human
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 vali date d 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 Netherl and s ) best explained the majority of the TEE variance not explained by TBW ( 53 % and 70 % , respectively ) and showed the most significant correlations with AEE ( r=0.71 , p<0.001 and r=0.70 , 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 . This study vali date s six activity monitors in the field against indirect calorimetry ( DLW ) in patients with COPD The use of movement registration for daily physical activity assessment was evaluated during a 7-day period in 30 free-living subjects . Body movement was registered with a Tracmor motion sensor consisting of a triaxial accelerometer and a data unit for on-line processing of accelerometer output over 1-min intervals . Average Tracmor output was correlated against four different energy estimates : 1 ) average daily metabolic rate ( ADMR ) , determined with doubly labeled water ; 2 ) ADMR-sleeping metabolic rate ( SMR ; determined in a respiration chamber ) ; 3 ) ( ADMR-SMR ) per kilogram of body mass ; and 4 ) the overall physical activity level ( PAL = ADMR/SMR ) . The highest correlation was found for the relationship between Tracmor output and PAL ( r = 0.58 ) . After correction for Tracmor values arising from vibrations produced by transportation means , this correlation was improved to 0.73 . There was no difference between Tracmor output and PAL in discriminating between overall activity levels with " low " ( PAL < 1.60 ) , " moderate " ( 1.60 < or = PAL < or = 1.85 ) , and " high " ( PAL > 1.85 ) intensity . It is concluded that the Tracmor can be used in free-living subjects to distinguish among interindividual as well as intraindividual levels of daily physical activity Background / Objectives : A combined heart rate ( HR ) and motion sensor ( Actiheart ) has been proposed as an accurate method for assessing total energy expenditure ( TEE ) and physical activity energy expenditure ( PAEE ) . However , the extent to which factors such as caffeine may affect the accuracy by which the estimated HR-related PAEE contribution will affect TEE and PAEE estimates is unknown . Therefore , we examined the validity of Actiheart in estimating TEE and PAEE in free-living adults under a caffeine trial compared with doubly labeled water ( DLW ) as reference criterion . Subjects/ Methods : Using a double-blind crossover trial ( Clinical trials.gov ID : # NCT01477294 ) with two conditions ( 4-day each with a 3-day-washout period ) , r and omly ordered as caffeine ( 5 mg/kg per day ) and placebo ( malt-dextrine ) intake , TEE was measured by DLW in 17 physically active men ( 20–38 years ) who were non-caffeine users . In each condition , resting energy expenditure ( REE ) was assessed by indirect calorimetry and PAEE was calculated as ( TEE−(REE+0.1 TEE ) ) . Simultaneously , PAEE and TEE were estimated by Actiheart using an individual calibration ( ACC+HRstep ) . Results : Under caffeine , ACC+HRstep explained 76 and 64 % of TEE and PAEE from DLW , respectively ; corresponding results for the placebo condition were 82 and 66 % . No mean bias was found between ACC+HRstep and DLW for TEE  ( caffeine:-468 kJ per day ; placebo:-407 kJ per day ) , although PAEE was slightly underestimated ( caffeine:-856 kJ per day ; placebo:-1147 kJ per day ) . Similar limits of agreement were observed in both conditions ranging from −2066 to 3002 and from −3488 to 1776 kJ per day for TEE and PAEE , respectively . Conclusions : Regardless of caffeine intake , the combined HR and motion sensor is valid for estimating free-living energy expenditure in a group of healthy men but is less accurate for an individual assessment We assessed the ability of the Polar activity recorder ( AR ) to measure energy expenditure ( EE ) during military training . Twenty-four voluntary male conscripts participated in the study and wore an AR on the non-dominant wrist 24 h a day for 7 d. The AR analyzed and stored the frequency of h and movements ( f_h and ) into memory at 1 min intervals . The relationship between f_h and and EE was studied over a 7 d period of military training using the doubly labeled water ( DLW ) technique . In addition , the relationship between f_h and and EE was analyzed during walking and running on a treadmill with an indirect calorimeter ( IC ) , and f_h and was measured during a supervised 45 min field march test where the conscripts carried combat gear .  EE was expressed as physical activity level ( PAL ) , total energy expenditure ( TEE ) , and activity-induced energy expenditure adjusted for body mass ( AEE/BM ) . Over the 7 d period , f_h and alone explained 46 % of inter-individual variation in PAL(DLW ) . After inclusion of body height and mass in the model used to predict PAL(DLW ) from f_h and , a very high positive correlation and a low st and ard error of estimate ( SEE ) were observed between the AR and DLW techniques : for TEE r = 0.86 ( p < 0.001 ) , the SEE was 6.3 % , and for AEE/BM r = 0.84 ( p < 0.001 ) , the SEE was 12.8 % . In the treadmill exercise , f_h and correlated highly with PAL(IC ) ( r = 0.97 ± 0.02 ) . In the 45 min field march test , the AR measured similar f_h and as on the treadmill at the same speed . In conclusion , the wrist-worn AR can be regarded as a reliable and valid method for assessing EE during intensive training OBJECTIVE The objective was to evaluate two accelerometers , the RT3 and the TriTrac-R3D for their ability to produce estimates of physical activity-related energy expenditure ( PAEE ) in overweight/obese adults . RESEARCH METHODS AND PROCEDURES PAEE estimates from both accelerometers were obtained in two experiments . In Experiment 1 , 13 overweight/obese subjects ( BMI 34.2+/-6.4 kg/m2 ) were monitored over 2 weeks in everyday life , PAEE being simultaneously measured by the doubly labeled water method ( DLW ) . In Experiment 2 , 8 overweight/obese subjects ( BMI 34.3+/-5.0 kg/m2 ) and 10 normal-weight subjects ( BMI 20.8+/-2.1 kg/m2 ) were monitored during a treadmill walking protocol , PAEE being simultaneously measured by indirect calorimetry . RESULTS In Experiment 1 , there was no significant difference between methods in mean PAEE ( DLW : 704+/-223 kcal/d , RT3 : 656+/-140 kcal/d , TriTrac-R3D 624+/-419 kcal/d ) . The relative difference between methods ( accelerometer vs. DLW ) was -17.1%+/-16.7 % for the RT3 and -20.0+/-44.6 % for the TriTrac-R3D . Correlation for PAEE between RT3 and DLW was higher than between TriTrac-R3D and DLW ( r=0.67 , p<0.05 and r=0.36 , p=0.25 , respectively ) . The 95 % confidence interval ( CI ) ( kcal/d ) of the mean difference between methods was large , amounting to -385 to 145 for the RT3 and -887 to 590 for the TriTrac-R3D . In Experiment 2 , both accelerometers were sensitive to the changes in treadmill speed , with no significant difference in mean PAEE between methods in overweight/obese subjects . CONCLUSIONS Although both accelerometers did not provide accurate estimates of PAEE at individual levels , the data suggest that RT3 has the potential to assess PAEE at group levels in overweight/obese subjects Background Current research practice employs wide-ranging accelerometer wear time criteria to identify a valid day of  physical activity ( PA ) measurement . Objective To evaluate the effects of varying amounts of daily accelerometer wear time on PA data . Methods A total of 1000 days of accelerometer data from 1000 participants ( age=38.7±14.3 years ; body mass index=28.2±6.7 kg/m2 ) were selected from the 2005–2006 National Health and Nutrition Examination Study data set . A reference data set was created using 200 r and om days with 14 h/day of wear time . Four additional sample s of 200 days were r and omly selected with a wear time of 10 , 11 , 12 and 13 h/day1 . These data sets were used in day-to-day comparison to create four semisimulation data sets ( 10 , 11 , 12 , 13 h/day ) from the reference data set . Differences in step count and time spent in inactivity ( < 100 cts/min ) , light ( 100–1951 cts/min ) , moderate ( 1952–5724 cts/min ) and vigorous ( ≥5725 cts/min ) intensity PA were assessed using repeated- measures analysis of variance and absolute percent error ( APE ) . Results There were significant differences for moderate intensity PA between the reference data set and semisimulation data sets of 10 and 11 h/day . Differences were observed in 10–13 h/day1 for inactivity and light intensity PA , and 10–12 h/day for steps ( all p values < 0.05 ) . APE increased with shorter wear time ( 13 h/day=3.9–14.1 % ; 12 h/day=9.9–15.2 % , 11 h/day=17.1–35.5 % ; 10 h/day=24.6–40.3 % ) . Discussion These data suggest that using accelerometer wear time criteria of 12 h/day or less may underestimate step count and time spent in various PA levels 
    Output:
    
agent
Conclusions : We conclude that there is large heterogeneity across studies in the explained variance of AEE when estimated based on accelerometry .
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 vali date d 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 Netherl and s ) best explained the majority of the TEE variance not explained by TBW ( 53 % and 70 % , respectively ) and showed the most significant correlations with AEE ( r=0.71 , p<0.001 and r=0.70 , 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 . This study vali date s six activity monitors in the field against indirect calorimetry ( DLW ) in patients with COPD The use of movement registration for daily physical activity assessment was evaluated during a 7-day period in 30 free-living subjects . Body movement was registered with a Tracmor motion sensor consisting of a triaxial accelerometer and a data unit for on-line processing of accelerometer output over 1-min intervals . Average Tracmor output was correlated against four different energy estimates : 1 ) average daily metabolic rate ( ADMR ) , determined with doubly labeled water ; 2 ) ADMR-sleeping metabolic rate ( SMR ; determined in a respiration chamber ) ; 3 ) ( ADMR-SMR ) per kilogram of body mass ; and 4 ) the overall physical activity level ( PAL = ADMR/SMR ) . The highest correlation was found for the relationship between Tracmor output and PAL ( r = 0.58 ) . After correction for Tracmor values arising from vibrations produced by transportation means , this correlation was improved to 0.73 . There was no difference between Tracmor output and PAL in discriminating between overall activity levels with " low " ( PAL < 1.60 ) , " moderate " ( 1.60 < or = PAL < or = 1.85 ) , and " high " ( PAL > 1.85 ) intensity . It is concluded that the Tracmor can be used in free-living subjects to distinguish among interindividual as well as intraindividual levels of daily physical activity Background / Objectives : A combined heart rate ( HR ) and motion sensor ( Actiheart ) has been proposed as an accurate method for assessing total energy expenditure ( TEE ) and physical activity energy expenditure ( PAEE ) . However , the extent to which factors such as caffeine may affect the accuracy by which the estimated HR-related PAEE contribution will affect TEE and PAEE estimates is unknown . Therefore , we examined the validity of Actiheart in estimating TEE and PAEE in free-living adults under a caffeine trial compared with doubly labeled water ( DLW ) as reference criterion . Subjects/ Methods : Using a double-blind crossover trial ( Clinical trials.gov ID : # NCT01477294 ) with two conditions ( 4-day each with a 3-day-washout period ) , r and omly ordered as caffeine ( 5 mg/kg per day ) and placebo ( malt-dextrine ) intake , TEE was measured by DLW in 17 physically active men ( 20–38 years ) who were non-caffeine users . In each condition , resting energy expenditure ( REE ) was assessed by indirect calorimetry and PAEE was calculated as ( TEE−(REE+0.1 TEE ) ) . Simultaneously , PAEE and TEE were estimated by Actiheart using an individual calibration ( ACC+HRstep ) . Results : Under caffeine , ACC+HRstep explained 76 and 64 % of TEE and PAEE from DLW , respectively ; corresponding results for the placebo condition were 82 and 66 % . No mean bias was found between ACC+HRstep and DLW for TEE  ( caffeine:-468 kJ per day ; placebo:-407 kJ per day ) , although PAEE was slightly underestimated ( caffeine:-856 kJ per day ; placebo:-1147 kJ per day ) . Similar limits of agreement were observed in both conditions ranging from −2066 to 3002 and from −3488 to 1776 kJ per day for TEE and PAEE , respectively . Conclusions : Regardless of caffeine intake , the combined HR and motion sensor is valid for estimating free-living energy expenditure in a group of healthy men but is less accurate for an individual assessment We assessed the ability of the Polar activity recorder ( AR ) to measure energy expenditure ( EE ) during military training . Twenty-four voluntary male conscripts participated in the study and wore an AR on the non-dominant wrist 24 h a day for 7 d. The AR analyzed and stored the frequency of h and movements ( f_h and ) into memory at 1 min intervals . The relationship between f_h and and EE was studied over a 7 d period of military training using the doubly labeled water ( DLW ) technique . In addition , the relationship between f_h and and EE was analyzed during walking and running on a treadmill with an indirect calorimeter ( IC ) , and f_h and was measured during a supervised 45 min field march test where the conscripts carried combat gear .  EE was expressed as physical activity level ( PAL ) , total energy expenditure ( TEE ) , and activity-induced energy expenditure adjusted for body mass ( AEE/BM ) . Over the 7 d period , f_h and alone explained 46 % of inter-individual variation in PAL(DLW ) . After inclusion of body height and mass in the model used to predict PAL(DLW ) from f_h and , a very high positive correlation and a low st and ard error of estimate ( SEE ) were observed between the AR and DLW techniques : for TEE r = 0.86 ( p < 0.001 ) , the SEE was 6.3 % , and for AEE/BM r = 0.84 ( p < 0.001 ) , the SEE was 12.8 % . In the treadmill exercise , f_h and correlated highly with PAL(IC ) ( r = 0.97 ± 0.02 ) . In the 45 min field march test , the AR measured similar f_h and as on the treadmill at the same speed . In conclusion , the wrist-worn AR can be regarded as a reliable and valid method for assessing EE during intensive training OBJECTIVE The objective was to evaluate two accelerometers , the RT3 and the TriTrac-R3D for their ability to produce estimates of physical activity-related energy expenditure ( PAEE ) in overweight/obese adults . RESEARCH METHODS AND PROCEDURES PAEE estimates from both accelerometers were obtained in two experiments . In Experiment 1 , 13 overweight/obese subjects ( BMI 34.2+/-6.4 kg/m2 ) were monitored over 2 weeks in everyday life , PAEE being simultaneously measured by the doubly labeled water method ( DLW ) . In Experiment 2 , 8 overweight/obese subjects ( BMI 34.3+/-5.0 kg/m2 ) and 10 normal-weight subjects ( BMI 20.8+/-2.1 kg/m2 ) were monitored during a treadmill walking protocol , PAEE being simultaneously measured by indirect calorimetry . RESULTS In Experiment 1 , there was no significant difference between methods in mean PAEE ( DLW : 704+/-223 kcal/d , RT3 : 656+/-140 kcal/d , TriTrac-R3D 624+/-419 kcal/d ) . The relative difference between methods ( accelerometer vs. DLW ) was -17.1%+/-16.7 % for the RT3 and -20.0+/-44.6 % for the TriTrac-R3D . Correlation for PAEE between RT3 and DLW was higher than between TriTrac-R3D and DLW ( r=0.67 , p<0.05 and r=0.36 , p=0.25 , respectively ) . The 95 % confidence interval ( CI ) ( kcal/d ) of the mean difference between methods was large , amounting to -385 to 145 for the RT3 and -887 to 590 for the TriTrac-R3D . In Experiment 2 , both accelerometers were sensitive to the changes in treadmill speed , with no significant difference in mean PAEE between methods in overweight/obese subjects . CONCLUSIONS Although both accelerometers did not provide accurate estimates of PAEE at individual levels , the data suggest that RT3 has the potential to assess PAEE at group levels in overweight/obese subjects Background Current research practice employs wide-ranging accelerometer wear time criteria to identify a valid day of  physical activity ( PA ) measurement . Objective To evaluate the effects of varying amounts of daily accelerometer wear time on PA data . Methods A total of 1000 days of accelerometer data from 1000 participants ( age=38.7±14.3 years ; body mass index=28.2±6.7 kg/m2 ) were selected from the 2005–2006 National Health and Nutrition Examination Study data set . A reference data set was created using 200 r and om days with 14 h/day of wear time . Four additional sample s of 200 days were r and omly selected with a wear time of 10 , 11 , 12 and 13 h/day1 . These data sets were used in day-to-day comparison to create four semisimulation data sets ( 10 , 11 , 12 , 13 h/day ) from the reference data set . Differences in step count and time spent in inactivity ( < 100 cts/min ) , light ( 100–1951 cts/min ) , moderate ( 1952–5724 cts/min ) and vigorous ( ≥5725 cts/min ) intensity PA were assessed using repeated- measures analysis of variance and absolute percent error ( APE ) . Results There were significant differences for moderate intensity PA between the reference data set and semisimulation data sets of 10 and 11 h/day . Differences were observed in 10–13 h/day1 for inactivity and light intensity PA , and 10–12 h/day for steps ( all p values < 0.05 ) . APE increased with shorter wear time ( 13 h/day=3.9–14.1 % ; 12 h/day=9.9–15.2 % , 11 h/day=17.1–35.5 % ; 10 h/day=24.6–40.3 % ) . Discussion These data suggest that using accelerometer wear time criteria of 12 h/day or less may underestimate step count and time spent in various PA levels 
    Output:
    