1 ADD RE_IGNITE
2 VO2, BMI, Gender
- 2.1 Confirmation of ballanced measures
  - 2.1.1 lm(vo2sum_peak_ml~rand_treat.factor*screen_site.factor,merged.df )
  - 2.1.2 lm(dur_day_total_MODVIG_min_pla~rand_treat.factor*screen_site.factor,merged.df )
3 Baseline Randomization
- 3.0.1 For Jermon
- 3.1 Play..

1 ADD RE_IGNITE

pvalue.func <- function(x, ...) {
    # Construct vectors of data y, and groups (strata) g
    y <- unlist(x)
    g <- factor(rep(1:length(x), times=sapply(x, length)))
    if (is.numeric(y)) {
        test <- aov(y ~ g)
        p<-summary(test)[[1]][1,5] 

    } else {
        # For categorical variables, perform a chi-squared test of independence
        p <- chisq.test(table(y, g))$p.value
    }
    # Format the p-value, using an HTML entity for the less-than sign.
    # The initial empty string places the output on the line below the variable label.
    c("", sub("<", "&lt;", format.pval(p, digits=3, eps=0.001)))
}

merged.df<-readr::read_csv("/Volumes/IGNITE_Admin/R_IGNITE/BASELINE_merged.csv")
merged.df$race.factor<-if_else(str_detect(merged.df$screen_race.factor, "White"), "White", "")
merged.df$race.factor<-if_else(str_detect(merged.df$screen_race.factor, "Black"), "Black", merged.df$race.factor)
merged.df$race.factor<-if_else(merged.df$race.factor=="", "Other", merged.df$race.factor)
merged.df$race.factor<-factor(merged.df$race.factor, levels=c("White", "Black", "Other"))
merged.df$sittime_total<-merged.df$sittime_weekend_total+merged.df$sittime_weekday_total_min

1.1 screen_mod_confirm.factor

merged.df$screen_mod_confirm.factor<-if_else(is.na(merged.df$screen_mod_confirm.factor), "No", as.character(merged.df$screen_mod_confirm.factor))

table1::table1(~screen_gender.factor+race.factor+season+dur_day_total_MODVIG_min_pla+vo2sum_peak_ml+sittime_total|screen_site.factor+screen_mod_confirm.factor  ,data= merged.df, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Kansas		Northeastern		Pitt
	No (N=202)	Yes (N=12)	No (N=209)	Yes (N=6)	No (N=215)	Yes (N=4)	P-value
screen_gender.factor
Female	146 (72.3%)	9 (75.0%)	144 (68.9%)	4 (66.7%)	154 (71.6%)	4 (100%)	0.787
Male	56 (27.7%)	3 (25.0%)	65 (31.1%)	2 (33.3%)	61 (28.4%)	0 (0%)
race.factor
White	170 (84.2%)	10 (83.3%)	148 (70.8%)	4 (66.7%)	158 (73.5%)	1 (25.0%)	<0.001
Black	23 (11.4%)	1 (8.3%)	42 (20.1%)	2 (33.3%)	51 (23.7%)	3 (75.0%)
Other	9 (4.5%)	1 (8.3%)	19 (9.1%)	0 (0%)	6 (2.8%)	0 (0%)
season
Fall	65 (32.2%)	4 (33.3%)	57 (27.3%)	1 (16.7%)	71 (33.0%)	2 (50.0%)	0.913
Spring	44 (21.8%)	2 (16.7%)	40 (19.1%)	1 (16.7%)	49 (22.8%)	0 (0%)
Summer	51 (25.2%)	4 (33.3%)	42 (20.1%)	2 (33.3%)	41 (19.1%)	0 (0%)
Winter	37 (18.3%)	2 (16.7%)	46 (22.0%)	1 (16.7%)	47 (21.9%)	1 (25.0%)
Missing	5 (2.5%)	0 (0%)	24 (11.5%)	1 (16.7%)	7 (3.3%)	1 (25.0%)
dur_day_total_MODVIG_min_pla
Mean (SD)	51.2 (29.3)	61.6 (38.1)	50.1 (30.5)	50.4 (12.2)	46.5 (28.9)	51.3 (20.9)	0.436
Median [Min, Max]	45.3 [4.90, 166]	51.0 [23.8, 146]	43.6 [3.67, 188]	56.7 [36.5, 63.5]	38.4 [3.01, 182]	43.5 [35.5, 75.1]
Missing	5 (2.5%)	0 (0%)	24 (11.5%)	1 (16.7%)	7 (3.3%)	1 (25.0%)
vo2sum_peak_ml
Mean (SD)	22.2 (5.24)	24.6 (3.83)	22.2 (5.04)	23.4 (2.45)	20.6 (4.81)	20.0 (5.47)	0.00133
Median [Min, Max]	21.7 [12.1, 39.5]	24.7 [18.6, 32.0]	22.0 [11.5, 34.1]	23.8 [18.7, 25.9]	20.0 [10.1, 38.1]	19.3 [14.5, 26.8]
sittime_total
Mean (SD)	627 (254)	586 (231)	683 (289)	801 (309)	688 (333)	667 (134)	0.197
Median [Min, Max]	581 [107, 1540]	565 [213, 978]	641 [166, 1640]	675 [548, 1290]	615 [184, 3170]	641 [536, 851]
Missing	0 (0%)	0 (0%)	0 (0%)	1 (16.7%)	0 (0%)	0 (0%)

1.2 rand_treat.factor

table1::table1(~screen_mod_confirm.factor+race.factor+season+dur_day_total_MODVIG_min_pla+vo2sum_peak_ml+sittime_total+dur_day_total_IN_min_pla+bmi|rand_treat.factor  ,data= merged.df, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Group 1: 150 mins of aerobic exercise (N=215)	Group 2: 225 mins of aerobic exercise (N=215)	Group 3: Stretch and Tone (N=209)	P-value
screen_mod_confirm.factor
No	207 (96.3%)	208 (96.7%)	202 (96.7%)	0.962
Yes	8 (3.7%)	7 (3.3%)	7 (3.3%)
race.factor
White	159 (74.0%)	163 (75.8%)	161 (77.0%)	0.956
Black	43 (20.0%)	41 (19.1%)	38 (18.2%)
Other	13 (6.0%)	11 (5.1%)	10 (4.8%)
season
Fall	68 (31.6%)	70 (32.6%)	59 (28.2%)	0.774
Spring	41 (19.1%)	43 (20.0%)	52 (24.9%)
Summer	46 (21.4%)	46 (21.4%)	45 (21.5%)
Winter	48 (22.3%)	40 (18.6%)	43 (20.6%)
Missing	12 (5.6%)	16 (7.4%)	10 (4.8%)
dur_day_total_MODVIG_min_pla
Mean (SD)	52.5 (31.4)	50.1 (27.9)	45.5 (29.0)	0.0558
Median [Min, Max]	48.2 [3.67, 188]	42.8 [4.90, 129]	37.6 [3.01, 166]
Missing	12 (5.6%)	16 (7.4%)	10 (4.8%)
vo2sum_peak_ml
Mean (SD)	21.7 (4.75)	21.8 (5.24)	21.5 (5.14)	0.833
Median [Min, Max]	21.4 [12.6, 35.2]	21.4 [11.5, 38.1]	20.9 [10.1, 39.5]
sittime_total
Mean (SD)	683 (257)	658 (299)	665 (324)	0.669
Median [Min, Max]	660 [184, 1610]	611 [107, 2390]	600 [201, 3170]
Missing	0 (0%)	0 (0%)	1 (0.5%)
dur_day_total_IN_min_pla
Mean (SD)	823 (104)	825 (102)	819 (99.4)	0.84
Median [Min, Max]	816 [580, 1140]	815 [559, 1120]	822 [535, 1070]
Missing	12 (5.6%)	16 (7.4%)	10 (4.8%)
bmi
Mean (SD)	29.9 (5.86)	29.8 (6.00)	29.6 (5.40)	0.873
Median [Min, Max]	28.8 [19.1, 50.9]	29.2 [18.0, 49.5]	28.9 [18.8, 44.5]
Missing	0 (0%)	1 (0.5%)	0 (0%)

1.3 rand_treat.factor + screen_mod_confirm.factor

table1::table1(~screen_gender.factor+race.factor+screen_race_la_his.factor+vo2sum_peak_ml+NValidays4_3wk_1we+dur_day_total_MODVIG_min_pla+dur_day_total_IN_min_pla+sittime_total|rand_treat.factor  +screen_mod_confirm.factor,data= merged.df, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Group 1: 150 mins of aerobic exercise		Group 2: 225 mins of aerobic exercise		Group 3: Stretch and Tone
	No (N=207)	Yes (N=8)	No (N=208)	Yes (N=7)	No (N=202)	Yes (N=7)	P-value
screen_gender.factor
Female	145 (70.0%)	7 (87.5%)	146 (70.2%)	6 (85.7%)	146 (72.3%)	4 (57.1%)	0.736
Male	62 (30.0%)	1 (12.5%)	62 (29.8%)	1 (14.3%)	56 (27.7%)	3 (42.9%)
race.factor
White	154 (74.4%)	5 (62.5%)	159 (76.4%)	4 (57.1%)	155 (76.7%)	6 (85.7%)	0.902
Black	41 (19.8%)	2 (25.0%)	38 (18.3%)	3 (42.9%)	37 (18.3%)	1 (14.3%)
Other	12 (5.8%)	1 (12.5%)	11 (5.3%)	0 (0%)	10 (5.0%)	0 (0%)
screen_race_la_his.factor
No	199 (96.1%)	7 (87.5%)	202 (97.1%)	7 (100%)	194 (96.0%)	6 (85.7%)	0.469
Yes	8 (3.9%)	1 (12.5%)	6 (2.9%)	0 (0%)	8 (4.0%)	1 (14.3%)
vo2sum_peak_ml
Mean (SD)	21.7 (4.82)	23.5 (1.58)	21.7 (5.27)	22.7 (4.57)	21.4 (5.11)	24.0 (5.71)	0.639
Median [Min, Max]	21.2 [12.6, 35.2]	23.8 [20.6, 25.5]	21.4 [11.5, 38.1]	23.7 [16.8, 29.5]	20.8 [10.1, 39.5]	25.1 [14.5, 32.0]
NValidays4_3wk_1we
Not Valid	2 (1.0%)	0 (0%)	5 (2.4%)	0 (0%)	4 (2.0%)	0 (0%)	0.877
Valid	194 (93.7%)	7 (87.5%)	187 (89.9%)	7 (100%)	189 (93.6%)	6 (85.7%)
Missing	11 (5.3%)	1 (12.5%)	16 (7.7%)	0 (0%)	9 (4.5%)	1 (14.3%)
dur_day_total_MODVIG_min_pla
Mean (SD)	52.1 (31.0)	64.0 (42.7)	50.3 (28.2)	45.0 (17.4)	45.0 (28.9)	63.7 (26.9)	0.0931
Median [Min, Max]	48.2 [3.67, 188]	56.0 [23.8, 146]	43.6 [4.90, 129]	36.5 [28.4, 75.1]	36.8 [3.01, 166]	54.1 [38.3, 114]
Missing	11 (5.3%)	1 (12.5%)	16 (7.7%)	0 (0%)	9 (4.5%)	1 (14.3%)
dur_day_total_IN_min_pla
Mean (SD)	823 (105)	814 (103)	823 (102)	884 (99.5)	822 (98.3)	716 (85.3)	0.103
Median [Min, Max]	816 [580, 1140]	868 [664, 916]	813 [559, 1120]	881 [726, 1060]	825 [535, 1070]	736 [581, 829]
Missing	11 (5.3%)	1 (12.5%)	16 (7.7%)	0 (0%)	9 (4.5%)	1 (14.3%)
sittime_total
Mean (SD)	684 (256)	636 (310)	654 (302)	768 (170)	669 (327)	541 (186)	0.68
Median [Min, Max]	660 [184, 1610]	589 [338, 1290]	611 [107, 2390]	851 [548, 978]	606 [201, 3170]	557 [213, 729]
Missing	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (14.3%)

1.4 rand_treat.factor + screen_gender.factor

table1::table1(~screen_race_la_his.factor+screen_mod_confirm.factor+dur_day_total_MODVIG_min_pla+vo2sum_peak_ml+sittime_total|rand_treat.factor+screen_gender.factor   ,data= merged.df, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Group 1: 150 mins of aerobic exercise		Group 2: 225 mins of aerobic exercise		Group 3: Stretch and Tone
	Female (N=152)	Male (N=63)	Female (N=152)	Male (N=63)	Female (N=150)	Male (N=59)	P-value
screen_race_la_his.factor
No	145 (95.4%)	61 (96.8%)	149 (98.0%)	60 (95.2%)	145 (96.7%)	55 (93.2%)	0.632
Yes	7 (4.6%)	2 (3.2%)	3 (2.0%)	3 (4.8%)	5 (3.3%)	4 (6.8%)
screen_mod_confirm.factor
No	145 (95.4%)	62 (98.4%)	146 (96.1%)	62 (98.4%)	146 (97.3%)	56 (94.9%)	0.732
Yes	7 (4.6%)	1 (1.6%)	6 (3.9%)	1 (1.6%)	4 (2.7%)	3 (5.1%)
dur_day_total_MODVIG_min_pla
Mean (SD)	52.3 (30.5)	53.1 (33.7)	50.2 (27.6)	49.7 (28.7)	43.3 (26.9)	51.4 (33.5)	0.118
Median [Min, Max]	48.4 [6.89, 188]	47.1 [3.67, 182]	43.6 [4.90, 123]	41.0 [11.3, 129]	35.8 [3.01, 120]	40.2 [5.65, 166]
Missing	9 (5.9%)	3 (4.8%)	16 (10.5%)	0 (0%)	6 (4.0%)	4 (6.8%)
vo2sum_peak_ml
Mean (SD)	20.8 (4.02)	24.0 (5.58)	20.6 (4.69)	24.7 (5.37)	20.4 (4.58)	24.2 (5.51)	<0.001
Median [Min, Max]	20.6 [12.6, 32.5]	23.9 [13.9, 35.2]	20.2 [11.5, 33.0]	24.0 [12.2, 38.1]	20.0 [10.1, 34.1]	25.4 [11.3, 39.5]
sittime_total
Mean (SD)	673 (255)	705 (264)	657 (302)	659 (295)	634 (272)	746 (423)	0.192
Median [Min, Max]	642 [184, 1610]	669 [230, 1230]	613 [107, 2390]	581 [192, 1570]	581 [201, 1430]	625 [213, 3170]
Missing	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (1.7%)

1.5 rand_treat.factor + race.factor

table1::table1(~screen_gender.factor+screen_race_la_his.factor+screen_mod_confirm.factor+dur_day_total_MODVIG_min_pla+vo2sum_peak_ml+sittime_total|rand_treat.factor+race.factor   ,data= merged.df, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Group 1: 150 mins of aerobic exercise			Group 2: 225 mins of aerobic exercise			Group 3: Stretch and Tone
	White (N=159)	Black (N=43)	Other (N=13)	White (N=163)	Black (N=41)	Other (N=11)	White (N=161)	Black (N=38)	Other (N=10)	P-value
screen_gender.factor
Female	105 (66.0%)	37 (86.0%)	10 (76.9%)	113 (69.3%)	33 (80.5%)	6 (54.5%)	109 (67.7%)	35 (92.1%)	6 (60.0%)	0.0104
Male	54 (34.0%)	6 (14.0%)	3 (23.1%)	50 (30.7%)	8 (19.5%)	5 (45.5%)	52 (32.3%)	3 (7.9%)	4 (40.0%)
screen_race_la_his.factor
No	155 (97.5%)	43 (100%)	8 (61.5%)	159 (97.5%)	41 (100%)	9 (81.8%)	155 (96.3%)	37 (97.4%)	8 (80.0%)	<0.001
Yes	4 (2.5%)	0 (0%)	5 (38.5%)	4 (2.5%)	0 (0%)	2 (18.2%)	6 (3.7%)	1 (2.6%)	2 (20.0%)
screen_mod_confirm.factor
No	154 (96.9%)	41 (95.3%)	12 (92.3%)	159 (97.5%)	38 (92.7%)	11 (100%)	155 (96.3%)	37 (97.4%)	10 (100%)	0.845
Yes	5 (3.1%)	2 (4.7%)	1 (7.7%)	4 (2.5%)	3 (7.3%)	0 (0%)	6 (3.7%)	1 (2.6%)	0 (0%)
dur_day_total_MODVIG_min_pla
Mean (SD)	52.3 (30.9)	52.9 (34.5)	53.7 (30.0)	52.3 (27.9)	36.9 (25.1)	64.0 (23.8)	47.9 (29.9)	31.7 (16.7)	59.6 (36.2)	<0.001
Median [Min, Max]	49.0 [3.67, 188]	44.3 [8.69, 133]	47.1 [14.2, 126]	47.3 [7.21, 129]	33.2 [4.90, 117]	64.1 [33.8, 101]	39.9 [5.65, 166]	30.0 [3.01, 78.4]	51.0 [27.4, 143]
Missing	8 (5.0%)	4 (9.3%)	0 (0%)	10 (6.1%)	5 (12.2%)	1 (9.1%)	5 (3.1%)	3 (7.9%)	2 (20.0%)
vo2sum_peak_ml
Mean (SD)	22.5 (4.55)	19.0 (4.80)	20.8 (3.71)	22.7 (4.93)	17.4 (4.12)	24.6 (5.13)	22.7 (4.85)	16.7 (3.01)	21.0 (5.78)	<0.001
Median [Min, Max]	22.3 [13.4, 35.2]	18.0 [12.6, 30.2]	20.7 [14.8, 26.3]	22.4 [12.6, 38.1]	17.5 [11.5, 25.5]	24.0 [18.7, 35.2]	22.5 [11.3, 39.5]	17.0 [10.1, 21.7]	19.8 [13.2, 32.4]
sittime_total
Mean (SD)	664 (254)	763 (278)	647 (170)	635 (304)	745 (296)	667 (175)	656 (340)	673 (259)	779 (291)	0.192
Median [Min, Max]	640 [184, 1610]	682 [230, 1290]	610 [367, 917]	610 [107, 2390]	703 [306, 1640]	671 [325, 856]	577 [201, 3170]	653 [229, 1360]	801 [294, 1250]
Missing	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	0 (0%)	1 (0.6%)	0 (0%)	0 (0%)

2 VO2, BMI, Gender

TMP<-merged.df[complete.cases(merged.df$bmi),]
model_part<-lm(vo2sum_peak_ml~screen_gender.factor, TMP)
TMP$VO2_Pk.partial_sex<-model_part$residuals
TMP$VO2_Pk.partial_sex<-round(TMP$VO2_Pk.partial_sex, digits=3)
TMP$sittime_total<-TMP$sittime_weekend_total+TMP$sittime_weekday_total_min
model_part<-lm(vo2sum_peak_ml~bmi, TMP)
TMP$VO2_Pk.partial_bmi<-model_part$residuals
TMP$VO2_Pk.partial_bmi<-round(TMP$VO2_Pk.partial_bmi, digits=3)
table1::table1(~dur_day_total_MODVIG_min_pla+vo2sum_peak_ml+VO2_Pk.partial_sex+VO2_Pk.partial_bmi+sittime_total|screen_gender.factor+screen_mod_confirm.factor ,data= TMP, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Female		Male
	No (N=443)	Yes (N=17)	No (N=182)	Yes (N=5)	P-value
dur_day_total_MODVIG_min_pla
Mean (SD)	48.0 (28.3)	62.8 (33.3)	51.9 (32.3)	40.7 (13.3)	0.129
Median [Min, Max]	42.0 [3.01, 188]	56.0 [28.4, 146]	42.7 [3.67, 182]	38.3 [23.8, 57.0]
Missing	29 (6.5%)	2 (11.8%)	7 (3.8%)	0 (0%)
vo2sum_peak_ml
Mean (SD)	20.5 (4.44)	22.9 (4.06)	24.3 (5.53)	24.9 (4.03)	<0.001
Median [Min, Max]	20.1 [10.1, 34.1]	23.7 [14.5, 32.0]	24.2 [11.3, 39.5]	25.9 [18.7, 29.5]
VO2_Pk.partial_sex
Mean (SD)	-0.0902 (4.44)	2.35 (4.06)	-0.0162 (5.53)	0.598 (4.03)	0.225
Median [Min, Max]	-0.499 [-10.5, 13.5]	3.10 [-6.10, 11.4]	-0.192 [-13.0, 15.2]	1.56 [-5.64, 5.16]
VO2_Pk.partial_bmi
Mean (SD)	-1.12 (3.63)	-0.690 (3.87)	2.70 (4.88)	3.10 (4.07)	<0.001
Median [Min, Max]	-1.19 [-13.8, 9.48]	-0.183 [-9.37, 7.07]	3.03 [-12.5, 16.1]	2.26 [-2.78, 7.18]
sittime_total
Mean (SD)	651 (277)	680 (239)	705 (334)	538 (263)	0.165
Median [Min, Max]	610 [107, 2390]	670 [338, 1290]	639 [192, 3170]	543 [213, 853]
Missing	0 (0%)	0 (0%)	0 (0%)	1 (20.0%)

par(mfrow = c(1, 2))
ggpubr::gghistogram(TMP, x = "VO2_Pk.partial_sex",
   add = "mean", rug = TRUE,
   color = "screen_gender.factor", palette = c("#00AFBB", "#E7B800"))

ggpubr::gghistogram(TMP, x = "VO2_Pk.partial_bmi",
   add = "mean", rug = TRUE,
   color = "screen_gender.factor", palette = c("#00AFBB", "#E7B800"))

2.1 Confirmation of ballanced measures

2.1.1 lm(vo2sum_peak_ml~rand_treat.factor*screen_site.factor,merged.df )

mod<-(lm(vo2sum_peak_ml~rand_treat.factor*screen_site.factor,merged.df ))
emm <- emmeans::emmeans(mod, pairwise ~rand_treat.factor*screen_site.factor, level = 0.95)
summary.em<-summary(emm)
sig_VO2_diffs<-summary.em$contrasts%>% filter(p.value<0.05)
sig_VO2_diffs

2.1.2 lm(dur_day_total_MODVIG_min_pla~rand_treat.factor*screen_site.factor,merged.df )

mod<-(lm(dur_day_total_MODVIG_min_pla~rand_treat.factor*screen_site.factor,merged.df ))
emm <- emmeans::emmeans(mod, pairwise ~rand_treat.factor*screen_site.factor, level = 0.95)
summary.em<-summary(emm)
sig_ACELL_diffs<-summary.em$contrasts%>% filter(p.value<0.05)
sig_ACELL_diffs

3 Baseline Randomization

table1::table1(~screen_site.factor+screen_gender.factor+Heavy_Drinker.factor+Smoking.Status.factor+ dur_day_total_MODVIG_min_pla+vo2sum_peak_ml+VO2_Pk.partial_sex+VO2_Pk.partial_bmi|rand_treat.factor  ,data= TMP, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Group 1: 150 mins of aerobic exercise (N=215)	Group 2: 225 mins of aerobic exercise (N=214)	Group 3: Stretch and Tone (N=209)	P-value
screen_site.factor
Kansas	69 (32.1%)	71 (33.2%)	70 (33.5%)	0.999
Northeastern	72 (33.5%)	71 (33.2%)	69 (33.0%)
Pitt	74 (34.4%)	72 (33.6%)	70 (33.5%)
screen_gender.factor
Female	152 (70.7%)	151 (70.6%)	150 (71.8%)	0.956
Male	63 (29.3%)	63 (29.4%)	59 (28.2%)
Heavy_Drinker.factor
Heavy Drinker	28 (13.0%)	25 (11.7%)	26 (12.4%)	0.915
Not Heavy Drinker	187 (87.0%)	189 (88.3%)	183 (87.6%)
Smoking.Status.factor
Current	11 (5.1%)	10 (4.7%)	9 (4.3%)	0.95
Former	90 (41.9%)	88 (41.1%)	93 (44.5%)
Never	114 (53.0%)	116 (54.2%)	106 (50.7%)
Missing	0 (0%)	0 (0%)	1 (0.5%)
dur_day_total_MODVIG_min_pla
Mean (SD)	52.5 (31.4)	50.0 (28.0)	45.5 (29.0)	0.0565
Median [Min, Max]	48.2 [3.67, 188]	42.7 [4.90, 129]	37.6 [3.01, 166]
Missing	12 (5.6%)	16 (7.5%)	10 (4.8%)
vo2sum_peak_ml
Mean (SD)	21.7 (4.75)	21.8 (5.25)	21.5 (5.14)	0.828
Median [Min, Max]	21.4 [12.6, 35.2]	21.5 [11.5, 38.1]	20.9 [10.1, 39.5]
VO2_Pk.partial_sex
Mean (SD)	0.0219 (4.53)	0.0818 (4.90)	-0.161 (4.84)	0.862
Median [Min, Max]	-0.0990 [-10.4, 11.9]	-0.449 [-12.1, 13.8]	-0.299 [-13.0, 15.2]
VO2_Pk.partial_bmi
Mean (SD)	0.110 (4.07)	0.124 (4.46)	-0.241 (4.52)	0.622
Median [Min, Max]	-0.0360 [-9.84, 11.2]	-0.458 [-13.8, 16.0]	-0.564 [-12.5, 16.1]

table1::table1(~screen_mod_confirm.factor+season+dur_day_total_MODVIG_min_pla+NValidays4_3wk_1we+vo2sum_peak_ml+VO2_Pk.partial_sex+VO2_Pk.partial_bmi|rand_treat.factor  ,data= TMP, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Group 1: 150 mins of aerobic exercise (N=215)	Group 2: 225 mins of aerobic exercise (N=214)	Group 3: Stretch and Tone (N=209)	P-value
screen_mod_confirm.factor
No	207 (96.3%)	207 (96.7%)	202 (96.7%)	0.964
Yes	8 (3.7%)	7 (3.3%)	7 (3.3%)
season
Fall	68 (31.6%)	69 (32.2%)	59 (28.2%)	0.791
Spring	41 (19.1%)	43 (20.1%)	52 (24.9%)
Summer	46 (21.4%)	46 (21.5%)	45 (21.5%)
Winter	48 (22.3%)	40 (18.7%)	43 (20.6%)
Missing	12 (5.6%)	16 (7.5%)	10 (4.8%)
dur_day_total_MODVIG_min_pla
Mean (SD)	52.5 (31.4)	50.0 (28.0)	45.5 (29.0)	0.0565
Median [Min, Max]	48.2 [3.67, 188]	42.7 [4.90, 129]	37.6 [3.01, 166]
Missing	12 (5.6%)	16 (7.5%)	10 (4.8%)
NValidays4_3wk_1we
Not Valid	2 (0.9%)	5 (2.3%)	4 (1.9%)	0.503
Valid	201 (93.5%)	193 (90.2%)	195 (93.3%)
Missing	12 (5.6%)	16 (7.5%)	10 (4.8%)
vo2sum_peak_ml
Mean (SD)	21.7 (4.75)	21.8 (5.25)	21.5 (5.14)	0.828
Median [Min, Max]	21.4 [12.6, 35.2]	21.5 [11.5, 38.1]	20.9 [10.1, 39.5]
VO2_Pk.partial_sex
Mean (SD)	0.0219 (4.53)	0.0818 (4.90)	-0.161 (4.84)	0.862
Median [Min, Max]	-0.0990 [-10.4, 11.9]	-0.449 [-12.1, 13.8]	-0.299 [-13.0, 15.2]
VO2_Pk.partial_bmi
Mean (SD)	0.110 (4.07)	0.124 (4.46)	-0.241 (4.52)	0.622
Median [Min, Max]	-0.0360 [-9.84, 11.2]	-0.458 [-13.8, 16.0]	-0.564 [-12.5, 16.1]

3.0.1 For Jermon

ggstatsplot::grouped_gghistostats(merged.df,ADI_NATRANK, grouping.var=screen_site.factor)

merged.df<-merged.df %>% 
  group_by(screen_site.factor) %>%
  mutate(
  ADI_NATRANK_Z=scale(ADI_NATRANK),
  ADI_STATERNK_Z=scale(ADI_STATERNK),
  tes_rank=scale(tes, center=T),
  tesctyscor_rank=scale(tes, center=T))


tmp<-merged.df %>% select(record_id,screen_site.factor ,race.factor,ADI_STATERNK_Z, ADI_NATRANK_Z) %>%reshape2::melt(.,id.vars=c("record_id","screen_site.factor","race.factor") )
tmp$value<-as.numeric(tmp$value)
ggpubr::gghistogram(tmp, x = "value",
   add = "mean", rug = TRUE,
   color = "variable", palette = c("#00AFBB", "#E7B800")) +facet_wrap(~screen_site.factor+race.factor,scale="free")

TMP<-merged.df[complete.cases(merged.df$ADI_STATERNK),]
summary(lm(ADI_NATRANK~poly(ADI_STATERNK,2)*screen_site.factor,TMP ))

## 
## Call:
## lm(formula = ADI_NATRANK ~ poly(ADI_STATERNK, 2) * screen_site.factor, 
##     data = TMP)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -28.193  -1.601   0.169   2.009  10.371 
## 
## Coefficients:
##                                                        Estimate Std. Error
## (Intercept)                                             50.3313     0.2942
## poly(ADI_STATERNK, 2)1                                 619.2057     7.4694
## poly(ADI_STATERNK, 2)2                                -118.2134     7.3346
## screen_site.factorNortheastern                         -35.2732     0.4480
## screen_site.factorPitt                                  -6.7429     0.4275
## poly(ADI_STATERNK, 2)1:screen_site.factorNortheastern -286.3476    14.7792
## poly(ADI_STATERNK, 2)2:screen_site.factorNortheastern  155.5476    12.5009
## poly(ADI_STATERNK, 2)1:screen_site.factorPitt          -21.9757    10.1623
## poly(ADI_STATERNK, 2)2:screen_site.factorPitt           87.2663    10.0537
##                                                       t value Pr(>|t|)    
## (Intercept)                                           171.066   <2e-16 ***
## poly(ADI_STATERNK, 2)1                                 82.899   <2e-16 ***
## poly(ADI_STATERNK, 2)2                                -16.117   <2e-16 ***
## screen_site.factorNortheastern                        -78.738   <2e-16 ***
## screen_site.factorPitt                                -15.773   <2e-16 ***
## poly(ADI_STATERNK, 2)1:screen_site.factorNortheastern -19.375   <2e-16 ***
## poly(ADI_STATERNK, 2)2:screen_site.factorNortheastern  12.443   <2e-16 ***
## poly(ADI_STATERNK, 2)1:screen_site.factorPitt          -2.162    0.031 *  
## poly(ADI_STATERNK, 2)2:screen_site.factorPitt           8.680   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.063 on 633 degrees of freedom
## Multiple R-squared:  0.9794, Adjusted R-squared:  0.9792 
## F-statistic:  3766 on 8 and 633 DF,  p-value: < 2.2e-16

mod<-(lm(ADI_NATRANK~poly(ADI_STATERNK,2)*screen_site.factor,TMP ))

par(mfrow = c(1, 2))
visreg::visreg(mod, "screen_site.factor",by="ADI_STATERNK", overlay=T )
visreg::visreg(mod, "ADI_STATERNK",by="screen_site.factor", overlay=T )

model.diag.metrics <- augment(mod)
class(model.diag.metrics$`poly(ADI_STATERNK, 2)`)<-"numeric"


mod<-(lm(ADI_NATRANK~ADI_STATERNK*screen_site.factor,TMP ))
model.diag.metrics <- augment(mod)

ggplot(model.diag.metrics, aes(ADI_STATERNK, ADI_NATRANK)) +
  geom_point() +
  stat_smooth(method = lm, se = FALSE) +
  geom_segment(aes(xend = ADI_STATERNK, yend = .fitted), color = "red", size = 0.3)+facet_wrap(~screen_site.factor)

summary(lm(ses_life_standing.factor~ses_community_standing.factor:tes+screen_site.factor+educ,merged.df ))

## 
## Call:
## lm(formula = ses_life_standing.factor ~ ses_community_standing.factor:tes + 
##     screen_site.factor + educ, data = merged.df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4996 -0.8985  0.0961  0.8888  3.9428 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                        0.0956795  0.4428385   0.216    0.829    
## screen_site.factorNortheastern     0.1560715  0.1362785   1.145    0.253    
## screen_site.factorPitt            -0.0896990  0.1360122  -0.659    0.510    
## educ                               0.1985580  0.0248347   7.995 6.43e-15 ***
## ses_community_standing.factor:tes  0.0047963  0.0003101  15.467  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.364 on 615 degrees of freedom
##   (28 observations deleted due to missingness)
## Multiple R-squared:  0.3595, Adjusted R-squared:  0.3553 
## F-statistic: 86.28 on 4 and 615 DF,  p-value: < 2.2e-16

mod<-(lm(ses_life_standing.factor~ses_community_standing.factor:tes+screen_site.factor+educ,merged.df ))
visreg::visreg(mod, "ses_community_standing.factor",by="tes", overlay=T)

model.diag.metrics <- augment(mod)
ggplot(model.diag.metrics, aes(ses_community_standing.factor, ses_life_standing.factor)) +
  geom_point() +
  stat_smooth(method = lm, se = FALSE) +
  geom_segment(aes(xend = ses_community_standing.factor, yend = .fitted), color = "red", size = 0.3) +facet_wrap(~screen_site.factor)

par(mfrow = c(2, 2))
autoplot(mod)

3.1 Play..

 table1::table1(~screen_age+AVG_cogTscore_rank+educ+ADI_NATRANK+ADI_STATERNK+`Walkability Index`+tes+tesctyscor|screen_site.factor  ,data= merged.df, overall=F,  extra.col=list(`P-value`=pvalue.func))

	Kansas (N=214)	Northeastern (N=215)	Pitt (N=219)	P-value
screen_age
Mean (SD)	69.4 (3.76)	70.5 (3.76)	69.4 (3.62)	0.00326
Median [Min, Max]	69.0 [64.0, 80.0]	70.0 [65.0, 80.0]	69.0 [65.0, 80.0]
AVG_cogTscore_rank
Mean (SD)	0.993 (0.119)	0.954 (0.132)	0.990 (0.125)	0.00165
Median [Min, Max]	1.01 [0.632, 1.21]	0.954 [0.630, 1.21]	1.00 [0.644, 1.26]
educ
Mean (SD)	16.4 (2.19)	16.5 (2.26)	16.1 (2.22)	0.183
Median [Min, Max]	16.0 [11.0, 21.0]	16.0 [12.0, 20.0]	16.0 [10.0, 20.0]
ADI_NATRANK
Mean (SD)	43.2 (24.3)	11.2 (8.76)	55.7 (25.5)	<0.001
Median [Min, Max]	38.0 [5.00, 100]	9.00 [1.00, 52.0]	54.0 [4.00, 100]
Missing	1 (0.5%)	5 (2.3%)	0 (0%)
ADI_STATERNK
Mean (SD)	2.95 (2.60)	2.87 (1.97)	5.04 (2.99)	<0.001
Median [Min, Max]	2.00 [1.00, 10.0]	2.00 [1.00, 10.0]	5.00 [1.00, 10.0]
Missing	1 (0.5%)	5 (2.3%)	0 (0%)
Walkability Index
Mean (SD)	10.9 (3.94)	15.2 (2.51)	13.4 (3.82)	<0.001
Median [Min, Max]	10.7 [2.67, 18.3]	15.5 [4.50, 19.3]	14.2 [4.17, 19.8]
tes
Mean (SD)	98.1 (7.95)	91.2 (8.70)	91.3 (10.0)	<0.001
Median [Min, Max]	100 [34.8, 100]	93.2 [37.4, 100]	93.2 [42.1, 100]
Missing	17 (7.9%)	0 (0%)	6 (2.7%)
tesctyscor
Mean (SD)	94.5 (4.17)	89.0 (4.12)	88.0 (3.19)	<0.001
Median [Min, Max]	95.7 [71.8, 100]	90.4 [68.1, 91.7]	87.0 [59.7, 95.3]
Missing	17 (7.9%)	0 (0%)	6 (2.7%)

IGNITE Baseline Tests

1 ADD RE_IGNITE

1.1 screen_mod_confirm.factor

1.2 rand_treat.factor

1.3 rand_treat.factor + screen_mod_confirm.factor

1.4 rand_treat.factor + screen_gender.factor

1.5 rand_treat.factor + race.factor

2 VO2, BMI, Gender

2.1 Confirmation of ballanced measures

2.1.1 lm(vo2sum_peak_ml~rand_treat.factor*screen_site.factor,merged.df )

2.1.2 lm(dur_day_total_MODVIG_min_pla~rand_treat.factor*screen_site.factor,merged.df )

3 Baseline Randomization

3.0.1 For Jermon

3.1 Play..