BACKGROUND:: Physical activity is the only nonpharmacological therapy that is proven to be effective in heart failure (HF) patients in reducing morbidity. To date, little is known about the levels of daily physical activity in HF patients and about related factors. OBJECTIVE:: The objectives of this study were to (a) describe performance-based daily physical activity in HF patients, (b) compare it with physical activity guidelines, and (c) identify related factors of daily physical activity. METHODS:: The daily physical activity of 68 HF patients was measured using an accelerometer (SenseWear) for 48 hours. Psychological characteristics (self-efficacy, motivation, and depression) were measured using questionnaires. To have an indication how to interpret daily physical activity levels of the study sample, time spent on moderate- to vigorous-intensity physical activities was compared with the 30-minute activity guideline. Steps per day was compared with the criteria for healthy adults, in the absence of HF-specific criteria. Linear regression analyses were used to identify related factors of daily physical activity. RESULTS:: Forty-four percent were active for less than 30 min/d, whereas 56% were active for more than 30 min/d. Fifty percent took fewer than 5000 steps per day, 35% took 5000 to 10 000 steps per day, and 15% took more than 10 000 steps per day. Linear regression models showed that New York Heart Association classification and self-efficacy were the most important factors explaining variance in daily physical activity. CONCLUSIONS:: The variance in daily physical activity in HF patients is considerable. Approximately half of the patients had a sedentary lifestyle. Higher New York Heart Association classification and lower self-efficacy are associated with less daily physical activity. These findings contribute to the understanding of daily physical activity behavior of HF patients and can help healthcare providers to promote daily physical activity in sedentary HF patients.PMID:23416939DOI: 10.1097/JCN.0b013e318283ba14
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Background Physical activity after bariatric surgery is associated with sustained weight loss and improved quality of life. Some bariatric patients engage insufficiently in physical activity. The aim of this study was to examine whether and to what extent both physical activity and exercise cognitions have changed at one and two years post-surgery, and whether exercise cognitions predict physical activity. Methods Forty-two bariatric patients (38 women, 4 men; mean age 38 ± 8 years, mean body mass index prior to surgery 47 ± 6 kg/m²), filled out self-report instruments to examine physical activity and exercise cognitions pre- and post surgery. Results Moderate to large healthy changes in physical activity and exercise cognitions were observed after surgery. Perceiving less exercise benefits and having less confidence in exercising before surgery predicted less physical activity two years after surgery. High fear of injury one year after surgery predicted less physical activity two years after surgery. Conclusion After bariatric surgery, favorable changes in physical activity and exercise cognitions are observed. Our results suggest that targeting exercise cognitions before and after surgery might be relevant to improve physical activity.
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Aware of the consequences of their inactive lifestyles, many people still struggle to integrate enough physical activity into their busy lives. Interventions that nudge to reinforce existing active behaviour seem therefore more likely to be effective than those adding an activity to daily routines. To encourage people to increase their physical activity level, we designed Discov, a network of physical waypoints triggering people to lengthen their walks. Placed in a public park, Discov encourages people to explore their surroundings in a fun and challenging way by creating an interactive walking experience. Adopting a Research-through-Design approach, we explore the potential of the design of accessible infrastructures and human-environment interactions to impact public health by nudging citizens into being more physically active. We discuss insights gathered through this process and report on first user tests of this interactive walking experience.
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Objectives: Promoting unstructured outside play is a promising vehicle to increase children’s physical activity (PA). This study investigates if factors of the social environment moderate the relationship between the perceived physical environment and outside play. Study design: 1875 parents from the KOALA Birth Cohort Study reported on their child’s outside play around age five years, and 1516 parents around age seven years. Linear mixed model analyses were performed to evaluate (moderating) relationships among factors of the social environment (parenting influences and social capital), the perceived physical environment, and outside play at age five and seven. Season was entered as a random factor in these analyses. Results: Accessibility of PA facilities, positive parental attitude towards PA and social capital were associated with more outside play, while parental concern and restriction of screen time were related with less outside play. We found two significant interactions; both involving parent perceived responsibility towards child PA participation. Conclusion: Although we found a limited number of interactions, this study demonstrated that the impact of the perceived physical environment may differ across levels of parent responsibility.
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BACKGROUND: Although physical activity is beneficial for Parkinson's disease (PD) patients, many do not meet the recommended levels. The range of physical activity among sedentary PD patients is unknown, as are factors that determine this variability. Hence, we aimed to (1) assess daily physical activity in self-identified sedentary PD patients; (2) compare this with criteria of a daily physical activity guideline; and (3) identify determinants of daily physical activity. METHODS: Daily physical activity of 586 self-identified sedentary PD patients was measured with a tri-axial accelerometer for seven consecutive days. Physical fitness and demographic, disease-specific, and psychological characteristics were assessed. Daily physical activity was compared with the 30-min activity guideline. A linear mixed-effects model was estimated to identify determinants of daily physical activity. RESULTS: Accelerometer data of 467 patients who fulfilled all criteria revealed that >98% of their day was spent on sedentary to light-intensity activities. Eighty-two percent of the participants were 'physically inactive' (0 days/week of 30-min activity); 17% were 'semi-active' (1-4 days/week of 30-min activity). Age, gender, physical fitness, and scores on the Unified Parkinson's Disease Rating Scale explained 69% of the variability in daily physical activity. CONCLUSIONS: Performance-based measurements confirmed that most self-identified sedentary PD patients are 'physically inactive'. However, the variance in daily physical activity across subjects was considerable. Higher age, being female, and lower physical capacity were the most important determinants of reduced daily physical activity. Future therapeutic interventions should aim to improve daily physical activity in these high-risk patients, focusing specifically on modifiable risk factors.
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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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It has been suggested that physical education (PE) and active transport can make a meaningful contribution to children's physical activity (PA) levels. However, data on the contribution these activities to total PA is scarce, and PE's contribution to total physical activity energy expenditure (PAEE) has to our knowledge never been determined. This is probably explained by the methodological complexity of determining PAEE (Welk, 2002). In this paper, we present the first data of an ongoing study using combined heart rate monitoring and accelerometry, together with activity diaries. Over the six measurement days, PE contributed 5% to total PAEE, and 16% to school-related PAEE, whereas active transportation had a much larger contribution.
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OBJECTIVE: This scoping review aimed to gather current knowledge on accurately identifying and distinguishing between non-frail, pre-frail, and frail older adults using gait and daily physical activity (DPA) parameters and/or models that combine gait with DPA parameters in both controlled and daily life environments.METHODS: Following PRISMA-ScR guidelines, a systematic search was conducted across seven databases using key terms: "frail", "gait or walk", "IMU", and "age". Studies were included if they focused on gait analysis using Inertial Measurement Units (IMUs) for walking distances greater than 10 meters. Extracted data included study design, gait and DPA outcomes, walking conditions, and classification model performance. Gait parameters were grouped into four domains: spatio-temporal, frequency, amplitude, and dynamic gait. DPA parameters were synthesized into three categories: postural and transition, variability, and physical activity pattern.RESULTS: A total of 15 cross-sectional studies involving 2,366 participants met the inclusion criteria. Gait analysis showed (pre)frail individuals had slower, shorter steps with longer stride times compared to non-frail individuals. Pre-frail individuals showed distinct gait patterns in periodicity, magnitude range, and variability. In daily activities, (pre)frail individuals displayed shorter, fragmented walking periods and longer transitions between positions. Walking variation identified pre-frail status, showing progressive decreases from non-frail to frail states. Combined gait and daily physical activity models achieved over 97% accuracy, sensitivity and specificity in distinguishing between groups.DISCUSSION: This review provides an updated synthesis of the relationship between various gait and/or DPA parameters and physical frailty, highlighting gaps in pre-frailty detection and the variability in measurement protocols. It underscores the potential of long-term, sensor-based monitoring of daily physical activity for advancing pre-frailty screening and guiding future clinical trials. Structured Abstract BACKGROUND: Changes in gait and physical activity are critical indicators of frailty. With advancements in wearable sensor technology, long-term gait analysis using acceleration data has become more feasible. However, the contribution of parameters beyond gait speed, such as gait dynamics and daily physical activity (DPA), in identifying frail and pre-frail individuals remains unclear.OBJECTIVE: This scoping review aimed to gather knowledge on accurately identifying and differentiating physical pre-frail and frail individuals from non-frail individuals using gait parameters alone or models that combine gait and DPA parameters, both in controlled settings and daily life environments.METHODS: The review followed PRISMA-ScR guidelines. A search strategy incorporating key terms-"frail", "gait or walk", "IMU", and "age"-was applied across seven databases from inception to March 1, 2024. Studies were included if they focused on gait analysis in controlled or daily environments using Inertial Measurement Units (IMUs) and involved walking distances longer than 10 meters. Data on walking conditions, gait outcomes, classification methods, and results were extracted. Gait parameters were categorized into four domains: spatio-temporal, frequency, amplitude, and dynamic gait. DPA parameters were synthesized into three categories: postural and transition, variability, physical activity pattern.RESULTS: A total of 15 cross-sectional observational studies met the eligibility criteria, covering 2,366 participants, with females representing 27%-80% of the sample and ages ranging from 60 to 92 years. Regarding gait parameters, (pre)frail individuals exhibited longer stride times, slower walking speeds, shorter steps, and reduced cadence compared to non-frail individuals. In three studies, pre-frail could be distinguished from the non-frail and frail group through gait periodicity, range of magnitude, and gait variability. DPA patterns differed between groups, with (pre)frail individuals showing shorter and more fragmented walking periods, brief walking bouts and longer postural transitions. Walking bout variation (CoV) effectively identified pre-frail status, decreasing 53.73% from non-frail to pre-frail, and another 30.87% from pre-frail to frail. Models combining both gait and DPA parameters achieved the highest accuracy (97.25%), sensitivity (98.25%), and specificity (98.25%) in distinguishing between groups.DISCUSSION: This scoping review provides an updated overview of the current knowledge and gaps in understanding the relationship between gait parameters across different domains and DPA parameters along with physical frailty. Significant variability in gait measurement methods and protocols complicates direct comparisons between studies. The review emphasizes the need for further research, particularly in pre-frailty screening, and underscores the potential of inertial sensor-based long-term monitoring of daily physical activity for future clinical trials.
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Introduction Physical activity levels of children with disabilities are low, as these children and their parents face a wide variety of both personal and environmental barriers. Behavior change techniques support pediatric physical therapists to address these barriers together with parents and children. We developed the What Moves You?! intervention Toolkit (WMY Toolkit) filled with behavioral change tools for use in pediatric physical therapy practice. Objective To evaluate the feasibility of using the WMY Toolkit in daily pediatric physical therapy practice. Methods We conducted a feasibility study with a qualitative approach using semi-structured interviews with pediatric physical therapists (n = 11). After one day of training, the pediatric physical therapists used the WMY Toolkit for a period of 9 weeks, when facilitating physical activity in children with disabilities. We analyzed the transcripts using an inductive thematic analysis followed by a deductive analysis using a feasibility framework. Results For acceptability, pediatric physical therapists found that the toolkit facilitated conversation about physical activity in a creative and playful manner. The working mechanisms identified were in line with the intended working mechanisms during development of the WMY Toolkit, such as focusing on problem solving, self-efficacy and independence. For demand, the pediatric physical therapists mentioned that they were able to use the WMY Toolkit in children with and without disabilities with a broad range of physical activity goals. For implementation, education is important as pediatric physical therapists expressed the need to have sufficient knowledge and to feel confident using the toolkit. For practicality, pediatric physical therapists were positive about the ease of which tools could be adapted for individual children. Some of the design and materials of the toolkit needed attention due to fragility and hygiene. Conclusion The WMY Toolkit is a promising and innovative way to integrate behavior change techniques into pediatric physical therapy practice.
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Purpose: The purpose of this study was to validate optimized algorithm parameter settings for step count and physical behavior for a pocket worn activity tracker in older adults during ADL. Secondly, for a more relevant interpretation of the results, the performance of the optimized algorithm was compared to three reference applications Methods: In a cross-sectional validation study, 20 older adults performed an activity protocol based on ADL with MOXMissActivity versus MOXAnnegarn, activPAL, and Fitbit. The protocol was video recorded and analyzed for step count and dynamic, standing, and sedentary time. Validity was assessed by percentage error (PE), absolute percentage error (APE), Bland-Altman plots and correlation coefficients. Results: For step count, the optimized algorithm had a mean APE of 9.3% and a correlation coefficient of 0.88. The mean APE values of dynamic, standing, and sedentary time were 15.9%, 19.9%, and 9.6%, respectively. The correlation coefficients were 0.55, 0.91, and 0.92, respectively. Three reference applications showed higher errors and lower correlations for all outcome variables. Conclusion: This study showed that the optimized algorithm parameter settings can more validly estimate step count and physical behavior in older adults wearing an activity tracker in the trouser pocket during ADL compared to reference applications.
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