Six-Minute Walk Test in Patients With Down Syndrome:Validity and Reproducibility

Contains fulltext : 81543.pdf (publisher's version ) (Closed access)OBJECTIVES: To examine the validity of the six-minute walk test (6MWT) as a tool to evaluate functional exercise performance in patients with Down syndrome (DS). DESIGN: Comparison of the six-minute walk distance (6MWD) in 2 distinct groups of DS patients: with and without severe cardiac disease. To test reproducibility, a group of patients with DS performed the 6MWT twice. SETTING: Tertiary referral centers for patients with congenital heart defects and outpatient clinics for people with intellectual disabilities. PARTICIPANTS: Adult patients with DS with (n=29) and without (n=52) severe cardiac disease categorized by cardiac echocardiography. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Distance walked on the 6MWT. RESULTS: The mean 6MWD in the group with severe cardiac disease was 289+/-104 m and in the group without severe cardiac disease 280+/-104 m (P=.70). Older age, female sex, and severe level of intellectual disability were all found to be independently and significantly correlated with a lower 6MWD (r=.67, P<.001). The paired 6MWD was not significantly different (310+/-88 m vs 317+/-85 m; P=.40) in patients who performed the 6MWT twice. The coefficient of variation was 11%. CONCLUSIONS: The 6MWD between the 2 groups was not significantly different. However, the walking distance inversely correlated with the level of intellectual disability. Therefore, the 6MWT is not a valid test to examine cardiac restriction in adult patients with DS

Smartphone App with an Accelerometer Enhances Patients’ Physical Activity Following Elective Orthopedic Surgery: A Pilot Study

Low physical activity (PA) levels are common in hospitalized patients. Digital health tools could be valuable in preventing the negative effects of inactivity. We therefore developed Hospital Fit; which is a smartphone application with an accelerometer, designed for hospitalized patients. It enables objective activity monitoring and provides patients with insights into their recovery progress and offers a tailored exercise program. The aim of this study was to investigate the potential of Hospital Fit to enhance PA levels and functional recovery following orthopedic surgery. PA was measured with an accelerometer postoperatively until discharge. The control group received standard physiotherapy, while the intervention group used Hospital Fit in addition to physiotherapy. The time spent active and functional recovery (modified Iowa Level of Assistance Scale) on postoperative day one (POD1) were measured. Ninety-seven patients undergoing total knee or hip arthroplasty were recruited. Hospital Fit use, corrected for age, resulted in patients standing and walking on POD1 for an average increase of 28.43 min (95% confidence interval (CI): 5.55&ndash;51.32). The odds of achieving functional recovery on POD1, corrected for the American Society of Anesthesiologists classification, were 3.08 times higher (95% CI: 1.14&ndash;8.31) with Hospital Fit use. A smartphone app combined with an accelerometer demonstrates the potential to enhance patients&rsquo; PA levels and functional recovery during hospitalization

How to encourage patients to increase physical activity during their hospital stay

Patients are insufficiently physically active during their hospital stay. Therefore, it is important to develop, evaluate, and implement interventions that encourage patients to be physically active as much as possible. Hastings et al. studied the effect of a supervised walking program called STRIDE. This program appeared effective in terms of reducing discharges to a nursing home, however, the implementation had an exceptionally low reach. In this commentary article, we highlight multifaceted interventions that have an impact on various barriers and facilitators related to physical activity of patients during their hospital stay. We present the Dutch Moving Hospitals ('Beweegziekenhuizen') initiative and highlight three interventions: Ban Bedcentricity ('Beteruit bed'), Hospital Fit and Better by Moving ('BeterBewegen'). Now is the time for interprofessional collaboration to develop, evaluate, and implement interventions that encourage patients to be as physically active as possible during their hospital stay

Optimization and Validation of a Classification Algorithm for Assessment of Physical Activity in Hospitalized Patients

Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm’s performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (&gt;80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients

Barriers and enablers to physical activity behaviour in older adults during hospital stay:a qualitative study guided by the theoretical domains framework

BACKGROUND: Older adults admitted with an acute medical illness spent little time active during hospitalisation and this has been associated with negative health outcomes. Understanding which barriers and enablers influence the physical activity behaviour of hospitalised older adults is a first step towards identifying potentially modifiable factors and developing, evaluating and implementing targeted interventions aimed at increasing their physical activity behaviour. Using a theoretical framework has been found to be more successful in changing behaviour than using a non-theory driven approach. This study aimed to explore barriers and enablers to physical activity behaviour in older adults admitted to a hospital with an acute medical illness, as perceived by patients and healthcare professionals, and to categorise them using the Theoretical Domains Framework (TDF). METHODS: A qualitative study was conducted at a combined university and regional hospital in the Netherlands between January 2019 and February 2020. Older adults (≥70 years) admitted with an acute medical illness, and healthcare professionals (nurses, physicians, physiotherapists) were recruited using purposive sampling. Semi-structured interviews were audiotaped, transcribed and analysed using directed qualitative content analysis. Barriers and enablers to physical activity behaviour during hospitalisation were identified and coded using the TDF. RESULTS: Meaning saturation was determined after interviews with 12 patients and 16 healthcare professionals. A large number of barriers and enablers were identified and each categorised to 11 of the 14 domains of the TDF. The ‘Environmental Context and Resources’ domain in particular yielded many examples, and revealed that the hospital environment exerts an inactivating influence on patients. CONCLUSIONS: The large number of identified barriers and enablers highlights the complexity of influencing older adults’ physical activity behaviour during hospitalisation. This overview of barriers and enablers to physical activity behaviour in older adults admitted to a hospital with an acute medical illness represents an initial step towards developing, evaluating and implementing theory-informed behaviour change interventions to improve hospitalised older adults’ physical activity levels. It can assist clinicians and researchers in selecting modifiable factors that can be targeted in future interventions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-02887-x

Development and internal validation of a prediction model to identify older adults at risk of low physical activity levels during hospitalisation:a prospective cohort study

BACKGROUND: Inactive behaviour is common in older adults during hospitalisation and associated with poor health outcomes. If patients at high risk of spending little time standing/walking could be identified early after admission, they could be given interventions aimed at increasing their time spent standing/walking. This study aims to identify older adults at high risk of low physical activity (PA) levels during hospitalisation. METHODS: Prospective cohort study of 165 older adults (≥ 70 years) admitted to the department of Internal Medicine of Maastricht University Medical Centre for acute medical illness. Two prediction models were developed to predict the probability of low PA levels during hospitalisation. Time spent standing/walking per day was measured with an accelerometer until discharge (≤ 12 days). The average time standing/walking per day between inclusion and discharge was dichotomized into low/high PA levels by dividing the cohort at the median (50.0%) in model 1, and lowest tertile (33.3%) in model 2. Potential predictors-Short Physical Performance Battery (SPPB), Activity Measure for Post-Acute Care (AM-PAC), age, sex, walking aid use, and disabilities in activities of daily living-were selected based on literature and analysed using logistic regression analysis. Models were internally validated using bootstrapping. Model performance was quantified using measures of discrimination (area under the receiver operating characteristic curve (AUC)) and calibration (Hosmer and Lemeshow (H-L) goodness-of-fit test and calibration plots). RESULTS: Model 1 predicts a probability of spending ≤ 64.4 min standing/walking and holds the predictors SPPB, AM-PAC and sex. Model 2 predicts a probability of spending ≤ 47.2 min standing/walking and holds the predictors SPPB, AM-PAC, age and walking aid use. AUCs of models 1 and 2 were .80 (95% confidence interval (CI) = .73-.87) and .86 (95%CI = .79-.92), respectively, indicating good discriminative ability. Both models demonstrate near perfect calibration of the predicted probabilities and good overall performance, with model 2 performing slightly better. CONCLUSIONS: The developed and internally validated prediction models may enable clinicians to identify older adults at high risk of low PA levels during hospitalisation. External validation and determining the clinical impact are needed before applying the models in clinical practise