BackgroundThe relationship between physical activity, disease severity, health status and prognosis in patients with COPD has not been systematically assessed. Our aim was to identify and summarise studies assessing associations between physical activity and its determinants and/or outcomes in patients with COPD and to develop a conceptual model for physical activity in COPD.MethodsWe conducted a systematic search of four databases (Medline, Embase, CINAHL and Psychinfo) prior to November 2012. Teams of two reviewers independently selected articles, extracted data and used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to assess quality of evidence.Results86 studies were included: 59 were focused on determinants, 23 on outcomes and 4 on both. Hyperinflation, exercise capacity, dyspnoea, previous exacerbations, gas exchange, systemic inflammation, quality of life and self-efficacy were consistently related to physical activity, but often based on cross-sectional studies and low-quality evidence. Results from studies of pharmacological and non-pharmacological treatments were inconsistent and the quality of evidence was low to very low. As outcomes, COPD exacerbations and mortality were consistently associated with low levels of physical activity based on moderate quality evidence. Physical activity was associated with other outcomes such as dyspnoea, health-related quality of life, exercise capacity and FEV1 but based on cross-sectional studies and low to very low quality evidence.ConclusionsPhysical activity level in COPD is consistently associated with mortality and exacerbations, but there is poor evidence about determinants of physical activity, including the impact of treatment.

Reduced physical activity is an important feature of Chronic Obstructive Pulmonary Disease (COPD). Various activity monitors are available but their validity is poorly established. The aim was to evaluate the validity of six monitors in patients with COPD. We hypothesized triaxial monitors to be more valid compared to uniaxial monitors. Thirty-nine patients (age 68±7years, FEV1 54±18%predicted) performed a one-hour standardized activity protocol. Patients wore 6 monitors (Kenz Lifecorder (Kenz), Actiwatch, RT3, Actigraph GT3X (Actigraph), Dynaport MiniMod (MiniMod), and SenseWear Armband (SenseWear)) as well as a portable metabolic system (Oxycon Mobile). Validity was evaluated by correlation analysis between indirect calorimetry (VO2) and the monitor outputs: Metabolic Equivalent of Task [METs] (SenseWear, MiniMod), activity counts (Actiwatch), vector magnitude units (Actigraph, RT3) and arbitrary units (Kenz) over the whole protocol and slow versus fast walking. Minute-by-minute correlations were highest for the MiniMod (r = 0.82), Actigraph (r = 0.79), SenseWear (r = 0.73) and RT3 (r = 0.73). Over the whole protocol, the mean correlations were best for the SenseWear (r = 0.76), Kenz (r = 0.52), Actigraph (r = 0.49) and MiniMod (r = 0.45). The MiniMod (r = 0.94) and Actigraph (r = 0.88) performed better in detecting different walking speeds. The Dynaport MiniMod, Actigraph GT3X and SenseWear Armband (all triaxial monitors) are the most valid monitors during standardized physical activities. The Dynaport MiniMod and Actigraph GT3X discriminate best between different walking speeds.

The assessment of physical activity in healthy populations and in those with chronic diseases is challenging. The aim of this systematic review was to identify whether available activity monitors (AM) have been appropriately validated for use in assessing physical activity in these groups. Following a systematic literature search we found 134 papers meeting the inclusion criteria; 40 conducted in a field setting (validation against doubly labelled water), 86 in a laboratory setting (validation against a metabolic cart, metabolic chamber) and 8 in a field and laboratory setting. Correlation coefficients between AM outcomes and energy expenditure (EE) by the criterion method (doubly labelled water and metabolic cart/chamber) and percentage mean differences between EE estimation from the monitor and EE measurement by the criterion method were extracted. Random-effects meta-analyses were performed to pool the results across studies where possible. Types of devices were compared using meta-regression analyses. Most validation studies had been performed in healthy adults (n = 118), with few carried out in patients with chronic diseases (n = 16). For total EE, correlation coefficients were statistically significantly lower in uniaxial compared to multisensor devices. For active EE, correlations were slightly but not significantly lower in uniaxial compared to triaxial and multisensor devices. Uniaxial devices tended to underestimate TEE (−12.07 (95%CI; -18.28 to −5.85) %) compared to triaxial (−6.85 (95%CI; -18.20 to 4.49) %, p = 0.37) and were statistically significantly less accurate than multisensor devices (−3.64 (95%CI; -8.97 to 1.70) %, p<0.001). TEE was underestimated during slow walking speeds in 69% of the lab validation studies compared to 37%, 30% and 37% of the studies during intermediate, fast walking speed and running, respectively. The high level of heterogeneity in the validation studies is only partly explained by the type of activity monitor and the activity monitor outcome. Triaxial and multisensor devices tend to be more valid monitors. Since activity monitors are less accurate at slow walking speeds and information about validated activity monitors in chronic disease populations is lacking, proper validation studies in these populations are needed prior to their inclusion in clinical trials.

Symptoms during physical activity and physical inactivity are hallmarks of chronic obstructive pulmonary disease (COPD). Our aim was to evaluate the validity and usability of six activity monitors in patients with COPD against the doubly labelled water (DLW) indirect calorimetry method. 80 COPD patients (mean¡SD age 68¡6 years and forced expiratory volume in 1 s 57¡19% predicted) recruited in four centres each wore simultaneously three or four out of six commercially available monitors validated in chronic conditions for 14 consecutive days. A priori validity criteria were defined. These included the ability to explain total energy expenditure (TEE) variance through multiple regression analysis, using TEE as the dependent variable with total body water (TBW) plus several physical activity monitor outputs as independent variables; and correlation with activity energy expenditure (AEE) measured by DLW.The Actigraph GT3X (Actigraph LLC, Pensacola, FL, USA), and DynaPort MoveMonitor (McRoberts BV, The Hague, the Netherlands) best explained the majority of the TEE variance not explained by TBW (53% and 70%, respectively) and showed the most significant correlations with AEE (r50.71, p,0.001 and r50.70, p,0.0001, respectively).The results of this study should guide users in choosing valid activity monitors for research or for clinical use in patients with chronic diseases such as COPD. @ERSpublications This study validates six activity monitors in the field against indirect calorimetry (DLW) in patients with COPD

BackgroundChanges in physical activity (PA) are difficult to interpret because no framework of minimal important difference (MID) exists. We aimed to determine the minimal important difference (MID) in physical activity (PA) in patients with Chronic Obstructive Pulmonary Disease and to clinically validate this MID by evaluating its impact on time to first COPD-related hospitalization.MethodsPA was objectively measured for one week in 74 patients before and after three months of rehabilitation (rehabilitation sample). In addition the intraclass correlation coefficient was measured in 30 patients (test-retest sample), by measuring PA for two consecutive weeks. Daily number of steps was chosen as outcome measurement. Different distribution and anchor based methods were chosen to calculate the MID. Time to first hospitalization due to an exacerbation was compared between patients exceeding the MID and those who did not.ResultsCalculation of the MID resulted in 599 (Standard Error of Measurement), 1029 (empirical rule effect size), 1072 (Cohen's effect size) and 1131 (0.5SD) steps.day-1. An anchor based estimation could not be obtained because of the lack of a sufficiently related anchor. The time to the first hospital admission was significantly different between patients exceeding the MID and patients who did not, using the Standard Error of Measurement as cutoff.ConclusionsThe MID after pulmonary rehabilitation lies between 600 and 1100 steps.day-1. The clinical importance of this change is supported by a reduced risk for hospital admission in those patients with more than 600 steps improvement.