active 38 ± 21 minutes (mean ± SD) per day, and post-implementation 50 ± 31 minutes per day (Δ12, P = 0.031). Perceived difficulty to move decreased from 3.4 to 1.7 (0-10) (Δ1.7, P = 0.008). No significant differences were found in other secondary outcomes. Conclusions: The goal-directed movement intervention seems to increase physical activity levels during hospitalization. Therefore, this intervention might be useful for other hospitals to stimulate inpatient physical activity.
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Children’s motor competence (MC) has declined in the past decades, while sedentary behavior (SB) has increased. This study examined the association between MC and physical activity (PA) levels among primary schoolchildren. Demographics, body height and weight, MC (Athletic Skills Track), and PA levels (ActiGraph, GT3X+) were assessed among 595 children (291 boys, mean age = 9.1 years, SD = 1.1). MC was standardized into five categories: from very low to very high. PA levels were classified into SB, light PA (LPA), and moderate-to-vigorous PA (MVPA). Mixed-model analyses were conducted with PA levels as dependent variables and MC as the independent variable, while adjusting for age, gender, and body mass index (BMI) z-score on the individual level. A negative association between MC and SB and a positive association between MC and MVPA were found. The strength of both associations increased as children expressed lower or higher levels of MC. MC is an important correlate of both SB and MVPA, particularly for children with very high or low MC. Developing and improving children’s MC may contribute to spending less time in SB and more time in MVPA, particularly for high-risk groups, i.e., children with low MC. Moreover, addressing MC development and PA promotion simultaneously might create positive feedback loops for both children’s MC and PA levels.
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Background: Multiple sclerosis often leads to fatigue and changes in physical behavior (PB). Changes in PB are often assumed as a consequence of fatigue, but effects of interventions that aim to reduce fatigue by improving PB are not sufficient. Since the heterogeneous nature of MS related symptoms, levels of PB of fatigued patients at the start of interventions might vary substantially. Better understanding of the variability by identification of PB subtypes in fatigued patients may help to develop more effective personalized rehabilitation programs in the future. This study aimed to identify PB subtypes in fatigued patients with multiple sclerosis based on multidimensional PB outcome measures. Methods: Baseline accelerometer (Actigraph) data, demographics and clinical characteristics of the TREFAMS-ACE participants (n = 212) were used for secondary analysis. All patients were ambulatory and diagnosed with severe fatigue based on a score of ≥35 on the fatigue subscale of the Checklist Individual Strength (CIS20r). Fifteen PB measures were used derived from 7 day measurements with an accelerometer. Principal component analysis was performed to define key outcome measures for PB and two-step cluster analysis was used to identify PB types. Results: Analysis revealed five key outcome measures: percentage sedentary behavior, total time in prolonged moderate-to-vigorous physical activity, number of sedentary bouts, and two types of change scores between day parts (morning, afternoon and evening). Based on these outcomes three valid PB clusters were derived. Conclusions: Patients with severe MS-related fatigue show three distinct and homogeneous PB subtypes. These PB subtypes, based on a unique set of PB outcome measures, may offer an opportunity to design more individually-tailored interventions in rehabilitation.
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