README.md
In mhajihos/GPAQ: Global Physical Activity Questionnaire
GPAQ
Overview
GPAQ packages is based on The Global Physical Activity Questionnaire which was developed by WHO for physical activity surveillance in countries. It collects information on physical activity participation in three settings (or domains) as well as sedentary behaviour, comprising 16 questions (P1-P16). The domains are:\
• Activity at work\
• Travel to and from places\
• Recreational activities\
GPAQ package has 2 dependencies listed in the DESCRIPTION file : survey, dplyr.
Installation
# install.packages("devtools")
devtools::install_github("mhajihos/GPAQ")
require(GPAQ)
#Functions
?gpaq
?As_svy_mean
?PtotalCat_svy_mean
?GPAQ_Shiny
Read_Data For Mac or Windows
if(.Platform$OS.type == "unix")
{
# MacOS
library(Hmisc) # needed to load mdb data in MacOS
data1 <- mdb.get("Dataset.mdb", tables = "data1")
data2 <- mdb.get("Dataset.mdb", tables = "data2")
} else {
# Windows
library(RODBC)
channel <- odbcConnectAccess("Dataset.mdb")
data1 <- sqlFetch(channel,"data1", as.is=TRUE)
data2 <- sqlFetch(channel,"data2", as.is=TRUE)
odbcClose(channel)
}
my_data<- data.frame(merge(data1,data2, by="QR"))
#Change variable names to lowercase
names(my_data)<- tolower(names(my_data))
#Creat a new categorical age variable
my_data$age4y<-NA
my_data$age4y[18<=my_data$age & my_data$age<=29]<-"18-29"
my_data$age4y[30<=my_data$age & my_data$age<=44]<-"30-44"
my_data$age4y[45<=my_data$age & my_data$age<=59]<-"45-59"
my_data$age4y[60<=my_data$age & my_data$age<=69]<-"60-69"
table(my_data$age4y)
gpaq (Data)
data<- gpaq (my_data)
class (data)
[1] "data.frame"
As_svy_mean (Outcome,Group=NULL,Data,id,weights,strata,CLN,Median=FALSE)
## Examples for Mean
As_svy_mean(~meet,~age4y,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 18-29 0.0658 0.0386-0.0931
2 30-44 0.1100 0.0819-0.1382
3 45-59 0.1385 0.1068-0.1701
4 60-69 0.1863 0.1471-0.2255
As_svy_mean(~meet,~age4y+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 18-29.Men 0.0703 0.0304-0.1103
2 30-44.Men 0.1002 0.0687-0.1317
3 45-59.Men 0.1474 0.101-0.1937
4 60-69.Men 0.1842 0.1269-0.2416
5 18-29.Women 0.0601 0.0285-0.0918
6 30-44.Women 0.1207 0.084-0.1574
7 45-59.Women 0.1313 0.0981-0.1645
8 60-69.Women 0.1879 0.1387-0.2371
As_svy_mean(~meet,~age4y+UrbanRural+sex,Data=Data,id=psu,weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 18-29.Rural.Men 0.0872 0.0315-0.1428
2 30-44.Rural.Men 0.1332 0.0763-0.19
3 45-59.Rural.Men 0.1489 0.104-0.1937
4 60-69.Rural.Men 0.1923 0.1333-0.2512
5 18-29.Urban.Men 0.0675 0.0218-0.1132
6 30-44.Urban.Men 0.0914 0.0546-0.1281
7 45-59.Urban.Men 0.1467 0.0842-0.2093
8 60-69.Urban.Men 0.1814 0.1068-0.256
9 18-29.Rural.Women 0.1057 0.0346-0.1768
10 30-44.Rural.Women 0.1717 0.1176-0.2257
11 45-59.Rural.Women 0.1142 0.0731-0.1553
12 60-69.Rural.Women 0.2232 0.1654-0.2809
13 18-29.Urban.Women 0.0535 0.0188-0.0882
14 30-44.Urban.Women 0.1085 0.0648-0.1521
15 45-59.Urban.Women 0.1360 0.0952-0.1768
16 60-69.Urban.Women 0.1779 0.1168-0.2389
As_svy_mean(~meet,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 meet1) doesn't meet recs 0.1157 0.0968-0.1347
## Examples for Median
As_svy_mean(~ptotalday,~age4y,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 18-29 122.1429 107.1429-145.7143
2 30-44 124.2857 111.4286-150
3 45-59 128.5714 120-157.1429
4 60-69 75.0000 60-90
As_svy_mean(~ptotalday,~age4y+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 18-29.Men 141.4286 111.4286-180
2 30-44.Men 171.4286 140-214.2857
3 45-59.Men 180.0000 154.2857-211.4286
4 60-69.Men 85.7143 66.4286-120
5 18-29.Women 102.8571 85.7143-128.5714
6 30-44.Women 100.0000 90-120
7 45-59.Women 111.4286 98.5714-128.5714
8 60-69.Women 65.0000 60-85.7143
As_svy_mean(~ptotalday,~age4y+UrbanRural+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 18-29.Rural.Men 168.5714 102.8571-244.2857
2 30-44.Rural.Men 248.5714 180-300
3 45-59.Rural.Men 210.0000 171.4286-244.2857
4 60-69.Rural.Men 115.7143 68.5714-162.8571
5 18-29.Urban.Men 141.4286 111.4286-194.2857
6 30-44.Urban.Men 150.7143 127.8571-204.2857
7 45-59.Urban.Men 171.4286 131.4286-192.8571
8 60-69.Urban.Men 81.4286 60-100
9 18-29.Rural.Women 90.0000 71.4286-180
10 30-44.Rural.Women 131.4286 102.8571-188.5714
11 45-59.Rural.Women 150.0000 120-192.8571
12 60-69.Rural.Women 77.1429 60-102.8571
13 18-29.Urban.Women 102.8571 85.7143-132.8571
14 30-44.Urban.Women 94.2857 82.8571-111.4286
15 45-59.Urban.Women 102.8571 85.7143-120
16 60-69.Urban.Women 64.2857 60-90
As_svy_mean(~ptotalday,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 ptotalday 120 112.8571-137.1429
PtotalCat_svy_mean (Outcome,Group=NULL,Data,id,weights,strata,CLN)
PtotalCat_svy_mean(~ptotalCat,~age4y,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category ptotalCat1..Low.Level 95%CI_Low ptotalCat2..Moderate.level
1 18-29 0.1193 0.081-0.1576 0.2355
2 30-44 0.1610 0.1253-0.1966 0.2951
3 45-59 0.1892 0.1528-0.2256 0.2941
4 60-69 0.2260 0.1853-0.2666 0.4093
95%CI_Moderate ptotalCat3..High.level 95%CI_High
1 0.1901-0.2809 0.6452 0.5909-0.6995
2 0.257-0.3333 0.5439 0.499-0.5888
3 0.2557-0.3325 0.5167 0.4751-0.5582
4 0.3644-0.4542 0.3647 0.3165-0.413
PtotalCat_svy_mean(~ptotalCat,~age4y+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category ptotalCat1..Low.Level 95%CI_Low ptotalCat2..Moderate.level
1 18-29.Men 0.1223 0.071-0.1737 0.1282
2 30-44.Men 0.1501 0.1117-0.1884 0.2299
3 45-59.Men 0.2167 0.1665-0.2668 0.2169
4 60-69.Men 0.2187 0.1617-0.2756 0.3899
5 18-29.Women 0.1155 0.0725-0.1586 0.3702
6 30-44.Women 0.1728 0.1259-0.2198 0.3662
7 45-59.Women 0.1671 0.1269-0.2073 0.3563
8 60-69.Women 0.2319 0.1832-0.2806 0.4252
95%CI_Moderate ptotalCat3..High.level 95%CI_High
1 0.077-0.1794 0.7495 0.6755-0.8234
2 0.1801-0.2798 0.6200 0.5648-0.6751
3 0.1745-0.2593 0.5664 0.5111-0.6217
4 0.318-0.4618 0.3915 0.3179-0.465
5 0.2857-0.4546 0.5143 0.4313-0.5973
6 0.3181-0.4143 0.4610 0.4047-0.5172
7 0.309-0.4036 0.4766 0.4254-0.5279
8 0.3747-0.4756 0.3429 0.2909-0.3949
PtotalCat_svy_mean(~ptotalCat,~age4y+UrbanRural+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category ptotalCat1..Low.Level 95%CI_Low
1 18-29.Rural.Men 0.1454 0.0692-0.2216
2 30-44.Rural.Men 0.1882 0.1252-0.2512
3 45-59.Rural.Men 0.1903 0.1418-0.2389
4 60-69.Rural.Men 0.2399 0.1789-0.301
5 18-29.Urban.Men 0.1184 0.0601-0.1768
6 30-44.Urban.Men 0.1399 0.0944-0.1853
7 45-59.Urban.Men 0.2272 0.1601-0.2944
8 60-69.Urban.Men 0.2112 0.1373-0.285
9 18-29.Rural.Women 0.1517 0.0716-0.2319
10 30-44.Rural.Women 0.2310 0.1662-0.2957
11 45-59.Rural.Women 0.1683 0.1117-0.2249
12 60-69.Rural.Women 0.2658 0.2041-0.3274
13 18-29.Urban.Women 0.1103 0.0624-0.1581
14 30-44.Urban.Women 0.1588 0.1028-0.2149
15 45-59.Urban.Women 0.1668 0.1179-0.2157
16 60-69.Urban.Women 0.2223 0.1622-0.2823
ptotalCat2..Moderate.level 95%CI_Moderate ptotalCat3..High.level
1 0.1915 0.1034-0.2795 0.6632
2 0.1613 0.1138-0.2087 0.6505
3 0.1971 0.1486-0.2456 0.6126
4 0.2888 0.2231-0.3545 0.4712
5 0.1175 0.0605-0.1745 0.7641
6 0.2483 0.1867-0.3099 0.6118
7 0.2249 0.1689-0.281 0.5478
8 0.4254 0.3314-0.5195 0.3634
9 0.3999 0.2995-0.5003 0.4484
10 0.2375 0.1875-0.2875 0.5316
11 0.2872 0.237-0.3374 0.5445
12 0.3525 0.2973-0.4078 0.3817
13 0.3658 0.2699-0.4617 0.5239
14 0.3971 0.3389-0.4554 0.4440
15 0.3754 0.3171-0.4337 0.4578
16 0.4459 0.3832-0.5086 0.3318
95%CI_High
1 0.5707-0.7556
2 0.5686-0.7325
3 0.5535-0.6716
4 0.3922-0.5503
5 0.6805-0.8477
6 0.5455-0.6781
7 0.4747-0.6209
8 0.268-0.4588
9 0.338-0.5587
10 0.4575-0.6057
11 0.4781-0.6109
12 0.3171-0.4463
13 0.4299-0.618
14 0.3766-0.5115
15 0.3953-0.5204
16 0.2676-0.3961
PtotalCat_svy_mean(~ptotalCat,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category PtotalCat 95%CI
1 ptotalCat1) Low Level 0.1658 0.1415-0.1901
2 ptotalCat2) Moderate level 0.2943 0.2734-0.3152
3 ptotalCat3) High level 0.5399 0.5103-0.5696
GPAQ_Shiny:Shiny app for the Global Physical Activity Questionnaire (GPAQ)
This function will Launch a shiny app on the local server for weighted and unweighted data analysis.
GPAQ_Shiny()
Useful Links
WHO European Office for the Prevention and Control of NCDs (NCD Office)\
WHO STEPS Program\
WHO Physical Activity Surveillance
mhajihos/GPAQ documentation built on Nov. 21, 2023, 6:52 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GPAQ
Overview
GPAQ packages is based on The Global Physical Activity Questionnaire which was developed by WHO for physical activity surveillance in countries. It collects information on physical activity participation in three settings (or domains) as well as sedentary behaviour, comprising 16 questions (P1-P16). The domains are:\ • Activity at work\ • Travel to and from places\ • Recreational activities\ GPAQ package has 2 dependencies listed in the DESCRIPTION file : survey, dplyr.
Installation
# install.packages("devtools")
devtools::install_github("mhajihos/GPAQ")
require(GPAQ)
#Functions
?gpaq
?As_svy_mean
?PtotalCat_svy_mean
?GPAQ_Shiny
Read_Data For Mac or Windows
if(.Platform$OS.type == "unix")
{
# MacOS
library(Hmisc) # needed to load mdb data in MacOS
data1 <- mdb.get("Dataset.mdb", tables = "data1")
data2 <- mdb.get("Dataset.mdb", tables = "data2")
} else {
# Windows
library(RODBC)
channel <- odbcConnectAccess("Dataset.mdb")
data1 <- sqlFetch(channel,"data1", as.is=TRUE)
data2 <- sqlFetch(channel,"data2", as.is=TRUE)
odbcClose(channel)
}
my_data<- data.frame(merge(data1,data2, by="QR"))
#Change variable names to lowercase
names(my_data)<- tolower(names(my_data))
#Creat a new categorical age variable
my_data$age4y<-NA
my_data$age4y[18<=my_data$age & my_data$age<=29]<-"18-29"
my_data$age4y[30<=my_data$age & my_data$age<=44]<-"30-44"
my_data$age4y[45<=my_data$age & my_data$age<=59]<-"45-59"
my_data$age4y[60<=my_data$age & my_data$age<=69]<-"60-69"
table(my_data$age4y)
gpaq (Data)
data<- gpaq (my_data)
class (data)
[1] "data.frame"
As_svy_mean (Outcome,Group=NULL,Data,id,weights,strata,CLN,Median=FALSE)
## Examples for Mean
As_svy_mean(~meet,~age4y,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 18-29 0.0658 0.0386-0.0931
2 30-44 0.1100 0.0819-0.1382
3 45-59 0.1385 0.1068-0.1701
4 60-69 0.1863 0.1471-0.2255
As_svy_mean(~meet,~age4y+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 18-29.Men 0.0703 0.0304-0.1103
2 30-44.Men 0.1002 0.0687-0.1317
3 45-59.Men 0.1474 0.101-0.1937
4 60-69.Men 0.1842 0.1269-0.2416
5 18-29.Women 0.0601 0.0285-0.0918
6 30-44.Women 0.1207 0.084-0.1574
7 45-59.Women 0.1313 0.0981-0.1645
8 60-69.Women 0.1879 0.1387-0.2371
As_svy_mean(~meet,~age4y+UrbanRural+sex,Data=Data,id=psu,weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 18-29.Rural.Men 0.0872 0.0315-0.1428
2 30-44.Rural.Men 0.1332 0.0763-0.19
3 45-59.Rural.Men 0.1489 0.104-0.1937
4 60-69.Rural.Men 0.1923 0.1333-0.2512
5 18-29.Urban.Men 0.0675 0.0218-0.1132
6 30-44.Urban.Men 0.0914 0.0546-0.1281
7 45-59.Urban.Men 0.1467 0.0842-0.2093
8 60-69.Urban.Men 0.1814 0.1068-0.256
9 18-29.Rural.Women 0.1057 0.0346-0.1768
10 30-44.Rural.Women 0.1717 0.1176-0.2257
11 45-59.Rural.Women 0.1142 0.0731-0.1553
12 60-69.Rural.Women 0.2232 0.1654-0.2809
13 18-29.Urban.Women 0.0535 0.0188-0.0882
14 30-44.Urban.Women 0.1085 0.0648-0.1521
15 45-59.Urban.Women 0.1360 0.0952-0.1768
16 60-69.Urban.Women 0.1779 0.1168-0.2389
As_svy_mean(~meet,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_meet")
Category Meet1) doesn't meet recs 95%CI
1 meet1) doesn't meet recs 0.1157 0.0968-0.1347
## Examples for Median
As_svy_mean(~ptotalday,~age4y,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 18-29 122.1429 107.1429-145.7143
2 30-44 124.2857 111.4286-150
3 45-59 128.5714 120-157.1429
4 60-69 75.0000 60-90
As_svy_mean(~ptotalday,~age4y+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 18-29.Men 141.4286 111.4286-180
2 30-44.Men 171.4286 140-214.2857
3 45-59.Men 180.0000 154.2857-211.4286
4 60-69.Men 85.7143 66.4286-120
5 18-29.Women 102.8571 85.7143-128.5714
6 30-44.Women 100.0000 90-120
7 45-59.Women 111.4286 98.5714-128.5714
8 60-69.Women 65.0000 60-85.7143
As_svy_mean(~ptotalday,~age4y+UrbanRural+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 18-29.Rural.Men 168.5714 102.8571-244.2857
2 30-44.Rural.Men 248.5714 180-300
3 45-59.Rural.Men 210.0000 171.4286-244.2857
4 60-69.Rural.Men 115.7143 68.5714-162.8571
5 18-29.Urban.Men 141.4286 111.4286-194.2857
6 30-44.Urban.Men 150.7143 127.8571-204.2857
7 45-59.Urban.Men 171.4286 131.4286-192.8571
8 60-69.Urban.Men 81.4286 60-100
9 18-29.Rural.Women 90.0000 71.4286-180
10 30-44.Rural.Women 131.4286 102.8571-188.5714
11 45-59.Rural.Women 150.0000 120-192.8571
12 60-69.Rural.Women 77.1429 60-102.8571
13 18-29.Urban.Women 102.8571 85.7143-132.8571
14 30-44.Urban.Women 94.2857 82.8571-111.4286
15 45-59.Urban.Women 102.8571 85.7143-120
16 60-69.Urban.Women 64.2857 60-90
As_svy_mean(~ptotalday,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal",Median=TRUE)
Category Ptotalday 25%le-75%le
1 ptotalday 120 112.8571-137.1429
PtotalCat_svy_mean (Outcome,Group=NULL,Data,id,weights,strata,CLN)
PtotalCat_svy_mean(~ptotalCat,~age4y,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category ptotalCat1..Low.Level 95%CI_Low ptotalCat2..Moderate.level
1 18-29 0.1193 0.081-0.1576 0.2355
2 30-44 0.1610 0.1253-0.1966 0.2951
3 45-59 0.1892 0.1528-0.2256 0.2941
4 60-69 0.2260 0.1853-0.2666 0.4093
95%CI_Moderate ptotalCat3..High.level 95%CI_High
1 0.1901-0.2809 0.6452 0.5909-0.6995
2 0.257-0.3333 0.5439 0.499-0.5888
3 0.2557-0.3325 0.5167 0.4751-0.5582
4 0.3644-0.4542 0.3647 0.3165-0.413
PtotalCat_svy_mean(~ptotalCat,~age4y+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category ptotalCat1..Low.Level 95%CI_Low ptotalCat2..Moderate.level
1 18-29.Men 0.1223 0.071-0.1737 0.1282
2 30-44.Men 0.1501 0.1117-0.1884 0.2299
3 45-59.Men 0.2167 0.1665-0.2668 0.2169
4 60-69.Men 0.2187 0.1617-0.2756 0.3899
5 18-29.Women 0.1155 0.0725-0.1586 0.3702
6 30-44.Women 0.1728 0.1259-0.2198 0.3662
7 45-59.Women 0.1671 0.1269-0.2073 0.3563
8 60-69.Women 0.2319 0.1832-0.2806 0.4252
95%CI_Moderate ptotalCat3..High.level 95%CI_High
1 0.077-0.1794 0.7495 0.6755-0.8234
2 0.1801-0.2798 0.6200 0.5648-0.6751
3 0.1745-0.2593 0.5664 0.5111-0.6217
4 0.318-0.4618 0.3915 0.3179-0.465
5 0.2857-0.4546 0.5143 0.4313-0.5973
6 0.3181-0.4143 0.4610 0.4047-0.5172
7 0.309-0.4036 0.4766 0.4254-0.5279
8 0.3747-0.4756 0.3429 0.2909-0.3949
PtotalCat_svy_mean(~ptotalCat,~age4y+UrbanRural+sex,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category ptotalCat1..Low.Level 95%CI_Low
1 18-29.Rural.Men 0.1454 0.0692-0.2216
2 30-44.Rural.Men 0.1882 0.1252-0.2512
3 45-59.Rural.Men 0.1903 0.1418-0.2389
4 60-69.Rural.Men 0.2399 0.1789-0.301
5 18-29.Urban.Men 0.1184 0.0601-0.1768
6 30-44.Urban.Men 0.1399 0.0944-0.1853
7 45-59.Urban.Men 0.2272 0.1601-0.2944
8 60-69.Urban.Men 0.2112 0.1373-0.285
9 18-29.Rural.Women 0.1517 0.0716-0.2319
10 30-44.Rural.Women 0.2310 0.1662-0.2957
11 45-59.Rural.Women 0.1683 0.1117-0.2249
12 60-69.Rural.Women 0.2658 0.2041-0.3274
13 18-29.Urban.Women 0.1103 0.0624-0.1581
14 30-44.Urban.Women 0.1588 0.1028-0.2149
15 45-59.Urban.Women 0.1668 0.1179-0.2157
16 60-69.Urban.Women 0.2223 0.1622-0.2823
ptotalCat2..Moderate.level 95%CI_Moderate ptotalCat3..High.level
1 0.1915 0.1034-0.2795 0.6632
2 0.1613 0.1138-0.2087 0.6505
3 0.1971 0.1486-0.2456 0.6126
4 0.2888 0.2231-0.3545 0.4712
5 0.1175 0.0605-0.1745 0.7641
6 0.2483 0.1867-0.3099 0.6118
7 0.2249 0.1689-0.281 0.5478
8 0.4254 0.3314-0.5195 0.3634
9 0.3999 0.2995-0.5003 0.4484
10 0.2375 0.1875-0.2875 0.5316
11 0.2872 0.237-0.3374 0.5445
12 0.3525 0.2973-0.4078 0.3817
13 0.3658 0.2699-0.4617 0.5239
14 0.3971 0.3389-0.4554 0.4440
15 0.3754 0.3171-0.4337 0.4578
16 0.4459 0.3832-0.5086 0.3318
95%CI_High
1 0.5707-0.7556
2 0.5686-0.7325
3 0.5535-0.6716
4 0.3922-0.5503
5 0.6805-0.8477
6 0.5455-0.6781
7 0.4747-0.6209
8 0.268-0.4588
9 0.338-0.5587
10 0.4575-0.6057
11 0.4781-0.6109
12 0.3171-0.4463
13 0.4299-0.618
14 0.3766-0.5115
15 0.3953-0.5204
16 0.2676-0.3961
PtotalCat_svy_mean(~ptotalCat,Data=Data,id=psu, weights=wstep1,strata =stratum,CLN="cln_Ptotal")
Category PtotalCat 95%CI
1 ptotalCat1) Low Level 0.1658 0.1415-0.1901
2 ptotalCat2) Moderate level 0.2943 0.2734-0.3152
3 ptotalCat3) High level 0.5399 0.5103-0.5696
GPAQ_Shiny:Shiny app for the Global Physical Activity Questionnaire (GPAQ)
This function will Launch a shiny app on the local server for weighted and unweighted data analysis.
GPAQ_Shiny()
Useful Links
WHO European Office for the Prevention and Control of NCDs (NCD Office)\ WHO STEPS Program\ WHO Physical Activity Surveillance
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