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: The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities . Forty-eight participants [ age 35 yr ( 11.4 ) ] performed various activities chosen to represent sedentary , light , moderate , and vigorous intensities . Eighteen activities were split into three routines with each routine being performed by 20 individuals , for a total of 60 tests . Forty-five tests were r and omly selected for the development of the new equation , and 15 tests were used to cross-vali date the new equation and compare it against already existing equations . During each routine , the participant wore an Actigraph accelerometer on the hip , and oxygen consumption was simultaneously measured by a portable metabolic system . For each activity , the coefficient of variation ( CV ) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity . If the CV was < or=10 , then a walk/run regression equation was used , whereas if the CV was > 10 , a lifestyle/leisure time physical activity regression was used . In the cross-validation group , the mean estimates using the new algorithm ( 2-regression model with an inactivity threshold ) were within 0.75 metabolic equivalents ( METs ) of measured METs for each of the activities performed ( P > or= 0.05 ) , which was a substantial improvement over the single-regression models . The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer This study examined the predictive validity of accelerometers ( ACC ) to estimate physical activity intensity ( PAI ) across age and differences in intensity predictions when expressed in relative and absolute PAI terms . Ninety adults categorized into 3 age groups ( 20 - 29 , 40 - 49 , and 60 - 69 yr ) completed a treadmill calibration study with simultaneous ACC ( 7164 Actigraph ) and oxygen-consumption assessment . Results revealed strong linear relations between ACC output and measured PAI ( R2 = .62-.89 ) across age and similar ACC cut-point ranges across age delineating absolute PAI ranges compared with previous findings . Comparing measured metabolic equivalents ( METs ) with estimated METs derived from previously published regression equations revealed that age did not affect predictive validity of ACC estimates of absolute PAI . Comparing ACC output expressed in relative vs. absolute terms across age revealed substantial differences in PAI ACC count ranges . Further work is warranted to increase the applicability ofACC use relative to PAI differences associated with physiological changes with age Background Underst and ing the relationships between  physical activity ( PA ) and disease has become a major area of research interest . Activity monitors , devices that quantify free-living PA for prolonged periods of time ( days or weeks ) , are increasingly being used to estimate PA . A range of different activity monitors br and s are available for investigators to use , but little is known about how they respond to different levels of PA in the field , nor if data conversion between br and s is possible . Methods 56 women and men were fitted with two different activity monitors , the Actigraph ™ ( Actigraph LLC ; AGR ) and the Actical ™ ( Mini-Mitter Co. ; MM ) for 15 days . Both activity monitors were fixed to an elasticized belt worn over the hip , with the anterior and posterior position of the activity monitors r and omized . Differences between activity monitors and the validity of br and inter-conversion were measured by t-tests , Pearson correlations , Bl and -Altman plots , and coefficients of variation ( CV ) . Results The AGR detected a significantly greater amount of daily PA ( 216.2 ± 106.2 vs. 188.0 ± 101.1 counts/min , P < 0.0001 ) . The average difference between activity monitors expressed as a CV were 3.1 and 15.5 % for log-transformed and raw data , respectively . When a conversion equation was applied to convert data sets from one br and to another , the differences were no longer significant , with CV 's of 2.2 and 11.7 % , log-transformed and raw data , respectively . Conclusion Although activity monitors predict PA on the same scale ( counts/min ) , the results between these two br and s are not directly comparable . However , the data are comparable if a conversion equation is applied , with better results for log-transformed data The primary purpose of this study was to investigate the accuracy of the activPAL physical activity monitor in measuring step number and cadence in older adults .  Two pedometers ( New-Lifestyles Digi-Walker SW-200 and New-Lifestyles NL2000 ) used in clinical practice to count steps were simultaneously evaluated . Observation was the criterion measure . Twenty-one participants ( 65 - 87 yr old ) recruited from community-based exercise classes walked on a treadmill at 5 speeds ( 0.67 , 0.90 , 1.12 , 1.33 , and 1.56 m/s ) and outdoors at 3 self-selected speeds ( slow , normal , and fast ) . The absolute percentage error of the activPAL was < 1 % for all treadmill and outdoor conditions for measuring steps and cadence . With the exception of the slowest treadmill speed , the NL-2000 error was < 2 % . The SW-200 was the least accurate device , particularly at slower walking speeds . The activPAL monitor accurately recorded step number and cadence . Combined with its ability to identify primary postures , the activPAL might be a useful and versatile device for measuring activity in older adults Objective . To determine whether the energy expenditure recorded with the physical activity monitor SenseWear ™ Pro2 Armb and differs from that recorded with indirect calorimetry . Design . Cross‐sectional comparison of measures of energy expenditure . Setting . A convenient sample recruited from a r and omized controlled trial . Population . Twenty‐nine pregnant women ( 24–43years old ) . Methods . Energy expenditure was recorded with SenseWear ™ Pro2 Armb and and a portable oxygen analyzer for 90minutes while carrying out a variety of activities of different intensities . Main Outcome Measures . Energy expenditure recorded with the physical activity monitor SenseWear ™ Pro2 Armb and . Results . Comparing energy expenditure during free‐living activities , the mean differences and limits of agreements from Bl and –Altman plots was −136±343kJ , giving an underestimation of energy expenditure by 9 % . The intraclass correlation coefficient was 0.85 ( 95 % confidence interval 0.71–0.93 ; p<0.001 ) , giving 85 % of the variance explained by differences among individuals . Conclusions . SenseWear ™ Pro2 Armb and is a valid measure of energy expenditure during pregnancy PURPOSE The accuracy of pedometers has not been thoroughly tested with older adult population s. The purpose of the present study was to examine the effects of walking speed and gait disorders on the accuracy of Yamax pedometers with nursing home residents ( NH ) relative to older adults living in the community . METHODS Pedometer accuracy was evaluated against observed steps taken during a self-paced walking test ( slow , normal , and fast speeds ) in 26 NH residents and 28 seniors ' recreation center members ( SC ) . Devices were attached to clothing at the waist . Walking speed was ascertained from the timed walk and a gait assessment was conducted . Percent error was calculated as ( [ pedometer steps - observed steps]/observed steps ) x 100 . RESULTS The walking speeds of both sample s increased across self-selected paces ( P < 0.0001 ) . The community-dwelling older adults  walked significantly faster ( P < 0.0001 ) in all trials and had significantly higher ( P < 0.0001 ) gait assessment scores ( indicating fewer gait problems ) . Gait scores were positively associated with walking speed and pedometer percent error .  Pedometers significantly underestimated NH residents '  observed steps taken by 74 % ( slow ) , 55 % ( normal ) , and 46 % ( fast ) paces ( P < 0.0001 ) . In the SC sample , the instruments failed to detect 25 % , 13 % , and 7 % of actual steps taken , respectively ( P < 0.0001 ) . The magnitude of the error was greater for NH versus SC older adults ( P < 0.0001 ) across all trials . CONCLUSIONS Slow walking speed and gait disorders hamper the utility of pedometers for physical activity measurement in frail seniors , such as NH residents , when worn at the usual attachment site . Pedometers , however , can be confidently used with ostensibly healthy older adult population s for both assessment and motivation purpose We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration ( gravity-removal physical activity classification algorithm , GRPACA ) measured by a triaxial accelerometer . The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer .  A total of sixty-six subjects were r and omly assigned into validation ( n 44 ) and cross-validation ( n 22 ) groups . All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting . During each activity , energy expenditure was measured by indirect calorimetry , and physical activity intensities were expressed as metabolic equivalents ( MET ) . The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household ( r 0·907 , P < 0·001 ) and locomotive ( r 0·961 , P < 0·001 ) activities . In the cross-validation group , two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities . Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions . Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts . Therefore , the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities , compared with previously reported cut-off classification models . This method may be useful for field investigations as well as for self-monitoring by general users In our study of 125 subjects ( 53 men and 72 women ) for two 24-h periods , we vali date d energy expenditure ( EE ) , estimated by a triaxial accelerometer ( Tritrac-R3D ) , by using a whole-room indirect calorimeter  under close-to-normal living conditions . The estimated EE was correlated with the measured total EE for the 2 days ( r = 0 . 925 and r = 0.855 ; P < 0.001 ) and in minute-by-minute EE ( P < 0.01 ) . Resting EE formulated by the Tritrac was found to be similar to the measured values [ st and ard errors of estimation ( SEE ) = 0.112 W/kg ; P = 0.822 ] . The Tritrac significantly underestimated total EE , EE for physical activities , EE of sedentary and light-intensity activities , and EE for exercise such as stepping ( all P < 0.001 ) . We developed a linear and a nonlinear model to predict EE by using the acceleration components from the Tritrac . Predicted EE was significantly improved with both models in estimating total EE , total EE for physical activities , EE in low-intensity activities , minute-by-minute averaged relative difference , and minute-by-minute SEE ( all P < 0 . 05 ) . Furthermore , with our generalized models and by using subjects ' physical characteristics and body acceleration , EE can be estimated with higher accuracy ( averaged SEE = 0.418 W/kg ) than with the Tritrac model We compared the accuracy of two physical activity recall question naires and a motion detector in 45- to 84-yr-old women ( n = 35 ) and men ( n = 32 ) , using doubly labeled water ( DLW ) in conjunction with indirect calorimetry as the criterion measure . Subjects were administered the  Yale Physical Activity Survey ( YPAS ) and  Minnesota Leisure Time Physical Activity Question naire ( LTA ) .  Physical activity energy expenditure was determined over a 10-day period by using a Caltrac uniaxial accelerometer and DLW in conjunction with indirect calorimetry . In older women , Minnesota LTA ( 386 + /- 228 kcal/day ) and Caltrac ( 379 + /- 162 kcal/day ) underestimated physical activity by approximately 55 % compared with DLW ( 873 + /- 244 kcal/day ) . No difference was observed between daily physical activity measured by the YPAS ( 863 + /- 447 kcal/day ) and DLW in older women . In older men , Minnesota LTA ( 459 + /- 288 kcal/day ) and Caltrac ( 554 + /- 242 kcal/day ) underestimated daily physical activity by approximately 50 - 60 % compared with DLW ( 1,211 + /- 429 kcal/day ) . No difference was found between physical activity measured by the YPAS ( 1,107 + /- 612 kcal/day ) and DLW in older men . Despite no difference in mean physical activity levels between YPAS and DLW in women and men , Bl and and Altman ( Lancet 1 : 307 - 310 , 1986 ) analyses demonstrated poor concordance between DLW and YPAS ( i.e. , limits of agreement = -1,310 - 1,518 kcal/day ) . Our data suggest that the Minnesota LTA recall and Caltrac uniaxial accelerometer may significantly underestimate free-living daily physical activity energy expenditure in older women and men . Although the YPAS compares favorably with DLW on a group basis , its use as a proxy measure of individual daily physical activity energy exp 
    Output:
    
agent
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 .
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 .
***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: The purpose of this study was to develop a new two-regression model relating Actigraph activity counts to energy expenditure over a wide range of physical activities . Forty-eight participants [ age 35 yr ( 11.4 ) ] performed various activities chosen to represent sedentary , light , moderate , and vigorous intensities . Eighteen activities were split into three routines with each routine being performed by 20 individuals , for a total of 60 tests . Forty-five tests were r and omly selected for the development of the new equation , and 15 tests were used to cross-vali date the new equation and compare it against already existing equations . During each routine , the participant wore an Actigraph accelerometer on the hip , and oxygen consumption was simultaneously measured by a portable metabolic system . For each activity , the coefficient of variation ( CV ) for the counts per 10 s was calculated to determine whether the activity was walking/running or some other activity . If the CV was < or=10 , then a walk/run regression equation was used , whereas if the CV was > 10 , a lifestyle/leisure time physical activity regression was used . In the cross-validation group , the mean estimates using the new algorithm ( 2-regression model with an inactivity threshold ) were within 0.75 metabolic equivalents ( METs ) of measured METs for each of the activities performed ( P > or= 0.05 ) , which was a substantial improvement over the single-regression models . The new algorithm is more accurate for the prediction of energy expenditure than currently published regression equations using the Actigraph accelerometer This study examined the predictive validity of accelerometers ( ACC ) to estimate physical activity intensity ( PAI ) across age and differences in intensity predictions when expressed in relative and absolute PAI terms . Ninety adults categorized into 3 age groups ( 20 - 29 , 40 - 49 , and 60 - 69 yr ) completed a treadmill calibration study with simultaneous ACC ( 7164 Actigraph ) and oxygen-consumption assessment . Results revealed strong linear relations between ACC output and measured PAI ( R2 = .62-.89 ) across age and similar ACC cut-point ranges across age delineating absolute PAI ranges compared with previous findings . Comparing measured metabolic equivalents ( METs ) with estimated METs derived from previously published regression equations revealed that age did not affect predictive validity of ACC estimates of absolute PAI . Comparing ACC output expressed in relative vs. absolute terms across age revealed substantial differences in PAI ACC count ranges . Further work is warranted to increase the applicability ofACC use relative to PAI differences associated with physiological changes with age Background Underst and ing the relationships between  physical activity ( PA ) and disease has become a major area of research interest . Activity monitors , devices that quantify free-living PA for prolonged periods of time ( days or weeks ) , are increasingly being used to estimate PA . A range of different activity monitors br and s are available for investigators to use , but little is known about how they respond to different levels of PA in the field , nor if data conversion between br and s is possible . Methods 56 women and men were fitted with two different activity monitors , the Actigraph ™ ( Actigraph LLC ; AGR ) and the Actical ™ ( Mini-Mitter Co. ; MM ) for 15 days . Both activity monitors were fixed to an elasticized belt worn over the hip , with the anterior and posterior position of the activity monitors r and omized . Differences between activity monitors and the validity of br and inter-conversion were measured by t-tests , Pearson correlations , Bl and -Altman plots , and coefficients of variation ( CV ) . Results The AGR detected a significantly greater amount of daily PA ( 216.2 ± 106.2 vs. 188.0 ± 101.1 counts/min , P < 0.0001 ) . The average difference between activity monitors expressed as a CV were 3.1 and 15.5 % for log-transformed and raw data , respectively . When a conversion equation was applied to convert data sets from one br and to another , the differences were no longer significant , with CV 's of 2.2 and 11.7 % , log-transformed and raw data , respectively . Conclusion Although activity monitors predict PA on the same scale ( counts/min ) , the results between these two br and s are not directly comparable . However , the data are comparable if a conversion equation is applied , with better results for log-transformed data The primary purpose of this study was to investigate the accuracy of the activPAL physical activity monitor in measuring step number and cadence in older adults .  Two pedometers ( New-Lifestyles Digi-Walker SW-200 and New-Lifestyles NL2000 ) used in clinical practice to count steps were simultaneously evaluated . Observation was the criterion measure . Twenty-one participants ( 65 - 87 yr old ) recruited from community-based exercise classes walked on a treadmill at 5 speeds ( 0.67 , 0.90 , 1.12 , 1.33 , and 1.56 m/s ) and outdoors at 3 self-selected speeds ( slow , normal , and fast ) . The absolute percentage error of the activPAL was < 1 % for all treadmill and outdoor conditions for measuring steps and cadence . With the exception of the slowest treadmill speed , the NL-2000 error was < 2 % . The SW-200 was the least accurate device , particularly at slower walking speeds . The activPAL monitor accurately recorded step number and cadence . Combined with its ability to identify primary postures , the activPAL might be a useful and versatile device for measuring activity in older adults Objective . To determine whether the energy expenditure recorded with the physical activity monitor SenseWear ™ Pro2 Armb and differs from that recorded with indirect calorimetry . Design . Cross‐sectional comparison of measures of energy expenditure . Setting . A convenient sample recruited from a r and omized controlled trial . Population . Twenty‐nine pregnant women ( 24–43years old ) . Methods . Energy expenditure was recorded with SenseWear ™ Pro2 Armb and and a portable oxygen analyzer for 90minutes while carrying out a variety of activities of different intensities . Main Outcome Measures . Energy expenditure recorded with the physical activity monitor SenseWear ™ Pro2 Armb and . Results . Comparing energy expenditure during free‐living activities , the mean differences and limits of agreements from Bl and –Altman plots was −136±343kJ , giving an underestimation of energy expenditure by 9 % . The intraclass correlation coefficient was 0.85 ( 95 % confidence interval 0.71–0.93 ; p<0.001 ) , giving 85 % of the variance explained by differences among individuals . Conclusions . SenseWear ™ Pro2 Armb and is a valid measure of energy expenditure during pregnancy PURPOSE The accuracy of pedometers has not been thoroughly tested with older adult population s. The purpose of the present study was to examine the effects of walking speed and gait disorders on the accuracy of Yamax pedometers with nursing home residents ( NH ) relative to older adults living in the community . METHODS Pedometer accuracy was evaluated against observed steps taken during a self-paced walking test ( slow , normal , and fast speeds ) in 26 NH residents and 28 seniors ' recreation center members ( SC ) . Devices were attached to clothing at the waist . Walking speed was ascertained from the timed walk and a gait assessment was conducted . Percent error was calculated as ( [ pedometer steps - observed steps]/observed steps ) x 100 . RESULTS The walking speeds of both sample s increased across self-selected paces ( P < 0.0001 ) . The community-dwelling older adults  walked significantly faster ( P < 0.0001 ) in all trials and had significantly higher ( P < 0.0001 ) gait assessment scores ( indicating fewer gait problems ) . Gait scores were positively associated with walking speed and pedometer percent error .  Pedometers significantly underestimated NH residents '  observed steps taken by 74 % ( slow ) , 55 % ( normal ) , and 46 % ( fast ) paces ( P < 0.0001 ) . In the SC sample , the instruments failed to detect 25 % , 13 % , and 7 % of actual steps taken , respectively ( P < 0.0001 ) . The magnitude of the error was greater for NH versus SC older adults ( P < 0.0001 ) across all trials . CONCLUSIONS Slow walking speed and gait disorders hamper the utility of pedometers for physical activity measurement in frail seniors , such as NH residents , when worn at the usual attachment site . Pedometers , however , can be confidently used with ostensibly healthy older adult population s for both assessment and motivation purpose We have recently developed a simple algorithm for the classification of household and locomotive activities using the ratio of unfiltered to filtered synthetic acceleration ( gravity-removal physical activity classification algorithm , GRPACA ) measured by a triaxial accelerometer . The purpose of the present study was to develop a new model for the immediate estimation of daily physical activity intensities using a triaxial accelerometer .  A total of sixty-six subjects were r and omly assigned into validation ( n 44 ) and cross-validation ( n 22 ) groups . All subjects performed fourteen activities while wearing a triaxial accelerometer in a controlled laboratory setting . During each activity , energy expenditure was measured by indirect calorimetry , and physical activity intensities were expressed as metabolic equivalents ( MET ) . The validation group displayed strong relationships between measured MET and filtered synthetic accelerations for household ( r 0·907 , P < 0·001 ) and locomotive ( r 0·961 , P < 0·001 ) activities . In the cross-validation group , two GRPACA-based linear regression models provided highly accurate MET estimation for household and locomotive activities . Results were similar when equations were developed by non-linear regression or sex-specific linear or non-linear regressions . Sedentary activities were also accurately estimated by the specific linear regression classified from other activity counts . Therefore , the use of a triaxial accelerometer in combination with a GRPACA permits more accurate and immediate estimation of daily physical activity intensities , compared with previously reported cut-off classification models . This method may be useful for field investigations as well as for self-monitoring by general users In our study of 125 subjects ( 53 men and 72 women ) for two 24-h periods , we vali date d energy expenditure ( EE ) , estimated by a triaxial accelerometer ( Tritrac-R3D ) , by using a whole-room indirect calorimeter  under close-to-normal living conditions . The estimated EE was correlated with the measured total EE for the 2 days ( r = 0 . 925 and r = 0.855 ; P < 0.001 ) and in minute-by-minute EE ( P < 0.01 ) . Resting EE formulated by the Tritrac was found to be similar to the measured values [ st and ard errors of estimation ( SEE ) = 0.112 W/kg ; P = 0.822 ] . The Tritrac significantly underestimated total EE , EE for physical activities , EE of sedentary and light-intensity activities , and EE for exercise such as stepping ( all P < 0.001 ) . We developed a linear and a nonlinear model to predict EE by using the acceleration components from the Tritrac . Predicted EE was significantly improved with both models in estimating total EE , total EE for physical activities , EE in low-intensity activities , minute-by-minute averaged relative difference , and minute-by-minute SEE ( all P < 0 . 05 ) . Furthermore , with our generalized models and by using subjects ' physical characteristics and body acceleration , EE can be estimated with higher accuracy ( averaged SEE = 0.418 W/kg ) than with the Tritrac model We compared the accuracy of two physical activity recall question naires and a motion detector in 45- to 84-yr-old women ( n = 35 ) and men ( n = 32 ) , using doubly labeled water ( DLW ) in conjunction with indirect calorimetry as the criterion measure . Subjects were administered the  Yale Physical Activity Survey ( YPAS ) and  Minnesota Leisure Time Physical Activity Question naire ( LTA ) .  Physical activity energy expenditure was determined over a 10-day period by using a Caltrac uniaxial accelerometer and DLW in conjunction with indirect calorimetry . In older women , Minnesota LTA ( 386 + /- 228 kcal/day ) and Caltrac ( 379 + /- 162 kcal/day ) underestimated physical activity by approximately 55 % compared with DLW ( 873 + /- 244 kcal/day ) . No difference was observed between daily physical activity measured by the YPAS ( 863 + /- 447 kcal/day ) and DLW in older women . In older men , Minnesota LTA ( 459 + /- 288 kcal/day ) and Caltrac ( 554 + /- 242 kcal/day ) underestimated daily physical activity by approximately 50 - 60 % compared with DLW ( 1,211 + /- 429 kcal/day ) . No difference was found between physical activity measured by the YPAS ( 1,107 + /- 612 kcal/day ) and DLW in older men . Despite no difference in mean physical activity levels between YPAS and DLW in women and men , Bl and and Altman ( Lancet 1 : 307 - 310 , 1986 ) analyses demonstrated poor concordance between DLW and YPAS ( i.e. , limits of agreement = -1,310 - 1,518 kcal/day ) . Our data suggest that the Minnesota LTA recall and Caltrac uniaxial accelerometer may significantly underestimate free-living daily physical activity energy expenditure in older women and men . Although the YPAS compares favorably with DLW on a group basis , its use as a proxy measure of individual daily physical activity energy exp 
    Output:
    