Validity of Six Activity Monitors in Chronic Obstructive Pulmonary Disease: A Comparison with Indirect Calorimetry

<div><p>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.</p> </div

Heart Rate Recovery After 6-min Walking Test Predicts Acute Exacerbation in COPD

INTRODUCTION: Abnormalities of autonomic function have been reported in patients with chronic obstructive pulmonary disease (COPD). Our objectives were to identify determinants of abnormal heart rate recovery at 1 min (HRR1) following completion of the 6-min walk test (6MWT) in COPD and to establish whether abnormal HRR1 predicts acute exacerbations (AECOPD). METHODS: Hundred one COPD patients (FEV1 (SD) 53 (19) % predicted) were prospectively recruited in a multi-center study. HRR1 after the 6MWT was evaluated as the difference between heart rate at the end of the test and 1 min into the recovery (HRR1). Linear and logistic regression was used to identify predictors of HRR1 and AECOPD, respectively. The best HRR1 cut-off point to predict AECOPD was selected using the receiver operating characteristics (ROC) curves. The follow-up period was 12 months. RESULTS: Distance covered during the 6MWT (m) and DLco (% predicted) were independently associated with HRR1 (r 2 = 0.51, p = 0.001). Among several potential covariates, HRR1 emerged as the most significant predictor of AECOPD (Odds ratio [OR], 0.91 per beat of recovery; 95% confidence interval [CI], 0.85-0.97; p = 0.02). The ROC analysis indicated that subjects with HRR1 less than 14 beats (AUC, 0.71 [CI] 0.60-0.80; p = 0.0001) were more likely to suffer an exacerbation during the follow-up period (for HRR1, p = 0.004 [log-rank test]). CONCLUSIONS: HRR1 after the 6MWT is an independent predictor factor for AECOPD. Further studies are warranted to examine the physiological mechanisms associating a delayed HRR and acute exacerbations in COPD patients

Validity of activity monitors in health and chronic disease: a systematic review

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

Characteristics of the 39 patients.

<p>Data are expressed as mean ± std. FEV₁; forced expiratory volume in 1 s, FVC; forced vital capacity, 6 MWD; six-minute walking distance, MRC; Medical Research Council, CAT; COPD Assessment Test, SGRQ; St George’s Respiratory Questionnaire.</p

Heart Rate Recovery After 6-min Walking Test Predicts Acute Exacerbation in COPD

INTRODUCTION: Abnormalities of autonomic function have been reported in patients with chronic obstructive pulmonary disease (COPD). Our objectives were to identify determinants of abnormal heart rate recovery at 1 min (HRR1) following completion of the 6-min walk test (6MWT) in COPD and to establish whether abnormal HRR1 predicts acute exacerbations (AECOPD). METHODS: Hundred one COPD patients (FEV1 (SD) 53 (19) % predicted) were prospectively recruited in a multi-center study. HRR1 after the 6MWT was evaluated as the difference between heart rate at the end of the test and 1 min into the recovery (HRR1). Linear and logistic regression was used to identify predictors of HRR1 and AECOPD, respectively. The best HRR1 cut-off point to predict AECOPD was selected using the receiver operating characteristics (ROC) curves. The follow-up period was 12 months. RESULTS: Distance covered during the 6MWT (m) and DLco (% predicted) were independently associated with HRR1 (r 2 = 0.51, p = 0.001). Among several potential covariates, HRR1 emerged as the most significant predictor of AECOPD (Odds ratio [OR], 0.91 per beat of recovery; 95% confidence interval [CI], 0.85-0.97; p = 0.02). The ROC analysis indicated that subjects with HRR1 less than 14 beats (AUC, 0.71 [CI] 0.60-0.80; p = 0.0001) were more likely to suffer an exacerbation during the follow-up period (for HRR1, p = 0.004 [log-rank test]). CONCLUSIONS: HRR1 after the 6MWT is an independent predictor factor for AECOPD. Further studies are warranted to examine the physiological mechanisms associating a delayed HRR and acute exacerbations in COPD patients

Minute-by-minute correlations (R) between activity monitor outputs and metabolic equivalents of task (METs) per patient (white dots).

<p>MM; MiniMod, AG; Actigraph, SW; SenseWear, AW; Actiwatch, VMU; vector magnitude unit, AC, activity count, AU; arbitrary unit. Dotted line corresponds to a correlation of 0.7, defined <i>a priori</i> as supporting monitor validity. Median (interquartile range) correlation for each activity monitor is reflected by cross bars, *p<0.05.</p

Partial variance (pR²) of the activity monitors (AM) in the six linear regression models.

<p>Partial variance (pR²) of the activity monitors (AM) in the six linear regression models.</p

Details of type, location and output of the six activity monitors.

<p>AC; activity counts, VMU: vector magnitude unit, EE; energy expenditure.</p

Relation between the activity monitor outputs and indirect calorimetry (METs).

<p>Data points represent mean values over the whole protocol. MM; MiniMod, AG; Actigraph, SW; SenseWear, AW; Actiwatch, VMU; vector magnitude unit, AC; activity count, AU; arbitrary unit.</p

Minute-by-minute correlations between indirect calorimetry (METs) and activity monitor output in mild to moderate COPD (GOLD I/II) and severe to very severe COPD (GOLD III/IV).

<p>Data are expressed as median [interquartile range]. METs; Metabolic Equivalents of Task, VMUs; Vector Magnitude Units.</p