Analyses HRV - support pour la these de Marie Viellard

1 Description de l’echantillon de RELADO

16/03/2021 : On a enlevé les 12 individus pour lesquels on avait pas de valeur pour RS2 HF.

On a maintenant 45 individus

1.1 VALEURS MANQUANTES

Insérer ici les graphes des valeurs manquantes

1.2 EGF

EGF : Echelle Globale de fonctionnnement

On a un score EGF et un score EGF hospitalisations : qu’est ce que le 2eme ?

Table 1.1: Score a l’Echelle Globale de Fonctionnement

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
40	55	65	66.6	75	99

Figure 1.1: Distribution des scores EGF

1.3 Hospitalisations

Table 1.2: Duree d’hospitalisation de jour

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
0	0	0	3.4	5.2	28	2

1.4 Automutilations

Table 1.3: Automutilations

	Frequence	% du Total	% Cumul
non	24	53.3	53.3
oui	21	46.7	100.0
<NA>	0	0.0	100.0
Total	45	100.0	100.0

1.5 Tentatives de suicide

Table 1.4: Nombre de TS

	Frequence	% du Total	% Cumul
0	18	40.0	40.0
1	12	26.7	66.7
>=2	15	33.3	100.0
<NA>	0	0.0	100.0
Total	45	100.0	100.0

1.6 Evenements de Vie

Table 1.5: Proportion de l’occurence pour chaque evenement de vie

Evenement	N	Ntotal	Proportion	NA
separation ou divorce des parents	23	45	51.1%	0
separation avec un parent avant l’âge d’un an	9	45	20%	0
separation avec les 2 parents de plus d’un an	1	45	2.2%	0
adoption	4	45	8.9%	0
placement en foyer	4	45	8.9%	0
placement en famille d’accueil	1	45	2.2%	0
mort du pere	2	45	4.4%	0
mort de la mere	0	61	0%	0
Deces d’un parent proche	3	45	6.7%	0
Rupture amoureuse	1	45	2.2%	0
Avortement	1	45	2.2%	0
Accident / catastrophe	0	61	0%	0
Autre	18	0	40%	2

Table 1.5: Score evenement de vie

	Frequence	% du Total	% Cumul
0	14	31.1	31.1
1	18	40.0	71.1
2	10	22.2	93.3
3	2	4.4	97.8
4	1	2.2	100.0
<NA>	0	0.0	100.0
Total	45	100.0	100.0

1.7 Meditation

Table 1.6: Meditation

	Frequence	% du Total	% Cumul
0	32	71.1	71.1
1	13	28.9	100.0
<NA>	0	0.0	100.0
Total	45	100.0	100.0

1.8 SCL 90

D’apres le document scanne a l’IMM (bouquin de Corinne), voila les differents scores calculables pour la SCL-90 :

Scans du codage de SCL90

Trop lourd, pas uploader sur internet

GSI : Gravite Globale = score total / 90
PST : diversite des symptomes = Nombres de reponses autres que 0
PSDI : Degre de malaise = score total divise par le PST

Voici les statistiques pour ces 3 indicateurs :

Table 1.7: Scores SCL 90

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
SCL_GSI	0.09	0.77	1.04	1.26	1.69	3.14
SCL_PST	7.00	39.00	47.00	51.27	70.00	86.00
SCL_PSDI	1.00	1.60	1.96	2.04	2.36	3.54

Statistiques par Sexe

Garçons :

	vars	n	mean	sd	min	max	range	se
SCL_GSI	1	12	0.94	0.52	0.14	1.97	1.82	0.15
SCL_PST	2	12	45.17	19.90	9.00	79.00	70.00	5.75
SCL_PSDI	3	12	1.79	0.32	1.30	2.36	1.05	0.09

Filles :

	vars	n	mean	sd	min	max	range	se
SCL_GSI	1	33	1.38	0.83	0.09	3.14	3.06	0.14
SCL_PST	2	33	53.48	21.00	7.00	86.00	79.00	3.66
SCL_PSDI	3	33	2.13	0.66	1.00	3.54	2.54	0.11

Statistiques Alexithymiques / Non-Alexityhmiques

NON-Alexithymiques :

	vars	n	mean	sd	min	max	range	se
SCL_GSI	1	21	0.76	0.48	0.09	2.21	2.12	0.11
SCL_PST	2	21	37.00	17.08	7.00	80.00	73.00	3.73
SCL_PSDI	3	21	1.70	0.41	1.00	2.49	1.49	0.09

Alexithymiques :

	vars	n	mean	sd	min	max	range	se
SCL_GSI	1	24	1.71	0.72	0.47	3.14	2.68	0.15
SCL_PST	2	24	63.75	15.09	30.00	86.00	56.00	3.08
SCL_PSDI	3	24	2.33	0.59	1.40	3.54	2.14	0.12

Figure 1.2: Distributions des 3 indicateurs SCL90

On a aussi la structure factorielle suivante, que j’ai pour l’instant laisse de côte :

Figure 1.3: Valeurs moyennes des sous dimensions de la SCL 90

1.9 TAS 20

La TAS 20 est composee de 20 items.

La TAS 20 se divise en 3 sous-scores correspondants aux 3 dimensions suivantes :

• la difficulte a identifier ses etats emotionnels (TAS1)
• la difficulte a decrire ses etats emotionnels a autrui (TAS2)
• la pensee operatoire. (TAS3)

Le Score total est l’addition de ces 3 sous-scores.

On a calcule les 3 sous-scores, ainsi que les categories definies selon des seuils :

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
TAS1	9	18	23	22.27	26	33
TAS2	5	11	16	15.67	20	25
TAS3	8	13	18	17.33	20	29
TASTOT	25	43	60	55.91	66	78

Figure 1.4: Scores aux sous dimensions de la TAS

Figure 1.5: Distribution des scores TAS totaux

Les scores a la TAS 20 peuvent être utilises selon plusieurs classifications :

	Freq	% Total	% Total Cum.
Alexithymie (>=56)	24	53.3	53.3
Modéré (44< <56)·	8	17.8	71.1
Non-Alexithtymie(<=44)	13	28.9	100.0
Total	45	100.0	100.0

	Freq	% Total	% Total Cum.
a Non-Alexithymie (<56)	21	46.7	46.7
b Alexithymie (>=56)	24	53.3	100.0
Total	45	100.0	100.0

Statistiques de la TAS20 par Sexe

Table 1.8: TAS categorielle par Sexe

	Alexithymie (>=56)	Modéré (44< <56)	Non-Alexithtymie(<=44)
F	57.6	21.2	21.2
M	41.7	8.3	50.0

Table 1.8: TAS binaire par Sexe

	a Non-Alexithymie (<56)	b Alexithymie (>=56)
F	42.4	57.6
M	58.3	41.7

Garçons :

Table 1.9: Scores TAS chez les Garçons

	vars	n	mean	sd	min	max	range	se
TAS1	1	12	19.8	3.8	15	26	11	1.1
TAS2	2	12	14.2	3.8	8	20	12	1.1
TAS3	3	12	16.9	3.3	11	22	11	0.9
TASTOT	4	12	50.4	9.3	41	63	22	2.7

Filles :

Table 1.10: Scores TAS chez les Filles

	vars	n	mean	sd	min	max	range	se
TAS1	1	33	23.2	5.7	9	33	24	1.0
TAS2	2	33	16.2	5.9	5	25	20	1.0
TAS3	3	33	17.5	5.2	8	29	21	0.9
TASTOT	4	33	57.9	15.3	25	78	53	2.7

=> HETEROGENEITE PLUS FORTE CHEZ LES GARCONS

1.10 MAIA

Table 1.11: Scores MAIA

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
MAIA_noticing	1.00	2.75	3.25	3.22	3.75	4.75
MAIA_notdistracting	0.00	1.33	2.00	2.16	3.00	5.00
MAIA_notworrying	0.33	1.67	3.33	2.89	4.00	5.00
MAIA_attentionreg	0.14	1.43	2.43	2.56	3.57	5.00
MAIA_emoaware	0.40	2.20	3.80	3.29	4.20	5.00
MAIA_selfregul	0.00	0.75	1.75	2.00	3.50	4.75
MAIA_bodylisten	0.00	0.33	1.33	1.67	2.33	4.67
MAIA_trust	0.00	0.33	2.00	2.21	4.00	5.00
MAIA_total	41.00	65.00	82.00	82.47	97.00	135.00

Figure 1.6: Multidimensional Assessment of Interoceptive Awareness

2 HF : Repos

On presente ici les statistiques des indicateurs HF_p (pourcentage) et HF_abs (valeur absolue)

On presente cet indicateur pendant les phases :

• de repos au debut de l’experimentation (RS2)

Table 2.1: RS2 HF_p

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
2.4	10.5	18.1	23.1	32.3	65.3

Table 2.1: RS2 HF_abs

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.
64.3	290.1	642.6	1202.5	1261.9	8932.7

On ventile les moyennes des quatre indicateurs par des caracteristiques socio-economiques :

Lorsque possible (la variable categorielle a seulement 2 categories), j’ai ajoute la p-value d’un test de difference de moyenne). Une p-value inferieure a 0,05 veut dire que la difference est statistiquement significative.

NOTE sur les CATEGORIES :

Pour les CSP, du aux faibles effectifs, j’ai binarise la CSP en “cadre” ou “Autre”.

Il n’y a qu’une fugue qui a les infos, donc pas possible calculer la moyenne ni le test

sexe	mean_RS2_HF_p	mean_RS2_HF_abs	mean_CC_HF_p	mean_CC_HF_abs
F	25.3	1261.4	11.5	1901.6
M	17.0	1040.6	11.2	1859.2
p-value T-test	0.064	0.598	0.924	0.956
age_median
<=17 ans	21.7	1309.1	11.2	1887.9
>17ans	25.0	1056.6	11.6	1894.4
p-value T-test	0.502	0.586	0.878	0.993
divorce
non	21.6	979.3	10.0	1465.9
oui	24.5	1416.0	12.5	2233.9
p-value T-test	0.567	0.386	0.371	0.249
fugues
0	23.3	1212.5	11.1	1824.7
NA	NaN	NaN	NaN	NaN
hospi_psy_nb_C
1	25.2	890.9	13.1	1807.5
2	31.0	1973.3	7.1	1326.9
3	17.6	891.8	11.1	1512.7
NA	NA	NA	NA	NA
CSP_mere_C
CSP autre	22.8	1338.9	11.7	2067.0
CSP cadre	23.5	997.9	10.9	1606.8
p-value T-test	0.896	0.45	0.739	0.451
CSP_pere_C
CSP autre	24.2	1239.6	11.0	1941.0
CSP cadre	22.2	1172.8	11.9	1833.6
p-value T-test	0.69	0.895	0.75	0.875
ATCD_mal_phy
0	23.7	1264	11.6	1916.4
1	21.7	1051.2	10.9	1836.1
p-value T-test	0.722	0.633	0.809	0.926

2.1 plots : HF_abs repos

[[1]] [[2]] [[3]] [[4]] [[5]] [[6]] [[7]] [[8]] [[9]] [[10]] [[11]] [[12]] [[13]] [[14]] [[15]]

2.2 plots : HF_p repos

[[1]] [[2]] [[3]] [[4]] [[5]] [[6]] [[7]] [[8]] [[9]] [[10]] [[11]] [[12]] [[13]] [[14]] [[15]]

2.3 Correlations avec MAIA

Avec MAIA :

BDD54  <-readRDS(paste(dropbox,"UIMM/RELADO/BDD DESC"         ,sep=""))

cor.test(BDD54$MAIA_total,BDD54$RS2_HF_p, use="pairwise.complete.obs" , method="pearson")

    Pearson's product-moment correlation

data:  BDD54$MAIA_total and BDD54$RS2_HF_p
t = 1.3555, df = 43, p-value = 0.1823
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.09685771  0.46814786
sample estimates:
      cor 
0.2024315 

cor.test(BDD54$MAIA_total,BDD54$RS2_HF_abs, use="pairwise.complete.obs", method ="pearson")

    Pearson's product-moment correlation

data:  BDD54$MAIA_total and BDD54$RS2_HF_abs
t = 1.2813, df = 43, p-value = 0.207
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.1078361  0.4594398
sample estimates:
     cor 
0.191768 

cor.test(BDD54$MAIA_selfregul,BDD54$RS2_HF_p, use="pairwise.complete.obs", method ="pearson")

    Pearson's product-moment correlation

data:  BDD54$MAIA_selfregul and BDD54$RS2_HF_p
t = 1.6121, df = 43, p-value = 0.1143
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.05893039  0.49741039
sample estimates:
      cor 
0.2387331 

cor.test(BDD54$MAIA_selfregul,BDD54$RS2_HF_abs, use="pairwise.complete.obs", method ="pearson")

    Pearson's product-moment correlation

data:  BDD54$MAIA_selfregul and BDD54$RS2_HF_abs
t = 2.279, df = 43, p-value = 0.02769
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.0384583 0.5671662
sample estimates:
      cor 
0.3282863 

3 DELTA HF

On cree les nouveaux indicateurs suivants :

La difference est calculee entre l’epreuve de Cyber ball et le Repos 2

delta_HF_abs <- LOG(CBT_HF_abs) - LOG(RS2_HF_abs)

Table 3.1: Delta HF (valeur absolue)

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
-2.6	-0.6	-0.1	0	0.6	2.3	15

3.1 DELTA HF et autres echelles

Tests de différences de moyennes des sous dimensions de la MAIA entre HRV reactivity positive et negative modérée.

BDDex <- BDD54[which(BDD54$delta_HF_ex>=0),]

t.test(BDDex$MAIA_total ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_total by BDDex$delta_HF_ex
t = 3.0199, df = 33.43, p-value = 0.004817
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  7.016872 35.947834
sample estimates:
mean in group 0 mean in group 1 
       95.88235        74.40000 

t.test(BDDex$MAIA_emoaware ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_emoaware by BDDex$delta_HF_ex
t = 2.121, df = 31.734, p-value = 0.04183
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.03318507 1.65387376
sample estimates:
mean in group 0 mean in group 1 
       3.823529        2.980000 

t.test(BDDex$MAIA_noticing ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_noticing by BDDex$delta_HF_ex
t = 0.84104, df = 34.996, p-value = 0.406
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.3056371  0.7379900
sample estimates:
mean in group 0 mean in group 1 
       3.441176        3.225000 

t.test(BDDex$MAIA_notdistracting ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_notdistracting by BDDex$delta_HF_ex
t = -1.1196, df = 33.987, p-value = 0.2708
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.2061433  0.3492806
sample estimates:
mean in group 0 mean in group 1 
       1.921569        2.350000 

t.test(BDDex$MAIA_notworrying ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_notworrying by BDDex$delta_HF_ex
t = 1.1493, df = 34.6, p-value = 0.2583
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.3768111  1.3591640
sample estimates:
mean in group 0 mean in group 1 
       3.274510        2.783333 

t.test(BDDex$MAIA_attentionreg ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_attentionreg by BDDex$delta_HF_ex
t = 0.81577, df = 34.466, p-value = 0.4202
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.5246162  1.2288178
sample estimates:
mean in group 0 mean in group 1 
       2.823529        2.471429 

t.test(BDDex$MAIA_selfregul ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_selfregul by BDDex$delta_HF_ex
t = 2.3108, df = 34.916, p-value = 0.02687
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.1308569 2.0250254
sample estimates:
mean in group 0 mean in group 1 
       2.602941        1.525000 

t.test(BDDex$MAIA_bodylisten ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_bodylisten by BDDex$delta_HF_ex
t = 3.6051, df = 34.967, p-value = 0.0009625
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.5717789 2.0458682
sample estimates:
mean in group 0 mean in group 1 
       2.392157        1.083333 

t.test(BDDex$MAIA_trust ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_trust by BDDex$delta_HF_ex
t = 2.6007, df = 34.73, p-value = 0.01357
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.3075081 2.4983742
sample estimates:
mean in group 0 mean in group 1 
       3.019608        1.616667 

Croisements avec autres échelles

variables	moyenne de Delta HF (abs)
age_median
<=17 ans	0.11
>17ans	-0.23
sexe
F	0.06
M	-0.29
EGF_Q4
(29.9,47.2]	0.63
(47.2,64.5]	-0.16
(64.5,81.8]	0.11
(81.8,99.1]	-0.32
SCL_GSI_Q4
(0.0858,0.853]	-0.30
(0.853,1.62]	0.23
(1.62,2.38]	-0.29
(2.38,3.15]	0.49
TAS1_Q4
(8.98,15]	-0.45
(15,21]	0.02
(21,27]	-0.19
(27,33]	0.50
TAS2_Q4
(4.98,10]	0.19
(10,15]	-0.28
(15,20]	-0.31
(20,25]	0.67
TAS3_Q4
(7.98,13.2]	-0.26
(13.2,18.5]	0.18
(18.5,23.8]	0.09
(23.8,29]	-0.37
TASTOT_Q4
(24.9,38.2]	-0.41
(38.2,51.5]	-0.09
(51.5,64.8]	0.01
(64.8,78.1]	0.15
meditation
0	-0.09
1	0.12
autoM
non	-0.23
oui	0.17
TS_nb
0	-0.15
1	0.52
>=2	-0.33
MAIA_noticing_Q
(0.996,1.94]	-0.27
(1.94,2.88]	-0.47
(2.88,3.81]	0.12
(3.81,4.75]	0.23
MAIA_notdistracting_Q
(-0.005,1.25]	0.40
(1.25,2.5]	-0.33
(2.5,3.75]	-0.03
(3.75,5]	0.61
MAIA_notworrying_Q
(0.329,1.5]	-0.07
(1.5,2.67]	-0.70
(2.67,3.83]	0.25
(3.83,5]	0.17
MAIA_attentionreg_Q
(0.138,1.36]	-0.04
(1.36,2.57]	-0.33
(2.57,3.79]	0.13
(3.79,5]	0.24
MAIA_emoaware_Q
(0.395,1.55]	0.22
(1.55,2.7]	-0.11
(2.7,3.85]	-0.27
(3.85,5]	0.06
MAIA_selfregul_Q
(-0.00475,1.19]	0.24
(1.19,2.38]	-0.13
(2.38,3.56]	0.02
(3.56,4.75]	-0.33
MAIA_bodylisten_Q
(-0.00467,1.17]	0.12
(1.17,2.33]	0.00
(2.33,3.5]	0.01
(3.5,4.67]	-0.68
MAIA_trust_Q
(-0.005,1.25]	0.12
(1.25,2.5]	-0.03
(2.5,3.75]	-0.07
(3.75,5]	-0.21
MAIA_total_Q
(40.9,64.5]	-0.04
(64.5,88]	-0.14
(88,112]	0.02
(112,135]	0.15

3.2 plots

[[1]] [[2]] [[3]] [[4]] [[5]] [[6]] [[7]] [[8]] [[9]] [[10]] [[11]] [[12]] [[13]] [[14]] [[15]]

4 Delta HF extremes

On créé un indicateur de Delta HF en 3 catégories : - HRV reactivite positive : >= 0 - HRV reactivite negative moderee : < 0 - HRV reactivite negative forte : ( < (moyenne - 1sd) )

Figure 4.1: Distribution de Delta HF

Table 4.1: Caracteristiques Delta HF extremes vs. moyens

	Delta HF moyen	Delta HF “extreme”
TAS1	22.30	23.33
TAS2	15.39	16.22
TAS3	16.76	18.33
age	17.73	18.22
Sexe01	0.79	0.56
SCL_GSI	1.28	1.34
SCL_PSDI	2.03	2.07
SCL_PST	51.06	55.78
coherence.ratio	4.15	2.30
RS2_HF_p	24.10	20.83
RS2_HF_abs	1096.88	1747.43
autoM01	0.42	0.78
TS_bin01	0.55	0.67

4.1 Tests avec MAIA

On fait ici des tests de differences de moyennes des sous dimensions de la MAIA entre les HRV reactivity>=0 et <0 (on laisse de cote les tres bas)

BDDex <- BDD54[which(BDD54$delta_HF_ex>=0),]

t.test(BDDex$MAIA_total ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_total by BDDex$delta_HF_ex
t = 3.0199, df = 33.43, p-value = 0.004817
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  7.016872 35.947834
sample estimates:
mean in group 0 mean in group 1 
       95.88235        74.40000 

t.test(BDDex$MAIA_emoaware ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_emoaware by BDDex$delta_HF_ex
t = 2.121, df = 31.734, p-value = 0.04183
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.03318507 1.65387376
sample estimates:
mean in group 0 mean in group 1 
       3.823529        2.980000 

t.test(BDDex$MAIA_noticing ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_noticing by BDDex$delta_HF_ex
t = 0.84104, df = 34.996, p-value = 0.406
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.3056371  0.7379900
sample estimates:
mean in group 0 mean in group 1 
       3.441176        3.225000 

t.test(BDDex$MAIA_notdistracting ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_notdistracting by BDDex$delta_HF_ex
t = -1.1196, df = 33.987, p-value = 0.2708
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.2061433  0.3492806
sample estimates:
mean in group 0 mean in group 1 
       1.921569        2.350000 

t.test(BDDex$MAIA_notworrying ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_notworrying by BDDex$delta_HF_ex
t = 1.1493, df = 34.6, p-value = 0.2583
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.3768111  1.3591640
sample estimates:
mean in group 0 mean in group 1 
       3.274510        2.783333 

t.test(BDDex$MAIA_attentionreg ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_attentionreg by BDDex$delta_HF_ex
t = 0.81577, df = 34.466, p-value = 0.4202
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.5246162  1.2288178
sample estimates:
mean in group 0 mean in group 1 
       2.823529        2.471429 

t.test(BDDex$MAIA_selfregul ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_selfregul by BDDex$delta_HF_ex
t = 2.3108, df = 34.916, p-value = 0.02687
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.1308569 2.0250254
sample estimates:
mean in group 0 mean in group 1 
       2.602941        1.525000 

t.test(BDDex$MAIA_bodylisten ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_bodylisten by BDDex$delta_HF_ex
t = 3.6051, df = 34.967, p-value = 0.0009625
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.5717789 2.0458682
sample estimates:
mean in group 0 mean in group 1 
       2.392157        1.083333 

t.test(BDDex$MAIA_trust ~ BDDex$delta_HF_ex)

    Welch Two Sample t-test

data:  BDDex$MAIA_trust by BDDex$delta_HF_ex
t = 2.6007, df = 34.73, p-value = 0.01357
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.3075081 2.4983742
sample estimates:
mean in group 0 mean in group 1 
       3.019608        1.616667 

5 Coherence ratio

Indicateur de coherence ratio fourni par Marie.

Mise a Jour 13/12/20 : Indicateur de coherence cardiaque mis a jour

(#tab:coherence ratio)Coherence ratio (log)

Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
-1.7	0.5	1	0.9	1.6	3	4

TABLEAU : Moyenne de Coherence ratio par categories

variables	moyenne de coherence log
age_median
<=17 ans	1.01
>17ans	0.75
sexe
F	0.91
M	0.85
EGF_Q4
(29.9,47.2]	0.73
(47.2,64.5]	0.62
(64.5,81.8]	1.00
(81.8,99.1]	1.40
SCL_GSI_Q4
(0.0858,0.853]	0.99
(0.853,1.62]	0.87
(1.62,2.38]	1.06
(2.38,3.15]	0.18
TAS1_Q4
(8.98,15]	1.04
(15,21]	0.88
(21,27]	0.93
(27,33]	0.61
TAS2_Q4
(4.98,10]	1.21
(10,15]	0.99
(15,20]	0.60
(20,25]	0.80
TAS3_Q4
(7.98,13.2]	1.20
(13.2,18.5]	0.80
(18.5,23.8]	0.64
(23.8,29]	1.11
TASTOT_Q4
(24.9,38.2]	1.35
(38.2,51.5]	0.88
(51.5,64.8]	0.83
(64.8,78.1]	0.70
meditation
0	1.03
1	0.6
autoM
non	0.96
oui	0.84
TS_nb
0	0.68
1	0.82
>=2	1.19
MAIA_noticing_Q
(0.996,1.94]	0.12
(1.94,2.88]	0.58
(2.88,3.81]	1.03
(3.81,4.75]	1.09
MAIA_notdistracting_Q
(-0.005,1.25]	0.91
(1.25,2.5]	0.87
(2.5,3.75]	0.82
(3.75,5]	1.23
MAIA_notworrying_Q
(0.329,1.5]	0.96
(1.5,2.67]	0.88
(2.67,3.83]	0.56
(3.83,5]	1.09
MAIA_attentionreg_Q
(0.138,1.36]	0.96
(1.36,2.57]	0.73
(2.57,3.79]	1.04
(3.79,5]	0.81
MAIA_emoaware_Q
(0.395,1.55]	0.97
(1.55,2.7]	0.85
(2.7,3.85]	0.82
(3.85,5]	0.90
MAIA_selfregul_Q
(-0.00475,1.19]	0.78
(1.19,2.38]	0.80
(2.38,3.56]	1.06
(3.56,4.75]	0.98
MAIA_bodylisten_Q
(-0.00467,1.17]	0.76
(1.17,2.33]	1.01
(2.33,3.5]	0.63
(3.5,4.67]	1.21
MAIA_trust_Q
(-0.005,1.25]	0.86
(1.25,2.5]	0.99
(2.5,3.75]	1.13
(3.75,5]	0.70
MAIA_total_Q
(40.9,64.5]	0.92
(64.5,88]	0.79
(88,112]	0.94
(112,135]	0.85

5.1 plots

[[1]] [[2]] [[3]] [[4]] [[5]] [[6]] [[7]] [[8]] [[9]] [[10]] [[11]] [[12]] [[13]] [[14]] [[15]]

5.2 HF repos et coherence ratio

Graphe de HF repos en fonction du coherence ratio (log) (couleur = valeur de delta HF_extreme)

cor.test(BDD54$coherence.log,BDD54$RS2_HF_p, use = "complete.obs")

    Pearson's product-moment correlation

data:  BDD54$coherence.log and BDD54$RS2_HF_p
t = 0.45006, df = 43, p-value = 0.6549
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.2296777  0.3548738
sample estimates:
       cor 
0.06847251 

6 Delta EVA

Table 6.1: Statistiques des EVA

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
EVA_RS1_Exp	-3	-1.00	0.5	0.61	2.00	6	3
EVA_RS1_P	0	1.00	2.0	2.79	4.00	9	4
EVA_RS1_E	-7	0.00	0.0	1.08	2.00	10	4
EVA_MM_Exp	-5	-1.75	0.0	-0.22	1.00	3	3
EVA_MM_P	0	1.00	3.0	3.21	5.00	10	4
EVA_MM_E	-8	-1.00	0.0	0.89	3.00	10	4
EVA_HBDT_Exp	-4	-0.50	0.0	0.73	2.00	6	2
EVA_HBDT_P	0	1.00	3.0	3.42	5.00	10	4
EVA_HBDT_E	-9	0.00	0.0	1.13	3.00	10	3
EVA_RS2_Exp	-3	-1.00	0.0	0.62	2.00	5	2
EVA_RS2_P	-1	1.00	2.0	2.98	5.00	10	3
EVA_RS2_E	-8	-1.00	0.0	1.17	3.00	10	3
EVA_CBT_Exp	-6	-3.00	-1.5	-1.44	0.00	4	3
EVA_CBT_P	0	1.00	4.0	3.77	6.00	10	4
EVA_CBT_E	-10	-2.00	0.0	0.49	3.00	10	4
EVA_TSST_Exp	-8	-3.50	-2.0	-1.71	-1.00	5	2
EVA_TSST_P	0	2.00	4.0	4.06	6.00	10	3
EVA_TSST_E	-10	-2.75	0.0	-0.06	3.00	10	3
EVA_CC_Exp	-3	0.00	2.0	1.42	2.50	6	2
EVA_CC_P	0	1.00	2.0	2.80	4.75	10	3
EVA_CC_E	-9	0.00	1.0	1.15	5.00	10	4
EVA_IGT_Exp	-6	-1.00	0.0	0.48	2.00	6	3
EVA_IGT_P	0	1.00	2.0	3.29	5.25	10	5
EVA_IGT_E	-8	-1.00	0.0	1.11	2.00	10	12

On cree les nouveaux indicateurs suivants :

La difference est calculee entre l’epreuve de Cyber ball et le Repos 2

delta_EVA_P <- EVA_CBT_P - EVA_RS2_P

delta_EVA_E <- EVA_CBT_E - EVA_RS2_E

	Min.	1st Qu.	Median	Mean	3rd Qu.	Max.	NA’s
P	-6	0	0	0.72	1	10	4
E	-12	-1	0	-0.62	0	9	4

Figure 6.1: Distribution des scores DELTA EVA P

Figure 6.2: Distribution des scores DELTA EVA E

6.1 Delta EVA et autres echelles

6.1.1 EVA physique

variables	moyenne de delta_EVA_P
age_median
<=17 ans	0.25
>17ans	1.24
sexe
F	0.51
M	1.29
EGF_Q4
(29.9,47.2]	2.00
(47.2,64.5]	0.00
(64.5,81.8]	1.05
(81.8,99.1]	1.12
SCL_GSI_Q4
(0.0858,0.853]	0.79
(0.853,1.62]	0.60
(1.62,2.38]	0.88
(2.38,3.15]	1.20
TAS1_Q4
(8.98,15]	1.78
(15,21]	-0.23
(21,27]	0.90
(27,33]	0.89
TAS2_Q4
(4.98,10]	0.45
(10,15]	1.73
(15,20]	0.11
(20,25]	0.88
TAS3_Q4
(7.98,13.2]	1.31
(13.2,18.5]	0.56
(18.5,23.8]	1.18
(23.8,29]	-1.75
TASTOT_Q4
(24.9,38.2]	0.14
(38.2,51.5]	1.38
(51.5,64.8]	1.00
(64.8,78.1]	0.15
meditation
0	1
1	0.12
autoM
non	0.60
oui	0.82
TS_nb
0	0.83
1	0.43
>=2	0.81
MAIA_noticing_Q
(0.996,1.94]	2.00
(1.94,2.88]	0.67
(2.88,3.81]	0.83
(3.81,4.75]	0.47
MAIA_notdistracting_Q
(-0.005,1.25]	-0.36
(1.25,2.5]	1.95
(2.5,3.75]	0.55
(3.75,5]	-6.00
MAIA_notworrying_Q
(0.329,1.5]	0.45
(1.5,2.67]	2.45
(2.67,3.83]	0.67
(3.83,5]	-0.13
MAIA_attentionreg_Q
(0.138,1.36]	1.00
(1.36,2.57]	1.69
(2.57,3.79]	0.40
(3.79,5]	-0.17
MAIA_emoaware_Q
(0.395,1.55]	0.40
(1.55,2.7]	0.57
(2.7,3.85]	0.60
(3.85,5]	1.04
MAIA_selfregul_Q
(-0.00475,1.19]	0.64
(1.19,2.38]	0.55
(2.38,3.56]	1.00
(3.56,4.75]	1.20
MAIA_bodylisten_Q
(-0.00467,1.17]	0.91
(1.17,2.33]	0.06
(2.33,3.5]	0.71
(3.5,4.67]	2.33
MAIA_trust_Q
(-0.005,1.25]	0.81
(1.25,2.5]	1.67
(2.5,3.75]	-0.36
(3.75,5]	1.21
MAIA_total_Q
(40.9,64.5]	1.33
(64.5,88]	0.44
(88,112]	0.79
(112,135]	0.62

6.1.2 EVA Emotionnel

variables	moyenne de delta_EVA_E
age_median
<=17 ans	-0.39
>17ans	-0.88
sexe
F	-0.87
M	0.07
EGF_Q4
(29.9,47.2]	-3.00
(47.2,64.5]	-0.65
(64.5,81.8]	-0.23
(81.8,99.1]	-0.75
SCL_GSI_Q4
(0.0858,0.853]	-0.05
(0.853,1.62]	-0.85
(1.62,2.38]	-0.12
(2.38,3.15]	-2.80
TAS1_Q4
(8.98,15]	-1.78
(15,21]	1.38
(21,27]	-0.71
(27,33]	-2.22
TAS2_Q4
(4.98,10]	0.09
(10,15]	-0.87
(15,20]	-0.50
(20,25]	-1.50
TAS3_Q4
(7.98,13.2]	-0.15
(13.2,18.5]	-0.83
(18.5,23.8]	-1.12
(23.8,29]	0.75
TASTOT_Q4
(24.9,38.2]	-0.71
(38.2,51.5]	0.23
(51.5,64.8]	-0.58
(64.8,78.1]	-1.54
meditation
0	-0.89
1	-0.06
autoM
non	0.24
oui	-1.39
TS_nb
0	-0.13
1	-1.00
>=2	-1.00
MAIA_noticing_Q
(0.996,1.94]	-2.50
(1.94,2.88]	-0.20
(2.88,3.81]	-1.06
(3.81,4.75]	-0.07
MAIA_notdistracting_Q
(-0.005,1.25]	-0.45
(1.25,2.5]	-1.20
(2.5,3.75]	-0.15
(3.75,5]	-1.00
MAIA_notworrying_Q
(0.329,1.5]	-0.64
(1.5,2.67]	-2.18
(2.67,3.83]	-1.53
(3.83,5]	1.40
MAIA_attentionreg_Q
(0.138,1.36]	-0.78
(1.36,2.57]	-0.94
(2.57,3.79]	-0.93
(3.79,5]	0.25
MAIA_emoaware_Q
(0.395,1.55]	-0.2
(1.55,2.7]	-0.5
(2.7,3.85]	-0.9
(3.85,5]	-0.7
MAIA_selfregul_Q
(-0.00475,1.19]	-1.14
(1.19,2.38]	-0.25
(2.38,3.56]	-0.75
(3.56,4.75]	-0.60
MAIA_bodylisten_Q
(-0.00467,1.17]	-0.45
(1.17,2.33]	-0.94
(2.33,3.5]	-0.71
(3.5,4.67]	-0.33
MAIA_trust_Q
(-0.005,1.25]	-0.71
(1.25,2.5]	-1.00
(2.5,3.75]	0.73
(3.75,5]	-1.43
MAIA_total_Q
(40.9,64.5]	-1.42
(64.5,88]	-0.06
(88,112]	-0.50
(112,135]	-1.00

6.1.3 plots delta_EVA Physique

[[1]] [[2]] [[3]] [[4]] [[5]] [[6]] [[7]] [[8]] [[9]] [[10]] [[11]] [[12]] [[13]] [[14]] [[15]]

6.1.4 plots delta_EVA Emotionnel

[[1]] [[2]] [[3]] [[4]] [[5]] [[6]] [[7]] [[8]] [[9]] [[10]] [[11]] [[12]] [[13]] [[14]] [[15]]

6.1.5 plots delta_EVA et delta_HRV

[[1]] [[2]] [[1]] [[2]]

6.2 delta_EVA + et -

Table 6.2: Frequences delta_EVA_E

	Frequence	% du Total	% Cumul
negatif	25	43.9	43.9
positif (ou nul)	28	49.1	93.0
<NA>	4	7.0	100.0
Total	57	100.0	100.0

Table 6.2: Frequences delta_EVA_P

	Frequence	% du Total	% Cumul
negatif	11	19.3	19.3
positif (ou nul)	42	73.7	93.0
<NA>	4	7.0	100.0
Total	57	100.0	100.0

6.2.1 croisement avec HRV

delta_EVA_E_positif	mean_coherence.ratio
negatif	2.9
positif (ou nul)	4.0
p-value T-test	0.253
delta_EVA_E_positif	mean_delta_HF_abs
negatif	0.1
positif (ou nul)	-0.2
p-value T-test	0.248
delta_EVA_P_positif	mean_coherence.ratio
negatif	5.5
positif (ou nul)	2.9
p-value T-test	0.203
delta_EVA_P_positif	mean_delta_HF_abs
negatif	0.2
positif (ou nul)	-0.1
p-value T-test	0.354

7 ANALYSES MARS 2021

delta_HF_extreme : -1 , 0 , 1

-1 si delta HF < ( moyenne - 1 sd)

1 si delta HF >= 0

0 si delta_HF entre les deux

7.1 HRV reactivity

% de TS en fonction de delta HF_ex

   
           -1         0         1
  0 0.4000000 0.5882353 0.3000000
  1 0.6000000 0.4117647 0.7000000

    Pearson's Chi-squared test with Yates' continuity correction

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$TS_bin and BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
X-squared = 2.047, df = 1, p-value = 0.1525

Automutilations :

proportions en fonction de Delta HF extreme (-1,0,1)

test du chi-2 parmi ceux ayant une reactivity positive.

     
             -1         0         1
  non 0.4000000 0.7647059 0.3000000
  oui 0.6000000 0.2352941 0.7000000

    Pearson's Chi-squared test with Yates' continuity correction

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$autoM and BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
X-squared = 6.1922, df = 1, p-value = 0.01283

SCL : t-tests (différences de moyennes) des sous dimensions puis score total de SCL parmi ceux qui ont une reactivité positive

    Welch Two Sample t-test

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$SCL_GSI by BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
t = -2.1862, df = 33.986, p-value = 0.03579
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.04911569 -0.03826993
sample estimates:
mean in group 0 mean in group 1 
       1.001307        1.545000 

    Welch Two Sample t-test

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$SCL_PST by BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
t = -1.0457, df = 31.2, p-value = 0.3037
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -22.280166   7.174284
sample estimates:
mean in group 0 mean in group 1 
       47.64706        55.20000 

    Welch Two Sample t-test

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$SCL_PSDI by BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
t = -3.3001, df = 33.305, p-value = 0.00231
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.9670895 -0.2295872
sample estimates:
mean in group 0 mean in group 1 
       1.730986        2.329325 

    Welch Two Sample t-test

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$SCL_PSDI by BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
t = -3.3001, df = 33.305, p-value = 0.00231
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.9670895 -0.2295872
sample estimates:
mean in group 0 mean in group 1 
       1.730986        2.329325 

MAIA :

moyennes selon le Delta HF extreme

de MAIA total

de MAIA selfregul

  Group.1        x
1      -1 71.00000
2       0 95.88235
3       1 74.40000

  Group.1        x
1      -1 1.950000
2       0 2.602941
3       1 1.525000

t-tests (différences de moyennes) de MAIA total parmi ceux qui ont une reactivité positive

    Welch Two Sample t-test

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$MAIA_total by BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
t = 3.0199, df = 33.43, p-value = 0.004817
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
  7.016872 35.947834
sample estimates:
mean in group 0 mean in group 1 
       95.88235        74.40000 

EGF :

t-tests (différences de moyennes) de EGF parmi ceux qui ont une reactivité positive

    Welch Two Sample t-test

data:  BDD54[which(BDD54$delta_HF_ex >= 0), ]$EGF by BDD54[which(BDD54$delta_HF_ex >= 0), ]$delta_HF_ex
t = 1.1545, df = 34.229, p-value = 0.2563
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.881702 14.099349
sample estimates:
mean in group 0 mean in group 1 
       70.05882        64.95000 

7.2 Comparaisons deltas

Comparer :

Delta EVA CBT-Repos2 avec Delta_HF_ex

Delta EVA CBT-CC

physique et Emotionnel à chaque fois.

BDD54$delta_EVA_CBT_RS2_E <- NA
BDD54$delta_EVA_CBT_RS2_E <- BDD54$EVA_CBT_E - BDD54$EVA_RS2_E

BDD54$delta_EVA_CBT_RS2_P <- NA
BDD54$delta_EVA_CBT_RS2_P <- BDD54$EVA_CBT_P - BDD54$EVA_RS2_P

BDD54$delta_EVA_CBT_CC_E <- NA
BDD54$delta_EVA_CBT_CC_E <- BDD54$EVA_CBT_E - BDD54$EVA_CC_E

BDD54$delta_EVA_CBT_CC_P <- NA
BDD54$delta_EVA_CBT_CC_P <- BDD54$EVA_CBT_P - BDD54$EVA_CC_P

library(ggpubr)
ggboxplot(BDD54, x = "delta_HF_ex", y = "delta_EVA_CBT_RS2_E")

aggregate(BDD54$delta_EVA_CBT_RS2_E, list(BDD54$delta_HF_ex), mean, na.rm=TRUE)

  Group.1           x
1      -1 -0.80000000
2       0  0.05882353
3       1 -0.94444444

ggboxplot(BDD54, x = "delta_HF_ex", y = "delta_EVA_CBT_RS2_P")

aggregate(BDD54$delta_EVA_CBT_RS2_P, list(BDD54$delta_HF_ex), mean, na.rm=TRUE)

  Group.1         x
1      -1 2.6000000
2       0 0.4705882
3       1 0.3888889

ggboxplot(BDD54, x = "delta_HF_ex", y = "delta_EVA_CBT_CC_E")

aggregate(BDD54$delta_EVA_CBT_CC_E, list(BDD54$delta_HF_ex), mean, na.rm=TRUE)

  Group.1          x
1      -1 -1.4000000
2       0 -0.3750000
3       1 -0.5555556

ggboxplot(BDD54, x = "delta_HF_ex", y = "delta_EVA_CBT_CC_P")

aggregate(BDD54$delta_EVA_CBT_CC_P, list(BDD54$delta_HF_ex), mean, na.rm=TRUE)

  Group.1         x
1      -1 2.6000000
2       0 1.2352941
3       1 0.4444444