This paper reports on the first stage of a research project1) that aims to incorporate objective measures of physical activity into health and lifestyle surveys. Physical activity is typically measured with questionnaires that are known to have measurement issues, and specifically, overestimate the amount of physical activity of the population. In a lab setting, 40 participants wore four different sensors on five different body parts, while performing various activities (sitting, standing, stepping with two intensities, bicycling with two intensities, walking stairs and jumping). During the first four activities, energy expenditure was measured by monitoring heart rate and the gas volume of in‐ and expired O2 and CO2. Participants subsequently wore two sensor systems (the ActivPAL on the thigh and the UKK on the waist) for a week. They also kept a diary keeping track of their physical activities, work and travel hours. Machine learning algorithms were trained with different methods to determine which sensor and which method was best able to differentiate the various activities and the intensity with which they were performed. It was found that the ActivPAL had the highest overall accuracy, possibly because the data generated on the upper tigh seems to be best distinguishing between different types of activities and therefore led to the highest accuracy. Accuracy could be slightly increased by including measures of heartrate. For recognizing intensity, three different measures were compared: allocation of MET values to activities (used by ActivPAL), median absolute deviation, and heart rate. It turns out that each method has merits and disadvantages, but median absolute deviation seems to be the most promishing metric. The search for the best method of gauging intensity is still ongoing. Subsequently, the algorithms developed for the lab data were used to determine physical activity in the week people wore the devices during their everyday activities. It quickly turned out that the models are far from ready to be used on free living data. Two approaches are suggested to remedy this: additional research with meticulously labelled free living data, e.g., by combining a Time Use Survey with accelerometer measurements. The second is to focus on better determining intensity of movement, e.g., with the help of unsupervised pattern recognition techniques. Accuracy was but one of the requirements for choosing a sensor system for subsequent research and ultimate implementation of sensor measurement in health surveys. Sensor position on the body, wearability, costs, usability, flexibility of analysis, response, and adherence to protocol equally determine the choice for a sensor. Also from these additional points of view, the activPAL is our sensor of choice.
Background A high sedentary time is associated with increased mortality risk. Previous studies indicate that replacement of sedentary time with light- and moderate-to-vigorous physical activity attenuates the risk for adverse outcomes and improves cardiovascular risk factors. Patients with cardiovascular disease are more sedentary compared to the general population, while daily time spent sedentary remains high following contemporary cardiac rehabilitation programmes. This clinical trial investigated the effectiveness of a sedentary behaviour intervention as a personalised secondary prevention strategy (SIT LESS) on changes in sedentary time among patients with coronary artery disease participating in cardiac rehabilitation. Methods Patients were randomised to usual care (n = 104) or SIT LESS (n = 108). Both groups received a comprehensive 12-week centre-based cardiac rehabilitation programme with face-to-face consultations and supervised exercise sessions, whereas SIT LESS participants additionally received a 12-week, nurse-delivered, hybrid behaviour change intervention in combination with a pocket-worn activity tracker connected to a smartphone application to continuously monitor sedentary time. Primary outcome was the change in device-based sedentary time between pre- to post-rehabilitation. Changes in sedentary time characteristics (prevalence of prolonged sedentary bouts and proportion of patients with sedentary time ≥ 9.5 h/day); time spent in light-intensity and moderate-to-vigorous physical activity; step count; quality of life; competencies for self-management; and cardiovascular risk score were assessed as secondary outcomes. Results Patients (77% male) were 63 ± 10 years and primarily diagnosed with myocardial infarction (78%). Sedentary time decreased in SIT LESS (− 1.6 [− 2.1 to − 1.1] hours/day) and controls (− 1.2 [ ─1.7 to − 0.8]), but between group differences did not reach statistical significance (─0.4 [─1.0 to 0.3]) hours/day). The post-rehabilitation proportion of patients with a sedentary time above the upper limit of normal (≥ 9.5 h/day) was significantly lower in SIT LESS versus controls (48% versus 72%, baseline-adjusted odds-ratio 0.4 (0.2–0.8)). No differences were observed in the other predefined secondary outcomes. Conclusions Among patients with coronary artery disease participating in cardiac rehabilitation, SIT LESS did not induce significantly greater reductions in sedentary time compared to controls, but delivery was feasible and a reduced odds of a sedentary time ≥ 9.5 h/day was observed.
MULTIFILE
Objective. Hospital in Motion is a multidimensional implementation project aiming to improve movement behavior during hospitalization. The purpose of this study was to investigate the effectiveness of Hospital in Motion on movement behavior. Methods. This prospective study used a pre-implementation and post-implementation design. Hospital in Motion was conducted at 4 wards of an academic hospital in the Netherlands. In each ward, multidisciplinary teams followed a 10-month step-by-step approach, including the development and implementation of a ward-specific action plan with multiple interventions to improve movement behavior. Inpatient movement behavior was assessed before the start of the project and 1 year later using a behavioral mapping method in which patients were observed between 9:00 am and 4:00 pm. The primary outcome was the percentage of time spent lying down. In addition, sitting and moving, immobility-related complications, length of stay, discharge destination home, discharge destination rehabilitation setting, mortality, and 30-day readmissions were investigated. Differences between pre-implementation and post-implementation conditions were analyzed using the chi-square test for dichotomized variables, the Mann Whitney test for non-normal distributed data, or independent samples t test for normally distributed data. Results. Patient observations demonstrated that the primary outcome, the time spent lying down, changed from 60.1% to 52.2%. For secondary outcomes, the time spent sitting increased from 31.6% to 38.3%, and discharges to a rehabilitation setting reduced from 6 (4.4%) to 1 (0.7%). No statistical differences were found in the other secondary outcome measures. Conclusion. The implementation of the multidimensional project Hospital in Motion was associated with patients who were hospitalized spending less time lying in bed and with a reduced number of discharges to a rehabilitation setting. Impact. Inpatient movement behavior can be influenced by multidimensional interventions. Programs implementing interventions that specifically focus on improving time spent moving, in addition to decreasing time spent lying, are recommended.