思覺失調症患者之暴力行為對總膽固醇影響
Shang Chi Lee
2022-03-11
Abstract
Background: Many studies have demonstrated that TC (total cholesterol), High-density lipoprotein (HDL), Low-density lipoprotein (LDL), and Triglyceride can affect violence in patients with schizophrenia, especially when cholesterol is low, violence is more serious.
PURPOSE: It is hoped that the experimental group can restore cholesterol to a balanced state (at least 160 mg/dl) through 3 months of multi-modal lifestyle intervention (nutrition, exercise, lifestyle changes), thereby improving the risk of violence, aggression, impulsivity and cognitive function. This case-control study aims to assess the association between serum cholesterol and multimodal lifestyles.
Methods: A total of 62 subjects were enrolled and admitted in the hospital. They were divided into two groups: 31 patients were the experimental group with violent schizophrenia with serum cholesterol ≤160mg/dl; 31 patients were the control group, with normal serum cholesterol 160-200mg/dl, in line with the following inclusion criteria: 1. All diagnosed as schizophrenia 2. Violent attacks occurred in the past year.
In the experimental group, the intervention measures were multimodal lifestyle intervention and received general clinical treatment; the control group only received general clinical treatment. TC (total cholesterol), HDL, LDL and triglycerides, mental health (depression, anxiety, hostility) and cognitive function at baseline, 3 months (3 months after pretest) and 6 months (3 after pretest) Month).
Four questionnaires are used, 1. Modified overt aggression scales (MOAS) ; 2.Violence risk (V-Risk 10), the higher the risk; 3. Aggression (Buss and Perry, 1992, The Aggression Questionnaire AQ attack scale), the higher the attack; 4. impulsivity scale (Barratt’s Impulsiveness scale, BIS), the higher the more impulsive.
Expected outcome: 1. The cholesterol concentration can be increased by intervening in the experimental group. 2. The overall violent behavior of schizophrenia can be improved.
研究問題說明
本研究因收集資料變相眾多，此研究專注探討思覺失調症患者於活動介入後， 1.總膽固醇濃度是否提升 2.其暴力行為是否下降 3.總膽固醇與暴力關係
一、 資料整理
1.1 讀檔
使用openxlsx進行讀檔，並運用 dplyr進行數據整理操作。 資料型態上，人口學變項有性別、年齡、教育程度、婚姻狀況、職業別、喝酒、抽菸、是否運動、體重、BMI。
結果變項為總膽固醇(cholesterol)；問卷測量部分四份，此研究以Modified overt aggression scales(MOAS)攻擊(頻率)量表及Violence Risk(V-Risk 10)危險評估量表為主要分析依據。
pacman::p_load(openxlsx, dplyr,gtsummary )pacman::p_load(openxlsx)
dta <- read.xlsx(xlsxFile = "cholesterol_20210903.xlsx", sheet = 'all')knitr::kable(head(dta))| id | 組別編號 | Intervention | sex | age | edu | marriage | occupation | income | alcohol | smoking | activity | BW | BMI | Age.of.onset | cholesterol | Triglyceride | HDL | LDL | time | MOAS1_1 | MOAS1_2 | MOAS1_3 | MOAS1_4 | MOAS2 | MOAS3 | MOAS4 | MOAS5 | MOAS_oral | MOAS_item | MOAS_lself | MOAS_body | MOAS_Total | risk1 | risk2 | risk3 | risk4 | risk5 | risk6 | risk7 | risk8 | risk9 | risk10 | Risk_total | AQ1 | AQ2 | AQ3 | AQ4 | AQ5 | AQ6 | AQ7 | AQ8 | AQ9 | AQ10 | AQ11 | AQ12 | AQ13 | AQ14 | AQ15 | AQ16 | AQ17 | AQ18 | AQ19 | AQ20 | AQ21 | AQ22 | AQ23 | AQ24 | AQ25 | AQ26 | AQ27 | AQ28 | AQ29 | AQ_anger | AQ_verbal | AQ_physical | AQ_suspicious | AQ_Total | BIS1 | BIS2 | BIS3 | BIS4 | BIS5 | BIS6 | BIS7 | BIS8 | BIS9 | BIS10 | BIS11 | BIS12 | BIS13 | BIS14 | BIS15 | BIS16 | BIS17 | BIS18 | BIS19 | BIS20 | BIS21 | BIS22 | BIS23 | BIS24 | BIS25 | BIS26 | BIS27 | BIS28 | BIS29 | BIS30 | BIS_attention | BIS_action | BIS_Noplan | BIS_Total | MMSE1 | MMSE2 | MMSE3 | MMSE4 | MMSE5 | MMSE6 | MMSE7 | MMSE8 | MMSE9 | MMSE10 | MMSE11_1 | MMSE11_2 | MMSE11_3 | MMSE12_1 | MMSE12_2 | MMSE12_3 | MMSE12_4 | MMSE12_5 | MMSE13 | MMSE14 | MMSE15 | MMSE16 | MMSE17 | MMSE18 | MMSE19 | MMSE20_1 | MMSE20_2 | MMSE20_3 | MMSE21 | MMSE22 | MMSE_Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 32 | 低膽1 | 1 | 1 | 28 | 5 | 1 | 5 | 2 | 1 | 2 | 1 | 71.1 | 25.65 | 23 | 148 | 102 | 38 | 95 | 1 | 2 | 2 | 1 | 2 | 4 | 3 | 0 | 0 | 4 | 6 | 0 | 0 | 10 | 2 | 3 | 1 | 3 | 1 | 2 | 3 | 2 | 1 | 2 | 20 | 5 | 5 | 4 | 1 | 2 | 5 | 3 | 5 | 2 | 2 | 1 | 5 | 1 | 2 | 4 | 2 | 2 | 2 | 4 | 5 | 1 | 4 | 4 | 2 | 1 | 4 | 4 | 4 | 3 | 26 | 13 | 24 | 26 | 89 | 3 | 2 | 4 | 1 | 4 | 3 | 1 | 1 | 3 | 2 | 4 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 3 | 1 | 1 | 4 | 2 | 4 | 4 | 2 | 4 | 1 | 4 | 15 | 22 | 33 | 70 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 27 |
| 33 | 低膽2 | 1 | 2 | 47 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 62.0 | 20.60 | 30 | 128 | 110 | 30 | 137 | 1 | 2 | 2 | 1 | 2 | 4 | 3 | 0 | 4 | 4 | 6 | 0 | 16 | 26 | 2 | 3 | 1 | 3 | 1 | 2 | 3 | 2 | 2 | 2 | 21 | 4 | 4 | 4 | 2 | 5 | 4 | 4 | 4 | 5 | 4 | 3 | 3 | 3 | 4 | 4 | 2 | 5 | 4 | 5 | 2 | 5 | 5 | 2 | 2 | 5 | 4 | 4 | 2 | 4 | 25 | 19 | 35 | 29 | 108 | 4 | 3 | 4 | 3 | 4 | 4 | 2 | 2 | 4 | 4 | 1 | 4 | 4 | 1 | 4 | 2 | 3 | 1 | 1 | 2 | 1 | 1 | 4 | 1 | 4 | 4 | 2 | 2 | 1 | 4 | 20 | 30 | 31 | 81 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 15 |
| 34 | 低膽3 | 1 | 2 | 40 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 70.0 | 25.80 | 36 | 156 | 160 | 44 | 99 | 1 | 2 | 2 | 1 | 1 | 3 | 2 | 0 | 0 | 3 | 4 | 0 | 0 | 7 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 12 | 5 | 5 | 2 | 2 | 5 | 4 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 3 | 3 | 5 | 2 | 5 | 1 | 1 | 5 | 1 | 5 | 1 | 2 | 4 | 2 | 3 | 25 | 20 | 29 | 26 | 100 | 4 | 1 | 4 | 3 | 2 | 3 | 1 | 3 | 3 | 4 | 1 | 4 | 4 | 1 | 4 | 1 | 4 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 3 | 2 | 1 | 4 | 1 | 4 | 20 | 22 | 31 | 73 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 21 |
| 35 | 低膽4 | 1 | 1 | 29 | 3 | 1 | 1 | 1 | 2 | 2 | 1 | 78.6 | 27.60 | 24 | 159 | 105 | 44 | 109 | 1 | 2 | 1 | 2 | 1 | 4 | 0 | 2 | 0 | 4 | 0 | 6 | 0 | 10 | 2 | 2 | 1 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 17 | 4 | 2 | 4 | 4 | 3 | 4 | 5 | 4 | 4 | 2 | 5 | 4 | 2 | 4 | 1 | 4 | 4 | 4 | 4 | 2 | 3 | 4 | 4 | 2 | 2 | 1 | 4 | 4 | 3 | 28 | 19 | 29 | 21 | 97 | 3 | 1 | 3 | 3 | 1 | 1 | 2 | 3 | 2 | 4 | 1 | 3 | 2 | 1 | 3 | 1 | 4 | 1 | 1 | 3 | 3 | 2 | 2 | 2 | 3 | 3 | 1 | 1 | 1 | 2 | 18 | 20 | 25 | 63 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 28 |
| 36 | 低膽5 | 1 | 2 | 43 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 53.0 | 20.90 | 15 | 138 | 120 | 32 | 104 | 1 | 2 | 2 | 1 | 1 | 4 | 3 | 0 | 0 | 4 | 6 | 0 | 0 | 10 | 2 | 3 | 1 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 18 | 4 | 5 | 4 | 2 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 5 | 4 | 4 | 3 | 4 | 5 | 2 | 2 | 1 | 5 | 5 | 2 | 2 | 2 | 1 | 4 | 1 | 4 | 21 | 20 | 38 | 26 | 105 | 3 | 3 | 4 | 1 | 4 | 4 | 3 | 4 | 3 | 4 | 1 | 3 | 3 | 1 | 4 | 2 | 2 | 1 | 2 | 2 | 1 | 1 | 4 | 1 | 4 | 4 | 2 | 1 | 1 | 2 | 19 | 28 | 28 | 75 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 21 |
| 37 | 低膽6 | 1 | 2 | 38 | 2 | 1 | 5 | 2 | 1 | 1 | 1 | 51.1 | 20.65 | 23 | 138 | 102 | 38 | 95 | 1 | 2 | 2 | 1 | 2 | 3 | 2 | 0 | 3 | 3 | 4 | 0 | 12 | 19 | 2 | 2 | 1 | 3 | 1 | 2 | 2 | 3 | 1 | 1 | 18 | 2 | 4 | 4 | 2 | 4 | 4 | 4 | 5 | 4 | 4 | 2 | 5 | 2 | 2 | 4 | 2 | 4 | 2 | 5 | 2 | 4 | 5 | 2 | 2 | 3 | 2 | 4 | 2 | 4 | 22 | 16 | 31 | 26 | 95 | 4 | 3 | 4 | 1 | 4 | 4 | 2 | 3 | 3 | 4 | 2 | 3 | 3 | 1 | 3 | 2 | 1 | 1 | 2 | 3 | 1 | 1 | 4 | 1 | 4 | 4 | 2 | 2 | 1 | 4 | 18 | 27 | 32 | 77 | 1 | 1 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 22 |
#崁入式呈現資料
DT::datatable(dta, rownames=FALSE, options=list(scrollY = 300, pageLength=10), fillContainer =TRUE)1.2資料型態
使用str()列出資料內每個欄位的狀態，可用來確認屬性結構。以下可得知只有”組別編號”為 character為文字字串，其他皆為numeric實數。
str(dta)## 'data.frame': 186 obs. of 143 variables:
## $ id : num 32 33 34 35 36 37 38 39 40 41 ...
## $ 組別編號 : chr "低膽1" "低膽2" "低膽3" "低膽4" ...
## $ Intervention : num 1 1 1 1 1 1 1 1 1 1 ...
## $ sex : num 1 2 2 1 2 2 1 2 1 2 ...
## $ age : num 28 47 40 29 43 38 29 43 23 40 ...
## $ edu : num 5 2 3 3 3 2 5 3 3 3 ...
## $ marriage : num 1 1 1 1 1 1 3 3 1 1 ...
## $ occupation : num 5 1 1 1 1 5 1 1 1 1 ...
## $ income : num 2 1 1 1 1 2 1 1 1 1 ...
## $ alcohol : num 1 1 1 2 1 1 2 1 2 1 ...
## $ smoking : num 2 1 1 2 1 1 2 1 2 1 ...
## $ activity : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BW : num 71.1 62 70 78.6 53 51.1 88 49.5 84 54.6 ...
## $ BMI : num 25.6 20.6 25.8 27.6 20.9 ...
## $ Age.of.onset : num 23 30 36 24 15 23 18 22 24 15 ...
## $ cholesterol : num 148 128 156 159 138 138 153 134 158 137 ...
## $ Triglyceride : num 102 110 160 105 120 102 171 125 105 130 ...
## $ HDL : num 38 30 44 44 32 38 41 35 44 32 ...
## $ LDL : num 95 137 99 109 104 95 147 87 109 104 ...
## $ time : num 1 1 1 1 1 1 1 1 1 1 ...
## $ MOAS1_1 : num 2 2 2 2 2 2 2 1 2 2 ...
## $ MOAS1_2 : num 2 2 2 1 2 2 1 2 2 2 ...
## $ MOAS1_3 : num 1 1 1 2 1 1 1 1 1 1 ...
## $ MOAS1_4 : num 2 2 1 1 1 2 1 2 2 1 ...
## $ MOAS2 : num 4 4 3 4 4 3 4 0 3 4 ...
## $ MOAS3 : num 3 3 2 0 3 2 0 2 2 4 ...
## $ MOAS4 : num 0 0 0 2 0 0 0 0 0 0 ...
## $ MOAS5 : num 0 4 0 0 0 3 0 3 3 0 ...
## $ MOAS_oral : num 4 4 3 4 4 3 4 0 3 4 ...
## $ MOAS_item : num 6 6 4 0 6 4 0 4 4 8 ...
## $ MOAS_lself : num 0 0 0 6 0 0 0 0 0 0 ...
## $ MOAS_body : num 0 16 0 0 0 12 0 12 12 0 ...
## $ MOAS_Total : num 10 26 7 10 10 19 4 16 19 12 ...
## $ risk1 : num 2 2 1 2 2 2 3 2 2 3 ...
## $ risk2 : num 3 3 1 2 3 2 2 2 2 2 ...
## $ risk3 : num 1 1 1 1 1 1 1 1 1 1 ...
## $ risk4 : num 3 3 3 3 3 3 3 1 3 3 ...
## $ risk5 : num 1 1 1 1 1 1 1 1 1 1 ...
## $ risk6 : num 2 2 1 2 2 2 2 1 2 2 ...
## $ risk7 : num 3 3 1 2 2 2 2 1 2 2 ...
## $ risk8 : num 2 2 1 2 2 3 1 1 1 2 ...
## $ risk9 : num 1 2 1 1 1 1 2 1 2 1 ...
## $ risk10 : num 2 2 1 1 1 1 1 2 1 2 ...
## $ Risk_total : num 20 21 12 17 18 18 18 13 17 19 ...
## $ AQ1 : num 5 4 5 4 4 2 3 5 3 3 ...
## $ AQ2 : num 5 4 5 2 5 4 3 4 3 4 ...
## $ AQ3 : num 4 4 2 4 4 4 3 2 4 4 ...
## $ AQ4 : num 1 2 2 4 2 2 1 4 2 2 ...
## $ AQ5 : num 2 5 5 3 5 4 1 4 2 1 ...
## $ AQ6 : num 5 4 4 4 5 4 3 3 3 4 ...
## $ AQ7 : num 3 4 5 5 5 4 2 5 4 2 ...
## $ AQ8 : num 5 4 5 4 5 5 4 4 4 1 ...
## $ AQ9 : num 2 5 5 4 5 4 3 3 3 1 ...
## $ AQ10 : num 2 4 5 2 5 4 2 4 4 1 ...
## $ AQ11 : num 1 3 5 5 4 2 2 3 3 1 ...
## $ AQ12 : num 5 3 5 4 5 5 4 3 3 1 ...
## $ AQ13 : num 1 3 5 2 4 2 2 2 2 1 ...
## $ AQ14 : num 2 4 5 4 4 2 2 5 4 5 ...
## $ AQ15 : num 4 4 3 1 3 4 5 2 4 4 ...
## $ AQ16 : num 2 2 3 4 4 2 2 4 2 4 ...
## $ AQ17 : num 2 5 5 4 5 4 2 3 3 2 ...
## $ AQ18 : num 2 4 2 4 2 2 2 3 2 3 ...
## $ AQ19 : num 4 5 5 4 2 5 4 3 3 5 ...
## $ AQ20 : num 5 2 1 2 1 2 1 3 3 5 ...
## $ AQ21 : num 1 5 1 3 5 4 1 3 1 1 ...
## $ AQ22 : num 4 5 5 4 5 5 5 5 3 3 ...
## $ AQ23 : num 4 2 1 4 2 2 4 4 2 3 ...
## $ AQ24 : num 2 2 5 2 2 2 1 2 2 1 ...
## $ AQ25 : num 1 5 1 2 2 3 1 4 2 2 ...
## $ AQ26 : num 4 4 2 1 1 2 1 3 4 4 ...
## $ AQ27 : num 4 4 4 4 4 4 4 4 3 4 ...
## $ AQ28 : num 4 2 2 4 1 2 2 3 3 1 ...
## $ AQ29 : num 3 4 3 3 4 4 3 3 3 4 ...
## $ AQ_anger : num 26 25 25 28 21 22 22 24 19 17 ...
## $ AQ_verbal : num 13 19 20 19 20 16 11 19 13 16 ...
## $ AQ_physical : num 24 35 29 29 38 31 23 33 24 21 ...
## $ AQ_suspicious: num 26 29 26 21 26 26 17 24 28 23 ...
## $ AQ_Total : num 89 108 100 97 105 95 73 100 84 77 ...
## $ BIS1 : num 3 4 4 3 3 4 3 4 4 4 ...
## $ BIS2 : num 2 3 1 1 3 3 1 3 1 3 ...
## $ BIS3 : num 4 4 4 3 4 4 3 4 3 4 ...
## $ BIS4 : num 1 3 3 3 1 1 3 3 3 3 ...
## $ BIS5 : num 4 4 2 1 4 4 2 4 2 4 ...
## $ BIS6 : num 3 4 3 1 4 4 2 3 3 3 ...
## $ BIS7 : num 1 2 1 2 3 2 3 2 3 4 ...
## $ BIS8 : num 1 2 3 3 4 3 1 1 3 1 ...
## $ BIS9 : num 3 4 3 2 3 3 2 4 4 3 ...
## $ BIS10 : num 2 4 4 4 4 4 4 3 4 4 ...
## $ BIS11 : num 4 1 1 1 1 2 2 2 1 1 ...
## $ BIS12 : num 2 4 4 3 3 3 3 4 4 4 ...
## $ BIS13 : num 2 4 4 2 3 3 1 2 2 3 ...
## $ BIS14 : num 1 1 1 1 1 1 1 1 2 1 ...
## $ BIS15 : num 1 4 4 3 4 3 3 3 3 4 ...
## $ BIS16 : num 2 2 1 1 2 2 1 3 1 2 ...
## $ BIS17 : num 1 3 4 4 2 1 2 4 3 3 ...
## $ BIS18 : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BIS19 : num 2 1 2 1 2 2 1 2 1 2 ...
## $ BIS20 : num 3 2 1 3 2 3 2 4 3 3 ...
## $ BIS21 : num 1 1 1 3 1 1 1 1 2 1 ...
## [list output truncated]1.3整理資料屬性
將所需的類別變項整理，此時使用forcats，可將屬性變更外也可將多個因子合併，交互處理多的因子數據。此時先將人口學資料整理完成。
pacman::p_load(forcats)
raw<-dtapacman::p_load(forcats)#reordering factor levels
#設定資料屬性與名稱
raw<-dta
raw$sex<-factor(raw$sex, levels = c("1","2"),exclude = NA, ordered = FALSE)
raw$edu<-factor(raw$edu, levels = c("2","3","5"),exclude = NA, ordered = FALSE)
raw$marriage<-factor(raw$marriage, levels = c("1","3"),exclude = NA, ordered = FALSE)
raw$occupation<-factor(raw$occupation, levels = c("1","5"),exclude = NA, ordered = FALSE)
raw$income<-factor(raw$income, levels = c("1","2"),exclude = NA, ordered = FALSE)
raw$alcohol<-factor(raw$alcohol, levels = c("1","2"),exclude = NA, ordered = FALSE)
raw$smoking<-factor(raw$smoking, levels = c("1","2"),exclude = NA, ordered = FALSE)
raw$activity<-factor(raw$activity, levels = c("1","2"),exclude = NA, ordered = FALSE)
raw$time<-factor(raw$time, levels = c("1","2","3"),exclude = NA, ordered = FALSE)
raw$Intervention <-factor(raw$Intervention , levels = c("1","2"),exclude = NA, ordered = FALSE)
raw$sex<-fct_collapse(raw$sex,"M" = "1","F" = "2")
raw$edu<-fct_collapse(raw$edu,"Elementary" = "2","Secondary" = "3","Junior college" = "5")
raw$marriage<-fct_collapse(raw$marriage,"Unmarried" = "1","
Divorce" = "3")
raw$occupation<-fct_collapse(raw$occupation,"No job " = "1","Worker" = "5")
raw$income<-fct_collapse(raw$income,"Below NT5,000" = "1","NT 25,001-50,000" = "2")
raw$alcohol<-fct_collapse(raw$alcohol,"No" = "1","Yes" = "2")
raw$smoking<-fct_collapse(raw$smoking,"No" = "1","Yes" = "2")
raw$activity<-fct_collapse(raw$activity,"No" = "1","Yes" = "2")
raw$Intervention <-fct_collapse(raw$Intervention,"Intervention" = "1","Control" = "2")二、 探索資料
2.1 總膽固醇的資料分布-箱型圖
2.1.1 總膽固醇的分布
實驗組與對照組，總膽固醇在第三次測量有提升。實驗組思覺失調症患者的總膽固醇皆低於對照組的濃度，實驗組的平均值明顯低於對照組。 註:第二次測量並無測量總膽固醇濃度
## Warning: Removed 62 rows containing non-finite values (stat_boxplot).
## Warning: Removed 62 rows containing non-finite values (stat_boxplot).
總膽固醇:長條圖及曲線圖 看第1次及第3次測量的分布如下，第三次的測量明顯總膽固醇濃度較高。
ggplot(data=raw)+
geom_histogram(mapping=aes(x=cholesterol, color=time))+
facet_wrap(~time, nrow=2)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 62 rows containing non-finite values (stat_bin).
ggplot(data=raw,mapping=aes(x=cholesterol, color=time))+
geom_freqpoly(binwidth=1)+
facet_wrap(~time, nrow=2)## Warning: Removed 62 rows containing non-finite values (stat_bin).
2.2 暴力頻率的分布
實驗組的暴力頻率明顯高於對照組，在第二次及第三次測量後兩組差異不明顯，箱型大小非常接近。
2.2.1 不同時間介入的兩組比較
ggplot(data=raw ,mapping=aes(x=time,y=MOAS_Total))+
geom_boxplot()+
facet_wrap(~Intervention, nrow=2)
ggplot(data=raw ,mapping=aes(x=Intervention,y=MOAS_Total))+
geom_boxplot()+
facet_wrap(~time, nrow=2)
2.2.2 兩組間三個時間點比較
不同的排列方式，在暴力頻率的箱型圖比較上，更可看出實驗組的前後差異。
ggplot(raw, aes(x=time, y=MOAS_Total, group=time,color=Intervention))+
facet_wrap(~ Intervention)+geom_boxplot()
2.2.3 以個人角度，兩組間三個時間點比較 以下圖可看出在實驗組個別的差異非常明顯，對照組與實驗組相比，曲線則趨近緩和。
ggplot(raw, aes(x=time, y=MOAS_Total, group=id))+
geom_line(mapping = aes(color=Intervention))+
geom_point()+
facet_wrap(~ Intervention)+#facet_wrap前面有用過，分類一個變項用
labs(x="Time", y="暴力頻率")+
theme_minimal()
加上平均值，可得知每個時間點的平均值移動的情況，實驗組隨時間暴力頻率有下降。
ggplot(raw, aes(x=time, y=MOAS_Total, group=id))+
geom_line(mapping = aes(color=Intervention))+
geom_point()+
stat_summary( aes(group= 1) ,geom="point",fun.y=mean ,
shape=17 , size=3 )+
facet_wrap(~ Intervention)+#facet_wrap前面有用過，分類一個變項用
labs(x="Time", y="暴力頻率")+
theme_minimal()## Warning: `fun.y` is deprecated. Use `fun` instead.
這裡我們選擇使用局部加權回歸（lowess）添加一條“平滑”的線， 加上誤差區間，看起來有線性感覺。
ggplot(raw, aes(x=time, y=MOAS_Total, group=id))+
geom_line(mapping=aes(color=Intervention))+
stat_smooth(aes(group=1))+stat_summary( aes(group= 1) ,
geom = "point" , fun.y = mean , shape = 17 ,size=3)+
facet_grid (.~Intervention)## Warning: `fun.y` is deprecated. Use `fun` instead.## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 1.0946e-016## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.99## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2.01## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 1.0946e-016## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4.0401## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : pseudoinverse used at 0.99## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : neighborhood radius 2.01## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : reciprocal condition number 3.886e-016## Warning in simpleLoess(y, x, w, span, degree = degree, parametric =
## parametric, : There are other near singularities as well. 4.0401## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at
## 0.99## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 2.01## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 3.886e-016## Warning in predLoess(object$y, object$x, newx = if
## (is.null(newdata)) object$x else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : There are other near
## singularities as well. 4.0401
因上張圖有線性感，故拿掉誤差項，直接畫圖線性圖。
ggplot(raw, aes(x=time, y=MOAS_Total, group=id))+
geom_line(mapping=aes(color=Intervention))+
geom_point()+
stat_smooth(aes(group=1),method="lm",formula=y~x*I(x>1),se=FALSE)+
stat_summary(aes(group=1) ,
geom="point",fun.y =mean ,shape=17,size=3 )+
facet_wrap(~ Intervention)+
labs(x="Time", y="暴力頻率")+
theme_minimal()## Warning: `fun.y` is deprecated. Use `fun` instead.## Warning in predict.lm(model, newdata = new_data_frame(list(x = xseq)), se.fit =
## se, : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(model, newdata = new_data_frame(list(x = xseq)), se.fit =
## se, : prediction from a rank-deficient fit may be misleading
2.3 暴力危險評估的分布
危險評估分布，對照組三次測量差異不明顯，實驗組在第三次測量分數明顯有下降。在第一次及第二次測量時危險評估分數兩組差異不大。
ggplot(data=raw ,mapping=aes(x=time,y=Risk_total))+
geom_boxplot()+
facet_wrap(~Intervention, nrow=2)
ggplot(data=raw ,mapping=aes(x=Intervention,y=Risk_total))+
geom_boxplot()+
facet_wrap(~time, nrow=2)
暴力危險評估，實驗組在介入後第三次的測量危險評估明顯下降。
ggplot(raw, aes(x=time, y=Risk_total, group=time,color=Intervention))+
facet_wrap(~ Intervention)+geom_boxplot()
以個人角度來看，暴力危險評估上依然是實驗組有明顯的差異，隨介入而下降，第一次測量的分數明顯高於第二及第三次測量。
ggplot(raw, aes(x=time, y=Risk_total, group=id))+
geom_line(mapping=aes(color=Intervention))+
geom_point()+
stat_smooth(aes(group= 1),method="lm",formula=y~x * I(x>1),se=FALSE)+
stat_summary(aes(group=1) ,
geom="point",fun.y =mean ,shape=17,size=3 )+
facet_wrap(~ Intervention)+
labs(x="Time", y="暴力危險")+
theme_minimal()## Warning: `fun.y` is deprecated. Use `fun` instead.## Warning in predict.lm(model, newdata = new_data_frame(list(x = xseq)), se.fit =
## se, : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(model, newdata = new_data_frame(list(x = xseq)), se.fit =
## se, : prediction from a rank-deficient fit may be misleading
2.4 總膽固醇與暴力頻率的關係
探討總膽固醇與暴力頻率的關係，由圖得知，實驗組與對照組介入的測量(藍色)暴力頻率分數較低且總膽固醇濃度也較高。介入似乎有成效。
註:第二次測量並無測量總膽固醇濃度
par(mfrow=c(4,4))
ggplot(data=raw)+geom_point(mapping=aes(x=MOAS_Total,y=cholesterol,color=time))+facet_wrap(~Intervention, nrow=2)## Warning: Removed 62 rows containing missing values (geom_point).
ggplot(data=raw)+geom_smooth(mapping=aes(x=MOAS_Total,y=cholesterol,color=time))+facet_wrap(~Intervention, nrow=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 62 rows containing non-finite values (stat_smooth).
ggplot(data=raw)+geom_point(mapping=aes(x=MOAS_Total,y=cholesterol,color=time))+
geom_smooth(mapping=aes(x=Risk_total,y=cholesterol,color=time))+
facet_wrap(~Intervention, nrow=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 62 rows containing non-finite values (stat_smooth).
## Warning: Removed 62 rows containing missing values (geom_point).
2.5 總膽固醇與危險評估的關係
探討總膽固醇與暴力危險評估的關係，以暴力危險評估來看，實驗組與對照組皆可看出介入後總膽固醇有提升，尤以對照組更為符合。
attach(raw)
par(mfrow=c(4,2))
ggplot(data=raw)+geom_point(mapping=aes(x=Risk_total,y=cholesterol,color=time))+facet_wrap(~Intervention, nrow=2)## Warning: Removed 62 rows containing missing values (geom_point).
ggplot(data=raw)+geom_smooth(mapping=aes(x=Risk_total,y=cholesterol,color=time))+facet_wrap(~Intervention, nrow=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 62 rows containing non-finite values (stat_smooth).
ggplot(data=raw)+geom_point(mapping=aes(x=Risk_total,y=cholesterol,color=time))+
geom_smooth(mapping=aes(x=Risk_total,y=cholesterol,color=time))+
facet_wrap(~Intervention, nrow=2)## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 62 rows containing non-finite values (stat_smooth).
## Warning: Removed 62 rows containing missing values (geom_point).
三、統計表格
表一人口學變項
以表一人口學變項而言，僅有性別、教育程度、婚姻狀況、職業別、收入及活動力等皆沒有差異，其他變項如暴力頻率、暴力危險量表及總膽固醇皆有差異。
pacman::p_load(gtsummary)
#去除第二次測量資料， 沒有測量總膽固醇，以畫圖而言有沒有去除NA資料都可
raw13<-na.omit(raw)
tl<- raw13 %>%
select(sex,age, BW,BMI,edu, marriage, occupation,income,alcohol,smoking, activity, cholesterol,Triglyceride, HDL, LDL,Intervention) %>%
tbl_summary(by = Intervention, label = list(sex ~ "Sex", age ~ "Age", edu ~ "Education",marriage ~ "Marriage", occupation ~ "Occupation", income ~ "Income", alcohol ~ "Alcohol", smoking ~ "Smoking", activity ~ "Activity")) %>%
add_p()%>%
add_stat_label() %>%
bold_labels() %>%
modify_header(list(label ~ "**Variable**", all_stat_cols() ~ "**{level}**")) %>%
modify_spanning_header(all_stat_cols() ~ "**Group**") %>%
as_gt() %>%
gt::tab_header(
title = gt::md("**Table 1. Demography**"))
tl| Table 1. Demography | |||
|---|---|---|---|
| Variable | Group | p-value1 | |
| Intervention | Control | ||
| Sex, n (%) | 0.7 | ||
| M | 36 (56%) | 32 (53%) | |
| F | 28 (44%) | 28 (47%) | |
| Age, Median (IQR) | 40 (29, 43) | 43 (37, 48) | 0.020 |
| BW, Median (IQR) | 68 (56, 76) | 81 (63, 86) | <0.001 |
| BMI, Median (IQR) | 23.70 (21.20, 25.88) | 27.80 (24.54, 29.60) | <0.001 |
| Education, n (%) | >0.9 | ||
| Elementary | 14 (22%) | 12 (20%) | |
| Secondary | 40 (62%) | 38 (63%) | |
| Junior college | 10 (16%) | 10 (17%) | |
| Marriage, n (%) | 0.9 | ||
| Unmarried | 56 (88%) | 52 (87%) | |
| Divorce | 8 (12%) | 8 (13%) | |
| Occupation, n (%) | 0.8 | ||
| No job | 50 (78%) | 46 (77%) | |
| Worker | 14 (22%) | 14 (23%) | |
| Income, n (%) | 0.8 | ||
| Below NT5,000 | 50 (78%) | 46 (77%) | |
| NT 25,001-50,000 | 14 (22%) | 14 (23%) | |
| Alcohol, n (%) | 8 (12%) | 16 (27%) | 0.046 |
| Smoking, n (%) | 10 (16%) | 20 (33%) | 0.021 |
| Activity, n (%) | 0 (0%) | 0 (0%) | >0.9 |
| cholesterol, Median (IQR) | 150 (138, 157) | 191 (186, 196) | <0.001 |
| Triglyceride, Median (IQR) | 122 (106, 136) | 143 (126, 155) | <0.001 |
| HDL, Median (IQR) | 41 (35, 44) | 44 (42, 48) | 0.002 |
| LDL, Median (IQR) | 102 (95, 115) | 122 (97, 140) | <0.001 |
|
1
Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test
|
|||
表二問卷測量
表二問卷測量，以暴力頻率及暴力危險問卷來看，隨時間皆有下降，且達顯著意義，也證實介入確實有效。
t2<- raw %>%
select(MOAS_Total,
Risk_total,
AQ_Total,
BIS_Total,
time) %>%
tbl_summary(by = time) %>%
add_p()%>%
add_stat_label() %>%
bold_labels() %>%
modify_header(list(label ~ "**Variable**", all_stat_cols() ~ "**{level}**")) %>%
modify_spanning_header(all_stat_cols() ~ "**Time**") %>%
as_gt() %>%
gt::tab_header(
title = gt::md("**Table 2. Questionnaire**"))
t2| Table 2. Questionnaire | ||||
|---|---|---|---|---|
| Variable | Time | p-value1 | ||
| 1 | 2 | 3 | ||
| MOAS_Total, Median (IQR) | 10.5 (6.0, 16.8) | 8.0 (4.0, 10.0) | 4.0 (3.0, 8.0) | <0.001 |
| Risk_total, Median (IQR) | 18.00 (17.00, 19.00) | 17.00 (14.00, 18.00) | 15.50 (12.00, 17.00) | <0.001 |
| AQ_Total, Median (IQR) | 88 (79, 98) | 81 (74, 92) | 79 (68, 88) | <0.001 |
| BIS_Total, Median (IQR) | 72 (62, 76) | 67 (58, 75) | 64 (54, 73) | 0.002 |
|
1
Kruskal-Wallis rank sum test
|
||||
pacman::p_load(gtsummary)raw## id 組別編號 Intervention sex age edu marriage occupation
## 1 32 低膽1 Intervention M 28 Junior college Unmarried Worker
## 2 33 低膽2 Intervention F 47 Elementary Unmarried No job
## 3 34 低膽3 Intervention F 40 Secondary Unmarried No job
## 4 35 低膽4 Intervention M 29 Secondary Unmarried No job
## 5 36 低膽5 Intervention F 43 Secondary Unmarried No job
## 6 37 低膽6 Intervention F 38 Elementary Unmarried Worker
## 7 38 低膽7 Intervention M 29 Junior college \nDivorce No job
## 8 39 低膽8 Intervention F 43 Secondary \nDivorce No job
## 9 40 低膽9 Intervention M 23 Secondary Unmarried No job
## 10 41 低膽10 Intervention F 40 Secondary Unmarried No job
## 11 42 低膽11 Intervention F 33 Junior college Unmarried Worker
## 12 43 低膽12 Intervention F 56 Elementary Unmarried No job
## 13 44 低膽13 Intervention F 33 Secondary Unmarried No job
## 14 45 低膽14 Intervention M 43 Secondary Unmarried No job
## 15 46 低膽15 Intervention M 28 Secondary Unmarried No job
## 16 47 低膽16 Intervention F 47 Elementary Unmarried Worker
## 17 48 低膽17 Intervention F 40 Secondary Unmarried No job
## 18 49 低膽18 Intervention F 29 Secondary Unmarried No job
## 19 50 低膽19 Intervention F 43 Elementary Unmarried Worker
## 20 51 低膽20 Intervention M 23 Junior college \nDivorce No job
## 21 52 低膽21 Intervention F 47 Secondary \nDivorce No job
## 22 53 低膽22 Intervention M 23 Secondary Unmarried No job
## 23 54 低膽23 Intervention M 40 Secondary Unmarried No job
## 24 55 低膽24 Intervention M 21 Junior college Unmarried Worker
## 25 56 低膽25 Intervention M 33 Elementary Unmarried No job
## 26 57 低膽26 Intervention M 56 Secondary Unmarried No job
## 27 58 低膽27 Intervention M 33 Secondary Unmarried No job
## 28 59 低膽28 Intervention M 43 Secondary Unmarried No job
## 29 60 低膽29 Intervention M 37 Elementary Unmarried Worker
## 30 61 低膽30 Intervention M 40 Secondary Unmarried No job
## 31 62 低膽31 Intervention M 51 Secondary Unmarried No job
## 32 63 低膽32 Intervention M 49 Secondary Unmarried No job
## 33 64 控制1 Control M 47 Secondary Unmarried No job
## 34 65 控制2 Control F 28 Secondary Unmarried No job
## 35 66 控制3 Control M 40 Elementary Unmarried Worker
## 36 67 控制4 Control M 44 Secondary Unmarried No job
## 37 68 控制5 Control M 40 Secondary Unmarried No job
## 38 69 控制6 Control M 43 Junior college Unmarried Worker
## 39 70 控制7 Control F 23 Elementary Unmarried No job
## 40 71 控制8 Control M 40 Secondary Unmarried No job
## 41 72 控制9 Control M 51 Secondary Unmarried No job
## 42 73 控制10 Control M 49 Secondary Unmarried No job
## 43 74 控制11 Control M 22 Elementary Unmarried Worker
## 44 75 控制12 Control M 60 Secondary Unmarried No job
## 45 76 控制13 Control M 29 Secondary Unmarried No job
## 46 77 控制14 Control F 30 Junior college Unmarried Worker
## 47 78 控制15 Control M 43 Secondary Unmarried No job
## 48 79 控制16 Control M 43 Secondary Unmarried No job
## 49 80 控制17 Control F 43 Elementary Unmarried Worker
## 50 81 控制18 Control M 37 Junior college \nDivorce No job
## 51 82 控制19 Control F 51 Secondary \nDivorce No job
## 52 83 控制20 Control F 34 Secondary Unmarried No job
## 53 84 控制21 Control F 57 Elementary Unmarried Worker
## 54 85 控制22 Control F 45 Junior college \nDivorce No job
## 55 86 控制23 Control F 59 Secondary \nDivorce No job
## 56 87 控制24 Control F 55 Secondary Unmarried No job
## 57 88 控制25 Control F 32 Secondary Unmarried No job
## 58 89 控制26 Control F 40 Junior college Unmarried Worker
## 59 90 控制27 Control F 38 Elementary Unmarried No job
## 60 91 控制28 Control F 48 Secondary Unmarried No job
## 61 92 控制29 Control M 46 Secondary Unmarried No job
## 62 93 控制30 Control M 40 Secondary Unmarried No job
## 63 32 低膽1 Intervention M 28 Junior college Unmarried Worker
## 64 33 低膽2 Intervention F 47 Elementary Unmarried No job
## 65 34 低膽3 Intervention F 40 Secondary Unmarried No job
## 66 35 低膽4 Intervention M 29 Secondary Unmarried No job
## 67 36 低膽5 Intervention F 43 Secondary Unmarried No job
## 68 37 低膽6 Intervention F 38 Elementary Unmarried Worker
## 69 38 低膽7 Intervention M 29 Junior college \nDivorce No job
## 70 39 低膽8 Intervention F 43 Secondary \nDivorce No job
## 71 40 低膽9 Intervention M 23 Secondary Unmarried No job
## 72 41 低膽10 Intervention F 40 Secondary Unmarried No job
## 73 42 低膽11 Intervention F 33 Junior college Unmarried Worker
## 74 43 低膽12 Intervention F 56 Elementary Unmarried No job
## 75 44 低膽13 Intervention F 33 Secondary Unmarried No job
## 76 45 低膽14 Intervention M 43 Secondary Unmarried No job
## 77 46 低膽15 Intervention M 28 Secondary Unmarried No job
## 78 47 低膽16 Intervention F 47 Elementary Unmarried Worker
## 79 48 低膽17 Intervention F 40 Secondary Unmarried No job
## 80 49 低膽18 Intervention F 29 Secondary Unmarried No job
## 81 50 低膽19 Intervention F 43 Elementary Unmarried Worker
## 82 51 低膽20 Intervention M 23 Junior college \nDivorce No job
## 83 52 低膽21 Intervention F 47 Secondary \nDivorce No job
## 84 53 低膽22 Intervention M 23 Secondary Unmarried No job
## 85 54 低膽23 Intervention M 40 Secondary Unmarried No job
## 86 55 低膽24 Intervention M 21 Junior college Unmarried Worker
## 87 56 低膽25 Intervention M 33 Elementary Unmarried No job
## 88 57 低膽26 Intervention M 56 Secondary Unmarried No job
## 89 58 低膽27 Intervention M 33 Secondary Unmarried No job
## 90 59 低膽28 Intervention M 43 Secondary Unmarried No job
## 91 60 低膽29 Intervention M 37 Elementary Unmarried Worker
## 92 61 低膽30 Intervention M 40 Secondary Unmarried No job
## 93 62 低膽31 Intervention M 51 Secondary Unmarried No job
## 94 63 低膽32 Intervention M 49 Secondary Unmarried No job
## 95 64 控制1 Control M 47 Secondary Unmarried No job
## 96 65 控制2 Control F 28 Secondary Unmarried No job
## 97 66 控制3 Control M 40 Elementary Unmarried Worker
## 98 67 控制4 Control M 44 Secondary Unmarried No job
## 99 68 控制5 Control M 40 Secondary Unmarried No job
## 100 69 控制6 Control M 43 Junior college Unmarried Worker
## 101 70 控制7 Control F 23 Elementary Unmarried No job
## 102 71 控制8 Control M 40 Secondary Unmarried No job
## 103 72 控制9 Control M 51 Secondary Unmarried No job
## 104 73 控制10 Control M 49 Secondary Unmarried No job
## 105 74 控制11 Control M 22 Elementary Unmarried Worker
## 106 75 控制12 Control M 60 Secondary Unmarried No job
## 107 76 控制13 Control M 29 Secondary Unmarried No job
## 108 77 控制14 Control F 30 Junior college Unmarried Worker
## 109 78 控制15 Control M 43 Secondary Unmarried No job
## 110 79 控制16 Control M 43 Secondary Unmarried No job
## 111 80 控制17 Control F 43 Elementary Unmarried Worker
## 112 81 控制18 Control M 37 Junior college \nDivorce No job
## 113 82 控制19 Control F 51 Secondary \nDivorce No job
## 114 83 控制20 Control F 34 Secondary Unmarried No job
## 115 84 控制21 Control F 57 Elementary Unmarried Worker
## 116 85 控制22 Control F 45 Junior college \nDivorce No job
## 117 86 控制23 Control F 59 Secondary \nDivorce No job
## 118 87 控制24 Control F 55 Secondary Unmarried No job
## 119 88 控制25 Control F 32 Secondary Unmarried No job
## 120 89 控制26 Control F 40 Junior college Unmarried Worker
## 121 90 控制27 Control F 38 Elementary Unmarried No job
## 122 91 控制28 Control F 48 Secondary Unmarried No job
## 123 92 控制29 Control M 46 Secondary Unmarried No job
## 124 93 控制30 Control M 40 Secondary Unmarried No job
## 125 32 低膽1 Intervention M 28 Junior college Unmarried Worker
## 126 33 低膽2 Intervention F 47 Elementary Unmarried No job
## 127 34 低膽3 Intervention F 40 Secondary Unmarried No job
## 128 35 低膽4 Intervention M 29 Secondary Unmarried No job
## 129 36 低膽5 Intervention F 43 Secondary Unmarried No job
## 130 37 低膽6 Intervention F 38 Elementary Unmarried Worker
## 131 38 低膽7 Intervention M 29 Junior college \nDivorce No job
## 132 39 低膽8 Intervention F 43 Secondary \nDivorce No job
## 133 40 低膽9 Intervention M 23 Secondary Unmarried No job
## 134 41 低膽10 Intervention F 40 Secondary Unmarried No job
## 135 42 低膽11 Intervention F 33 Junior college Unmarried Worker
## 136 43 低膽12 Intervention F 56 Elementary Unmarried No job
## 137 44 低膽13 Intervention F 33 Secondary Unmarried No job
## 138 45 低膽14 Intervention M 43 Secondary Unmarried No job
## 139 46 低膽15 Intervention M 28 Secondary Unmarried No job
## 140 47 低膽16 Intervention F 47 Elementary Unmarried Worker
## 141 48 低膽17 Intervention F 40 Secondary Unmarried No job
## 142 49 低膽18 Intervention F 29 Secondary Unmarried No job
## 143 50 低膽19 Intervention F 43 Elementary Unmarried Worker
## 144 51 低膽20 Intervention M 23 Junior college \nDivorce No job
## 145 52 低膽21 Intervention F 47 Secondary \nDivorce No job
## 146 53 低膽22 Intervention M 23 Secondary Unmarried No job
## 147 54 低膽23 Intervention M 40 Secondary Unmarried No job
## 148 55 低膽24 Intervention M 21 Junior college Unmarried Worker
## 149 56 低膽25 Intervention M 33 Elementary Unmarried No job
## 150 57 低膽26 Intervention M 56 Secondary Unmarried No job
## 151 58 低膽27 Intervention M 33 Secondary Unmarried No job
## 152 59 低膽28 Intervention M 43 Secondary Unmarried No job
## 153 60 低膽29 Intervention M 37 Elementary Unmarried Worker
## 154 61 低膽30 Intervention M 40 Secondary Unmarried No job
## 155 62 低膽31 Intervention M 51 Secondary Unmarried No job
## 156 63 低膽32 Intervention M 49 Secondary Unmarried No job
## 157 64 控制1 Control M 47 Secondary Unmarried No job
## 158 65 控制2 Control F 28 Secondary Unmarried No job
## 159 66 控制3 Control M 40 Elementary Unmarried Worker
## 160 67 控制4 Control M 44 Secondary Unmarried No job
## 161 68 控制5 Control M 40 Secondary Unmarried No job
## 162 69 控制6 Control M 43 Junior college Unmarried Worker
## 163 70 控制7 Control F 23 Elementary Unmarried No job
## 164 71 控制8 Control M 40 Secondary Unmarried No job
## 165 72 控制9 Control M 51 Secondary Unmarried No job
## 166 73 控制10 Control M 49 Secondary Unmarried No job
## 167 74 控制11 Control M 22 Elementary Unmarried Worker
## 168 75 控制12 Control M 60 Secondary Unmarried No job
## 169 76 控制13 Control M 29 Secondary Unmarried No job
## 170 77 控制14 Control F 30 Junior college Unmarried Worker
## 171 78 控制15 Control M 43 Secondary Unmarried No job
## 172 79 控制16 Control M 43 Secondary Unmarried No job
## 173 80 控制17 Control F 43 Elementary Unmarried Worker
## 174 81 控制18 Control M 37 Junior college \nDivorce No job
## 175 82 控制19 Control F 51 Secondary \nDivorce No job
## 176 83 控制20 Control F 34 Secondary Unmarried No job
## 177 84 控制21 Control F 57 Elementary Unmarried Worker
## 178 85 控制22 Control F 45 Junior college \nDivorce No job
## 179 86 控制23 Control F 59 Secondary \nDivorce No job
## 180 87 控制24 Control F 55 Secondary Unmarried No job
## 181 88 控制25 Control F 32 Secondary Unmarried No job
## 182 89 控制26 Control F 40 Junior college Unmarried Worker
## 183 90 控制27 Control F 38 Elementary Unmarried No job
## 184 91 控制28 Control F 48 Secondary Unmarried No job
## 185 92 控制29 Control M 46 Secondary Unmarried No job
## 186 93 控制30 Control M 40 Secondary Unmarried No job
## income alcohol smoking activity BW BMI Age.of.onset
## 1 NT 25,001-50,000 No Yes No 71.1 25.65 23
## 2 Below NT5,000 No No No 62.0 20.60 30
## 3 Below NT5,000 No No No 70.0 25.80 36
## 4 Below NT5,000 Yes Yes No 78.6 27.60 24
## 5 Below NT5,000 No No No 53.0 20.90 15
## 6 NT 25,001-50,000 No No No 51.1 20.65 23
## 7 Below NT5,000 Yes Yes No 88.0 28.30 18
## 8 Below NT5,000 No No No 49.5 22.30 22
## 9 Below NT5,000 Yes Yes No 84.0 27.60 24
## 10 Below NT5,000 No No No 54.6 20.90 15
## 11 NT 25,001-50,000 No No No 60.0 20.65 23
## 12 Below NT5,000 No No No 55.0 22.60 30
## 13 Below NT5,000 No No No 64.0 22.80 36
## 14 Below NT5,000 Yes Yes No 79.0 27.60 24
## 15 Below NT5,000 No No No 75.0 27.90 15
## 16 NT 25,001-50,000 No No No 67.0 20.65 23
## 17 Below NT5,000 No No No 78.0 26.60 24
## 18 Below NT5,000 No No No 47.0 20.90 15
## 19 NT 25,001-50,000 No No No 67.0 24.65 23
## 20 Below NT5,000 No No No 89.0 28.30 18
## 21 Below NT5,000 No No No 45.0 23.30 22
## 22 Below NT5,000 No No No 82.0 26.60 24
## 23 Below NT5,000 No No No 75.5 24.90 15
## 24 NT 25,001-50,000 No No No 81.1 25.65 23
## 25 Below NT5,000 No No No 77.0 24.60 30
## 26 Below NT5,000 No No No 78.0 22.80 36
## 27 Below NT5,000 No No No 71.5 27.60 24
## 28 Below NT5,000 No No No 66.0 21.90 15
## 29 NT 25,001-50,000 No No No 61.1 20.65 23
## 30 Below NT5,000 No No No 88.5 28.60 24
## 31 Below NT5,000 No No No 45.0 21.90 15
## 32 Below NT5,000 No No No 65.0 22.80 36
## 33 Below NT5,000 No No No 92.6 29.60 24
## 34 Below NT5,000 No No No 66.6 21.90 15
## 35 NT 25,001-50,000 No No No 61.1 27.65 23
## 36 Below NT5,000 Yes Yes No 83.6 29.60 24
## 37 Below NT5,000 Yes Yes No 83.6 28.90 15
## 38 NT 25,001-50,000 No No No 81.1 29.65 23
## 39 Below NT5,000 No No No 59.0 23.60 30
## 40 Below NT5,000 No No No 86.0 28.80 36
## 41 Below NT5,000 No No No 80.6 27.60 24
## 42 Below NT5,000 Yes Yes No 93.6 28.90 15
## 43 NT 25,001-50,000 No No No 75.1 22.65 23
## 44 Below NT5,000 No No No 80.6 29.60 24
## 45 Below NT5,000 No No No 85.6 26.90 15
## 46 NT 25,001-50,000 No No No 61.1 20.65 23
## 47 Below NT5,000 Yes Yes No 89.0 29.60 30
## 48 Below NT5,000 Yes Yes No 83.6 28.90 15
## 49 NT 25,001-50,000 No No No 58.1 20.65 23
## 50 Below NT5,000 No No No 82.0 28.30 18
## 51 Below NT5,000 No No No 71.5 25.30 22
## 52 Below NT5,000 No No No 59.6 21.90 15
## 53 NT 25,001-50,000 Yes Yes No 61.1 26.65 23
## 54 Below NT5,000 Yes Yes No 82.0 28.30 18
## 55 Below NT5,000 No No No 64.5 23.30 22
## 56 Below NT5,000 No No No 80.6 29.60 24
## 57 Below NT5,000 No No No 63.6 23.90 15
## 58 NT 25,001-50,000 No No No 61.1 20.65 23
## 59 Below NT5,000 No No No 62.0 25.60 30
## 60 Below NT5,000 No Yes No 60.0 22.80 36
## 61 Below NT5,000 No Yes No 80.6 29.60 24
## 62 Below NT5,000 Yes Yes No 93.6 30.90 15
## 63 NT 25,001-50,000 No Yes No NA NA 23
## 64 Below NT5,000 No No No NA NA 30
## 65 Below NT5,000 No No No NA NA 36
## 66 Below NT5,000 Yes Yes No NA NA 24
## 67 Below NT5,000 No No No NA NA 15
## 68 NT 25,001-50,000 No No No NA NA 23
## 69 Below NT5,000 Yes Yes No NA NA 18
## 70 Below NT5,000 No No No NA NA 22
## 71 Below NT5,000 Yes Yes No NA NA 24
## 72 Below NT5,000 No No No NA NA 15
## 73 NT 25,001-50,000 No No No NA NA 23
## 74 Below NT5,000 No No No NA NA 30
## 75 Below NT5,000 No No No NA NA 36
## 76 Below NT5,000 Yes Yes No NA NA 24
## 77 Below NT5,000 No No No NA NA 15
## 78 NT 25,001-50,000 No No No NA NA 23
## 79 Below NT5,000 No No No NA NA 24
## 80 Below NT5,000 No No No NA NA 15
## 81 NT 25,001-50,000 No No No NA NA 23
## 82 Below NT5,000 No No No NA NA 18
## 83 Below NT5,000 No No No NA NA 22
## 84 Below NT5,000 No No No NA NA 24
## 85 Below NT5,000 No No No NA NA 15
## 86 NT 25,001-50,000 No No No NA NA 23
## 87 Below NT5,000 No No No NA NA 30
## 88 Below NT5,000 No No No NA NA 36
## 89 Below NT5,000 No No No NA NA 24
## 90 Below NT5,000 No No No NA NA 15
## 91 NT 25,001-50,000 No No No NA NA 23
## 92 Below NT5,000 No No No NA NA 24
## 93 Below NT5,000 No No No NA NA 15
## 94 Below NT5,000 No No No NA NA 36
## 95 Below NT5,000 No No No NA NA 24
## 96 Below NT5,000 No No No NA NA 15
## 97 NT 25,001-50,000 No No No NA NA 23
## 98 Below NT5,000 Yes Yes No NA NA 24
## 99 Below NT5,000 Yes Yes No NA NA 15
## 100 NT 25,001-50,000 No No No NA NA 23
## 101 Below NT5,000 No No No NA NA 30
## 102 Below NT5,000 No No No NA NA 36
## 103 Below NT5,000 No No No NA NA 24
## 104 Below NT5,000 Yes Yes No NA NA 15
## 105 NT 25,001-50,000 No No No NA NA 23
## 106 Below NT5,000 No No No NA NA 24
## 107 Below NT5,000 No No No NA NA 15
## 108 NT 25,001-50,000 No No No NA NA 23
## 109 Below NT5,000 Yes Yes No NA NA 30
## 110 Below NT5,000 Yes Yes No NA NA 15
## 111 NT 25,001-50,000 No No No NA NA 23
## 112 Below NT5,000 No No No NA NA 18
## 113 Below NT5,000 No No No NA NA 22
## 114 Below NT5,000 No No No NA NA 15
## 115 NT 25,001-50,000 Yes Yes No NA NA 23
## 116 Below NT5,000 Yes Yes No NA NA 18
## 117 Below NT5,000 No No No NA NA 22
## 118 Below NT5,000 No No No NA NA 24
## 119 Below NT5,000 No No No NA NA 15
## 120 NT 25,001-50,000 No No No NA NA 23
## 121 Below NT5,000 No No No NA NA 30
## 122 Below NT5,000 No Yes No NA NA 36
## 123 Below NT5,000 No Yes No NA NA 24
## 124 Below NT5,000 Yes Yes No NA NA 15
## 125 NT 25,001-50,000 No Yes No 71.1 25.65 23
## 126 Below NT5,000 No No No 62.0 20.60 30
## 127 Below NT5,000 No No No 70.0 25.80 36
## 128 Below NT5,000 Yes Yes No 75.6 26.50 24
## 129 Below NT5,000 No No No 53.0 20.90 15
## 130 NT 25,001-50,000 No No No 51.1 20.65 23
## 131 Below NT5,000 Yes Yes No 77.0 25.10 18
## 132 Below NT5,000 No No No 50.5 21.30 22
## 133 Below NT5,000 Yes Yes No 78.0 25.50 24
## 134 Below NT5,000 No No No 54.6 20.90 15
## 135 NT 25,001-50,000 No No No 56.0 20.05 23
## 136 Below NT5,000 No No No 55.0 22.60 30
## 137 Below NT5,000 No No No 63.0 22.80 36
## 138 Below NT5,000 Yes Yes No 75.0 26.40 24
## 139 Below NT5,000 No No No 74.0 27.90 15
## 140 NT 25,001-50,000 No No No 67.0 20.65 23
## 141 Below NT5,000 No No No 78.0 26.40 24
## 142 Below NT5,000 No No No 47.5 19.90 15
## 143 NT 25,001-50,000 No No No 67.0 24.65 23
## 144 Below NT5,000 No No No 76.0 24.10 18
## 145 Below NT5,000 No No No 47.0 23.30 22
## 146 Below NT5,000 No No No 78.0 25.10 24
## 147 Below NT5,000 No No No 75.5 24.70 15
## 148 NT 25,001-50,000 No No No 76.1 24.15 23
## 149 Below NT5,000 No No No 75.0 24.10 30
## 150 Below NT5,000 No No No 74.0 21.80 36
## 151 Below NT5,000 No No No 71.5 27.60 24
## 152 Below NT5,000 No No No 66.0 21.90 15
## 153 NT 25,001-50,000 No No No 61.1 20.65 23
## 154 Below NT5,000 No No No 80.5 26.10 24
## 155 Below NT5,000 No No No 45.0 21.90 15
## 156 Below NT5,000 No No No 65.0 22.80 36
## 157 Below NT5,000 No No No 95.6 29.90 24
## 158 Below NT5,000 No No No 70.6 22.90 15
## 159 NT 25,001-50,000 No No No 65.1 27.95 23
## 160 Below NT5,000 Yes Yes No 86.6 29.90 24
## 161 Below NT5,000 Yes Yes No 87.6 29.30 15
## 162 NT 25,001-50,000 No No No 84.1 29.95 23
## 163 Below NT5,000 No No No 59.0 23.60 30
## 164 Below NT5,000 No No No 88.0 28.90 36
## 165 Below NT5,000 No No No 85.6 28.20 24
## 166 Below NT5,000 Yes Yes No 93.6 30.90 15
## 167 NT 25,001-50,000 No No No 77.0 22.75 23
## 168 Below NT5,000 No No No 86.6 30.20 24
## 169 Below NT5,000 No No No 89.0 27.10 15
## 170 NT 25,001-50,000 No No No 63.1 24.75 23
## 171 Below NT5,000 Yes Yes No 89.0 29.60 30
## 172 Below NT5,000 Yes Yes No 83.6 28.90 15
## 173 NT 25,001-50,000 No No No 61.0 20.90 23
## 174 Below NT5,000 No No No 84.0 28.50 18
## 175 Below NT5,000 No No No 71.5 25.30 22
## 176 Below NT5,000 No No No 62.6 22.10 15
## 177 NT 25,001-50,000 Yes Yes No 69.5 27.25 23
## 178 Below NT5,000 Yes Yes No 82.0 28.30 18
## 179 Below NT5,000 No No No 64.5 26.30 22
## 180 Below NT5,000 No No No 82.0 29.70 24
## 181 Below NT5,000 No No No 63.6 25.90 15
## 182 NT 25,001-50,000 No No No 61.1 25.65 23
## 183 Below NT5,000 No No No 62.0 25.60 30
## 184 Below NT5,000 No Yes No 67.0 25.10 36
## 185 Below NT5,000 No Yes No 87.0 31.10 24
## 186 Below NT5,000 Yes Yes No 99.0 31.50 15
## cholesterol Triglyceride HDL LDL time MOAS1_1 MOAS1_2 MOAS1_3 MOAS1_4 MOAS2
## 1 148.0 102 38 95 1 2 2 1 2 4
## 2 128.0 110 30 137 1 2 2 1 2 4
## 3 156.0 160 44 99 1 2 2 1 1 3
## 4 159.0 105 44 109 1 2 1 2 1 4
## 5 138.0 120 32 104 1 2 2 1 1 4
## 6 138.0 102 38 95 1 2 2 1 2 3
## 7 153.0 171 41 147 1 2 1 1 1 4
## 8 134.0 125 35 87 1 1 2 1 2 0
## 9 158.0 105 44 109 1 2 2 1 2 3
## 10 137.0 130 32 104 1 2 2 1 1 4
## 11 146.0 102 48 95 1 2 1 1 2 2
## 12 134.0 110 35 137 1 2 2 2 2 4
## 13 148.0 128 44 109 1 2 2 1 2 2
## 14 159.0 105 44 99 1 2 2 1 2 4
## 15 134.0 111 31 104 1 2 2 2 2 4
## 16 138.0 102 38 95 1 2 1 1 2 2
## 17 156.0 105 44 89 1 2 2 1 1 4
## 18 130.0 107 30 84 1 2 2 2 2 2
## 19 137.0 102 34 95 1 2 2 2 2 3
## 20 157.0 171 41 147 1 2 2 1 1 4
## 21 132.0 136 35 97 1 1 2 1 2 0
## 22 155.0 105 44 89 1 2 2 1 1 3
## 23 138.0 140 32 84 1 2 1 2 2 3
## 24 140.0 102 38 95 1 2 1 1 2 4
## 25 137.0 138 35 117 1 1 2 1 2 0
## 26 130.0 116 34 129 1 1 2 2 2 0
## 27 159.0 105 44 89 1 2 2 1 1 4
## 28 138.0 129 36 84 1 2 1 1 1 2
## 29 132.0 102 38 95 1 2 1 2 1 4
## 30 153.0 105 44 89 1 2 1 1 1 4
## 31 137.0 136 32 84 1 2 2 1 2 4
## 32 130.0 123 34 89 1 2 2 2 1 4
## 33 191.0 162 44 139 1 2 2 1 1 2
## 34 192.0 151 42 124 1 2 1 1 1 4
## 35 189.0 142 41 105 1 2 1 1 1 3
## 36 196.0 155 40 129 1 2 2 1 1 4
## 37 196.0 140 42 114 1 2 2 2 1 4
## 38 188.0 132 48 105 1 2 1 2 1 2
## 39 176.0 129 55 97 1 2 1 1 1 2
## 40 177.0 143 44 89 1 2 1 1 1 2
## 41 192.0 155 34 119 1 2 1 2 2 4
## 42 198.0 152 42 134 1 2 1 2 2 4
## 43 174.0 120 48 95 1 2 1 1 1 4
## 44 196.0 123 44 149 1 2 2 1 1 4
## 45 186.0 146 42 124 1 2 1 1 2 2
## 46 178.0 112 48 95 1 2 2 1 1 4
## 47 197.0 153 35 147 1 2 2 2 2 4
## 48 193.5 142 38 124 1 2 1 1 1 2
## 49 171.0 122 48 95 1 2 1 1 1 2
## 50 198.0 151 41 147 1 2 2 1 2 2
## 51 176.0 147 55 97 1 2 1 1 1 4
## 52 178.0 107 62 94 1 1 1 2 1 0
## 53 190.0 136 48 115 1 2 2 1 1 3
## 54 199.0 161 35 147 1 2 2 1 2 3
## 55 185.0 126 45 97 1 2 2 1 1 2
## 56 197.0 105 44 159 1 2 2 1 2 2
## 57 188.0 142 48 134 1 2 1 1 1 3
## 58 179.0 102 48 95 1 2 1 1 1 4
## 59 172.0 123 55 87 1 1 2 1 1 0
## 60 186.0 126 44 129 1 2 1 1 1 2
## 61 196.0 105 40 159 1 2 1 1 2 4
## 62 198.0 156 32 148 1 2 2 1 2 4
## 63 NA NA NA NA 2 2 2 1 1 4
## 64 NA NA NA NA 2 2 1 1 1 4
## 65 NA NA NA NA 2 2 2 1 1 3
## 66 NA NA NA NA 2 2 1 1 1 4
## 67 NA NA NA NA 2 2 2 1 1 4
## 68 NA NA NA NA 2 2 2 1 1 3
## 69 NA NA NA NA 2 2 1 1 1 4
## 70 NA NA NA NA 2 1 2 1 2 0
## 71 NA NA NA NA 2 2 2 1 1 3
## 72 NA NA NA NA 2 2 2 1 1 4
## 73 NA NA NA NA 2 2 1 1 2 2
## 74 NA NA NA NA 2 2 2 1 1 4
## 75 NA NA NA NA 2 2 2 1 1 2
## 76 NA NA NA NA 2 2 2 1 1 4
## 77 NA NA NA NA 2 2 2 1 1 4
## 78 NA NA NA NA 2 2 1 1 2 2
## 79 NA NA NA NA 2 2 2 1 1 4
## 80 NA NA NA NA 2 2 2 2 1 2
## 81 NA NA NA NA 2 2 1 2 1 3
## 82 NA NA NA NA 2 2 2 1 1 4
## 83 NA NA NA NA 2 1 2 1 1 0
## 84 NA NA NA NA 2 2 2 1 1 2
## 85 NA NA NA NA 2 2 2 1 1 3
## 86 NA NA NA NA 2 2 1 1 2 4
## 87 NA NA NA NA 2 1 2 1 2 0
## 88 NA NA NA NA 2 1 2 2 2 0
## 89 NA NA NA NA 2 2 2 1 1 3
## 90 NA NA NA NA 2 2 1 1 2 2
## 91 NA NA NA NA 2 2 1 2 1 4
## 92 NA NA NA NA 2 2 1 1 2 4
## 93 NA NA NA NA 2 2 2 1 2 4
## 94 NA NA NA NA 2 2 2 1 1 4
## 95 NA NA NA NA 2 2 2 1 1 2
## 96 NA NA NA NA 2 2 1 1 1 4
## 97 NA NA NA NA 2 2 1 1 1 3
## 98 NA NA NA NA 2 2 2 1 1 4
## 99 NA NA NA NA 2 2 2 1 1 4
## 100 NA NA NA NA 2 2 1 2 1 2
## 101 NA NA NA NA 2 2 1 1 1 2
## 102 NA NA NA NA 2 2 1 1 1 2
## 103 NA NA NA NA 2 2 1 2 2 4
## 104 NA NA NA NA 2 2 1 2 2 4
## 105 NA NA NA NA 2 2 1 1 1 4
## 106 NA NA NA NA 2 2 2 1 1 4
## 107 NA NA NA NA 2 2 1 1 2 2
## 108 NA NA NA NA 2 2 2 1 1 4
## 109 NA NA NA NA 2 2 2 2 2 4
## 110 NA NA NA NA 2 2 1 1 1 3
## 111 NA NA NA NA 2 2 1 1 1 2
## 112 NA NA NA NA 2 2 2 1 2 2
## 113 NA NA NA NA 2 2 1 1 1 4
## 114 NA NA NA NA 2 1 1 2 1 0
## 115 NA NA NA NA 2 2 2 1 1 3
## 116 NA NA NA NA 2 2 2 1 2 3
## 117 NA NA NA NA 2 2 2 1 1 2
## 118 NA NA NA NA 2 2 2 1 2 2
## 119 NA NA NA NA 2 2 1 1 1 3
## 120 NA NA NA NA 2 2 1 1 1 4
## 121 NA NA NA NA 2 1 2 1 1 0
## 122 NA NA NA NA 2 2 1 1 1 2
## 123 NA NA NA NA 2 2 1 1 2 4
## 124 NA NA NA NA 2 2 2 1 2 3
## 125 148.0 121 38 95 3 1 2 1 1 0
## 126 148.0 132 41 137 3 2 1 1 1 2
## 127 156.0 160 44 109 3 2 1 1 1 3
## 128 168.0 118 54 120 3 2 1 1 1 4
## 129 149.0 120 32 104 3 2 1 1 1 4
## 130 146.0 125 48 115 3 2 1 1 1 3
## 131 164.0 161 52 137 3 1 1 1 1 0
## 132 150.0 119 45 114 3 2 1 1 1 1
## 133 169.0 142 56 132 3 2 2 1 1 3
## 134 156.0 129 42 101 3 2 1 1 1 4
## 135 166.0 112 58 115 3 2 1 1 1 2
## 136 154.0 138 45 117 3 2 2 1 1 1
## 137 148.0 128 44 109 3 2 1 1 1 2
## 138 159.0 105 44 119 3 2 1 1 1 4
## 139 146.0 111 41 104 3 2 2 1 1 2
## 140 154.0 122 52 105 3 2 1 1 1 3
## 141 156.0 135 44 105 3 2 1 1 1 4
## 142 149.0 117 42 94 3 2 2 1 1 1
## 143 157.0 126 34 95 3 2 1 2 1 3
## 144 169.0 141 59 142 3 2 1 1 1 4
## 145 148.0 112 45 90 3 1 2 1 1 0
## 146 165.0 100 54 109 3 1 2 1 1 0
## 147 152.0 140 32 91 3 2 1 1 1 4
## 148 152.0 114 48 99 3 2 1 1 2 4
## 149 157.0 138 35 117 3 1 2 1 1 0
## 150 142.0 136 44 139 3 1 2 1 1 0
## 151 159.0 112 44 89 3 2 2 1 1 3
## 152 138.0 129 36 84 3 2 1 1 1 4
## 153 156.0 138 38 95 3 2 1 2 1 3
## 154 176.0 136 59 98 3 2 1 1 2 4
## 155 157.0 136 32 84 3 2 1 1 2 4
## 156 159.0 142 41 108 3 2 2 1 1 4
## 157 191.0 162 44 139 3 2 2 1 1 2
## 158 192.0 151 42 124 3 2 1 1 1 4
## 159 189.0 142 41 105 3 2 1 1 1 3
## 160 196.0 155 40 129 3 2 2 1 1 4
## 161 196.0 140 42 114 3 2 2 1 1 4
## 162 188.0 148 48 105 3 2 1 2 1 2
## 163 176.0 129 55 97 3 2 1 1 1 2
## 164 191.0 162 44 89 3 2 1 1 1 2
## 165 192.0 155 34 119 3 2 1 2 1 4
## 166 217.0 189 42 134 3 2 1 1 2 4
## 167 174.0 120 48 95 3 2 1 1 1 4
## 168 215.0 177 49 171 3 2 2 1 1 4
## 169 197.0 162 47 139 3 2 1 1 2 2
## 170 178.0 112 48 95 3 2 2 1 1 4
## 171 213.0 172 39 165 3 2 2 1 2 4
## 172 193.5 142 38 124 3 2 1 1 1 3
## 173 190.0 155 50 112 3 2 1 1 1 2
## 174 198.0 151 41 147 3 2 2 1 2 2
## 175 196.0 147 45 97 3 2 1 1 1 4
## 176 189.0 136 42 94 3 1 1 2 1 0
## 177 190.0 136 48 115 3 2 2 1 1 3
## 178 218.0 183 46 158 3 2 2 1 2 3
## 179 185.0 126 45 97 3 2 2 1 1 2
## 180 197.0 145 44 159 3 2 2 1 2 2
## 181 188.0 163 48 134 3 1 2 1 1 0
## 182 192.0 124 48 95 3 2 1 1 1 4
## 183 172.0 123 55 87 3 1 2 1 1 0
## 184 186.0 159 46 141 3 2 1 1 1 2
## 185 196.0 143 42 178 3 2 1 1 2 4
## 186 227.0 185 48 172 3 2 2 1 1 3
## MOAS3 MOAS4 MOAS5 MOAS_oral MOAS_item MOAS_lself MOAS_body MOAS_Total risk1
## 1 3 0 0 4 6 0 0 10 2
## 2 3 0 4 4 6 0 16 26 2
## 3 2 0 0 3 4 0 0 7 1
## 4 0 2 0 4 0 6 0 10 2
## 5 3 0 0 4 6 0 0 10 2
## 6 2 0 3 3 4 0 12 19 2
## 7 0 0 0 4 0 0 0 4 3
## 8 2 0 3 0 4 0 12 16 2
## 9 2 0 3 3 4 0 12 19 2
## 10 4 0 0 4 8 0 0 12 3
## 11 0 0 3 2 0 0 12 14 2
## 12 3 1 2 4 6 3 8 21 2
## 13 2 0 2 2 4 0 8 14 2
## 14 2 0 0 4 4 0 0 8 2
## 15 2 1 4 4 4 3 16 27 3
## 16 0 0 3 2 0 0 12 14 2
## 17 4 0 0 4 8 0 0 12 2
## 18 2 3 4 2 4 9 16 31 2
## 19 2 2 1 3 4 6 4 17 2
## 20 1 0 0 4 2 0 0 6 3
## 21 2 0 4 0 4 0 16 20 2
## 22 2 0 0 3 4 0 0 7 2
## 23 0 2 3 3 0 6 12 21 2
## 24 0 0 2 4 0 0 8 12 2
## 25 4 0 2 0 8 0 8 16 2
## 26 3 1 3 0 6 3 12 21 2
## 27 1 0 0 4 2 0 0 6 2
## 28 0 0 4 2 0 0 16 18 2
## 29 0 4 0 4 0 12 0 16 2
## 30 0 0 1 4 0 0 4 8 2
## 31 2 0 3 4 4 0 12 20 2
## 32 2 3 0 4 4 9 0 17 2
## 33 3 0 0 2 6 0 0 8 2
## 34 0 0 0 4 0 0 0 4 3
## 35 0 0 0 3 0 0 0 3 3
## 36 2 0 0 4 4 0 0 8 2
## 37 2 2 0 4 4 6 0 14 3
## 38 0 3 0 2 0 9 0 11 1
## 39 0 0 0 2 0 0 0 2 1
## 40 0 0 0 2 0 0 0 2 1
## 41 0 1 2 4 0 3 8 15 2
## 42 0 1 2 4 0 3 8 15 2
## 43 0 0 0 4 0 0 0 4 1
## 44 1 0 0 4 2 0 0 6 2
## 45 0 0 8 2 0 0 0 10 2
## 46 2 0 0 4 4 0 0 8 1
## 47 1 2 1 4 2 6 4 16 3
## 48 0 0 0 2 0 0 0 2 2
## 49 0 0 0 4 0 0 0 4 1
## 50 2 0 3 2 4 0 12 18 3
## 51 0 0 0 4 0 0 0 4 1
## 52 0 1 0 0 0 3 0 3 1
## 53 2 0 0 3 4 0 0 7 2
## 54 2 0 3 3 4 0 12 19 3
## 55 2 0 0 2 4 0 0 6 2
## 56 2 0 1 2 4 0 4 10 2
## 57 3 0 0 3 6 0 0 9 2
## 58 0 0 0 1 0 0 0 4 1
## 59 2 0 0 0 4 0 0 4 2
## 60 0 0 0 2 0 0 0 2 2
## 61 0 0 2 4 0 0 8 12 3
## 62 2 0 3 4 4 0 12 20 3
## 63 3 0 0 4 6 0 0 10 2
## 64 0 0 0 4 0 0 0 4 2
## 65 2 0 0 3 4 0 0 7 1
## 66 0 0 0 4 0 0 0 4 2
## 67 3 0 0 4 6 0 0 10 2
## 68 2 0 0 3 4 0 0 7 2
## 69 0 0 0 4 0 0 0 4 3
## 70 2 0 1 0 4 0 4 8 2
## 71 1 0 0 3 2 0 0 5 2
## 72 4 0 0 4 8 0 0 12 2
## 73 0 0 3 2 0 0 12 14 2
## 74 0 0 0 4 0 0 0 4 2
## 75 2 0 0 2 4 0 0 6 2
## 76 2 0 0 4 4 0 0 8 2
## 77 2 0 0 4 4 0 0 8 3
## 78 0 0 2 2 0 0 8 10 2
## 79 3 0 0 4 6 0 0 10 2
## 80 2 3 0 2 4 9 0 15 2
## 81 0 2 0 3 0 6 0 9 2
## 82 1 0 0 4 2 0 0 6 2
## 83 2 0 0 0 4 0 0 4 2
## 84 2 0 0 2 4 0 0 6 2
## 85 3 0 0 3 6 0 0 9 2
## 86 0 0 1 4 0 0 4 8 2
## 87 4 0 2 0 8 0 8 16 2
## 88 3 1 3 0 6 3 12 21 2
## 89 1 0 0 3 2 0 0 5 2
## 90 0 0 2 2 0 0 8 10 2
## 91 0 3 0 4 0 9 0 13 2
## 92 0 0 1 4 0 0 4 8 2
## 93 2 0 1 4 4 0 4 12 2
## 94 2 0 0 4 4 0 0 8 2
## 95 2 0 0 2 4 0 0 6 2
## 96 0 0 0 4 0 0 0 4 2
## 97 0 0 0 3 0 0 0 3 2
## 98 2 0 0 4 4 0 0 8 2
## 99 2 0 0 4 4 0 0 8 2
## 100 0 3 0 2 0 9 0 11 1
## 101 0 0 0 2 0 0 0 2 1
## 102 0 0 0 2 0 0 0 2 1
## 103 0 1 2 4 0 3 8 15 2
## 104 0 1 1 4 0 3 4 11 2
## 105 0 0 0 4 0 0 0 4 1
## 106 2 0 0 4 4 0 0 8 2
## 107 0 0 1 2 0 0 4 6 2
## 108 2 0 0 4 4 0 0 8 1
## 109 1 1 1 4 2 3 4 13 2
## 110 0 0 0 3 0 0 0 3 2
## 111 0 0 0 2 0 0 0 2 1
## 112 2 0 1 2 4 0 4 10 2
## 113 0 0 0 4 0 0 0 4 1
## 114 0 1 0 0 0 3 0 3 1
## 115 1 0 0 3 2 0 0 5 2
## 116 2 0 1 3 4 0 4 11 3
## 117 2 0 0 2 4 0 0 6 2
## 118 2 0 1 2 4 0 4 10 2
## 119 3 0 0 3 6 0 0 9 2
## 120 0 0 0 4 0 0 0 4 1
## 121 2 0 0 0 4 0 0 4 2
## 122 0 0 0 2 0 0 0 2 2
## 123 0 0 2 4 0 0 8 12 3
## 124 2 0 1 3 4 0 4 11 2
## 125 1 0 0 0 2 0 0 2 2
## 126 0 0 0 2 0 0 0 2 2
## 127 0 0 0 3 0 0 0 3 1
## 128 0 0 0 4 0 0 0 4 1
## 129 0 0 0 4 0 0 0 4 2
## 130 0 0 0 3 0 0 0 3 2
## 131 0 0 0 0 0 0 0 0 1
## 132 0 0 0 1 0 0 0 1 2
## 133 1 0 0 3 2 0 0 5 1
## 134 0 0 0 4 0 0 0 4 1
## 135 0 0 0 2 0 0 0 2 1
## 136 1 0 0 1 2 0 0 3 2
## 137 0 0 0 2 0 0 0 2 2
## 138 0 0 0 4 0 0 0 4 1
## 139 2 0 0 2 4 0 0 6 1
## 140 0 0 0 3 0 0 0 3 2
## 141 0 0 0 4 0 0 0 4 1
## 142 2 0 0 1 4 0 0 5 2
## 143 0 2 0 3 0 6 0 9 2
## 144 0 0 0 4 0 0 0 4 1
## 145 2 0 0 0 4 0 0 4 2
## 146 2 0 0 0 4 0 0 4 1
## 147 0 0 0 4 0 0 0 4 2
## 148 0 0 1 4 0 0 4 8 2
## 149 3 0 0 0 6 0 0 6 2
## 150 3 0 0 0 6 0 0 6 2
## 151 1 0 0 3 2 0 0 5 1
## 152 0 0 0 4 0 0 0 2 2
## 153 0 2 0 3 0 6 0 9 1
## 154 0 0 1 4 0 0 4 8 1
## 155 0 0 1 4 0 0 4 8 2
## 156 2 0 0 4 4 0 0 8 2
## 157 2 0 0 2 4 0 0 6 2
## 158 0 0 0 4 0 0 0 4 1
## 159 0 0 0 3 0 0 0 3 2
## 160 2 0 0 4 4 0 0 8 2
## 161 2 0 0 4 4 0 0 8 2
## 162 0 3 0 2 0 9 0 11 1
## 163 0 0 0 2 0 0 0 2 1
## 164 0 0 0 2 0 0 0 2 1
## 165 0 1 0 4 0 3 0 7 2
## 166 0 0 1 4 0 0 4 8 1
## 167 0 0 0 4 0 0 0 4 1
## 168 2 0 0 4 4 0 0 8 2
## 169 0 0 1 2 0 0 4 6 2
## 170 2 0 0 4 4 0 0 8 1
## 171 1 0 1 4 2 0 4 10 2
## 172 0 0 0 3 0 0 0 3 2
## 173 0 0 0 2 0 0 0 2 1
## 174 2 0 1 2 4 0 4 10 2
## 175 0 0 0 4 0 0 0 4 1
## 176 0 1 0 0 0 3 0 3 1
## 177 1 0 0 3 2 0 0 5 2
## 178 2 0 1 3 4 0 4 11 3
## 179 2 0 0 2 4 0 0 6 2
## 180 2 0 1 2 4 0 4 10 2
## 181 2 0 0 0 4 0 0 4 2
## 182 0 0 0 4 0 0 0 4 1
## 183 2 0 0 0 4 0 0 4 2
## 184 0 0 0 2 0 0 0 2 2
## 185 0 0 2 4 0 0 8 12 3
## 186 2 0 0 3 4 0 0 7 2
## risk2 risk3 risk4 risk5 risk6 risk7 risk8 risk9 risk10 Risk_total AQ1 AQ2
## 1 3 1 3 1 2 3 2 1 2 20 5 5
## 2 3 1 3 1 2 3 2 2 2 21 4 4
## 3 1 1 3 1 1 1 1 1 1 12 5 5
## 4 2 1 3 1 2 2 2 1 1 17 4 2
## 5 3 1 3 1 2 2 2 1 1 18 4 5
## 6 2 1 3 1 2 2 3 1 1 18 2 4
## 7 2 1 3 1 2 2 1 2 1 18 3 3
## 8 2 1 1 1 1 1 1 1 2 13 5 4
## 9 2 1 3 1 2 2 1 2 1 17 3 3
## 10 2 1 3 1 2 2 2 1 2 19 3 4
## 11 2 1 3 1 2 2 3 1 1 18 3 3
## 12 2 1 3 1 2 2 3 2 2 20 4 3
## 13 2 1 3 1 2 2 2 1 1 17 3 4
## 14 2 1 3 1 2 2 2 1 1 17 4 4
## 15 2 1 3 1 3 3 3 2 1 22 5 5
## 16 2 1 3 1 2 2 2 2 1 18 3 3
## 17 2 1 3 1 2 2 2 1 1 17 3 2
## 18 2 1 3 1 2 2 2 1 2 18 3 4
## 19 2 1 3 1 2 3 2 1 2 19 3 4
## 20 3 1 3 1 2 2 1 2 1 19 3 3
## 21 1 1 1 1 1 1 2 1 2 13 3 2
## 22 2 1 3 1 2 2 2 0 1 16 3 3
## 23 2 1 3 1 2 3 2 2 1 19 5 5
## 24 2 1 3 1 2 3 2 1 1 18 5 5
## 25 2 1 3 1 2 2 2 1 2 18 5 5
## 26 2 1 3 1 2 4 2 1 2 20 5 5
## 27 2 1 3 1 2 3 1 2 1 18 3 4
## 28 2 1 3 1 2 2 2 2 1 18 5 5
## 29 2 1 3 1 2 3 2 1 2 19 5 5
## 30 2 1 3 1 2 2 1 1 1 16 5 2
## 31 3 1 3 1 2 2 2 2 2 20 5 5
## 32 2 1 3 1 2 3 2 2 2 20 5 5
## 33 3 1 3 1 2 2 2 2 1 19 3 5
## 34 2 1 3 1 2 2 2 2 1 19 3 3
## 35 3 1 3 1 2 2 2 2 1 20 3 5
## 36 2 1 3 1 2 2 2 2 1 18 3 5
## 37 2 1 3 1 2 2 2 2 1 19 3 5
## 38 1 1 3 1 2 3 2 2 1 17 3 5
## 39 1 1 3 1 1 1 1 1 1 12 1 4
## 40 1 1 3 1 1 1 1 1 1 12 1 2
## 41 2 1 3 1 2 2 2 2 1 18 3 5
## 42 2 1 3 1 2 2 2 2 1 18 3 5
## 43 1 1 3 1 1 1 1 1 1 12 1 4
## 44 2 1 3 1 2 2 2 2 1 18 3 5
## 45 2 1 3 1 2 1 2 2 1 17 3 5
## 46 1 1 3 1 1 1 1 1 1 12 1 4
## 47 2 1 3 1 2 1 2 2 1 18 3 5
## 48 3 1 3 1 2 1 2 2 1 18 3 5
## 49 1 1 3 1 1 1 1 1 1 12 1 1
## 50 2 1 3 1 2 2 2 2 1 19 4 5
## 51 1 1 3 1 1 1 1 1 1 12 1 2
## 52 1 1 3 1 1 1 1 1 1 12 1 4
## 53 2 1 3 1 2 2 1 2 1 17 2 4
## 54 3 1 3 1 2 2 2 2 1 20 2 3
## 55 1 1 1 1 1 1 1 1 1 11 3 2
## 56 2 1 3 1 2 2 2 2 1 18 2 2
## 57 2 1 3 1 2 2 2 2 1 18 4 2
## 58 1 1 3 1 1 1 1 1 1 12 1 4
## 59 2 1 3 1 1 1 1 1 1 14 1 4
## 60 3 1 3 1 2 1 2 2 1 18 2 4
## 61 2 1 3 1 2 1 2 2 1 18 3 5
## 62 3 1 3 1 2 1 2 2 1 19 3 5
## 63 3 1 3 1 2 2 2 1 2 19 5 5
## 64 3 1 3 1 2 2 2 2 1 19 4 4
## 65 1 1 3 1 1 1 1 1 1 12 5 2
## 66 2 1 3 1 1 1 1 1 1 14 4 1
## 67 2 1 3 1 2 2 2 1 1 17 4 5
## 68 2 1 3 1 2 2 3 1 1 18 2 4
## 69 2 1 3 1 1 1 1 2 1 16 3 3
## 70 2 1 1 1 1 1 1 1 1 12 5 4
## 71 2 1 3 1 1 1 1 2 1 15 3 3
## 72 2 1 3 1 1 2 1 1 1 15 3 4
## 73 2 1 3 1 1 1 1 1 1 14 3 3
## 74 2 1 3 1 1 2 3 2 1 18 4 3
## 75 2 1 3 1 2 2 2 1 1 17 3 4
## 76 2 1 3 1 1 1 1 1 1 14 4 4
## 77 2 1 3 1 3 3 3 2 1 22 5 5
## 78 2 1 3 1 1 2 2 2 1 17 3 3
## 79 2 1 3 1 1 1 2 1 1 15 3 2
## 80 2 1 3 1 2 2 2 1 2 18 3 4
## 81 2 1 3 1 2 3 2 1 1 18 3 4
## 82 2 1 3 1 1 1 1 2 1 15 3 3
## 83 1 1 1 1 1 1 2 1 2 13 3 2
## 84 2 1 3 1 1 1 1 0 1 13 3 3
## 85 2 1 3 1 2 3 2 2 1 19 5 5
## 86 2 1 3 1 2 3 2 1 1 18 5 5
## 87 2 1 3 1 1 2 2 1 1 16 5 5
## 88 2 1 3 1 2 4 2 1 2 20 5 5
## 89 2 1 3 1 1 3 1 2 1 17 3 4
## 90 2 1 3 1 2 2 2 2 1 18 5 5
## 91 2 1 3 1 1 3 2 1 1 17 5 5
## 92 2 1 3 1 1 2 1 1 1 15 5 2
## 93 3 1 3 1 2 2 2 2 1 19 5 5
## 94 2 1 3 1 2 2 2 2 1 18 5 5
## 95 3 1 3 1 2 2 2 2 1 19 3 5
## 96 2 1 3 1 2 2 2 2 2 19 3 3
## 97 2 1 3 1 1 1 2 2 1 16 3 5
## 98 2 1 3 1 2 2 2 2 1 18 3 5
## 99 2 1 3 1 2 2 2 2 1 18 3 5
## 100 1 1 3 1 1 3 2 2 1 16 3 5
## 101 1 1 3 1 1 1 1 1 1 12 1 4
## 102 1 1 3 1 1 1 1 1 1 12 1 2
## 103 2 1 3 1 2 2 2 2 1 18 3 5
## 104 2 1 3 1 2 2 2 2 2 19 3 5
## 105 1 1 3 1 1 1 1 1 1 12 1 4
## 106 2 1 3 1 2 2 2 2 2 19 3 5
## 107 2 1 3 1 2 1 2 2 1 17 3 5
## 108 1 1 3 1 1 1 1 1 1 12 1 4
## 109 2 1 3 1 2 1 2 2 2 18 3 5
## 110 3 1 3 1 2 1 1 2 1 17 3 5
## 111 1 1 3 1 1 1 1 1 1 12 1 1
## 112 2 1 3 1 2 2 2 2 2 19 4 5
## 113 1 1 3 1 1 1 1 1 1 12 1 2
## 114 1 1 3 1 1 1 1 1 1 12 1 4
## 115 2 1 3 1 2 2 1 2 1 17 2 4
## 116 3 1 3 1 2 2 2 2 2 21 2 3
## 117 1 1 1 1 1 1 1 1 1 11 3 2
## 118 2 1 3 1 2 2 2 2 1 18 2 2
## 119 2 1 3 1 2 2 2 2 1 18 4 2
## 120 1 1 3 1 1 1 1 1 1 12 1 4
## 121 2 1 3 1 1 1 1 1 1 14 1 4
## 122 2 1 3 1 1 1 2 2 1 16 2 4
## 123 2 1 3 1 1 1 2 2 1 17 3 5
## 124 2 1 3 1 2 1 2 2 2 18 3 5
## 125 2 1 3 1 2 2 2 1 1 17 3 5
## 126 2 1 3 1 2 2 1 2 1 17 3 4
## 127 1 1 3 1 1 1 1 1 1 12 2 2
## 128 1 1 3 1 1 1 1 1 1 12 1 1
## 129 2 1 3 1 2 2 2 1 1 17 4 5
## 130 2 1 3 1 2 2 2 1 1 17 2 4
## 131 1 1 3 1 1 1 1 1 1 12 1 3
## 132 2 1 3 1 1 1 1 1 1 14 5 4
## 133 1 1 3 1 1 1 1 1 1 12 1 3
## 134 1 1 3 1 1 1 1 1 1 12 2 4
## 135 1 1 3 1 1 1 1 1 1 12 1 3
## 136 2 1 3 1 1 1 1 2 1 15 3 3
## 137 2 1 3 1 2 2 2 1 1 17 3 4
## 138 1 1 3 1 1 1 1 1 1 12 2 4
## 139 2 1 3 1 3 2 1 2 1 17 5 5
## 140 1 1 3 1 1 1 1 2 1 14 2 3
## 141 1 1 3 1 1 1 1 1 1 12 2 2
## 142 2 1 3 1 2 2 2 1 2 18 2 4
## 143 2 1 3 1 2 1 2 1 1 16 3 4
## 144 1 1 3 1 1 1 1 2 1 13 1 3
## 145 1 1 3 1 1 1 2 1 2 15 3 2
## 146 1 1 3 1 1 1 1 1 1 12 1 3
## 147 2 1 3 1 2 1 1 1 1 15 3 5
## 148 2 1 3 1 2 1 1 1 1 15 3 5
## 149 2 1 3 1 1 1 1 1 1 14 3 5
## 150 2 1 3 1 2 2 2 1 2 18 5 5
## 151 1 1 3 1 1 1 1 1 1 12 3 4
## 152 2 1 3 1 2 2 2 2 1 18 5 5
## 153 1 1 3 1 1 1 2 1 1 13 3 5
## 154 1 1 3 1 1 1 1 1 1 12 1 2
## 155 3 1 3 1 2 1 2 2 1 18 2 5
## 156 2 1 3 1 2 1 2 2 1 17 2 5
## 157 3 1 3 1 2 2 2 2 1 19 2 5
## 158 1 1 3 1 2 2 2 2 2 17 2 3
## 159 2 1 3 1 1 1 2 2 1 16 1 5
## 160 2 1 3 1 2 2 2 2 1 18 3 5
## 161 2 1 3 1 2 2 2 2 1 18 3 5
## 162 1 1 3 1 2 3 2 2 1 17 3 5
## 163 1 1 3 1 1 1 1 1 1 12 1 4
## 164 1 1 3 1 1 1 1 1 1 12 1 2
## 165 2 1 3 1 2 2 2 2 1 18 3 5
## 166 1 1 3 1 2 2 2 2 2 17 3 5
## 167 1 1 3 1 1 1 1 1 1 12 1 4
## 168 2 1 3 1 2 2 2 2 2 19 3 5
## 169 2 1 3 1 2 1 2 2 1 17 3 5
## 170 1 1 3 1 1 1 1 1 1 12 1 4
## 171 2 1 3 1 2 1 2 2 2 18 3 5
## 172 1 1 3 1 2 1 1 2 1 15 3 5
## 173 1 1 3 1 2 2 1 1 1 14 1 1
## 174 2 1 3 1 2 2 2 2 2 19 2 5
## 175 1 1 3 1 1 1 1 1 1 12 1 2
## 176 1 1 3 1 1 1 1 1 1 12 1 4
## 177 2 1 3 1 2 2 1 2 1 17 2 4
## 178 1 1 3 1 2 2 2 2 2 19 2 3
## 179 1 1 3 1 1 1 1 1 1 13 3 2
## 180 2 1 3 1 2 2 2 2 1 18 2 2
## 181 2 1 3 1 2 2 2 2 1 18 2 2
## 182 1 1 3 1 1 1 1 1 1 12 1 4
## 183 2 1 3 1 1 1 1 1 1 14 1 4
## 184 2 1 3 1 1 1 2 2 1 16 2 4
## 185 2 1 3 1 1 1 2 2 1 17 3 5
## 186 2 1 3 1 2 1 2 2 2 18 3 5
## AQ3 AQ4 AQ5 AQ6 AQ7 AQ8 AQ9 AQ10 AQ11 AQ12 AQ13 AQ14 AQ15 AQ16 AQ17 AQ18
## 1 4 1 2 5 3 5 2 2 1 5 1 2 4 2 2 2
## 2 4 2 5 4 4 4 5 4 3 3 3 4 4 2 5 4
## 3 2 2 5 4 5 5 5 5 5 5 5 5 3 3 5 2
## 4 4 4 3 4 5 4 4 2 5 4 2 4 1 4 4 4
## 5 4 2 5 5 5 5 5 5 4 5 4 4 3 4 5 2
## 6 4 2 4 4 4 5 4 4 2 5 2 2 4 2 4 2
## 7 3 1 1 3 2 4 3 2 2 4 2 2 5 2 2 2
## 8 2 4 4 3 5 4 3 4 3 3 2 5 2 4 3 3
## 9 4 2 2 3 4 4 3 4 3 3 2 4 4 2 3 2
## 10 4 2 1 4 2 1 1 1 1 1 1 5 4 4 2 3
## 11 5 4 4 3 4 4 5 4 4 4 2 2 4 3 4 2
## 12 4 2 2 4 2 2 3 2 2 2 1 3 4 4 3 3
## 13 4 4 1 1 1 4 1 1 1 2 1 1 5 1 1 3
## 14 5 5 5 2 4 1 5 4 1 1 5 5 1 4 5 2
## 15 3 2 3 3 4 2 3 3 3 3 2 4 2 2 4 5
## 16 2 3 3 3 2 3 3 2 2 2 2 2 3 3 3 3
## 17 4 2 4 4 4 3 2 2 3 2 4 3 3 2 1 3
## 18 4 3 2 2 4 3 2 5 3 2 2 2 3 3 3 3
## 19 3 3 4 4 5 2 3 4 2 2 3 4 3 3 4 3
## 20 3 2 2 2 3 4 2 3 2 3 2 2 3 2 2 3
## 21 2 2 2 2 3 3 3 3 3 2 2 2 4 3 3 3
## 22 3 3 3 4 3 3 3 4 4 2 3 3 3 3 4 2
## 23 1 3 2 2 2 1 2 3 2 1 2 2 2 2 1 5
## 24 3 4 4 2 4 4 4 2 3 4 3 2 2 4 3 5
## 25 4 4 4 2 4 5 4 2 2 4 2 4 4 3 4 5
## 26 4 3 4 3 4 4 3 4 2 4 3 2 4 4 4 5
## 27 4 3 3 3 4 4 3 2 2 4 3 3 2 3 3 2
## 28 4 4 4 4 4 4 4 4 2 4 2 4 3 2 4 5
## 29 4 4 4 3 4 4 4 3 2 4 3 4 4 4 4 5
## 30 4 4 4 3 4 5 3 4 3 4 2 4 4 4 4 4
## 31 4 2 4 4 4 5 4 4 4 3 4 4 2 2 4 5
## 32 4 4 4 2 2 2 4 4 1 2 2 4 2 3 4 5
## 33 3 2 2 2 2 2 2 2 1 3 2 2 4 2 2 5
## 34 3 4 4 2 4 4 3 2 3 2 3 2 2 2 2 2
## 35 4 4 4 2 4 5 3 2 2 2 2 4 4 2 2 5
## 36 4 3 4 3 4 4 3 4 3 2 3 2 4 2 2 5
## 37 4 3 3 3 4 4 3 2 3 2 3 3 2 2 2 5
## 38 4 4 4 4 4 4 3 4 3 2 2 4 3 2 2 5
## 39 4 4 4 3 4 4 1 3 1 1 3 4 4 1 1 4
## 40 4 4 4 3 4 5 1 4 1 1 2 4 4 1 1 4
## 41 4 2 2 4 3 4 3 2 3 2 2 3 4 2 2 2
## 42 4 4 4 4 4 4 3 4 3 2 2 4 4 2 2 2
## 43 4 4 2 3 3 4 1 3 1 1 2 2 4 1 1 2
## 44 3 4 4 2 4 4 3 2 3 2 3 2 2 2 2 5
## 45 4 4 4 2 4 5 3 2 2 2 2 4 4 2 2 5
## 46 4 3 4 3 4 4 1 4 1 1 3 2 4 1 1 3
## 47 4 3 3 3 4 4 3 2 2 2 3 3 2 2 3 5
## 48 4 4 4 4 4 4 3 4 2 2 2 4 3 2 4 5
## 49 4 4 4 3 4 4 1 3 1 1 3 4 4 1 1 2
## 50 4 4 4 3 4 5 3 4 3 2 2 4 4 2 2 5
## 51 4 2 2 4 3 4 1 2 1 1 2 3 4 1 1 2
## 52 4 4 4 4 4 4 1 4 1 1 2 4 4 1 1 2
## 53 4 4 2 3 3 4 2 3 2 2 2 2 4 2 2 2
## 54 4 2 2 2 4 4 2 4 2 3 2 2 4 2 2 2
## 55 4 2 2 2 4 4 3 4 2 2 2 4 3 2 2 2
## 56 2 2 4 4 4 2 3 4 2 2 4 3 1 2 2 4
## 57 3 4 4 2 4 4 3 2 1 4 3 2 2 2 2 3
## 58 4 4 4 2 4 5 1 2 1 2 2 4 4 1 1 2
## 59 4 3 4 3 4 4 1 4 1 2 3 2 4 1 1 2
## 60 4 3 3 3 4 4 3 2 2 2 3 3 2 2 2 2
## 61 3 3 1 3 3 1 2 3 2 2 0 1 4 2 2 5
## 62 5 5 5 2 2 2 3 5 2 3 5 5 1 2 1 5
## 63 4 1 2 5 3 5 2 2 1 5 1 2 4 2 2 2
## 64 4 2 5 4 4 4 5 2 3 5 3 4 4 2 5 4
## 65 2 2 5 4 2 5 4 3 5 2 5 5 3 3 5 1
## 66 4 4 3 1 1 4 2 2 5 1 2 4 1 4 4 1
## 67 4 2 5 5 4 5 5 3 4 4 4 4 3 4 5 2
## 68 4 2 4 4 4 5 4 4 2 4 2 2 4 2 4 2
## 69 1 1 1 1 1 4 1 2 2 1 2 2 1 2 2 1
## 70 2 4 4 3 2 4 3 4 3 2 2 5 2 4 3 3
## 71 1 2 2 1 1 4 1 1 3 1 2 4 1 2 3 1
## 72 2 2 1 4 2 1 1 1 1 2 1 5 4 4 2 3
## 73 1 4 4 1 1 4 1 1 4 1 2 2 1 3 4 1
## 74 4 2 2 4 2 2 3 2 2 2 1 3 2 4 3 3
## 75 4 4 1 1 1 4 1 1 1 3 1 1 5 1 1 3
## 76 2 5 5 1 2 1 3 2 1 2 5 5 1 4 5 1
## 77 3 2 3 3 4 2 3 3 3 4 2 4 2 2 4 3
## 78 2 3 3 3 2 3 3 2 2 2 2 2 2 3 3 2
## 79 2 2 4 1 2 3 2 2 3 2 4 3 2 2 1 2
## 80 4 3 2 2 4 3 2 5 3 3 2 2 3 3 3 3
## 81 3 3 4 4 2 2 3 4 2 3 3 4 3 3 4 2
## 82 1 2 2 1 1 4 1 1 2 1 2 2 1 2 2 1
## 83 2 2 2 2 3 3 3 3 3 2 2 2 4 3 3 3
## 84 3 3 3 1 1 3 1 4 4 1 3 3 1 3 4 1
## 85 1 3 2 2 1 1 2 3 2 1 2 2 2 2 1 2
## 86 3 4 4 2 2 4 2 2 3 2 3 2 2 4 3 2
## 87 4 4 4 2 2 5 2 2 2 2 2 4 2 3 4 2
## 88 4 3 4 3 4 4 3 4 2 4 3 2 4 4 4 3
## 89 4 3 3 1 2 4 2 2 2 3 3 3 2 3 3 1
## 90 4 4 4 4 4 4 4 4 2 4 2 4 3 2 4 4
## 91 4 4 4 1 2 4 2 3 2 2 3 4 2 4 4 3
## 92 4 4 4 1 1 5 3 4 3 1 2 4 1 4 4 4
## 93 4 2 4 4 2 5 2 4 4 2 4 4 2 2 4 2
## 94 4 4 4 2 2 2 2 4 1 2 2 4 2 3 4 2
## 95 3 2 2 2 2 2 2 2 1 1 2 2 4 2 2 3
## 96 3 4 4 2 3 4 3 2 3 2 3 2 2 2 2 2
## 97 1 4 4 1 3 5 1 2 2 2 2 4 4 2 2 2
## 98 4 3 4 3 3 4 3 4 3 2 3 2 4 2 2 3
## 99 4 3 3 3 3 4 3 2 3 2 3 3 2 2 2 3
## 100 1 4 4 1 3 4 2 4 3 2 2 4 3 2 2 2
## 101 1 4 4 1 2 4 1 3 1 1 3 4 1 1 1 1
## 102 4 4 4 3 3 5 1 4 1 2 2 4 4 1 1 3
## 103 4 2 2 4 3 4 3 2 3 2 2 3 4 2 2 2
## 104 4 4 4 4 4 4 3 4 3 3 2 4 4 2 2 2
## 105 1 4 2 1 2 4 1 3 1 1 2 1 4 1 1 2
## 106 3 4 4 2 4 4 3 2 3 3 3 2 2 2 2 4
## 107 4 4 4 2 4 5 1 2 2 1 2 4 4 2 2 3
## 108 1 3 4 1 4 4 1 4 1 1 3 2 4 1 1 1
## 109 4 3 3 3 4 4 4 2 2 2 3 3 2 2 3 4
## 110 4 4 4 4 4 4 3 4 2 1 2 4 3 2 4 3
## 111 4 4 4 3 4 4 1 3 1 1 3 4 4 1 1 2
## 112 4 4 4 3 4 5 3 4 3 1 2 4 4 2 2 5
## 113 4 2 2 4 3 4 1 2 1 1 2 3 4 1 1 2
## 114 4 4 4 4 4 4 1 4 1 1 2 4 4 1 1 2
## 115 4 4 2 1 3 4 1 3 2 1 2 2 4 2 2 2
## 116 4 2 2 2 4 4 3 4 2 3 2 2 4 2 2 2
## 117 4 2 2 2 4 4 1 4 2 1 2 4 3 2 2 2
## 118 2 2 4 4 4 2 3 4 2 2 4 3 1 2 2 4
## 119 3 4 4 1 4 4 3 2 1 1 3 2 2 2 2 1
## 120 4 4 4 2 4 5 1 2 1 2 2 4 4 1 1 1
## 121 4 3 4 1 4 4 1 4 1 1 3 2 4 1 1 1
## 122 4 3 3 1 4 4 1 2 2 2 3 3 2 2 2 1
## 123 3 3 1 3 3 1 2 3 2 2 0 1 4 2 2 2
## 124 5 5 5 4 2 2 4 5 2 5 5 5 1 2 1 5
## 125 4 1 2 5 3 5 2 2 1 5 1 2 4 2 2 2
## 126 4 2 5 4 4 4 5 2 3 5 3 4 4 2 5 3
## 127 2 2 5 4 2 5 4 3 2 2 5 5 3 3 2 1
## 128 4 4 3 1 1 4 2 2 1 1 2 4 1 1 1 1
## 129 4 2 5 5 4 5 5 3 4 4 4 4 3 4 5 2
## 130 4 2 4 4 4 5 4 4 2 4 2 2 4 2 4 2
## 131 1 1 1 1 1 4 1 2 1 1 2 2 1 1 1 1
## 132 2 4 4 3 2 4 3 4 3 2 2 5 2 4 2 2
## 133 1 2 2 1 1 4 1 1 1 1 2 4 1 1 1 1
## 134 2 2 1 4 2 1 1 1 1 2 1 5 4 4 2 3
## 135 1 4 4 1 1 4 1 1 1 1 2 2 1 1 1 1
## 136 4 2 2 4 2 2 3 2 2 2 1 3 2 4 2 3
## 137 4 4 1 1 1 4 1 1 1 3 1 1 5 1 1 3
## 138 2 5 5 1 2 1 3 2 1 2 5 5 1 4 2 1
## 139 3 2 3 3 4 2 3 3 3 4 2 4 2 2 4 3
## 140 2 3 3 3 2 3 3 2 2 2 2 2 2 3 3 2
## 141 2 2 4 1 2 3 2 2 3 2 4 3 2 2 1 2
## 142 4 3 2 2 4 3 2 5 3 3 2 2 3 3 3 3
## 143 3 3 4 4 2 2 3 4 2 3 3 4 3 3 2 2
## 144 1 2 2 1 1 4 1 1 1 1 2 2 1 2 1 1
## 145 2 2 2 2 3 3 3 3 3 2 2 2 4 3 3 3
## 146 3 3 3 1 1 3 1 4 1 1 3 3 1 3 1 1
## 147 1 3 2 2 1 1 2 3 2 1 2 2 2 2 1 2
## 148 3 4 4 2 2 4 2 2 3 2 3 2 2 4 3 2
## 149 4 4 4 2 2 5 2 2 2 2 2 4 2 3 2 2
## 150 4 3 4 3 4 4 3 4 2 4 3 2 4 4 4 3
## 151 4 3 3 1 2 4 2 2 2 3 3 3 1 3 2 1
## 152 4 4 4 4 4 4 4 4 2 4 2 4 3 2 4 4
## 153 4 4 4 1 2 4 2 3 2 2 3 4 2 4 2 3
## 154 4 4 4 1 1 5 3 4 1 1 2 4 1 4 1 4
## 155 4 2 4 4 2 5 2 4 2 2 4 4 1 2 2 2
## 156 4 4 4 2 2 2 2 4 1 2 2 4 1 3 2 2
## 157 3 2 2 2 2 2 2 2 1 1 2 2 4 2 2 3
## 158 3 4 4 2 3 4 3 2 3 2 3 2 2 2 2 2
## 159 1 4 4 1 3 5 1 2 2 2 2 4 4 1 2 2
## 160 4 3 4 3 3 4 3 4 3 2 3 2 4 2 2 3
## 161 4 3 3 3 3 4 3 2 3 2 3 3 2 2 2 3
## 162 1 4 4 1 3 4 2 4 3 2 2 4 3 2 2 2
## 163 1 4 4 1 2 4 1 3 1 1 3 4 1 1 1 1
## 164 4 4 4 3 3 5 1 4 1 2 2 4 4 1 1 3
## 165 4 2 2 4 3 4 3 2 3 2 2 3 4 2 2 2
## 166 4 4 4 4 4 4 3 4 3 3 2 4 4 2 2 2
## 167 1 4 2 1 2 4 1 3 1 1 2 1 4 1 1 2
## 168 3 4 4 2 4 4 3 2 3 3 3 2 2 2 2 5
## 169 4 4 4 2 4 5 1 2 2 1 2 4 4 2 2 3
## 170 1 3 4 1 4 4 1 4 1 1 3 2 4 1 1 1
## 171 4 3 3 3 4 4 4 2 2 2 3 3 2 2 3 5
## 172 4 4 4 4 4 4 3 4 2 1 2 4 3 2 4 3
## 173 4 4 4 3 4 4 1 3 1 1 3 4 4 1 1 2
## 174 4 4 4 3 4 5 3 4 3 1 2 4 4 2 2 5
## 175 4 2 2 4 3 4 1 2 1 1 2 3 4 1 1 2
## 176 4 4 4 4 4 4 1 4 1 1 2 4 4 1 1 2
## 177 4 4 2 1 3 4 1 3 2 1 2 2 4 2 2 2
## 178 4 2 2 2 4 4 3 4 2 3 2 2 4 2 2 4
## 179 4 2 2 2 4 4 1 4 2 1 2 4 3 2 2 2
## 180 2 2 4 4 4 2 3 4 2 2 4 3 1 2 2 4
## 181 3 4 4 1 4 4 3 2 1 1 3 2 2 2 2 1
## 182 4 4 4 2 4 5 1 2 1 2 2 4 4 1 1 4
## 183 4 3 4 1 4 4 1 4 1 1 3 2 4 1 1 1
## 184 4 3 3 1 4 4 1 2 2 2 3 3 2 2 2 1
## 185 3 3 1 3 3 1 2 3 2 2 0 1 4 1 2 2
## 186 5 5 5 4 2 2 4 5 2 5 5 5 1 2 1 5
## AQ19 AQ20 AQ21 AQ22 AQ23 AQ24 AQ25 AQ26 AQ27 AQ28 AQ29 AQ_anger AQ_verbal
## 1 4 5 1 4 4 2 1 4 4 4 3 26 13
## 2 5 2 5 5 2 2 5 4 4 2 4 25 19
## 3 5 1 1 5 1 5 1 2 4 2 3 25 20
## 4 4 2 3 4 4 2 2 1 4 4 3 28 19
## 5 2 1 5 5 2 2 2 1 4 1 4 21 20
## 6 5 2 4 5 2 2 3 2 4 2 4 22 16
## 7 4 1 1 5 4 1 1 1 4 2 3 22 11
## 8 3 3 3 5 4 2 4 3 4 3 3 24 19
## 9 3 3 1 3 2 2 2 4 3 3 3 19 13
## 10 5 5 1 3 3 1 2 4 4 1 4 17 16
## 11 3 1 2 2 2 1 4 2 4 2 4 21 15
## 12 2 3 2 2 2 1 1 3 2 3 4 19 13
## 13 3 1 1 3 1 1 4 1 4 1 4 14 11
## 14 5 5 5 5 4 4 5 5 4 4 3 25 21
## 15 5 3 3 2 4 2 3 3 4 2 4 27 16
## 16 3 2 2 2 4 2 2 2 3 3 4 21 13
## 17 3 4 4 2 2 2 2 4 4 4 3 19 17
## 18 3 2 2 2 4 2 3 3 3 3 4 20 12
## 19 4 4 3 3 4 2 2 4 4 2 4 21 18
## 20 3 2 2 2 2 2 2 2 4 2 3 18 12
## 21 3 3 3 3 4 2 2 2 4 2 4 20 13
## 22 3 3 3 3 2 3 3 3 4 2 3 17 17
## 23 5 2 2 2 4 2 2 2 4 2 4 24 13
## 24 5 4 4 4 4 2 4 2 4 2 4 29 16
## 25 5 3 4 4 4 2 5 4 4 4 4 31 18
## 26 5 4 4 4 4 2 2 3 4 2 4 28 16
## 27 4 3 4 5 2 2 5 3 4 3 3 21 17
## 28 5 4 4 4 4 2 2 4 4 4 4 31 20
## 29 5 4 4 5 4 3 2 4 4 4 4 31 19
## 30 2 4 4 4 3 4 2 4 4 4 3 25 19
## 31 5 3 4 4 4 2 2 4 4 2 4 28 18
## 32 5 3 2 4 4 2 2 4 3 2 0 27 15
## 33 5 2 1 3 4 2 1 2 4 4 4 26 11
## 34 3 2 4 2 4 2 4 2 2 2 4 19 14
## 35 5 3 4 2 4 2 5 2 4 4 4 26 18
## 36 5 2 4 2 4 2 2 2 4 4 4 26 16
## 37 5 2 4 2 4 2 5 2 4 4 4 26 17
## 38 5 2 4 2 4 2 2 2 4 4 4 26 20
## 39 4 1 4 1 4 3 2 1 4 4 3 19 19
## 40 2 1 4 1 4 4 2 1 4 4 3 17 19
## 41 2 2 4 1 4 2 2 2 3 4 4 20 16
## 42 2 2 1 1 4 2 2 2 4 4 4 20 17
## 43 3 2 4 2 2 2 2 2 4 2 3 12 17
## 44 5 2 4 2 4 2 4 2 4 4 4 26 16
## 45 5 2 4 2 4 2 5 2 4 4 4 26 18
## 46 3 1 4 1 2 2 2 1 4 2 3 15 16
## 47 5 2 4 2 4 2 3 2 4 4 4 26 17
## 48 5 2 4 2 4 2 2 2 4 4 4 26 20
## 49 2 1 4 1 4 3 2 1 4 4 3 15 19
## 50 5 2 4 2 4 4 2 1 4 4 4 27 19
## 51 2 2 4 1 3 2 2 1 3 2 3 13 16
## 52 2 3 1 1 4 2 2 3 4 3 3 15 17
## 53 3 2 4 2 2 2 2 2 4 2 4 15 17
## 54 5 3 4 1 2 1 5 2 2 2 4 18 12
## 55 2 2 4 2 4 2 2 2 4 4 3 20 16
## 56 3 2 4 2 3 2 2 2 4 2 4 19 17
## 57 3 2 4 2 2 2 4 2 2 2 4 21 14
## 58 2 3 4 1 3 2 5 1 4 4 3 17 18
## 59 2 1 4 1 2 2 2 1 4 2 3 14 16
## 60 4 3 4 3 4 2 3 3 4 3 4 20 17
## 61 5 3 3 3 1 5 3 3 4 4 4 22 14
## 62 5 2 5 2 4 5 5 2 4 4 4 27 21
## 63 4 5 1 4 4 2 1 4 4 4 3 26 13
## 64 5 2 5 5 2 2 3 4 4 2 4 27 19
## 65 5 1 1 5 1 5 1 2 4 2 3 20 16
## 66 4 2 3 2 4 2 1 1 2 4 1 20 14
## 67 2 1 5 5 2 2 2 1 4 1 4 20 20
## 68 5 2 4 5 2 2 3 2 4 2 4 21 16
## 69 4 1 1 2 4 1 1 1 2 2 1 16 7
## 70 3 3 3 5 4 2 3 3 4 3 3 23 19
## 71 3 3 1 1 2 2 1 4 1 3 1 14 9
## 72 5 5 1 3 3 1 2 4 1 1 4 18 13
## 73 3 1 2 1 2 1 1 2 4 2 1 13 13
## 74 2 3 2 2 2 1 1 3 2 3 4 19 13
## 75 3 1 1 3 1 1 4 1 4 1 4 15 11
## 76 2 5 5 2 4 4 2 5 4 4 1 20 20
## 77 5 3 3 2 4 2 3 3 4 2 2 26 16
## 78 3 2 2 2 4 2 2 2 3 3 4 20 13
## 79 3 4 4 2 2 2 2 4 4 4 1 18 14
## 80 3 2 2 2 4 2 3 3 3 3 4 21 12
## 81 2 4 3 3 4 2 2 4 4 2 1 19 18
## 82 3 2 2 1 2 2 1 2 1 2 1 13 8
## 83 3 3 3 3 4 2 2 2 4 2 4 20 13
## 84 3 3 3 1 2 3 1 3 4 2 1 13 14
## 85 2 2 2 2 4 2 2 2 4 2 4 18 13
## 86 2 4 4 4 4 2 3 2 4 2 4 21 16
## 87 2 3 4 4 4 2 3 4 4 4 2 23 18
## 88 5 4 4 4 4 2 2 3 4 2 4 26 16
## 89 2 3 4 5 2 2 1 3 4 3 3 17 15
## 90 5 4 4 4 4 2 2 4 4 4 4 30 20
## 91 2 4 4 5 4 3 2 4 4 4 4 24 17
## 92 1 4 4 4 3 4 1 4 4 4 1 21 17
## 93 2 3 4 4 4 2 2 4 4 2 1 21 18
## 94 2 3 2 4 4 2 1 4 3 2 1 21 15
## 95 5 2 1 3 4 2 1 2 4 4 4 22 11
## 96 3 2 4 2 4 2 4 2 2 2 4 19 14
## 97 5 3 4 2 4 2 2 2 4 4 4 21 17
## 98 5 2 4 2 4 2 2 2 4 4 4 24 16
## 99 5 2 4 2 4 2 3 2 4 4 4 24 17
## 100 5 2 4 2 4 2 2 2 4 4 4 22 17
## 101 4 1 4 1 4 3 2 1 1 4 3 16 14
## 102 2 1 4 1 4 4 2 1 4 4 3 17 19
## 103 2 2 4 1 4 2 2 2 3 4 4 20 16
## 104 2 2 1 1 4 2 2 2 4 4 4 21 17
## 105 3 2 4 2 2 2 2 2 4 2 3 12 15
## 106 5 2 4 2 4 2 4 2 4 4 4 26 16
## 107 5 2 4 2 4 2 5 2 4 4 4 21 18
## 108 3 1 4 1 2 2 2 1 4 2 1 11 14
## 109 5 2 4 2 4 2 3 2 4 4 4 26 17
## 110 5 2 4 2 4 2 2 2 4 4 4 23 20
## 111 2 1 4 1 4 3 2 1 4 4 3 15 19
## 112 5 2 4 2 4 4 2 1 4 4 4 26 19
## 113 2 2 4 1 3 2 2 1 3 2 3 12 16
## 114 2 3 1 1 4 2 2 3 4 3 3 14 17
## 115 3 2 4 2 2 2 2 2 4 2 1 13 15
## 116 5 3 4 1 2 1 5 2 2 2 4 19 12
## 117 2 2 4 2 4 2 2 2 4 4 1 17 16
## 118 3 2 4 2 3 2 2 2 4 2 4 19 17
## 119 3 2 4 2 2 2 4 2 2 2 4 16 13
## 120 2 3 4 1 3 2 5 1 4 4 3 14 18
## 121 2 1 4 1 2 2 2 1 4 2 1 10 14
## 122 4 3 4 1 4 2 3 3 4 3 1 17 15
## 123 5 3 3 3 1 5 4 3 4 4 4 19 14
## 124 5 2 5 2 4 5 5 2 4 4 4 30 23
## 125 3 3 1 4 4 2 1 3 4 3 3 22 13
## 126 3 2 5 5 2 2 3 3 4 2 4 23 19
## 127 2 1 1 5 1 2 1 2 1 1 3 13 13
## 128 4 2 1 2 1 2 1 1 1 1 1 11 11
## 129 2 1 5 5 2 2 2 1 4 1 1 20 20
## 130 4 2 4 5 2 2 3 2 4 2 1 20 16
## 131 4 1 1 2 1 1 1 1 1 1 1 10 6
## 132 2 3 3 5 3 2 3 2 4 2 3 19 19
## 133 3 3 1 1 1 1 1 1 1 1 1 9 9
## 134 2 2 1 3 2 1 2 2 1 1 2 13 13
## 135 3 1 2 1 1 1 1 2 1 2 1 10 10
## 136 2 3 2 2 2 1 1 3 2 2 2 17 13
## 137 3 1 1 3 1 1 4 1 4 1 3 15 11
## 138 2 2 2 2 2 2 2 2 1 2 1 14 14
## 139 5 3 3 2 4 2 3 3 4 2 2 26 16
## 140 2 2 2 2 4 1 2 2 2 2 1 17 12
## 141 2 4 4 2 2 1 2 2 2 2 1 14 12
## 142 3 2 2 2 4 2 3 3 3 3 1 20 12
## 143 2 4 3 3 2 2 2 4 1 1 1 16 15
## 144 1 2 1 1 1 1 1 2 1 1 1 7 7
## 145 3 3 3 3 4 2 2 2 4 2 1 20 13
## 146 3 3 1 1 2 1 1 3 1 1 1 10 9
## 147 2 2 2 2 4 2 2 2 4 2 1 16 13
## 148 2 2 4 4 4 2 3 2 4 2 1 17 16
## 149 2 3 2 4 4 1 3 4 4 1 2 16 16
## 150 5 4 4 4 4 2 2 3 4 2 1 26 16
## 151 1 3 2 5 2 2 1 3 1 3 3 15 10
## 152 5 4 4 4 4 2 2 4 4 4 1 30 20
## 153 2 4 4 5 4 3 2 2 4 1 1 17 17
## 154 1 1 4 4 3 1 1 1 4 1 1 14 17
## 155 2 3 2 4 4 2 2 2 4 1 1 15 16
## 156 2 3 2 4 4 2 1 2 3 1 1 15 15
## 157 5 2 1 3 4 2 1 2 4 4 1 21 11
## 158 3 2 4 2 4 2 4 2 2 2 4 18 14
## 159 5 1 4 2 4 2 2 2 4 4 4 19 17
## 160 5 2 4 2 4 2 2 2 4 4 4 24 16
## 161 5 2 4 2 4 2 3 2 4 4 4 24 17
## 162 5 2 4 2 4 2 1 2 4 4 4 22 17
## 163 4 1 4 1 4 3 1 1 4 4 3 16 17
## 164 2 4 4 1 4 4 2 1 4 4 3 17 19
## 165 2 4 4 1 4 2 2 2 3 4 4 20 16
## 166 2 2 1 1 4 2 2 2 4 4 4 21 17
## 167 3 2 4 2 2 2 1 2 4 4 3 14 14
## 168 5 4 4 2 4 2 4 2 4 4 4 27 16
## 169 5 2 4 2 4 2 2 2 4 4 4 21 18
## 170 3 1 4 1 2 2 2 1 4 4 1 13 14
## 171 5 2 4 2 4 2 3 2 4 4 4 27 17
## 172 5 2 4 2 4 2 2 2 4 4 4 23 20
## 173 2 1 4 1 4 3 1 1 4 4 3 15 19
## 174 5 2 4 2 4 4 2 1 4 4 4 24 19
## 175 2 2 4 1 3 2 2 1 3 4 3 14 16
## 176 2 1 1 1 4 2 2 3 4 3 3 14 17
## 177 3 2 4 2 2 2 2 2 2 4 2 15 13
## 178 5 4 4 1 2 1 3 2 2 2 4 21 12
## 179 2 2 4 2 4 2 2 2 4 4 2 17 16
## 180 3 4 4 2 3 2 2 2 2 4 4 21 15
## 181 3 2 4 2 2 2 2 2 2 2 4 14 13
## 182 2 4 4 1 3 2 3 1 4 4 3 17 18
## 183 2 1 4 1 2 2 2 1 2 2 1 10 12
## 184 4 1 4 1 4 2 3 3 4 3 1 17 15
## 185 5 4 3 3 4 5 3 3 4 4 4 22 14
## 186 5 4 5 2 4 5 5 2 4 4 4 30 23
## AQ_physical AQ_suspicious AQ_Total BIS1 BIS2 BIS3 BIS4 BIS5 BIS6 BIS7 BIS8
## 1 24 26 89 3 2 4 1 4 3 1 1
## 2 35 29 108 4 3 4 3 4 4 2 2
## 3 29 26 100 4 1 4 3 2 3 1 3
## 4 29 21 97 3 1 3 3 1 1 2 3
## 5 38 26 105 3 3 4 1 4 4 3 4
## 6 31 26 95 4 3 4 1 4 4 2 3
## 7 23 17 73 3 1 3 3 2 2 3 1
## 8 33 24 100 4 3 4 3 4 3 2 1
## 9 24 28 84 4 1 3 3 2 3 3 3
## 10 21 23 77 4 3 4 3 4 3 4 1
## 11 30 25 91 3 2 4 1 4 2 2 1
## 12 21 22 75 4 3 4 2 4 4 2 1
## 13 23 15 63 3 2 4 3 4 2 3 1
## 14 33 33 112 3 1 3 3 2 2 2 1
## 15 26 24 93 4 3 4 3 4 2 2 3
## 16 24 18 76 4 3 4 3 4 2 2 1
## 17 25 24 85 3 1 2 2 2 1 2 1
## 18 26 26 84 4 2 4 3 4 2 3 1
## 19 27 29 95 4 3 4 3 4 4 2 1
## 20 22 20 72 4 1 1 1 2 1 2 1
## 21 24 22 79 4 3 4 2 4 2 3 1
## 22 28 26 88 4 1 2 1 2 1 2 1
## 23 22 15 74 4 3 4 2 4 4 2 3
## 24 35 22 102 4 2 4 3 4 4 2 4
## 25 34 27 110 4 3 4 3 4 4 2 3
## 26 32 29 105 4 3 4 2 4 4 2 2
## 27 32 23 93 3 1 2 1 2 2 3 1
## 28 29 29 109 4 3 4 2 4 4 3 1
## 29 33 30 113 4 2 4 2 4 4 3 1
## 30 29 32 105 3 1 2 1 2 2 2 1
## 31 34 27 107 4 2 4 2 4 4 1 1
## 32 23 25 90 4 1 4 1 4 4 2 1
## 33 22 19 78 4 2 4 2 4 1 1 1
## 34 29 19 81 2 1 4 1 4 1 2 2
## 35 31 23 98 3 2 4 1 4 1 2 2
## 36 29 24 95 3 2 4 2 4 3 3 3
## 37 31 20 94 2 2 4 2 4 2 3 3
## 38 28 23 97 4 2 4 2 3 2 1 2
## 39 23 21 82 4 1 2 1 1 1 2 2
## 40 21 23 80 4 1 1 1 1 1 2 1
## 41 25 21 82 3 2 4 2 3 1 1 1
## 42 27 24 88 2 2 4 1 4 2 2 1
## 43 21 21 71 3 1 2 1 1 2 3 1
## 44 31 19 92 3 2 3 2 4 3 2 1
## 45 31 22 97 4 2 3 2 4 2 2 1
## 46 23 21 75 3 1 1 1 1 2 1 1
## 47 28 21 92 4 3 4 2 3 2 2 2
## 48 27 25 98 2 1 3 1 2 1 2 2
## 49 20 21 75 4 3 1 1 1 1 1 3
## 50 29 25 100 4 2 4 1 2 1 1 1
## 51 18 19 66 3 3 1 3 1 1 1 1
## 52 22 25 79 2 2 1 2 1 2 1 1
## 53 24 22 78 3 2 4 2 4 2 2 1
## 54 25 24 79 3 3 4 2 4 3 2 1
## 55 21 23 80 4 3 3 2 3 2 3 1
## 56 24 19 79 3 3 4 2 4 2 2 1
## 57 26 19 80 4 3 4 2 3 2 2 2
## 58 26 21 82 2 1 1 1 1 1 2 2
## 59 23 21 74 4 3 1 1 1 1 1 3
## 60 28 22 87 2 1 4 1 4 1 2 2
## 61 21 26 83 3 2 4 1 4 1 2 2
## 62 32 23 103 3 3 4 3 2 3 1 3
## 63 24 26 89 3 2 4 1 4 3 1 1
## 64 33 27 106 4 3 4 3 4 4 2 2
## 65 37 23 96 4 1 4 3 2 1 1 3
## 66 24 17 75 3 1 3 1 1 1 1 3
## 67 38 23 101 3 3 4 1 4 4 3 4
## 68 31 26 94 4 3 4 1 4 4 2 3
## 69 18 10 51 3 1 3 1 2 1 1 1
## 70 32 21 95 4 3 4 3 4 3 2 1
## 71 19 16 58 4 1 3 1 2 1 1 3
## 72 21 21 73 4 3 4 3 4 1 4 1
## 73 23 12 61 3 2 4 1 4 1 1 1
## 74 21 20 73 4 3 4 2 4 1 2 1
## 75 23 15 64 3 2 4 3 4 2 3 1
## 76 25 26 91 3 1 3 3 2 1 1 1
## 77 24 24 90 4 3 4 3 4 2 2 3
## 78 24 17 74 4 3 4 1 4 2 2 1
## 79 23 19 74 3 1 2 1 2 1 2 1
## 80 26 26 85 4 2 4 3 4 2 3 1
## 81 24 26 87 4 3 4 1 4 2 2 1
## 82 18 12 51 4 1 1 1 2 1 1 1
## 83 24 22 79 4 3 4 2 4 2 3 1
## 84 22 22 71 4 1 2 1 2 1 1 1
## 85 22 14 67 4 3 4 2 4 4 2 3
## 86 34 20 91 4 2 4 2 4 4 2 4
## 87 30 23 94 4 3 4 3 4 2 1 3
## 88 32 29 103 4 3 4 2 4 4 2 2
## 89 28 21 81 3 1 2 1 2 1 1 1
## 90 29 29 108 4 3 4 2 4 4 3 1
## 91 33 26 100 4 2 4 1 4 2 3 1
## 92 26 26 90 3 1 2 1 2 1 2 1
## 93 31 25 95 4 2 4 2 4 2 1 1
## 94 23 25 84 4 1 4 1 4 2 2 1
## 95 22 19 74 4 2 4 2 4 1 1 1
## 96 29 18 80 2 1 4 1 4 1 2 2
## 97 28 19 85 3 2 4 1 4 1 2 2
## 98 29 23 92 3 2 4 2 4 3 2 3
## 99 29 19 89 2 2 4 2 4 2 2 3
## 100 28 19 86 4 2 4 2 3 2 1 2
## 101 23 13 66 4 1 2 1 1 1 1 2
## 102 21 22 79 4 1 1 1 1 1 2 1
## 103 25 21 82 3 2 4 2 3 1 1 1
## 104 27 24 89 2 2 4 1 4 2 2 1
## 105 21 17 65 3 1 2 1 1 2 1 1
## 106 31 19 92 3 2 3 2 4 3 2 1
## 107 31 22 92 4 2 3 2 4 2 2 1
## 108 21 18 64 3 1 1 1 1 2 1 1
## 109 28 21 92 4 3 4 2 3 2 2 2
## 110 27 25 95 2 1 3 1 2 1 2 2
## 111 20 21 75 4 3 1 1 1 1 1 3
## 112 29 25 99 4 2 4 1 2 1 1 1
## 113 18 19 65 3 3 1 2 1 1 1 1
## 114 22 25 78 2 2 1 1 1 2 1 1
## 115 21 22 71 3 2 4 1 4 2 2 1
## 116 25 24 80 3 3 4 2 4 3 2 1
## 117 19 23 75 4 3 3 1 3 2 1 1
## 118 24 19 79 3 3 4 2 4 2 2 1
## 119 26 19 74 4 3 4 1 3 2 1 2
## 120 26 21 79 2 1 1 1 1 1 2 2
## 121 21 21 66 4 3 1 1 1 1 1 3
## 122 23 22 77 2 1 4 1 4 1 1 2
## 123 22 26 81 3 2 4 1 4 1 2 2
## 124 32 23 108 3 3 4 3 2 3 1 3
## 125 24 23 82 3 2 4 1 4 3 1 1
## 126 33 26 101 4 3 4 3 4 4 2 2
## 127 31 17 74 4 1 4 3 2 1 1 3
## 128 16 14 52 3 1 3 1 1 1 1 3
## 129 35 23 98 3 3 4 1 4 4 3 4
## 130 28 26 90 4 3 4 1 4 4 2 3
## 131 16 9 41 3 1 3 1 2 1 1 1
## 132 32 19 89 4 3 4 3 4 3 2 1
## 133 16 10 44 4 1 3 1 2 1 1 3
## 134 19 16 61 4 3 4 3 4 1 4 1
## 135 18 9 47 3 2 4 1 4 1 1 1
## 136 19 19 68 4 3 4 2 4 1 2 1
## 137 22 15 63 3 2 4 3 4 2 3 1
## 138 25 15 68 3 1 3 3 2 1 1 1
## 139 24 24 90 4 3 4 3 4 2 2 3
## 140 21 16 66 4 3 4 1 4 2 2 1
## 141 23 16 65 3 1 2 1 2 1 2 1
## 142 23 26 81 4 2 4 3 4 2 3 1
## 143 24 24 79 4 3 4 1 4 2 2 1
## 144 17 10 41 4 1 1 1 2 1 1 1
## 145 21 22 76 4 3 4 2 4 2 3 1
## 146 19 17 55 4 1 2 1 2 1 1 1
## 147 19 14 62 4 3 4 2 4 4 2 3
## 148 31 18 82 4 2 4 2 4 4 2 4
## 149 30 20 82 4 3 4 3 4 2 1 3
## 150 29 29 100 4 3 4 2 4 4 2 2
## 151 28 19 72 3 1 2 1 2 1 1 1
## 152 26 29 105 4 3 4 2 4 4 3 1
## 153 30 22 86 4 2 4 1 4 2 3 1
## 154 24 14 69 3 1 2 1 2 1 2 1
## 155 29 20 80 4 2 4 2 4 2 1 1
## 156 23 20 73 4 1 4 1 4 2 2 1
## 157 19 19 70 4 2 4 2 4 1 1 1
## 158 29 18 79 2 1 4 1 4 1 2 2
## 159 27 17 80 3 2 4 1 4 1 2 2
## 160 29 23 92 3 2 4 2 4 3 2 3
## 161 29 19 89 2 2 4 2 4 2 2 3
## 162 27 19 85 4 2 4 2 3 2 1 2
## 163 22 13 68 4 1 2 1 1 1 1 2
## 164 21 25 82 4 1 1 1 1 1 2 1
## 165 25 23 84 3 2 4 2 3 1 1 1
## 166 27 24 89 2 2 4 1 4 2 2 1
## 167 20 17 65 3 1 2 1 1 2 1 1
## 168 31 21 95 3 2 3 2 4 3 2 1
## 169 28 22 89 4 2 3 2 4 2 2 1
## 170 21 18 66 3 1 1 1 1 2 1 1
## 171 28 21 93 4 3 4 2 3 2 2 2
## 172 27 25 95 2 1 3 1 2 1 2 2
## 173 19 21 74 4 3 1 1 1 1 1 3
## 174 29 25 97 4 2 4 1 2 1 1 1
## 175 18 19 67 3 3 1 2 1 1 1 1
## 176 22 23 76 2 2 1 1 1 2 1 1
## 177 22 22 72 3 2 4 1 4 2 2 1
## 178 23 25 81 3 3 4 2 4 3 2 1
## 179 20 23 76 4 3 3 1 3 2 1 1
## 180 24 21 81 3 3 4 2 4 2 2 1
## 181 24 19 70 4 3 4 1 3 2 1 2
## 182 24 22 81 2 1 1 1 1 1 2 2
## 183 21 21 64 4 3 1 1 1 1 1 3
## 184 23 20 75 2 1 4 1 4 1 1 2
## 185 20 27 83 3 2 4 1 4 1 2 2
## 186 32 25 110 3 3 4 3 2 3 1 3
## BIS9 BIS10 BIS11 BIS12 BIS13 BIS14 BIS15 BIS16 BIS17 BIS18 BIS19 BIS20
## 1 3 2 4 2 2 1 1 2 1 1 2 3
## 2 4 4 1 4 4 1 4 2 3 1 1 2
## 3 3 4 1 4 4 1 4 1 4 1 2 1
## 4 2 4 1 3 2 1 3 1 4 1 1 3
## 5 3 4 1 3 3 1 4 2 2 1 2 2
## 6 3 4 2 3 3 1 3 2 1 1 2 3
## 7 2 4 2 3 1 1 3 1 2 1 1 2
## 8 4 3 2 4 2 1 3 3 4 1 2 4
## 9 4 4 1 4 2 2 3 1 3 1 1 3
## 10 3 4 1 4 3 1 4 2 3 1 2 3
## 11 2 3 1 3 2 2 2 2 2 1 2 3
## 12 3 4 1 4 4 3 3 2 2 1 2 3
## 13 2 3 2 4 2 1 4 1 2 1 2 3
## 14 2 3 1 4 1 1 4 1 3 1 1 2
## 15 3 4 2 3 4 1 4 2 3 1 2 3
## 16 3 3 2 3 3 1 3 2 2 1 2 4
## 17 2 4 2 4 3 1 4 1 3 1 1 4
## 18 3 4 4 4 4 1 3 2 3 1 2 3
## 19 3 3 2 4 2 1 4 1 2 1 2 4
## 20 3 3 2 3 4 2 4 2 1 1 1 4
## 21 3 3 2 4 3 3 3 2 2 2 2 3
## 22 4 4 1 4 3 1 3 1 2 1 1 4
## 23 4 4 4 4 2 1 3 2 2 1 2 3
## 24 4 3 4 4 2 1 4 1 2 1 2 4
## 25 3 4 4 4 3 1 4 2 3 1 2 3
## 26 2 4 4 3 2 1 3 2 3 1 2 2
## 27 3 4 2 4 3 1 3 1 2 1 1 2
## 28 2 4 4 3 3 1 3 1 3 1 2 3
## 29 3 3 4 3 2 1 3 1 2 1 2 3
## 30 3 4 1 3 4 2 3 1 2 1 1 3
## 31 3 4 4 3 3 3 3 1 2 1 2 2
## 32 2 4 4 2 2 1 2 1 2 1 2 2
## 33 3 4 4 4 4 1 4 1 3 1 2 2
## 34 3 2 2 2 2 1 3 2 2 1 1 3
## 35 3 4 4 3 3 1 3 2 2 1 2 3
## 36 3 3 4 3 3 1 4 2 3 1 2 2
## 37 2 4 4 3 3 1 4 2 3 1 2 2
## 38 3 2 2 3 2 1 3 2 3 1 2 2
## 39 3 4 2 3 3 1 4 1 2 1 1 2
## 40 4 3 1 4 2 1 4 1 2 1 1 4
## 41 3 4 3 4 3 1 4 2 3 1 2 3
## 42 2 4 2 3 2 1 3 2 3 1 2 2
## 43 3 4 2 4 3 1 3 1 2 1 1 2
## 44 2 4 4 3 3 1 3 2 3 1 2 3
## 45 3 3 4 3 2 2 3 2 2 1 2 3
## 46 3 4 1 3 4 1 3 1 2 1 1 3
## 47 3 4 3 3 3 1 3 2 2 3 2 2
## 48 2 4 4 2 2 1 2 2 2 3 2 2
## 49 3 4 3 4 4 1 4 1 3 1 1 2
## 50 4 3 4 4 2 1 4 1 2 1 2 4
## 51 3 4 3 4 3 1 4 1 3 1 1 3
## 52 2 4 2 3 2 1 3 1 3 1 2 2
## 53 3 4 2 4 3 3 3 2 2 1 2 2
## 54 2 4 4 3 3 3 3 2 3 1 2 3
## 55 3 3 4 3 2 1 3 1 2 1 2 3
## 56 3 4 4 3 4 1 3 2 2 2 2 3
## 57 3 4 3 3 3 1 3 2 2 1 2 2
## 58 2 4 1 2 2 1 2 1 2 1 1 2
## 59 3 4 3 4 4 1 4 1 3 1 1 2
## 60 3 2 4 2 2 2 3 2 2 1 1 3
## 61 3 4 4 3 3 1 3 1 2 1 2 3
## 62 3 3 4 3 3 1 4 1 3 1 2 2
## 63 3 2 4 2 2 1 1 2 1 1 2 3
## 64 4 4 1 4 4 1 4 2 3 1 1 2
## 65 3 4 1 2 4 1 4 1 4 1 2 1
## 66 2 1 1 1 2 1 3 1 4 1 1 1
## 67 3 4 1 3 3 1 4 2 2 1 2 2
## 68 3 4 2 3 3 1 3 2 1 1 2 3
## 69 1 1 2 1 1 1 3 1 2 1 1 1
## 70 4 3 2 4 2 1 3 3 4 1 2 4
## 71 1 1 1 1 2 2 3 1 3 1 1 1
## 72 3 2 1 2 3 1 4 2 3 1 2 3
## 73 1 1 1 3 2 2 2 2 2 1 2 1
## 74 3 2 1 2 4 3 3 2 2 1 2 3
## 75 2 3 2 4 2 1 4 1 2 1 2 3
## 76 1 3 1 2 1 1 4 1 3 1 1 1
## 77 3 4 2 3 4 1 4 2 3 1 2 3
## 78 1 3 2 3 3 1 3 2 2 1 2 4
## 79 2 3 2 2 3 1 4 1 3 1 1 4
## 80 3 3 4 4 4 1 3 2 3 1 2 3
## 81 1 1 2 2 2 1 4 1 2 1 2 1
## 82 3 1 2 1 4 2 4 2 1 1 1 4
## 83 3 3 2 4 3 3 3 2 2 2 2 3
## 84 1 1 1 1 3 1 3 1 2 1 1 1
## 85 2 2 4 4 2 1 3 2 2 1 2 3
## 86 2 3 4 4 2 1 4 1 2 1 2 1
## 87 3 2 4 2 3 1 4 2 3 1 2 1
## 88 2 4 4 3 2 1 3 2 3 1 2 2
## 89 1 2 2 2 3 1 3 1 2 1 1 1
## 90 2 4 4 3 3 1 3 1 3 1 2 3
## 91 1 3 4 2 2 1 3 1 2 1 2 1
## 92 1 1 1 3 4 2 3 1 2 1 1 1
## 93 1 2 4 3 3 3 3 1 2 1 2 1
## 94 2 1 4 2 2 1 2 1 2 1 2 1
## 95 3 4 4 4 4 1 4 1 3 1 2 2
## 96 3 2 2 2 2 1 3 2 2 1 1 3
## 97 3 4 4 1 3 1 3 2 2 1 2 3
## 98 3 3 4 3 3 1 4 2 3 1 2 2
## 99 2 4 4 3 3 1 4 2 3 1 2 2
## 100 1 2 2 3 2 1 3 2 3 1 2 1
## 101 1 4 2 1 3 1 4 1 2 1 1 1
## 102 3 3 1 2 2 1 4 1 2 1 1 4
## 103 2 4 3 2 3 1 4 2 3 1 2 3
## 104 2 4 2 3 2 1 3 2 3 1 2 2
## 105 1 1 2 4 3 1 3 1 2 1 1 1
## 106 2 4 4 3 3 1 3 2 3 1 2 2
## 107 2 3 4 3 2 2 3 2 2 1 2 2
## 108 1 1 1 3 4 1 3 1 2 1 1 1
## 109 3 4 3 3 3 1 3 2 2 3 2 2
## 110 2 2 4 2 2 1 2 2 2 3 2 2
## 111 3 2 3 2 4 1 4 1 3 1 1 2
## 112 3 3 4 3 2 1 4 1 2 1 2 2
## 113 3 4 3 3 3 1 4 1 3 1 1 2
## 114 2 2 2 3 2 1 3 1 3 1 2 2
## 115 2 2 2 4 3 3 3 2 2 1 2 2
## 116 2 4 4 3 3 3 3 2 3 1 2 3
## 117 1 1 4 3 2 1 3 1 2 1 2 3
## 118 3 4 4 3 4 1 3 2 2 2 2 3
## 119 2 2 3 3 3 1 3 2 2 1 2 2
## 120 2 4 1 2 2 1 2 1 2 1 1 2
## 121 1 1 3 1 4 1 4 1 3 1 1 1
## 122 3 2 4 2 2 2 3 2 2 1 1 3
## 123 3 3 4 3 3 1 3 1 2 1 2 2
## 124 3 3 4 4 3 1 4 1 3 1 2 3
## 125 3 2 4 2 2 1 1 2 1 1 2 3
## 126 4 4 1 4 2 1 4 2 3 1 1 2
## 127 3 4 1 2 1 1 4 1 4 1 2 1
## 128 2 1 1 1 1 1 3 1 4 1 1 1
## 129 3 4 1 3 2 1 4 2 2 1 2 2
## 130 3 4 2 3 2 1 3 2 1 1 2 3
## 131 1 1 2 1 1 1 3 1 2 1 1 1
## 132 4 3 2 4 1 1 3 3 4 1 2 4
## 133 1 1 1 1 1 2 3 1 3 1 1 1
## 134 3 2 1 2 2 1 4 2 3 1 2 3
## 135 1 1 1 3 2 2 2 2 2 1 2 1
## 136 3 2 1 2 2 3 3 2 2 1 2 3
## 137 2 3 2 4 2 1 4 1 2 1 2 3
## 138 1 3 1 2 1 1 4 1 3 1 1 1
## 139 3 4 2 3 2 1 4 2 3 1 2 3
## 140 1 3 2 3 2 1 3 2 2 1 2 4
## 141 2 3 2 2 1 1 4 1 3 1 1 4
## 142 3 3 4 4 2 1 3 2 3 1 2 3
## 143 1 1 2 2 1 1 4 1 2 1 2 1
## 144 3 1 2 1 1 2 4 2 1 1 1 4
## 145 3 3 2 4 2 3 3 2 2 2 2 3
## 146 1 1 1 1 1 1 3 1 2 1 1 1
## 147 2 2 4 4 2 1 3 2 2 1 2 3
## 148 2 3 4 4 2 1 4 1 2 1 2 1
## 149 3 2 4 2 3 1 4 2 3 1 2 1
## 150 2 4 4 3 2 1 3 2 3 1 2 2
## 151 1 2 2 2 1 1 3 1 2 1 1 1
## 152 2 4 4 3 3 1 3 1 3 1 2 3
## 153 1 3 4 2 2 1 3 1 2 1 2 1
## 154 1 1 1 3 1 2 3 1 2 1 1 1
## 155 1 2 4 3 3 3 3 1 2 1 2 1
## 156 2 1 4 2 1 1 2 1 2 1 2 1
## 157 3 4 4 4 4 1 4 1 3 1 2 2
## 158 3 2 2 2 2 1 3 2 2 1 1 3
## 159 3 4 4 1 3 1 3 2 2 1 2 3
## 160 3 3 4 3 3 1 4 2 3 1 2 2
## 161 2 4 4 3 3 1 4 2 3 1 2 2
## 162 1 2 2 3 2 1 3 2 3 1 2 1
## 163 1 4 2 1 3 1 4 1 2 1 1 1
## 164 3 3 1 2 2 1 4 1 2 1 1 4
## 165 2 4 3 2 3 1 4 2 3 1 2 3
## 166 2 4 2 3 2 1 3 2 3 1 2 2
## 167 1 1 2 4 3 1 3 1 2 1 1 1
## 168 2 4 4 3 3 1 3 2 3 1 2 2
## 169 2 3 4 3 2 2 3 2 2 1 2 2
## 170 1 1 1 3 4 1 3 1 2 1 1 1
## 171 3 4 3 3 3 1 3 2 2 3 2 2
## 172 2 2 4 2 2 1 2 2 2 3 2 2
## 173 3 2 3 2 4 1 4 1 3 1 1 2
## 174 3 3 4 3 2 1 4 1 2 1 2 2
## 175 3 4 3 3 3 1 4 1 3 1 1 2
## 176 2 2 2 3 2 1 3 1 3 1 2 2
## 177 2 2 2 4 3 3 3 2 2 1 2 2
## 178 2 4 4 3 3 3 3 2 3 1 2 3
## 179 1 1 4 3 2 1 3 1 2 1 2 3
## 180 3 4 4 3 4 1 3 2 2 2 2 3
## 181 2 2 3 3 3 1 3 2 2 1 2 2
## 182 2 4 1 2 2 1 2 1 2 1 1 2
## 183 1 1 3 1 4 1 4 1 3 1 1 1
## 184 3 2 4 2 2 2 3 2 2 1 1 3
## 185 3 3 4 3 3 1 3 1 2 1 2 2
## 186 3 3 4 4 3 1 4 1 3 1 2 3
## BIS21 BIS22 BIS23 BIS24 BIS25 BIS26 BIS27 BIS28 BIS29 BIS30 BIS_attention
## 1 1 1 4 2 4 4 2 4 1 4 15
## 2 1 1 4 1 4 4 2 2 1 4 20
## 3 1 2 2 2 3 2 1 4 1 4 20
## 4 3 2 2 2 3 3 1 1 1 2 18
## 5 1 1 4 1 4 4 2 1 1 2 19
## 6 1 1 4 1 4 4 2 2 1 4 18
## 7 1 2 2 2 2 2 1 3 1 1 18
## 8 1 1 4 1 4 4 1 4 1 2 18
## 9 2 2 2 2 2 3 1 3 1 2 19
## 10 1 1 4 1 4 4 2 2 4 4 22
## 11 1 1 4 1 4 4 2 2 3 3 16
## 12 1 1 4 1 4 4 2 2 3 4 20
## 13 1 1 3 1 4 4 2 1 4 2 18
## 14 1 2 2 2 2 3 1 2 3 1 16
## 15 1 1 4 1 4 4 1 2 4 4 20
## 16 1 1 4 1 4 4 1 2 3 3 18
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## 18 1 1 4 1 4 4 2 2 1 3 22
## 19 1 1 4 1 4 4 2 1 1 3 17
## 20 1 2 2 1 2 2 1 2 1 3 17
## 21 2 1 4 1 4 4 2 2 1 3 19
## 22 2 2 2 1 1 2 1 2 1 4 16
## 23 1 1 4 1 4 4 1 1 1 3 17
## 24 1 1 4 1 4 4 3 1 4 2 18
## 25 1 1 4 1 4 4 2 2 3 4 20
## 26 1 1 4 1 4 4 2 2 4 3 18
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## 103 1 1 4 1 3 4 1 2 1 4 17
## 104 1 1 4 1 3 4 1 2 1 3 16
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## 107 1 1 4 1 3 4 2 2 1 3 17
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## 109 1 1 1 1 3 4 2 2 3 4 19
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## 112 1 1 4 1 2 4 2 1 1 2 14
## 113 1 1 1 2 3 1 1 2 1 4 16
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## 119 1 1 4 1 1 4 2 2 1 4 15
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## 121 1 1 1 2 1 1 1 2 1 4 13
## 122 1 1 4 1 3 1 2 1 1 2 13
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## 124 2 1 4 1 3 2 2 1 1 3 17
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## 133 2 2 1 2 1 3 1 3 1 1 11
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## 137 1 1 3 1 2 4 2 1 4 2 18
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## 147 1 1 4 1 2 2 1 1 1 2 15
## 148 1 1 4 1 2 2 3 1 4 2 17
## 149 1 1 2 1 2 2 2 2 3 3 17
## 150 1 1 4 1 2 4 2 2 4 3 18
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## 153 1 2 2 1 1 2 2 2 1 2 17
## 154 1 3 3 2 1 2 1 2 1 1 13
## 155 1 1 2 1 1 2 2 2 1 2 15
## 156 1 1 2 1 1 2 2 2 1 2 12
## 157 1 1 4 1 2 2 2 2 1 2 18
## 158 1 1 3 1 2 2 2 1 1 2 14
## 159 1 1 3 1 1 2 2 2 4 2 18
## 160 2 1 3 2 1 3 1 1 3 2 18
## 161 2 1 3 2 3 2 3 1 3 3 21
## 162 1 1 3 1 2 2 2 2 3 2 15
## 163 2 1 1 2 1 1 1 2 1 3 15
## 164 1 1 1 2 2 1 1 1 1 2 14
## 165 1 1 4 1 3 4 1 2 1 3 17
## 166 1 1 4 1 3 4 1 2 1 4 16
## 167 1 1 1 2 2 1 1 2 1 1 12
## 168 0 1 4 1 3 4 3 2 1 4 20
## 169 1 1 4 1 3 4 1 2 1 3 16
## 170 1 1 1 2 1 1 1 2 1 3 13
## 171 1 1 1 1 3 4 2 2 3 4 19
## 172 1 1 3 1 2 4 1 2 1 3 14
## 173 1 1 1 2 2 1 1 2 1 4 14
## 174 1 1 3 1 2 4 1 1 1 2 13
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## 176 1 1 1 2 3 1 1 2 1 3 13
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## 183 1 1 1 2 1 1 1 2 1 4 13
## 184 1 1 4 1 3 1 1 1 1 2 12
## 185 1 1 4 1 1 1 2 2 1 3 16
## 186 2 1 4 1 3 2 2 1 1 3 17
## BIS_action BIS_Noplan BIS_Total MMSE1 MMSE2 MMSE3 MMSE4 MMSE5 MMSE6 MMSE7
## 1 22 33 70 0 1 0 0 1 1 1
## 2 30 31 81 1 1 1 0 0 1 1
## 3 22 31 73 1 1 0 1 1 1 1
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## 153 17 26 60 0 1 0 1 1 1 1
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## 184 18 29 59 0 1 1 1 1 1 1
## 185 20 28 64 1 1 1 1 1 1 1
## 186 27 30 74 1 1 1 1 1 1 1
## MMSE8 MMSE9 MMSE10 MMSE11_1 MMSE11_2 MMSE11_3 MMSE12_1 MMSE12_2 MMSE12_3
## 1 1 1 1 1 1 1 1 1 1
## 2 1 0 1 1 1 1 0 0 0
## 3 1 1 1 1 1 1 1 0 0
## 4 1 1 1 1 1 1 1 1 1
## 5 1 1 1 1 1 1 1 0 0
## 6 1 1 1 1 1 1 1 1 0
## 7 1 1 1 1 1 1 1 1 1
## 8 1 1 1 1 1 1 1 1 1
## 9 1 1 1 1 1 1 1 1 1
## 10 1 1 1 1 1 1 1 1 1
## 11 0 1 1 1 1 1 1 1 1
## 12 0 1 1 1 1 1 1 1 1
## 13 1 1 1 1 1 1 1 1 1
## 14 1 1 1 1 1 1 1 1 0
## 15 1 1 1 1 1 1 1 0 0
## 16 1 1 1 1 1 1 1 1 1
## 17 1 1 1 1 1 1 1 1 1
## 18 1 1 1 1 1 1 1 1 0
## 19 0 1 1 1 1 1 1 0 0
## 20 1 1 1 1 1 1 1 1 1
## 21 1 1 1 1 1 1 1 1 1
## 22 1 1 1 1 1 1 1 1 0
## 23 1 1 1 1 1 1 1 1 1
## 24 1 1 1 1 1 1 1 1 1
## 25 1 1 1 1 1 1 1 0 0
## 26 1 1 1 1 1 1 1 1 1
## 27 1 1 1 1 1 1 1 0 0
## 28 1 1 1 1 1 1 1 1 1
## 29 1 1 1 1 1 1 1 0 0
## 30 1 1 1 1 1 1 1 1 1
## 31 1 1 1 1 1 1 1 1 1
## 32 1 1 1 1 1 1 1 1 1
## 33 1 1 1 1 1 1 1 1 1
## 34 1 1 1 1 1 1 0 0 1
## 35 1 1 1 1 1 1 1 1 1
## 36 1 1 1 1 1 1 1 1 1
## 37 1 1 1 1 1 1 1 1 1
## 38 1 1 1 1 1 1 1 1 1
## 39 1 1 1 1 1 1 1 1 1
## 40 1 1 1 1 1 1 1 1 1
## 41 1 1 1 1 1 1 1 1 1
## 42 1 1 1 1 1 1 1 1 1
## 43 1 1 1 1 1 1 1 0 1
## 44 1 1 1 1 1 1 1 1 1
## 45 1 1 1 1 1 1 1 1 1
## 46 1 1 1 1 1 1 1 1 1
## 47 1 1 1 1 1 1 1 1 1
## 48 1 1 1 1 1 1 1 1 1
## 49 1 1 1 1 1 1 1 1 1
## 50 1 1 1 1 1 1 1 1 1
## 51 1 1 1 1 1 1 1 1 1
## 52 1 1 1 1 1 1 1 1 1
## 53 1 1 1 1 1 1 1 0 0
## 54 1 1 1 1 1 1 1 1 1
## 55 1 1 1 1 1 1 1 1 1
## 56 1 1 1 1 1 1 1 1 1
## 57 1 1 1 1 1 1 1 1 1
## 58 1 1 1 1 1 1 1 1 1
## 59 1 1 1 1 1 1 1 1 1
## 60 1 1 1 1 1 1 0 0 1
## 61 1 1 1 1 1 1 1 1 1
## 62 1 1 1 1 1 1 1 1 1
## 63 1 1 1 1 1 1 1 1 1
## 64 1 0 1 1 1 1 0 0 0
## 65 1 1 1 1 1 1 1 0 0
## 66 1 1 1 1 1 1 1 1 1
## 67 1 1 1 1 1 1 1 0 0
## 68 1 1 1 1 1 1 1 1 0
## 69 1 1 1 1 1 1 1 1 1
## 70 1 1 1 1 1 1 1 1 1
## 71 1 1 1 1 1 1 1 1 1
## 72 1 1 1 1 1 1 1 1 1
## 73 0 1 1 1 1 1 1 1 1
## 74 0 1 1 1 1 1 1 1 1
## 75 1 1 1 1 1 1 1 1 1
## 76 1 1 1 1 1 1 1 1 0
## 77 1 1 1 1 1 1 1 0 0
## 78 1 1 1 1 1 1 1 1 1
## 79 1 1 1 1 1 1 1 1 1
## 80 1 1 1 1 1 1 1 1 0
## 81 0 1 1 1 1 1 1 1 1
## 82 1 1 1 1 1 1 1 1 1
## 83 1 1 1 1 1 1 1 1 1
## 84 1 1 1 1 1 1 1 1 0
## 85 1 1 1 1 1 1 1 1 1
## 86 1 1 1 1 1 1 1 1 1
## 87 1 1 1 1 1 1 1 0 0
## 88 1 1 1 1 1 1 1 1 1
## 89 1 1 1 1 1 1 1 0 0
## 90 1 1 1 1 1 1 1 1 1
## 91 1 1 1 1 1 1 1 0 0
## 92 1 1 1 1 1 1 1 1 1
## 93 1 1 1 1 1 1 1 1 1
## 94 1 1 1 1 1 1 1 1 1
## 95 1 1 1 1 1 1 1 1 1
## 96 1 1 1 1 1 1 0 0 1
## 97 1 1 1 1 1 1 1 1 1
## 98 1 1 1 1 1 1 1 1 1
## 99 1 1 1 1 1 1 1 1 1
## 100 1 1 1 1 1 1 1 1 1
## 101 1 1 1 1 1 1 1 1 1
## 102 1 1 1 1 1 1 1 1 1
## 103 1 1 1 1 1 1 1 1 1
## 104 1 1 1 1 1 1 1 1 1
## 105 1 1 1 1 1 1 1 0 1
## 106 1 1 1 1 1 1 1 1 1
## 107 1 1 1 1 1 1 1 1 1
## 108 1 1 1 1 1 1 1 1 1
## 109 1 1 1 1 1 1 1 1 1
## 110 1 1 1 1 1 1 1 1 1
## 111 1 1 1 1 1 1 1 1 1
## 112 1 1 1 1 1 1 1 1 1
## 113 1 1 1 1 1 1 1 1 1
## 114 1 1 1 1 1 1 1 1 1
## 115 1 1 1 1 1 1 1 0 0
## 116 1 1 1 1 1 1 1 1 1
## 117 1 1 1 1 1 1 1 1 1
## 118 1 1 1 1 1 1 1 1 1
## 119 1 1 1 1 1 1 1 1 1
## 120 1 1 1 1 1 1 1 1 1
## 121 1 1 1 1 1 1 1 1 1
## 122 1 1 1 1 1 1 0 0 1
## 123 1 1 1 1 1 1 1 1 1
## 124 1 1 1 1 1 1 1 1 1
## 125 1 1 1 1 1 1 1 1 1
## 126 1 0 1 1 1 1 0 0 0
## 127 1 1 1 1 1 1 1 0 0
## 128 1 1 1 1 1 1 1 1 1
## 129 1 1 1 1 1 1 1 0 0
## 130 1 1 1 1 1 1 1 1 0
## 131 1 1 1 1 1 1 1 1 1
## 132 1 1 1 1 1 1 1 1 1
## 133 1 1 1 1 1 1 1 1 1
## 134 1 1 1 1 1 1 1 1 1
## 135 0 1 1 1 1 1 1 1 1
## 136 0 1 1 1 1 1 1 1 1
## 137 1 1 1 1 1 1 1 1 1
## 138 1 1 1 1 1 1 1 1 0
## 139 1 1 1 1 1 1 1 0 0
## 140 1 1 1 1 1 1 1 1 1
## 141 1 1 1 1 1 1 1 1 1
## 142 1 1 1 1 1 1 1 1 0
## 143 0 1 1 1 1 1 1 1 1
## 144 1 1 1 1 1 1 1 1 1
## 145 1 1 1 1 1 1 1 1 1
## 146 1 1 1 1 1 1 1 1 0
## 147 1 1 1 1 1 1 1 1 1
## 148 1 1 1 1 1 1 1 1 1
## 149 1 1 1 1 1 1 1 0 1
## 150 1 1 1 1 1 1 1 1 1
## 151 1 1 1 1 1 1 1 0 1
## 152 1 1 1 1 1 1 1 1 1
## 153 1 1 1 1 1 1 1 0 0
## 154 1 1 1 1 1 1 1 1 1
## 155 1 1 1 1 1 1 1 1 1
## 156 1 1 1 1 1 1 1 1 1
## 157 1 1 1 1 1 1 1 1 1
## 158 1 1 1 1 1 1 0 0 1
## 159 1 1 1 1 1 1 1 1 1
## 160 1 1 1 1 1 1 1 1 1
## 161 1 1 1 1 1 1 1 1 1
## 162 1 1 1 1 1 1 1 1 1
## 163 1 1 1 1 1 1 1 1 1
## 164 1 1 1 1 1 1 1 1 1
## 165 1 1 1 1 1 1 1 1 1
## 166 1 1 1 1 1 1 1 1 1
## 167 1 1 1 1 1 1 1 0 1
## 168 1 1 1 1 1 1 1 1 1
## 169 1 1 1 1 1 1 1 1 1
## 170 1 1 1 1 1 1 1 1 1
## 171 1 1 1 1 1 1 1 1 1
## 172 1 1 1 1 1 1 1 1 1
## 173 1 1 1 1 1 1 1 1 1
## 174 1 1 1 1 1 1 1 1 1
## 175 1 1 1 1 1 1 1 1 1
## 176 1 1 1 1 1 1 1 1 1
## 177 1 1 1 1 1 1 1 0 0
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## 182 1 1 1 1 1 1 1 1 1
## 183 1 1 1 1 1 1 1 1 1
## 184 1 1 1 1 1 1 0 0 1
## 185 1 1 1 1 1 1 1 1 1
## 186 1 1 1 1 1 1 1 1 1
## MMSE12_4 MMSE12_5 MMSE13 MMSE14 MMSE15 MMSE16 MMSE17 MMSE18 MMSE19 MMSE20_1
## 1 1 1 1 1 1 1 1 1 1 1
## 2 0 0 0 0 0 1 0 0 1 1
## 3 1 0 0 0 0 1 1 0 1 1
## 4 1 1 1 0 1 1 1 1 1 1
## 5 0 0 1 0 0 1 0 0 1 1
## 6 0 1 0 0 0 1 1 1 1 1
## 7 1 1 1 1 1 1 0 1 1 1
## 8 1 1 1 1 1 1 1 1 1 1
## 9 1 1 1 1 1 1 1 1 1 1
## 10 1 1 1 1 1 1 1 1 1 1
## 11 1 1 1 0 1 1 1 1 1 1
## 12 1 1 1 0 1 1 1 1 1 1
## 13 1 0 1 1 1 1 1 1 1 1
## 14 0 0 1 1 1 1 0 0 0 0
## 15 0 0 1 1 1 1 0 0 1 1
## 16 1 1 1 1 1 1 1 1 1 1
## 17 1 1 1 1 1 1 1 1 1 1
## 18 1 0 1 1 1 1 1 1 1 1
## 19 0 0 1 1 1 1 1 1 1 1
## 20 1 1 1 1 1 1 1 1 1 1
## 21 1 1 1 1 1 1 0 0 1 1
## 22 1 1 1 1 1 1 1 1 1 1
## 23 1 1 1 1 1 1 1 1 1 1
## 24 1 1 1 1 1 1 1 1 0 0
## 25 0 0 1 1 1 1 0 0 1 1
## 26 1 1 1 1 1 1 0 0 0 0
## 27 0 0 0 0 0 1 1 1 1 1
## 28 1 1 1 1 1 1 0 0 0 0
## 29 0 0 1 1 1 1 0 0 1 0
## 30 1 1 1 1 1 0 0 0 0 0
## 31 1 1 1 1 0 1 0 0 1 1
## 32 1 1 1 1 1 1 1 1 1 1
## 33 1 1 1 1 1 1 1 1 1 1
## 34 0 0 1 0 1 0 0 0 1 1
## 35 1 1 1 1 1 1 1 1 1 1
## 36 1 1 0 0 1 0 0 0 1 1
## 37 1 1 1 1 1 1 0 0 1 1
## 38 1 0 1 1 1 1 1 1 1 1
## 39 1 1 1 1 1 1 1 1 1 1
## 40 1 1 1 1 1 1 1 1 0 1
## 41 0 0 1 1 1 1 0 0 1 1
## 42 1 1 1 1 1 0 0 0 1 1
## 43 1 1 1 1 1 1 1 1 1 1
## 44 1 1 1 1 1 0 1 1 1 1
## 45 1 0 1 1 1 0 1 1 1 1
## 46 1 1 1 1 1 1 1 1 1 1
## 47 1 1 1 1 0 1 0 1 1 1
## 48 1 1 1 1 1 1 1 1 1 1
## 49 1 1 0 0 1 1 1 1 1 1
## 50 1 1 1 1 0 0 1 1 0 1
## 51 0 0 1 1 1 1 1 1 1 1
## 52 1 1 1 1 1 1 1 1 1 1
## 53 0 0 1 1 1 1 1 1 1 1
## 54 1 1 1 1 1 1 1 1 1 1
## 55 0 0 1 1 1 1 1 1 1 1
## 56 1 1 1 1 1 1 1 0 1 1
## 57 1 1 1 1 0 1 1 1 1 1
## 58 1 1 1 1 1 1 1 1 1 1
## 59 1 1 0 0 1 1 1 1 1 1
## 60 0 0 1 0 1 1 1 1 1 0
## 61 1 1 1 1 1 1 1 0 1 1
## 62 1 1 0 0 1 1 1 0 1 1
## 63 1 1 1 1 1 1 1 1 1 1
## 64 0 0 0 0 0 1 0 0 1 1
## 65 1 0 0 0 0 1 1 0 1 1
## 66 1 1 1 0 1 1 1 1 1 1
## 67 0 0 1 0 0 1 0 0 1 1
## 68 0 1 0 0 0 1 1 1 1 1
## 69 1 1 1 1 1 1 1 1 1 1
## 70 1 1 1 1 1 1 1 1 1 1
## 71 1 1 1 1 1 1 1 1 1 1
## 72 1 1 1 1 1 1 1 1 1 1
## 73 1 1 1 0 1 1 1 1 1 1
## 74 1 1 1 0 1 1 1 1 1 1
## 75 1 0 1 1 1 1 1 1 1 1
## 76 0 0 1 1 1 1 1 1 1 1
## 77 0 0 1 1 1 1 0 0 1 1
## 78 1 1 1 1 1 1 1 1 1 1
## 79 1 1 1 1 1 1 1 1 1 1
## 80 1 0 1 1 1 1 1 1 1 1
## 81 1 1 1 1 1 1 1 1 1 1
## 82 1 1 1 1 1 1 1 1 1 1
## 83 1 1 1 1 1 1 0 0 1 1
## 84 1 1 1 1 1 1 1 1 1 1
## 85 1 1 1 1 1 1 1 1 1 1
## 86 1 1 1 1 1 1 1 1 0 0
## 87 0 0 1 1 1 1 1 1 1 1
## 88 1 1 1 1 1 1 0 0 0 0
## 89 0 0 0 0 0 1 1 1 1 1
## 90 1 1 1 1 1 1 0 0 0 0
## 91 0 0 1 1 1 1 1 1 1 1
## 92 1 1 1 1 1 1 1 1 1 1
## 93 1 1 1 1 0 1 0 0 1 1
## 94 1 1 1 1 1 1 1 1 1 1
## 95 1 1 1 1 1 1 1 1 1 1
## 96 0 0 1 0 1 0 0 0 1 1
## 97 1 1 1 1 1 1 1 1 1 1
## 98 1 1 0 0 1 0 0 0 1 1
## 99 1 1 1 1 1 1 0 0 1 1
## 100 1 0 1 1 1 1 1 1 1 1
## 101 1 1 1 1 1 1 1 1 1 1
## 102 1 1 1 1 1 1 1 1 1 1
## 103 0 0 1 1 1 1 0 0 1 1
## 104 1 1 1 1 1 0 1 1 1 1
## 105 1 1 1 1 1 1 1 1 1 1
## 106 1 1 1 1 1 0 1 1 1 1
## 107 1 0 1 1 1 0 1 1 1 1
## 108 1 1 1 1 1 1 1 1 1 1
## 109 1 1 1 1 0 1 0 1 1 1
## 110 1 1 1 1 1 1 1 1 1 1
## 111 1 1 0 0 1 1 1 1 1 1
## 112 1 1 1 1 0 0 1 1 0 1
## 113 0 0 1 1 1 1 1 1 1 1
## 114 1 1 1 1 1 1 1 1 1 1
## 115 0 0 1 1 1 1 1 1 1 1
## 116 1 1 1 1 1 1 1 1 1 1
## 117 0 0 1 1 1 1 1 1 1 1
## 118 1 1 1 1 1 1 1 0 1 1
## 119 1 1 1 1 0 1 1 1 1 1
## 120 1 1 1 1 1 1 1 1 1 1
## 121 1 1 0 0 1 1 1 1 1 1
## 122 0 0 1 0 1 1 1 1 1 0
## 123 1 1 1 1 1 1 1 0 1 1
## 124 1 1 0 0 1 1 1 0 1 1
## 125 1 1 1 1 1 1 1 1 1 1
## 126 1 1 1 1 0 1 1 1 1 1
## 127 1 0 0 0 1 1 1 0 1 1
## 128 1 1 1 0 1 1 1 1 1 1
## 129 0 0 1 0 0 1 0 0 1 1
## 130 0 1 0 0 0 1 1 1 1 1
## 131 1 1 1 1 1 1 1 1 1 1
## 132 1 1 1 1 1 1 1 1 1 1
## 133 1 1 1 1 1 1 1 1 1 1
## 134 1 1 1 1 1 1 1 1 1 1
## 135 1 1 1 0 1 1 1 1 1 1
## 136 1 1 1 0 1 1 1 1 1 1
## 137 1 0 1 1 1 1 1 1 1 1
## 138 1 1 1 1 1 1 1 1 1 1
## 139 0 0 1 1 1 1 1 1 1 1
## 140 1 1 1 1 1 1 1 1 1 1
## 141 1 1 1 1 1 1 1 1 1 1
## 142 1 1 1 1 1 1 1 1 1 1
## 143 1 1 1 1 1 1 1 1 1 1
## 144 1 1 1 1 1 1 1 1 1 1
## 145 1 1 1 1 1 1 0 0 1 1
## 146 1 1 1 1 1 1 1 1 1 1
## 147 1 1 1 1 1 1 1 1 1 1
## 148 1 1 1 1 1 1 1 1 0 1
## 149 1 1 1 1 1 1 1 1 1 1
## 150 1 1 1 1 1 1 0 0 0 0
## 151 1 0 1 1 1 1 1 1 1 1
## 152 1 1 1 1 1 1 0 0 0 0
## 153 0 0 1 1 1 1 1 1 1 1
## 154 1 1 1 1 1 1 1 0 1 1
## 155 1 1 1 1 0 1 1 1 1 1
## 156 1 1 1 1 1 1 1 1 1 1
## 157 1 0 0 0 1 1 0 1 0 1
## 158 0 0 1 0 1 0 0 1 1 1
## 159 1 1 1 1 1 1 1 1 1 1
## 160 1 1 0 0 1 0 0 0 1 0
## 161 1 1 1 1 1 1 0 0 1 1
## 162 1 0 1 1 1 1 1 1 1 1
## 163 1 1 1 1 1 1 1 1 1 1
## 164 1 0 1 0 1 0 1 1 0 1
## 165 0 0 1 1 1 1 0 0 1 1
## 166 1 1 1 1 1 0 1 1 0 0
## 167 1 1 1 1 1 1 1 1 1 1
## 168 1 1 1 1 1 0 1 0 0 0
## 169 1 0 1 0 1 0 1 0 1 0
## 170 1 0 1 0 1 1 0 0 1 0
## 171 1 1 1 1 0 1 0 1 1 0
## 172 1 1 1 1 1 1 1 1 1 1
## 173 1 1 0 0 1 1 1 1 1 1
## 174 1 1 1 1 0 0 1 0 0 1
## 175 0 0 1 1 1 1 1 1 1 1
## 176 1 1 1 1 1 1 1 1 1 1
## 177 0 0 1 1 1 1 1 1 1 1
## 178 1 1 1 0 1 1 0 1 1 0
## 179 0 0 1 1 1 1 1 1 1 1
## 180 1 1 1 1 0 1 1 0 1 0
## 181 1 1 1 1 0 1 1 1 1 1
## 182 1 1 1 1 1 1 1 1 1 1
## 183 1 1 0 0 1 1 1 1 1 1
## 184 0 0 1 0 1 1 1 1 1 1
## 185 1 1 1 1 1 1 1 0 1 1
## 186 1 0 0 0 1 0 1 0 1 0
## MMSE20_2 MMSE20_3 MMSE21 MMSE22 MMSE_Total
## 1 1 1 1 1 27
## 2 1 1 0 0 15
## 3 1 1 1 0 21
## 4 1 1 1 0 28
## 5 1 1 0 1 21
## 6 1 1 0 1 22
## 7 1 1 1 1 29
## 8 1 1 0 0 28
## 9 1 1 1 0 29
## 10 1 1 1 1 27
## 11 1 1 0 1 26
## 12 1 1 1 1 28
## 13 1 1 1 1 29
## 14 0 0 0 0 15
## 15 1 1 0 0 22
## 16 1 1 1 1 30
## 17 1 1 1 1 30
## 18 1 1 0 0 26
## 19 1 1 1 1 24
## 20 1 1 1 1 30
## 21 1 1 0 1 27
## 22 1 1 1 0 28
## 23 1 1 1 1 30
## 24 0 0 0 0 24
## 25 1 1 0 0 18
## 26 0 0 0 1 23
## 27 1 1 1 1 23
## 28 0 0 0 0 22
## 29 0 0 1 1 19
## 30 0 0 0 0 20
## 31 1 0 0 0 20
## 32 1 1 1 1 28
## 33 1 1 0 1 28
## 34 1 1 0 0 19
## 35 1 1 0 1 29
## 36 1 1 1 1 25
## 37 1 1 1 1 28
## 38 1 1 1 1 29
## 39 1 1 1 1 29
## 40 0 1 1 1 28
## 41 1 1 1 0 21
## 42 0 0 1 1 25
## 43 1 1 1 1 29
## 44 1 1 1 1 29
## 45 1 1 1 1 26
## 46 1 1 1 1 29
## 47 1 1 1 1 24
## 48 1 1 1 1 28
## 49 1 1 1 1 27
## 50 0 1 1 1 26
## 51 1 1 1 1 24
## 52 1 0 1 1 29
## 53 1 1 1 1 26
## 54 1 1 1 0 29
## 55 1 1 1 1 26
## 56 1 1 1 1 28
## 57 1 1 1 1 25
## 58 1 1 1 1 28
## 59 1 1 1 1 27
## 60 0 1 1 0 21
## 61 1 1 1 1 29
## 62 1 1 1 1 27
## 63 1 1 1 1 27
## 64 1 1 0 0 15
## 65 1 1 1 0 21
## 66 1 1 1 0 28
## 67 1 1 0 1 21
## 68 1 1 0 1 22
## 69 1 1 1 1 30
## 70 1 1 0 0 28
## 71 1 1 1 1 30
## 72 1 1 1 1 27
## 73 1 1 0 1 26
## 74 1 1 1 1 28
## 75 1 1 1 1 29
## 76 1 1 1 1 23
## 77 1 1 0 0 22
## 78 1 1 1 1 30
## 79 1 1 1 1 30
## 80 1 1 0 0 26
## 81 1 1 1 1 28
## 82 1 1 1 1 30
## 83 1 1 0 1 27
## 84 1 1 1 0 28
## 85 1 1 1 1 30
## 86 0 0 0 0 24
## 87 1 1 0 0 20
## 88 0 0 0 1 23
## 89 1 1 1 1 23
## 90 0 0 0 0 22
## 91 1 1 1 1 24
## 92 1 1 1 1 29
## 93 1 0 0 0 20
## 94 1 1 1 1 28
## 95 1 1 0 1 28
## 96 1 1 1 1 21
## 97 1 1 0 1 29
## 98 1 1 1 1 25
## 99 1 1 1 1 28
## 100 1 1 1 1 29
## 101 1 1 1 1 29
## 102 1 1 1 1 30
## 103 1 1 1 0 21
## 104 0 0 1 1 27
## 105 1 1 1 1 29
## 106 1 1 1 1 29
## 107 1 1 1 1 26
## 108 1 1 1 1 29
## 109 1 1 1 1 24
## 110 1 1 1 1 28
## 111 1 1 1 1 27
## 112 0 1 1 1 26
## 113 1 1 1 1 24
## 114 1 0 1 1 29
## 115 1 1 1 1 26
## 116 1 1 1 0 29
## 117 1 1 1 1 26
## 118 1 1 1 1 28
## 119 1 1 1 1 25
## 120 1 1 1 1 28
## 121 1 1 1 1 27
## 122 0 1 1 0 21
## 123 1 1 1 1 29
## 124 1 1 1 1 27
## 125 1 1 1 1 27
## 126 1 1 1 1 23
## 127 1 1 1 0 22
## 128 1 1 1 0 28
## 129 1 1 0 1 21
## 130 1 1 0 1 22
## 131 1 1 1 1 30
## 132 1 1 0 0 28
## 133 1 1 1 1 30
## 134 1 1 1 1 27
## 135 1 1 1 1 27
## 136 1 1 1 1 28
## 137 1 1 1 1 29
## 138 1 1 1 1 25
## 139 1 1 1 0 25
## 140 1 1 1 1 30
## 141 1 1 1 1 30
## 142 1 1 1 1 29
## 143 1 1 1 1 28
## 144 1 1 1 1 30
## 145 1 1 0 1 27
## 146 1 1 1 0 28
## 147 1 1 1 1 30
## 148 1 1 0 0 27
## 149 1 1 0 0 23
## 150 0 0 0 1 23
## 151 1 1 1 1 28
## 152 0 0 0 0 22
## 153 1 1 1 1 24
## 154 0 1 0 1 26
## 155 1 1 1 1 25
## 156 1 1 1 1 28
## 157 1 0 0 1 22
## 158 1 1 1 1 22
## 159 1 1 0 1 29
## 160 1 0 0 1 22
## 161 1 1 1 1 28
## 162 1 1 1 1 29
## 163 1 1 1 1 29
## 164 1 0 0 1 24
## 165 1 1 1 1 22
## 166 0 0 1 1 25
## 167 1 1 1 1 29
## 168 0 0 1 1 24
## 169 0 1 1 1 22
## 170 1 0 1 1 23
## 171 0 0 1 1 21
## 172 1 1 1 1 28
## 173 1 1 1 1 27
## 174 0 1 0 0 23
## 175 1 1 1 1 24
## 176 1 0 1 1 29
## 177 1 1 1 1 26
## 178 1 0 0 0 24
## 179 1 1 1 1 26
## 180 1 0 1 1 25
## 181 1 1 1 1 25
## 182 0 0 0 1 25
## 183 1 1 1 1 27
## 184 1 1 1 1 24
## 185 1 1 1 1 29
## 186 1 0 1 0 22inter1<-filter(raw, Intervention==`Intervention`)t21<- inter1 %>%
select(MOAS_Total,
Risk_total,
AQ_Total,
BIS_Total,
MMSE_Total,
time) %>%
tbl_summary(by = time ) %>%
add_p()%>%
add_stat_label() %>%
bold_labels() %>%
modify_header(list(label ~ "**Variable**", all_stat_cols() ~ "**{level}**")) %>%
modify_spanning_header(all_stat_cols() ~ "**Time**") %>%
as_gt() %>%
gt::tab_header(
title = gt::md("**Table 2. Intervention group**"))
t21| Table 2. Intervention group | ||||
|---|---|---|---|---|
| Variable | Time | p-value1 | ||
| 1 | 2 | 3 | ||
| MOAS_Total, Median (IQR) | 10.5 (6.0, 16.8) | 8.0 (4.0, 10.0) | 4.0 (3.0, 8.0) | <0.001 |
| Risk_total, Median (IQR) | 18.00 (17.00, 19.00) | 17.00 (14.00, 18.00) | 15.50 (12.00, 17.00) | <0.001 |
| AQ_Total, Median (IQR) | 88 (79, 98) | 81 (74, 92) | 79 (68, 88) | <0.001 |
| BIS_Total, Median (IQR) | 72 (62, 76) | 67 (58, 75) | 64 (54, 73) | 0.002 |
| MMSE_Total, Median (IQR) | 27.00 (23.25, 28.75) | 27.00 (24.00, 29.00) | 26.00 (23.25, 28.00) | 0.7 |
|
1
Kruskal-Wallis rank sum test
|
||||
control<-filter(dta, Intervention==2)t22<- control %>%
select(MOAS_Total,
Risk_total,
AQ_Total,
BIS_Total,
MMSE_Total,
time) %>%
tbl_summary(by = time ) %>%
add_p()%>%
add_stat_label() %>%
bold_labels() %>%
modify_header(list(label ~ "**Variable**", all_stat_cols() ~ "**{level}**")) %>%
modify_spanning_header(all_stat_cols() ~ "**Time**") %>%
as_gt() %>%
gt::tab_header(
title = gt::md("**Table 2. Control group**"))
t22| Table 2. Control group | ||||
|---|---|---|---|---|
| Variable | Time | p-value1 | ||
| 1 | 2 | 3 | ||
| MOAS_Total, Median (IQR) | 7.5 (4.0, 11.8) | 6.0 (4.0, 10.0) | 6.0 (4.0, 8.0) | 0.4 |
| Risk_total, Median (IQR) | 18.00 (12.00, 18.00) | 17.00 (12.00, 18.00) | 17.00 (13.25, 18.00) | 0.7 |
| AQ_Total, Median (IQR) | 82 (79, 94) | 80 (74, 89) | 81 (72, 89) | 0.2 |
| BIS_Total, Median (IQR) | 66 (61, 73) | 64 (58, 72) | 63 (57, 70) | 0.3 |
| MMSE_Total, Median (IQR) | 27.50 (25.25, 29.00) | 27.50 (26.00, 29.00) | 25.00 (23.00, 27.75) | 0.035 |
|
1
Kruskal-Wallis rank sum test
|
||||
其他資料整理
#處理矩形資料
pacman::p_load(dplyr)
g<-raw13 %>% group_by(Intervention,time)%>%
summarize(
count=n(),
MOAS_Total=mean(MOAS_Total,na.rm=T),
Risk_total=mean(Risk_total,na.rm=T),
AQ_Total=mean(AQ_Total,na.rm=T),
BIS_Total=mean(MOAS_Total,na.rm=T))## `summarise()` has grouped output by 'Intervention'. You can override using the `.groups` argument.g## # A tibble: 4 x 7
## # Groups: Intervention [2]
## Intervention time count MOAS_Total Risk_total AQ_Total BIS_Total
## <fct> <fct> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Intervention 1 32 15.0 17.9 92.2 15.0
## 2 Intervention 3 32 4.44 14.6 73.2 4.44
## 3 Control 1 30 8.33 16.2 84.8 8.33
## 4 Control 3 30 6.07 15.9 80.9 6.07四、結論
回顧以下研究問題， 1.總膽固醇濃度是否提升，由圖可得知介入組提升較對照組明顯。 2.其暴力行為是否下降，暴力行為包含頻率及危險性，隨時間介入後可看出分數皆有下降。 3.總膽固醇與暴力關係，總膽固醇越低報力量表分數越高，存有正相關。
本研究的介入策略，以以上圖形而言是有成效，並隨時間推移更明顯，透過介入能降低暴力行為產生，也能提升總膽固醇濃度。