Intellectually disabled adolescents Prepared in time



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Main research questions:

1. What is the life-time prevalence of smoking and drinking alcohol among 1st and 2nd graders in secondary

special needs schools?
2. Is the e-learning program “Prepared in time” a useful prevention intervention programme for students in 1st

and 2nd grade of secondary special needs schools?
Sub questions:

  • What attitudes do 1st and 2nd graders in secondary specials needs schools have towards tobacco and alcohol?

  • Which behavioural determinants can predict the use of tobacco and alcohol?

  • Is e-learning a useful and workable method for these adolescents?

  • Does “Prepared in time” extend the knowledge on alcohol & smoking among the ID adolescents?

  • Does “Prepared in time” change behavioural attitudes on alcohol & smoking among the ID adolescents?



Chapter 3 - Methods



3.1 Design

To answer the research questions a quasi experimental design with a pretest and post-test was used (Fig.1).



Fig 1. Schematic display research

Timeline

Week 1/2

Week 3/4

Week 6/7




1st Questionnaire

Prepared in time

2nd Questionnaire













Experimental group¹

x

x

x













Control group²

x

o

x

¹Experimental group = Special needs schools in Enschede & Zutphen

²Control group = Special needs schools in Almelo, Ommen & Zwolle

5 schools were willing to participate in this research, providing 232 students. They were assigned to either of 2 groups: experimental or control. Schools were non-random selected based on number of students, location, gender, number of students in 1st and 2nd class and if any prevention education was done already that schoolyear, trying to create comparable groups (Table 3.1).

Table 3.1 Participating schools, number of students, gender & class




Enschede

Zutphen

Almelo

Ommen

Zutphen

Total

Number of students

10

91

61

42

28

232

Male

3

49

32

29

19

132

Female

7

42

29

13

9

100

1st class

7

47

33

20

10

117

2nd class

3

44

28

22

18

115

Prevention education

No

No

No

No

No





3.2 Study Group

Several secondary special needs schools were first contacted by Tactus as they normally provide prevention education and they were asked if they would be willing to participate. 5 schools were willling to participate and were further contacted by researcher. Schools were asked to provide the number of students, number of boys/girls, how many were in 1st and 2nd grade, if they had had any interventions yet and the possibilities for working with all students in one class on computers at the same time. Based on this information schools were assigned; the special needs schools in Enschede and Zutphen to the experimental group and the special needs schools Almelo, Ommen and Zuthpen to the control group. A schedule was made, planning dates for the 1st questionnaire, working with Prepared in time and the 2nd questionnaire.

The study group consists of adolescents who visit a special needs school and are currently in the first or second grade. This means their age lies between 12 and 15 years. Their IQ levels lie between 51 and 90 IQ-points which indicates they have either a borderline or a mild intellectual disability. These adolescents grew up in a computer-era, with computers at home, connection to the world wide web and they have shown to be well capable of using these technologies as most of them have email, use chat programmes and maintain personal web-logs.

3.3 Procedures




3.3.1 Usability & appreciation

The usability and appreciation of Prepared in time with adolescents in special needs schools was measured by a short process-evaluation questionnaire as well as observation. The participants were asked to grade the computer programme, to indicate whether they thought it was fun, useful, interesting or childish and they had the opportunity to write down if they missed any information on both alcohol and tobacco. Participants worked classically with “Prepared in time” in the computerroom of their school. The questionnaire was filled out straigh after the participants worked with the programme. The researcher was present during the time the students worked with the program and whilst they were filling out the questionnaire. Participants were able to ask questions if things were unclear. After finishing the programme all students were asked to fill out a short proces-evaluation.


3.3.2. effectiveness

To measure the effectiveness of “Prepared in time” a pre-test post-test system was used (fig. 1). Both the experimental and control group were asked to answer the same questions in both the first and second questionnaire. By comparing the answers it is possible to tell whether the experimental group gained more knowledge after working with the program, whether their attitudes changed and is they feel more competent to stand up to peer pressure.


The baseline measurement was done with both the experimental group and the control group within the same 2 weeks. The experimental group would then work with the program “Prepared in time”, 2 weeks after the questionnaire. 3 weeks after working with “Prepared in time” they would fill out the follow-up questionnaire. The control group had no intervention between the baseline and follow-up questionnaire, filling out the last questionnaire 5 weeks after the baseline questionnaire. For measuring the usefullness of “Prepared in time” the experimental group also answered a short proces-evaluation questionnaire right after working with the program.
In week 1 and 2 all schools were visited by the researcher for the baseline questionnaire. The students filled them out during schooltime in their own classroom with the researcher present. This way students were able to ask questions if things were unclear and it gave the researcher the possibility to see if answers were given seriously and in the right way. The researcher would start with a short introduction, explaining how to fill out the questionnaire and garanteeing the anonimity of the students. Next the students would fill out the questionnaire, on average taking 25 to 35 minutes. All questionnaires were numbered with a letter-number code on the top right hand side of the first page. This way it was possible to compare the baseline and follow-up questionnaire.
The follow-up questionnaire was filled out the same way as the baseline questionnaire. All schools will be provided with the most important results of this study by means of a letter, including a school-based result.

3.5 Measurements


For this study 3 questionnaires were used. The baseline questionnaire (appendix 2), a proces-evaluation (appendix 3) and the follow-up questionnaire (appendix 4 & 5). Table 3.2 shows the topics from the 3 questionnaires.

Table 3.2 Topics in questionnaires



Baseline

Proces-Evaluation

Follow-up

Demographic variables

x







Knowledge

x




x

Behaviour

x




x

Parental influence

x




x

Intention

x




x

Attitude

x




x

Social influences

x




x

Self-Efficacy

x




x

Implementation PiT




x

x

Appreciation PiT




x

x

Subjective effects PIT




x

x

Improvements




x




Indirect effects




x

x

Appreciation of the questionnaire




x


3.5.1. Pretesting


The original questionnaires developed for the study “Prepared in time, a research into the use of e-learning for substance use prevention for primary school” by Ter Huure (2006) were used for this study. The original questionnaire was pretested for use with ID children. It showed that a 5-point Likert answering scale was not adding much information as they would choose only the extreme answering questions, which is corresponding with findings by Finlay & Lyons (2001). After pretesting the questionnaires were altered, changing all the 5-point scales into 3-point scales on the questions measuring intention, attitude, subjective norm, peer pressure, social support and self-efficacy. For these categories the option ‘I don’t know’ was removed as answering option forcing the participant to think about their opinions and not having an easy way out.

3.5.2. Baseline questionnaire



Demographic variables

The demographic variables consisted of gender, age, country of birth of respondent, country of birth parents and living situation.


Knowledge

Knowledge of tobacco and alcohol was measured with 10 questiones; 5 multiple choice questions about tobacco and 5 questions on alcohol. Questions were aimed at knowledge about addiction, harmful substances in tobacco, influence of alcohol on body etcetra. Every question had 4 answering possibilities; 1 correct answer, 2 wrong answers and the ‘I don’t know option’. Participants were instructed to choose ‘I don’t know’ rather then guess, if they did not know an answer. A knowledge smoking score was obtained by averaging the 5 smoking items. The same was done to compute a knowledge alcohol and a total knowledge score. Cronbachs Alpha on knowledge smoking was .42, on knowledge alcohol .32 and on total knowledge .49.


Behaviour on smoking

Smoking behaviour was measured by asking participants about lifetime prevalence, even if it was just inhaling once or twice. Answers were devided over 5 points going from ‘I’ve never smoked in my life’ (1); ‘I smoked once or twice’ (2); ‘I sometimes smoke, but not every day’ (3); ‘I smoke every day’ (4); ‘I used to smoke, but I’ve quit’ (5). Participants that did smoke were asked their monthly prevalence and daily smoking. There were 6 answering questions; ‘I didn’t smoke in the last 4 weeks’ (1); ‘Less then 1 cigarette a week’ (2); ‘Less then 1 cigarette a day’ (3); ‘1 to5 cigarettes a day’ (4); 6 to 20 cigarettes a day’ (5); ‘20 cigarettes or more a day’ (6). Also, smoking participants were asked at what age they started smoking. These questions are similar to the way national surveys ask participants about there lifetime prevalence, monthly prevelance and daily use. This way we were able to compare results from this study with national numbers (Monshouwer et.al. 2008; CBS 2009; Stivoro, 2009).



Behaviour on alcohol

Alcohol drinking behaviour was measured similairly to smoking behaviour. Participants were asked about their lifetime prevalence, giving them 3 answering options; ‘No, I never drank alcohol’ (1); ‘Yes, I drank (a sip of) alcohol once’ (2); ‘Yes I drank alcohol more then once’(3). Alcohol drinking participants were asked at what age they started drinking. Next they were asked how many times in their life and how many times in the last four weeks they drank alcohol indicating on a scale running from 0 to 11 times or more. Participants that drank alcohol more then once were asked some extra questions, starting with were they drink. There were 8 answering options and they were allowed to choose more then one. On a 2 points scale the amount of alcohol drank on a drinking occasion was asked giving the options of ‘less then one drink’ (1) or ‘ I drink approximatly ... glasses’ (insert answer)(2). Last they were asked if they had ever been tipsy/drunk in their life from drinking alcohol, scale running from 0 to 11 times or more. These questions are similar to the way national surveys ask participants about there lifetime prevalence, monthly prevelance and daily use. This way we were able to compare results from this study with national numbers (Monshouwer et.al. 2008; CBS 2009; Stivoro, 2009).


Intention

The intention of participants to start smoking or drinking alcohol was asked with 3 questions in which participants were asked whether they plans to start smoking in 6 months, in 2 years, in the future or to start drinking alcohol within 6 months, 2 years or before the age of 16. Respondents that already smoked or drank alcohol were asked if they had plans to stop smoking/drinking. By combining and averaging the scores of the seperate items on smoking & alcohol an combined intention was obtained. Cronbach’s Alpha for start smoking was 0.79. Cronbach’s Alpha for intention to stop smoking was 0.83. Cronbachs Alpha for combined intention (6 items) to start or stop smoking was 0.69. Cronbachs Alpha on intention to start drinking alcohol was 0.60. Cronbachs Alpha on intention to stop drinking alcohol was .83. Cronbachs Alpha for combined intention (6 items) to start or stop drinking alcohol was .70.


Attitude

Respondents attitude towards smoking and drinking alcohol was measured with 7 items. They were set out on a 3 point scale;’ I find smoking; bad for my health (1); neutral (2); good for my health (3); annormal (1) / normal (3); uncool (1) / cool (3); unsocial (1)/ social (3); stupid (1)/ smart (3), makes less populair (1)/ doesn’t change populairity (2)/ makes more populair (3). Attitudescore on smoking and alcohol was obtained by averaging the scores on the 7 previously mentioned items. Cronbach’s alpha on smoking was 0.82. and Cronbachs Alpha on alcohol 0.73.


Social influences

Social influences is a combination of multiple factors that might influence the participant to start smoking/drinking or gives them the support to stay away from alcohol and tobacco. It was measured with several questions:



Subjective norm: two questions: ‘My family/friends thinks I.....’ ‘shouldn’t smoke/drink’ (1); ‘should decide for

myself whether I smoke/drink’ (2); ‘should smoke/drink’ (3). Cronbach’s alpha for smoking was 0.50 and for alcohol 0.65.



Peer pressure: one question: ‘Do you sometimes feel like your friends want you to smoke/ drink alcohol?’ ‘No,

never’ (1); ‘sometimes’ (2), ‘all the time’ (3).



Social support: one question: ‘Do you sometimes feel like your friends do not want you to smoke/ drink

alcohol?’ ‘No, never’ (1); ‘sometimes’ (2), ‘all the time’ (3).



Modelling direct surroundings: 5 items on a 4 point scale: ‘Yes, smokes/drinks’ (1); ‘No, doesn’t smoke/drink’ (2);

‘Quit smoking/drinking’ (3); ‘I don’t know’ (4) on which the respondent could indicate whether father, mother, brother/sister, best friend and teacher smoke cigarette or drink alcohol. Cronbach’s alpha for smoking was 0.20 and for alcohol 0.38. Since both alpha’s are very low, the items will be seen separatly and not as scale.



Modelling classmates and friends: 2 questions: How many of the boys/girls in your class / of your friends

smoke/drink alcohol? Answered on a 5 point scale: ‘(almost) all (1); ‘Many’ (2); “one or two’ (3); ‘No one’ (4); ‘I don’t know’ (5). Cronbach’s alpha for smoking was 0.28 and for alcohol 0.69. Since Cronbach’s alpha is very low on smoking the items will be seen separatly and not as scale.


Self-efficacy

Self-efficacy was measured with 2 questions, which were different for non-smokers/non-drinkers and smokers/drinkers. The non-smokers & non-drinkers were asked whether they found it easy or hard to not smoke or drink alcohol until the age of 16. They were also asked whether they thought they were capable of not starting smoking or drinking alcohol. The smokers/drinkers were asked whether they found it easy or hard to stop smoking/drinking and if they thought they would be capable to stop if they really wanted to. The scores of the seperate items were combined and averaged to obtain a total score on self-efficacy. Cronbach’s alpha on smoking was 0.66 and 0.84 on alcohol.


Parents knowledge on participants behaviour

The part parents play and how they influence smoking- and drinking behaviour of the participants was measured with 3 questions in the smoking part of the questionnaire and with 4 questions in the alcohol part. First they were asked if their parents knew about them smoking/drinking alcohol, which could be answered with ‘Yes, they know’ (1) or ‘No, they don’t know’ (2). For alcohol they were also asked if parents know how much they drink, which could be answered in 4 ways; ‘Yes, they know how much I drink’ (1); ‘No, they think I don’t drink alcohol’ (2); ‘No, they think I drink less then I really do’ (3); ‘I don’t know’ (4). The questions ‘Are you (or would you be) allowed to smoke/ drink at home’ and ‘Do you have an agreement with someone that you won’t smoke/drink till a certain age’ were asked all the participants.




3.5.3. Proces evaluation questionnaire

Participants in the experimental group were asked to fill out a proces-evaluation questionnaire after working with Prepared in time. It contained 22 questions with mainly 3 answering options, none of them containing ‘I don’t know’. This way the respondents were forced to form an opinion and not have an easy way of quickly finishing this questionnaire.


Exposure

Firstly the participants were asked whether they finished the program on smoking and the program on alcohol. Both could be answered with ‘Yes’ (1) or ‘No’ (2).


Appreciation

The appreciation for the program “Prepared in time” was measured in different ways. First a general idea of the program was gained by asking on a 3 point scale whether the respondents thought the program was stupid/fun, difficult/easy, boring/interesting, childish/not childish, giving a neutral option in the middle. Then they were asked whether they thougt the program was ‘too long’ (1), ‘ok’ (2), ‘too short’ (3), measuring there opinion on the time it took them to finish. On a 3 point scale the repondents could indicate if they thought there were enough oppertunities to ask questions during the program; ‘Yes, (1); ‘sometimes’ (2); ‘no’ (3). More detailed information was gained by asking the opinion of the respondents on specific parts of the program. They were asked to evaluate 5 parts; ‘making your own portrait’; ‘Professor Profitacto’, ‘the movies’, ‘the games’ and ‘the quizes’ on a 4 point scale running from ‘stupid’ (1) to ‘brilliant’ (4). Extra evaluation on Professor Profitacto was asked by letting respondents indicate whether they thought that his explanations were ‘unclear’ (1); ‘neutral’ (2); ‘clear’ (3). A similair 3 point scale was used to see if respondents thought is was ‘irritating’ (1) or ‘nice’ (3) that they were not able to continue the program while Professor Profitacto was talking and had to wait for him to finish instructions. Finally respondents were asked to grade the program on a scale from 1-10, with one being the lowest and ten being the highest.


Subjective effects

Subjective effects were measured with two open answer questions, asking respondents what the most important thing was they learned in the smoking/alcohol programme.


Improvements

Whether respondents thought the programme could be improved was asked with 2 open answer questions, in which the respondents could give tips on improvments on the smoking/alcohol programme.



3.5.4. Follow-up questionnaire


The follow-up questionnaire was very similair to the baseline questionnaire, making it possible to compare them. Only the differences will be described here, as all other information has been given above. There were 2 versions of the follow-up questionnaire, one for the experimental group, one for the control group. The experimental group had a few extra questions about the computer program “Prepared in time”.

Exposure and appreciation

The implementation of “Prepared in time” and the appreciation of this programme was asked again in the experimental group. The questions were similair to the ones asked in the proces-evaluation. They were first asked whether they finished the programme on tobacco/alcohol. Next they had to indicate how many weeks ago they worked with the program ‘This week’ (1); ‘last week’ (2); ‘two weeks ago’ (3); ‘Three weeks ago’ (4); ‘Longer then 3 weeks ago’ (5).


Appreciation

Respondents in the experimental group were asked again to indicate what they thought about “Prepared in time” on a 3 point scale, ‘not fun’(1); ‘little bit of fun’ (2); ‘Great’ (3). They also graded the program on a scale from 1-10, one being the lowest and ten being the highest.


Subjective effects

The subjective effects of “Prepared in time” was measured with 2 questions. First respondents were asked if they learned ‘little’ (1); ‘ some’ (2); ‘a lot’ (3) from the program. Next they were asked whether they told at home that they worked with “Prepared in time”, answering options being ‘Yes’ (1) or ‘No’ (2).


Indirect effects

Both respondents in the experimental group and control group were asked 10 questions on a 2 point scale ‘yes’(1) or ‘no’ (2) on whether they talked with parents/friends/ in the classroom about smoking and alcohol in the period between the baseline and follow-up questionnaire. They were also asked whether they looked up extra information on smoking/alcohol and if they visited the website of Tactus.


Behaviour on smoking

Smoking behaviour was measured similairly as in the baseline questionnaire. Different was the question on smoking behaviour since filling out baseline questionnaire; ‘No, I quit’ (1); ‘Yes’ (2); ‘I don’t smoke’ (3). Parents knowing about their smoking behaviour was also asked with the same question as in the baseline questionnaire, only this time it was directed to all respondents so the option of ‘I never smoked’ was added as an answering possibility.




Behaviour on alcohol

Alcohol drinking behaviour was measured with multiple questions, starting with whether or not respondents had drank alcohol since filling out the baseline questionnaire. This time the students that already drank alcohol were asked which kind of alcoholic drinks they normally drank, giving 9 options (including beer, wine, Bacardi Breezer, Flugel etc.) and a ‘different, namely.....’ option, with multiple answers possible.


Appreciation questionnaires

Respondents were asked to indicate on a four point scale, running from ‘not at all’ (1); ‘a little’ (2); ‘pretty much’ (3) to ‘very much’ (4), whether they thought filling out the follow-up questionnaire was ‘fun’, ‘easy’ and ‘interesting’.



3.6 Statistical analysis


The statistical analyses in this study were performed with SPSS 16.0. The significance levels were in general set at P< 0.05. Exception was made in the comparison of behavioural determinants in the follow-up questionnaire. Because of multiple comparisons significance levels were set at P< 0.01.

The scales on behavioural determinants were tested for reliability, where a Cronbach’s Alpha of ≥ 0.6 was seen as reliable. More detailed description, including the alpha’s can be found in 3.5.2. Modeling family/friends scored low alpha’s therefore they were seen as seperate items.

Differences between the experimental group and control group on nominal scales as gender and origin were tested with a Chi-Square test. Ordinal scaled items were tested with one-way ANOVA. Differences between national percentages and percentages from this study on lifetime prevalence, monthly prevalence and daily smoking were tested with a significance test for comparing two proportions. As the reports on nationwide research only provided the total N of participants and the percentage, the count (X) was calculated to be able to perform this significance test.

To get a better insight into the effects of knowledge and behavioural determinants on smoking behaviour and drinking alcohol correlation analyses were performed. Multiple regression analyses were done to see which items were strong predictors of smoking- and drinking behaviour.

The effects of the program “Prepared in time” were measured by testing the differences between groups between scores on Q2 with ANOVA, taking scores on Q1 as covariate and keepin p< .01 because of multiple comparisons.

Chapter 4 - Results baseline questionnaire: Prevalence of substance use and behavioural determinants



4.1 study group

4.1.1. Respons


The respondents in this study were all students in 1st or 2nd grade of a special needs school. 5 school participated with a total number of 254 students. The schools were located in Enschede, Zutphen (experimental group), Almelo, Ommen and Zwolle (control group). The students were evenly devided over 1st and 2nd grade (table 4.1).

The baseline questionnaire was filled out by 232 students, as some of the total number weren’t in that day because of illness or other reasons. The second questionniare was filled out by 235 students. However, some of the students who were absent the first time, participated in the follow-up questionnaire and students who participated in the baseline questionnaire were absent when the follow-up was filled out. This resulted in a total of 210 students who participated in both baseline and follow-up questionnaire. In the analyses of this study only those 210 students were counted.



Table 4.1 Participating school, classes and respons




E-Learning

Control group

Total

Number of schools

2

3

5

Number of classes

9

11

20

Total number of students

111

143

254

Students 1st grade

61 (24,1%)

68 (26,8%)

129 (50,8%)

Students 2nd grade

50 (19,7%)

75 (29,6%)

125 (49,2%)

Respons 1st questionnaire

101

131

232

Respons 2nd questionnaire

103

134

235

Respons both 1st & 2nd questionnaire

93

117

210


4.1.2. Demographic variables

The percentage of boys that participated in this study were slighlty larger then the girls. The average age was 13.6 years. 75% of the respondents were native, the other 25% were either born abroad or one/both parents were born abroad, this is in line with the way the Central Bureau of Statistics counts native and foreign persons in the Netherlands (CBS, 2009). Looking at the living situation of the respondents, around two third lived at home with both parents and one or more brother(s)/sister(s). 20% lived at home with one parent and one or more brotehr(s)/sister(s).

Only a very small percentage did not live at home and were institutionalized (table 4.2). No significant differences were found between the experimental and control group for gender, age, origin or living situation (table 4.2).
Table 4.2 Demographic variables gender, age, origin and living situation of respondents by studygroup(N=210)







E-learning (88≥n≤93)

Control

(112≥n≤117)



Total

(200 ≥n≤210)



Significance¹

Gender (n=210)



















Male (n=121)

52.7%

61.5%

57.6%

n.s.




Female (n=89)

47.3%

38.5%

42.4%




Age

(n =210 )



















12

4.3%

6.0%

5.2%







13

36.6%

36.8%

36.7%







14

50.5%

41.9&

45.7%

n.s.




15 & older

8.6%

15.4%

12.4%







Mean age in years

13.63

13.67

13.65



Origin²

(n=200)


















Native (n=150)

73.9%

75.9%

75.0%

n.s.




Foreign (n=50)

26.1%

24.1%

25.0%



Living situation (n=208)



















2 parents with brother and/or sister home

62.6%

64.1%

63.5%







2 parents, only child at home

5.5%

10.3%

8.2%

n.s.




1 parent with brother and/or sister at home

22.0%

18.8%

20.2%







1 parent, only child at home

6.6 %

5.1%

5.8%







Not living at home

3.3%

1.7%

2.4%




¹ Differences between study groups between gender, origin and living situation were tested with Chi-Square and age was tested with one-way ANOVA.

2 A person is considered native when both parents are born in the Netherlands and foreign when at least 1 parent was born abroad (CBS, 1999).


4.2 Smoking Behaviour at baseline

Half of the respondents tried a cigarette at least once in their lives (table 4.3). The lifetime prevelance of the female respondents in the controlgroup (51%) was higher then in the experimental group (41%). For the males it was the other way around, more males in the experimentalgroup (55%) had smoked then in the controlgroup (51%). However both differences were not significant. No significant differences were found either between the different schools or between gender (not in table). Both groups scored significanlty higher then could be expected based on a national survey (Monschouwer et.al. 2007).


Table 4.3 Smoking status, smoking bevahiour in last 4 weeks & smoking behaviour daily by gender & studygroup (N=209)




E-learning

(86≥n≤93)



Control

(111≥n≤116)



Total

(197≥n≤209)



Signifi-cance¹

Regional PiT 2006²


Nation-wide³


Nation-wide³ VMBO-B

Signifi-cance⁴



Ever smoke (vs. never smoked)



















Male (n=120)

55.1%

50.7%

52.2%

n.s.

16.7%

-

-




Female (n=89)

40.9%

51.1%

46.1%




9.6%

-

-




Total (n=209)

48.4%

50.9%

49.8%




12.9%

39%

46%

p<.0004⁶




























Smoked in last 4 weeks (vs. never smoked)



















Male (n=113)

26.7%

32.4%

30.1%

n.s.

1.6%

-

-




Female (n=84)

19.5%

32.6%

26.2%




1.9%

-

-




Total (n=197)

23.3%

32.4%

28.4%




1.7%

19%

23%

p<.0002⁷




























Smokes on daily basis






















Male (n=113)

17.8%

23.5%

21.2%

p=.03⁵

-

8%

-




Female (n=84)

7.3%

25.6%

16.7%




0.3%

7%

-




Total (n=197)

12.8%

24.3%

19.3%




0.1%

7%

11%

p<.0002⁸

¹ Differences between groups were tested with Chi-Square

² Results from original study by Ter Huurne (2006); mainstream primaryschool, agegroup 9-13 years old (692≥n≤699)

³ Monschouwer et.al. (2007), students in secundary schools age 12-16

⁴ Difference between studygroup & nationwide, tested with significance test for comparing two proportions.

X count in nationwide unknown & calculated from percentage and total N.

⁵ Respondents in the controlgroup smoke significantly more on a daily basis

⁶ Ever smoke significantly higher in studygroup then nationwide

⁷ Significantly more smoking in last 4 weeks in studygroup compared to nationwide

⁸Significantly more daily smokers in studygroup compared to nationwide.
Most adolescents tried their first cigarette between the ages of 12 and 14 with a peak at age 13 (table 4.4). Again, no significant differences were found between the experimental and control group. Still, one in ten of adolescents already tried their first cigarette at the age of 10 years or younger. This is in corresponcence with the results from Monschouwer et.al. (2007) who found that one in ten started before age 11 (not in table). Almost a third of the students had an agreement with either their parents, school or someone else to not start smoking (table 6.7). This is comparible with the 27% Monschouwer et.al. found in their study (2007). Over half of the respondents however did not have an agreement and did not want one either. Around two third of the parents of smoking respondents knew that their children smoked. A third of the smoking students had not told their parents. However this was a self-answering questionnaire in which the respondents had to indicate if their parents knew. If their estimate was correct and parents really did not know cannot be said with certainty.
Table 4.4 Age of first cigarette of respondents by study group (N=91)




Frequency

(n=91)


E-Learning (37≤n≥38)

Control (53≤n≥54)

Total (n=91)

Significance¹

Age smoking 1st cigarette (n=91)
















10 years or younger

12

10.8%

14.8%

13.2%




11 years

16

10.8%

22.2%

17.6%




12 years

20

29.7%

16.7%

22.0%

n.s.

13 years

28

32.4%

29.6%

30.8%




14 years or older

15

16.2%

16.7%

16.5%






















Do parents know about smoking (n=91)
















Yes

57

60.5%

64.2%

62.6%

n.s.

No

34

39.5%

35.8%

37.4%




¹ Differences in age between groups were tested with Unianova variance analyses. Knowledge of parents was tested with Chi-Square.

4.3 Alcohol drinking behaviour at baseline

Over half of the respondents in this study drank alcohol more then once. Only 25% had never had alcohol in their lives. No significant differences were found between the experimental and control group for lifetime prevelance or starting age for drinking alcohol (table 4.5). The same counts for a comparison between schools, no significant differences were found their either (not in table). The lifetime prevelance of alcohol among the males was significantly higher then among the females with 81.5% versus 67.0%. The precentage of males drinking more then once was also higher then for the females, however this difference was not significant (table 4.5). The starting age of a first drink looks slightly younger then the starting age for smoking. Around 15% already had their first drink at age 10. With 18.9% and the average at age 12 this seemed to be the most common age at which adolescents drank for the first time however it is pretty evenly devided within this study. Compared to the National Drug Monitor 2007 (Trimbos, 2007) this age is below the national average age of 14.6 years. The percentage of ever drinking is not comparible as it is unclear whether ‘once (a sip of) alcohol’ was counted as ever use in their survey.


Table 4.5 Alcohol status by gender & studygroup and & age first drink (N=208)




E-learning

(91≥n≤92)



Control

(116≥n≤117)



Total

(206≥n≤208)



Significance¹

Signifi

cance²


Regional PiT 2006³

Nation-wide⁴

Signifi-cance⁵

Ever drank alcohol

























Male (n =119)

85.4%

78.9%

81.5%

n.s.

p =.02⁶

88.4%

81%




Female (n=89)

69.8%

64.4%

67.0%







79.0%

77%




Total (n=208)

78.0%

73.3%

75.4%







83.4%

79%

n.s.




























Once (a sip of) alcohol

























Male (n=24)

25.0%

16.9%

20.2%

-

-

32.1%

-




Female (n=19)

27.3%

15.6%

21.3%







36.4%

-




Total (n =43)

26.1%

16.4%

20.7%







34.4%

-































Drank alcohol more then once






















Male (n=73)

60.4%

62.0%

61.3%

-

-

56.3%

-




Female (n=40)

40.9%

48.9%

44.9%







42.6%

-




Total (n=113)

51.1%

56.9%

54.3%







48.9%

-































Age of drinking alcohol for 1st time (n=206)






















Never drank alcohol

22.2%

24.1%

23.3%







-

-




10 or younger

13.3%

16.4%

15.0%







-

-




11

10.0%

15.5%

13.1%

n.s.

n.s.

-

16%⁷




12

22.2%

16.4%

18.9%







-

-




13

20.0%

15.5%

17.5%







-

-




14 or older

12.2%

12.1%

12.1%







-

-































Average age 1st drink

12

12

12







-

-




¹ Differences between groups were tested with Chi-Square & Unianova variance analyses, looking at never alcohol versus once a sip versus drank more then once.

² Differences between gender were tested with Unianova

³ Results from original study by Ter Huurne (2006); mainstream primaryschool, agegroup 9-13 years old (n=703)

⁴ Monschouwer et.al. (2007), students in secundary schools age 12-16

⁵ Difference between studygroup & nationwide, tested with significance test for comparing two proportions. X count in nationwide unknown & calculated from percentage and total N.

⁶ Males drink significantly more then females on total group.

⁷ Nationwide: 11 years or younger having first drink

The respondents were asked about their lifetime and monthly prevelance of drinking alcohol (table 4.6). Looking at the lifetime prevalence of the boys almost a third drank more then 11 times and around half of them drank one to six times. The girls seemed to drink a little less. Only a fifth drank 11 times or more, a third drank only 1-3 times and a third indicates they never drank at all. This corresponts with the earlier questions about their drinking behaviour. The respondents did not seem to be regular drinkers yet as almost 60% did not drink alcohol in the last month. A fifth or them drank one to three times and 10% drank four to six times. This is slightly lower then the national numbers of 51% drinking in the last 4 weeks with 30% drinking 1-3 times (Monschouwer et.al. 2007). When respondents drink almost a quatre of them drinks two to three drinks. 20% drinks four to six drinks, bordering the binge-drinking line. Around one in ten seem to be binge-drinkers as they indicated to drink more then 7 drinks at a time. Again these numbers are the result of self-report. It is possible that respondents overrated their drinking in an attempt to show off. No significant differences were found in frequency of drinking or in the amount respondents drink on a drinking occasion (table 4.6). Looking at national numbers is shows that binge drinking increases as students get older, rapidly progressing between the ages of 13-15 (Monschouwer et.al., 2007).


Table 4.6 Alcohol frequencies in life by studygroup and gender (N=203)




E-Learning (n=90)

Control (n=113)

Total (n=203)

Signifi-cance¹

Signifi-cance²




Male (n=46)

Female (n=44)

Male (n=68)

Female (n=45)

Male (n=114)

Female

(n=89)








Alcohol frequency in whole life (n=203)




















Never

17.4%

29.5%

20.6%

33.3%

19.3%

31.5%







1-3 times

32.6%

34.1%

19.1%

24.4%

24.6%

29.2%







4-6 times

19.6%

18.2%

20.6%

8.9%

20.2%

13.5%

n.s.

n.s.

7-10 times

2.2%

6.8%

8.8%

6.7%

6.1%

6.7%







11 times or more

28.3%

11.4%

30.9%

26.7%

29.8%

19.1%







¹ Differences between studygroups were tested with Unianova variance analyses

² Differences between gender were tested with Unianova variance analyses


Most of the drinking happens at home or at a friends/family members place. Other populair drinking spots seemed to be ‘outside on the street’ (hanging around with friends), ‘in a bar, pub or drinkingshed’ and ‘on holidays’ (table 4.7). Significant differences between experimental and controlgroup were found on the items ‘Bar, pub or drinkingshed’ ‘On holidays’ and ‘Somewhere else’. Drinking in bars/pubs/drinkingsheds was done more often by respondents in the control group as was drinking somewhere else. Holidays seemed to be a populair drinking occasion for respondents in the experiment group (table 4.7).
Table 4.7 Alcohol frequencies in last 4 weeks, amount of alcohol on occasion and drinking places of respondents who drank alcohol more then once by study group (N=203)




E-learning

(64≤n≥89)



Control

(88≤n≥113)



Total

(101≤n≥202)



Significance¹

Regional PiT 2006

Alcohol frequency in last 4 weeks (n=202)













Never

64%

55.8%

59.4%




-

1-3 times

19.1%

19.5%

19.3%

n.s.

-

4-6 times

10.1%

10.6%

10.4%




-

7-10 times

5.6%

4.4%

5.0%




-

11 times or more

1.1%

9.7%

5.9%




-



















Amount respondents drink on occasion (n=152)













Never had a drink

31.2%

35.2%

33.6%




-

Less then 1 drink

20.3%

10.2%

14.5%




-

1 drink

12.5%

13.6%

13.2%




-

2-3 drinks

14.1%

17.0%

15.8%

n.s.

-

4-6 drinks

12.5%

14.8%

13.8%




-

7-10 drinks

6.1%

4.5%

5.3%




-

More then 10 drinks

3.1%

4.5%

3.9%




-



















Where do respondents drink (n=116)⁶













At home

52.0%

51.5%

51.7%

n.s.

75.8%

With family or friends

51.1%

43.9%

46.9%

n.s.

30.0%

On street, park etc.

19.0%

24.6%

22.4%

n.s.

1.5%

Bar, pub or drinkingshed

18.2%

34.8%

28.2%

p =.044 ³

7.0%

Restaurant

4.5%

1.5%

2.7%

n.s.

7.0%

On holidays

40.9%

18.2%

27.3%

p= .008⁴

28.3%

Sport canteen

0.0%

3.0%

1.8%

n.s.

0.6%

Somewhere else

11.1%

31.8%

23.4%

p= .009 ⁵

12.0%

¹ Differences between groups were tested with Unianova variance analyses for the first 2 variables, for the last variable a Chi-Square test was used.

² Results from original study by Ter Huurne (2006); normal primaryschool, agegroup 9-13 years old (n=343)

³ The percentage of respondents drinking in bars, pubs and/or drinkingsheds was significantly higher in the control group.

⁴ The percentage of respondents drinking on holidays was significantly higher in the e-learning group.

⁵The percentage of respondents drinking somewhere else was significantly higher in the control group.

⁶ Respondents were allowed to give more then one answer.


Looking at this more closely a significant difference was be found on school level aswel (not in table). Bars/pubs/drinkingsheds were most populair in Almelo and Ommen with 41.9% resp. 29.0%. The respondents in Zutphen, Enschede and Zwolle did not seem to have the oppertunities or interest in drinking in those kinds of places (19.4%, 6.5% and 3.2%). The significance in ‘On Holiday’ seems to be created by the respondents in Zutphen as 46.7% of them indicated to drink while on vacation. Respondents in the other schools do not seem to drink on this occasion or perhaps are not going on holidays as often. The drinking ‘somewhere else’, again, is most populair in Almelo and Ommen (both 38.5%). As no option was given for writing down what place somewhere else could be, it is unclear where they drink. It is possible that one of their drinking spots could be classified as a drinking shed or friends place but was not recognised as such by the respondents.
Even though 50% indicated to drink at home in previous question, only 30% says to be allowed to drink at home (table 6.7). Possibly the drinking happens when the parents are out . Almost a third of respondents said not to know whether they were allowed to drink at home or not indicating that this topic has so far not been discussed with their parents. Around a quatre of the respondents had an agreement with their parents to not drink at all or until a certain age. 65-70% did not have an agreement and were not interested in one either. Only a small 7% did not have an agreement (yet) but would like one.

Respondents who answered they drank more then once were asked whether their parents knew about them drinking and if they knew how much they drink. 79% of respondents said their parents were aware of them drinking, and 52% claims their parents also knew how much they drink. Almost a fifth was not as open with their parents though, claiming the parents thought the respondents did not drink as much as they actually do (not in table).


Table 4.8 Frequency of smoking and drinking alcohol by persons in environment of respondent by study group (N=202)




E-Learning (85≤n≥90)

Control

(105≤n≥113)



Total

(190≤n≥201)



Significance¹

Person smokes













Father (n=198)

48.9%

59.1%

54.5%

n.s.

Mother (n=201)

42.7%

53.6%

48.8%

n.s.

One or more brother(s)/ sister(s) (n=191)

23.3%

35.2%

29.8%

n.s.

Best friend (n=192)

30.6%

48.6%

40.6%

p =.03²

Teacher (n=198)

12.6%

42.3%

29.3%

p =.00³
















Person drinks alcohol













Father (n=202)

77.8%

70.5%

73.8%

n.s.

Mother (n=202)

67.4%

54.0%

59.9%

n.s.

One or more brother(s)/ sister(s) (n=196)

45.5%

53.7%

50.0%

n.s.

Best friend (n=198)

39.8%

54.5%

48.0%

n.s.

Teacher (n=202)

66.3%

63.7%

64.9%

n.s.

¹ Differences between groups were tested with Chi-Square

² Respondents in control group had significantly more times a best friend that smokes

³ Teachers in control group smoke significantly more in comparison to teachers e-learning group
Most parents drank alcohol themselves, 73.8% of the fathers drank alcohol compared to 59.9% of mothers (table 4.8). About half of the brother(s)/sister(s) drank alcohol, as did the best friend of the respondent. Around two-third of teachers drink alcohol, although this last one was not very objective and can not be seen from a rolemodel point of view. A lot of students asked their teachers directly as they did not know, instead of answering with the ‘I don’t know’ option. Next to that a lot of teachers twisted their answer by saying they did not drink or they “drank very rarely, which should be a no” in an attempt to set a good rolemodel. The drinking among parents is slightly lower then the national number of 85% drinking alcohol (Trimbos, 2007). No significance was found between experimental and control group.
Looking at smoking it showed that around 50% of mothers and 55% of fathers smoked (table 4.8). Almost a third of brother(s)/sister(s) smoked, setting an example for the respondents. This is very high when national average numbers show that 30.5% of men and 24.5% of females over 15 years old smoke (Trimbos, 2007). The differences between experiment- and controlgroup were not significant. The item on ‘best friend smokes’ did turn out a significant difference, with the respondents in the controlgroup more often having a best friend that smoked then the respondents in the experiment group (table 4.8). Also a significance was found on the item ‘does your teacher smoke’. Significantly more teachers seemed to smoke in the control group. However this item is not very reliable as students asked their teacher while filling out the questionnaire, because they did not know.
Overall we can state that looking at smoking, there is a big problem among this targetgroup. They smoke significantly more then could be expected based on national numbers. We also see that parents smoke more then the national average, probably setting an example for their children. The use of alcohol is comparible with national statistics, however considering that 15% of adolescents already had their first drink by the age of 10, it is safe to say that here also lies a big problem. We can conclude that the use of tobacco and alcohol is nothing strange in the lives of intellectually disabled adolescents.


4.4 Determinants of behaviour at baseline



4.4.1. Knowledge

A significant difference was found between knowledge on smoking between the males in the experimental group and control group (table 4.9). It seems to be caused by the question “which three substances are found in tobacco?” (not in table). More males in the experimental group knew the correct answer to this question. This also influences the total knowledge, creating a significant difference there between the males in both groups.


In general the average scores on knowledge are pretty low. Questions that were answered extremely badly were: What is tar? (both 1&2 correct; black sticky substance that sticks to your lungs & dasmages the cilium), what is the addicting substance in cigarettes (nicotine) and which is true about pure alcohol? (it is poisonous). Over 80% answered these questions incorrectly.
Table 4.9 Correct answered knowledge questions on avarage by theme and studygroup at baseline(N=202 )




E-Learning

(83≤n≥90)



Control

(108≤n≥112)



Total

(186≤n≥202)



Significance¹


Knowledge Smoking (0-5)(n=202)













Male

3.2

2.8

3.0

.03²

Female

2.9

3.0

2.9

n.s.

Total

3.0

2.9

3.0

n.s.

Knowledge Alcohol (0-5) (n=191)













Male

2.3

2.2

2.2

n.s.

Female

2.4

2.1

2.2

n.s.

Total

2.3

2.1

2.2

n.s.

Total Knowledge score (0-10)(n=186)













Male

5.4

5.0

5.1

.048³

Female

5.3

5.1

5.9

n.s.

Total

5.4

5.0

5.2

n.s.

¹ Differences between groups were tested with Unianova variance analyses

² Knowledge on smoking of males in experiment group was significantly higher then in control group.

³ Average total knowledge of the males in the experiment group was significantly higher then in the control group at Q1.

4.4.2. Intention

As table 4.10 shows the intention to start/stay drinking in the future is low. Intention to start/stay smoking is slightly higher but still in the low side. Almost 50% of respondents indicated to have no intention in ever smoking. 21% of males and 41% of females indicates to have no intention in ever drinking alcohol. However many students said they found it hard to say how they would feel on the subject in 2 years time (not in table).



4.4.3. Attitude

Attitude towards both alcohol & smoking were on the low to neutral side (table 4.10). Although 70-80% of the respondents found smoking bad for their health, almost 23% found smoking normal. It was seen as uncool but almost 1 in 5 also found it social. A big difference was seen between experimental- & controlgroup. In the experimentalgroup only 8% saw smoking as sociable compared to a 21% in the controlgroup. 66% found smoking stupid and they did not really like the smell. Besides these negatives 15% found that smoking makes them more populair (not in table). In general the females were a bit more negative towards smoking then the males. Looking at alcohol we see that a quarter of the respondent found drinking sociable. Almost 12% of females and 9% of males felt that drinking made them more populair. 30% of respondents indicate that they liked the taste of alcohol. There were no big differences between the males and females. Again on the social level there was a big difference between the experimental- and controlgroup. As 17% of the experimentalgroup found drinking social, the controlgroup scored much higher with 29%. Also, more students in the controlgroup found that drinking made them more populair (not in table).



4.4.4 Social influences



Subjective Norm

The subjective norm on both smoking and alcohol was low to neutral (table 4.10). 75% of respondents indicated that their family felt they should not smoke. With friends over 60% of males and 50% indicated that they should decide for themselves whether they should smoke or not. Almost 60% of males and nearly 70% of females said that their family felt they should not drink. Again with friends the majority said they should decide for themselves. There were no big differences between both groups.


Peer Pressure

The respondents did not feel much peer pressure on smoking or drinking alcohol. The male respondents felt slightly more pressured to have a smoke (6.6%) then the females (1.1%). 70% of both females and males did not feel pressured into drinking alcohol (table 4.10).




Social Support

50% of the respondents felt their friends support them in not smoking. For not drinking the support is even higher, 55% of males and 64% of females felt their friends did not want them to drink alcohol. There were no significant differences between groups or gender (table 4.10).



4.4.5 Self efficacy

The self-efficacy in not starting smoking or drinking (or capability of stopping smoking/drinking) was average to high on both smoking and drinking alcohol (table 4.10). Most respondents indicated that they found it easy to not start smoking and thought they would be able to not smoke when they get older. There were no big differences between the groups or gender. Most smokers and drinkers indicated that they would be capable of stopping if they wanted to. There were some differences between the experimental group and control group but since the N is low, no conclusions can be drawn from that.


Table 4.10 Behavioural determinants on smoking and alcohol by study group at baseline (N=209)




E-Learning

(51≥n≤70)



Control

(76≥n≤86)



Total

(177≥n≤209)



Significance¹

Intention (1=low - 3=high)













Start/Stay smoking (n=150)

1.4

1.4

1.4

n.s.

Start/Stay drinking alcohol (n=179)

1.2

1.1

1.1

n.s.
















Attitude (1= negative towards - 3 = positive towards)













Smoking (n=204)

1.5

1.6

1.6

n.s.

Alcohol (n=204)

1.7

1.7

1.7

n.s.
















Subjective Norm (1=negative towards – 3 = positive towards)













Smoking (n=201)

1.7

1.6

1.7

n.s.

Alcohol (n=199)

1.7

1.7

1.7

n.s.
















Peer pressure (1=low feeling of PP- 3=high feeling of PP)













Smoking (n=193)

1.3

1.4

1.4

n.s.

Alcohol (n=208)

1.4

1.3

1.3

n.s.
















Social Support ( 1=low feeling of SS - 3=high feeling of SS)













DON’T smoke (n=193)

1.7

1.7

1.7

n.s.

DON’T drink alcohol (n=209)

1.5

1.7

1.6

n.s.
















Self-Efficacy (1=low feeling of SE - 3=high feeling of SE)













Smoking (n=203)

2.5

2.5

2.5

n.s.

Alcohol (n=177)

2.4

2.4

2.4

n.s.
















¹ Differences between groups were tested with Unianova variance analyses

² Respondents in the controlgroup have significantly more classmates that smoke

³ Respondents in the controlgroup have significantly more classmates that drink alcohol

⁴Respondents in the controlgroup have significantly more friends that smoke



4.5 Multi-analysis on smoking behaviour & drinking behaviour at baseline

To see whether smoke status and alcohol status were connected to knowledge and the behavioural determinants correlation analyses were performed. Looking at smoking it shows that there were high correlations between smoking status and intention to start smoking. Also attitude towards smoking correlated highly with smoking status. As expected self-efficacy correlated in a negative way with smoking status. There also seemed to be a strong connection between attitude towards smoking and intention to start smoking. Intention and subjective norm also seemed to influence one another (table 4.11).


Table 4.11 Correlation Smoking behaviour, knowledge & behavioural determinants at baseline (148 ≥n≤209).¹







2

3

4

5

6

7

8

9

1

Smoke Status

.67

.29

.62

.75

.43

.26

.

-.34

2

Smoke in last

4 weeks


-

.22

.59

.69

.35

.

.

-.35

3

Knowledge




-

.35

.25

.25

.

.

.

4

Attitude







-

.53

.46

.

.

-.32

5

Intention










-

.42

.

.

-.23

6

Subjective norm













-

.24

.

-.25

7

Peer pressure
















-

.

.

8

Social Support



















-

.

9

Self-Efficacy






















-

¹ Table only shows correlations of r > .010 (two-tailed) p <.010 (Spearman’s Rho)
Table 4.12 showed that there is a high correlation between drinking alcohol and attitude towards drinking alcohol. This attitude also correlated highly with drinking in the last 4 weeks. Different from smoking, intention to start drinking did not seem to correlate with the actual drinking status or drinking in the last 4 weeks. Subjective norm however did seem to be connected to alcohol status. There was also a strong relation between attitude towards drinking and subjective norm.
Table 4.12 Correlation Alcohol behaviour, knowledge & behavioural determinants at baseline (127≥n≤208).¹







2

3

4

5

6

7

8

9

1

Alcohol Status

.45

.

.59

.

.41

.

.

-.41

2

Drinking in last 4 weeks

-

.

.69

.

.41

.

.

-.33

3

Knowledge




-

.

.

.

.

.

.

4

Attitude







-

.24

.61

.26

.

-.40

5

Intention










-

.

.

.

.

6

Subjective Norm













-

.29

-.18

-.31

7

Peer Pressure
















-

.

.

8

Social Support



















-

.

9

Self-Efficacy






















-

¹ Table only shows correlations of r > .010 (two-tailed) p <.010 (Spearman’s Rho)

To get a better insight in which determinants really influence smoking behaviour and intention a multiple regression analyses was performed (table 4.13). Looking at smoking behaviour it shows that 72.2% can be explained by the behavioural determinants. Attitude and intention prove to be the main predictors of smoking behaviour. Attitude is also connected to the intention to start smoking.

As could be expected by the correlation analyses, intention does not seem to play a role in alcoholdrinking behaviour. 60.5% of alcoholstatus can be explained by the behavioural determinants. Main predictor here is attitude towards alcohol. Subjective norm also seems to play a role in the choice to start drinking.

Table 4.13 Multiple regression analyses on smoking status, smoking last 4 weeks, intention to start smoking, alcohol status, drinking in last 4 weeks and intention to start drinking.¹




Smoking Status

β


Smoking last 4 weeks

β


Intention to start smoking

β


Alcohol Status

β


Drinking last 4 weeks

β


Intention to start drinking

β


Knowledge

.

.

.

.

.

-.21*

Attitude

.47***

.45***

.27**

.58***

.38**

.

Intention

.44***

.37***

-







-

Subjective Norm

.

.

.

.25**

.24*

.

Peer Pressure

.

.

.

.

.

.

Social Support

.

.

.

.

.

.

Self-efficacy

.

.

.

.

.

.23*



R² = 72.2%

F=(7,111)=41.11

p≤.001


R² = 64.3%

F=(7,109)=28.04

p≤.001


R² = 22.2%

F=(6,112)=5.33

p≤.001


R² = 60.5%

F=(7,101)=22.09

p≤.001


R² = 34.8%

F=(7,100)=7.63

p≤.001


R² = 17.6%

F=(6,102)=3.63

p≤.003


¹ Table shows significantly independent predictors if *=p<.05, **= p<.01, *** = p<.001 .


Chapter 5 - Results proces evaluation



5.1 Respons

The schools in Enschede and Zutphen were selected for the experimental group, which meant they got to work with the e-learning program Prepared in time. A total of 97 students, 10 in Enschede and 87 in Zutphen, worked with the programme and filled out an evaluation questionnaire.



5.2 Appreciation

The respondents were asked on a 3-pointscale whether they thought the program was fun, easy to do, interesting and childish where 1 was negative, 2 neutral and 3 positive (table 5.1). Scoring an average 2.48 out of 3, the program was seen as easy and well doable. Most children had no problems working with it what so ever. On the points of interesting (1.75) and fun (1.87) however they were not so positive.


The respondents were neutral on whether the programme was childish or not. They did not seem to mind working with it from that perspective. The respondents in Zutphen spent around an hour working with the program in which they did half the smoking part and the full alcohol part. Normally they would have been able to do the full programme in this time, which was unfortunately not possible because of a problem with their computers. The respondents in Enschede did both parts in around an hour. The respondents were not positive on this amount of time, finding it too long.
Respondents were asked to grade the programme on a scale from 1-10, 1 being the lowest and 10 being the highest. Their average grade overall was a 5.98, running from ones to tens, showing a great difference between the respondents (table 5.1). 14 students graded the programme with a one, pulling the average down. Looking at the median it showed that an axis around a 7, with 11 students grading a 6, 33 students grading a 7 and 12 students grading the programme with an 8. A ten was given by 8 students (not in table).

Table 5.1 Appreciation and average grades of e-learning program (N=97)





Percentage

Mean

(1=low, 3=high)



Did you think the program was: (n=97)




1.9

1.Stupid

34.0%




2.Neutral

45.4%




3.Fun

20.6%





Did you find the program: (n=96)




2.5

1.Difficult

5.2%




2.Neutral

41.7%




3.Easy

53.1%





Did you find the program: (n=97)




1.8

1.Boring

46.4%




2.Neutral

32.0%




3.Interesting

21.6%





Did you think the program was: (n=97)




1.9

1.Childish

28.9%




2.Neutral

49.5%




3.Cool

21.6%





Did you have enough time for working with the program? (n=97)




1.5

1.Time was too long

58.8%




2.Time was OK

36.1%




3.Time was too short

5.2%





Mean Grade (n=96)




6.0

To get a better impression of which parts of the programme the respondents liked, they were asked specifically about these parts (table 5.2). At the start of the program all students had to make a portrait of themselves. Almost 70% seemed to appreciate this aspect from allright to brilliant. Professor Profitacto was not as populair and found stupid by 40%. Over 60% did not appreciate the movies either, which is probably caused by the problems with the sound system, preventing most students to watch the movies properly. The games and quizzes were appreciated a lot more with only 20% finding them stupid.


Table 5.2 Appreciation different items on e-learning program (N=96)




Stupid (1)

Allright (2)

Fun (3)

Brilliant (4)

Mean(1=low–4=high)

Making own portret (n=95)

25.3%

38.9%

28.4%

7.4%

2.2

Professor Profitacto

39.1%

42.4%

17.4%

1.1%

1.8

Movies

61.1%

29.5%

8.4%

1.1%

1.5

Games

20.8%

31.2%

38.5%

9.4%

2.4

Tests & quizzes

21.1%

45.3%

23.2%

10.5%

2.2

Table 5.3 shows more closely why the students were no fans of Professor Proficacto. Even though his explenations were alright nearly half of the students got irritated waiting for him to finish his talks before they could move on to the next screen.


Table 5.3 Appreciation of Professor Profitacto (N=96)




Frequency

Percentage

Clear explanations







Very unclear

25

26.0%

Neutral

43

44.8%

Very clear

28

29.2%










Wait for Prof. Profitacto before going to next screen







Irritating

44

45.8%

Neutral

35

36.5%

Nice

17

17.7%

With 2 open questions respondents were asked what they learned from both the smoking and the alcohol part. They gave many different answers that were categorized afterwards. One in three students found they did not learn (anything new) on the subjects. Also seen a lot were answers like ‘it is bad for your health’, ‘you can get addicted’ and ‘ it is bad for your brain’ in case of alcohol. Table 5.4 shows the most given answers.


Table 5.4 What respondents learned about smoking and alcohol (N=99)




Frequency

Percentage

Smoking (n=91)







No, I learned nothing (new)

28

30.8%

I learned very much

3

3.3%










It is bad for you / your health

22

24.2%

Don’t start smoking

7

7.7%

It is bad for your lungs

4

4.4%

You can get addicted

4

4.4%

Smoking can kill you

1

1.1%

You can get sick

1

1.1%

28% of people in Holland smoke

1

1.1%

Something else

20

22.0%










Alcohol (n=90)







No, I learned nothing (new)

27

30.0%

I don’t know

5

5.6%

I learned a lot

2

2.2%










That alcohol is bad for you

14

15.6%

It is bad for your brain

11

12.2%

Don’t start drinking (before 16)

7

7.8%

How many people drink alcohol

3

3.3%

Alcohol is poisonous

2

2.2%

You shouldn’t drink too much

2

2.2%

Same amount of pure alcohol in different drinks

2

2.2%

Alcohol can kill you

2

2.2%

Don’t get addicted

1

1.1%

Something else

12

13.3%



5.3 Improvement

Many students had no ideas or tips on how to improve the programme. The given answers were categorized as can be seen in table 5.5. Some are very contradictory and personal, showed by one student wanting longer movies where as another wants shorter movies. Most important for future users is to check whether their computersystem is able to run the program correctly at normal speed and with proper sound.


Table 5.5 Improvement tips for programme according to respondents (N=99)




Frequency of mentioning

Smoking




No tips

65

Shorter movies

2

Chat with others

1

Images more clear

1

Programme should work faster

1

It should be just verbal

1

Programme is too long

1

Too much explanation

1







Alcohol




No tips

68

Movies should be longer

1

Images more clear

1

More explanation

1

Movies should be shorter

1

Less movies

1

Most students enjoyed working with the program but some elements were long and students lost their interest at those points. It would help a lot if teachers would be able to skip certain parts or movies. Games were appreciated most and are definitly a good part in the program. However, the explenation on how to play the games was not always clear, causing irretation among the students. Working with “Prepared in time” over 2 or 3 times will work better as well. Doing both smoking and alcohol in 1 hour is asking to much of these students. They find it difficult to concentrate and stay focused for so long.



5.4 Observations

Observations showed that the students were well capable of working with a computerprogram. One of the comments heard a lot was that the program, and especially some games, were moving to slow. The students were very focussed on their own screen, mainly seeking contact with classmates when they discovered a ‘cool’ game. They did try and keep track of eachother at the start, asking how far others were but became more focused on their own progress as time went on. The problems with the soundsystem proofed annoying to the students. They were not capable of waiting for a movie to finish and became restless. Classes that were allowed to do only a short part of the smoking module and move on to the alcohol part then were less restless and annoyed. Some teachers had the feeling that the students were capable of concentrating longer working behind the computer then they normally would be able to do in a classroom situation. As this wasn’t part of this study no measurements were made that could confirm this observation.


Observation also showed that some information on the programme was to difficult for this group of students. For example the explanation on pure alcohol and how it is a substance on its own was difficult for them to comprehend. When compared to lemonade and how you can change the strenght and taste by adding water most students seemed to grasp how pure alcohol is a substance on its own that is added to drinks, and that alcohol is not the drink itself.
In general it showed that students did not appreciate the programme very much, giving it a mere 6 out of 10. This did not show while they were working with the programme. Most students were participating very well, focused on what they had to do and not afraid to ask question when they did not understand or calling over the researcher to show how well they performed on a quiz.

Chapter 6 - Results follow-up questionnaire: Prevalence of substance use and behavioural determinants



6.1 Indirect behavioural aspects

About a third of students told at home that they filled out the baseline questionnaire. Only 12% talked with their parents about smoking and alcohol, showing that it is not a topic they talk about with their parents (table 6.1). Significantly more students in the experiment group looked up extra information on smoking compared to the control group. The question however does not ask whether they looked it up themselves or if it was a school assignment. Smoking and drinking alcohol does not seem to be a general topic of conversation among these students.


Table 6.1 Indirect behavioural effects of intervention by studygroup (N=198)




E-Learning

(82 ≤n≥84)



Control

(114≤n≥116)



Total

(197≤n≥198)



Significance¹

Told at home about baseline questionnaire (n=198)

37.3%

29.6%

32.8%

n.s.

Talked with parents about smoking (n=198)

12.2%

12.1%

12.1%

n.s.

Talked with parents about alcohol (n=199)

12.0%

11.2%

11.6%

n.s.

Talked with friends about smoking (n=199)

21.7%

12.9%

16.6%

n.s.

Talked with friends about alcohol (n=198)

14.5%

13.9%

14.1%

n.s.

Talked in class about smoking (n=197)

16.9%

11.4%

13.7%

n.s.

Talked in class about alcohol (n=198)

19.0%

13.2%

15.7%

n.s.

Searched for extra info smoking (n=198)

11.9%

2.6%

6.6%

p=.016²

Searched for extra info alcohol (n=197)

8.4%

4.4%

6.1%

n.s.

Visited website Tactus (n=197)

8.4%

3.5%

5.6%

n.s.

¹ Differences between studygroups tested with Chi-Square

² Significantly more students in E-learning group looked up extra information on smoking




6.2 Effects on knowledge & variables ASE-model

Looking at table 6.2 it shows that knowledge on smoking did not improve after working with “Prepared in time”. Infact, both groups scored worse on the follow-up questionnaire compared to the baseline. Explenation for this phenomena might be that the schoolyear was coming to an end and students were not motivated to fill out another questionnaire when the first one was only a few weeks earlier. Also, as the movies were not working properly the students in the experimental group only did a short bit on smoking in the e-learning program, preventing them from getting all the information. On alcohol however the knowledge did improve significantly when comparing the experimental group with the control group. Explenation for this could be that the experiment group students remembered the learned knowledge on alcohol better, as they did the full program on alcohol. If a student knows an answer it is easy to fill out a form, they do not have to think that much.




Table 6.2 Knowledge scores on smoking and alcohol on baseline (Q1) andfollow-up (Q2) by studygroup (N=210).




E-Learning (82≥n≤90)

Q1 Q2


Control (104≥n≤116)

Q1 Q2


Total (202≥n≤204)

Q1 Q2


Significance¹

Knowledge score













Smoking (0-5)

3.02 2.60

2.88 2.45

2.95 2.51

n.s.

Alcohol 0-5

2.34 2.52

2.14 2.14

2.23 2.30

p=.036

Total (0-10)

5.35 5.12

4.99 4.63

5.15 4.83

n.s.

¹ Differences between scores on Q2 were tested with ANOVA, taking scores on Q1 as covariate

² The knowledge about alcohol was significantly higher in the experimental group at Q2.


Looking at the behavioural determinants there were no significant differences there (table 6.3). “Prepared in time” did not seem to have changed students attitude towards or intention to start smoking and/or drinking. Both peer pressure and social support on drinking alcohol seem to have become slighlty higher and self-efficacy has come down a bit which, even though not significant, is a worrying point considering the wanted effect is the other way around.
Table 6.3 Behaviourdeterminants from ASE-Model on smoking and alcohol on baseline (Q1) and follow-up (Q2) by studygroup (N=208)




E-Learning

(62≥n≤69)



Control

(76≥n≤84)



Total

(96≥n≤208)



Significance¹




Q1

Q2

Q1

Q2

Q1

Q2




Intention (1=low-3=high)






















Start/stay smoking (n=146)

1.4

1.1

1.4

1.1

1.4

1.1

n.s.

Start/stay drinking alcohol (n=145)

1.2

1.0

1.1

1.0

1.1

1.0

n.s.

























Attitude (1=low-3=high)






















Smoking (n=205)

1.5

1.6

1.6

1.6

1.6

1.6

n.s.

Alcohol (n=202)

1.7

1.8

1.7

1.7

1.7

1.8

n.s.

























Subjective norm (1=low-3=high)






















Smoking(n=204)

1.7

1.6

1.8

1.7

1.7

1.6

n.s.

Alcohol(n=201)

1.6

1.6

1.7

1.7

1.7

1.7

n.s.

























Peer pressure (1=low-3=high)






















Smoking (n=193)

1.3

1.3

1.4

1.3

1.4

1.3

n.s.

Alcohol (n=208)

1.4

2.6

1.3

2.6

1.3

2.6

n.s.

























Social Support (1=low-3=high)






















Smoking (n=198)

1.7

1.8

1.7

1.7

1.7

1.8

n.s.

Alcohol (n=197)

1.5

2.4

1.8

2.5

1.6

2.4

n.s.

























Self-Efficacy (1=low-3=high)






















Smoking (n=143)

2.5

2.6

2.5

2.4

2.5

2.5

n.s.

Alchohol (n=141)

2.4

1.5

2.4

1.4

2.4

1.4

n.s.

























¹ Differences between scores on Q2 were tested with ANOVA, taking scores on Q1 as covariate, keepin p< .01 because of multiple comparisons

Again, there were no significant differences between the smoking and drinking behavior parents/brothers/sisters and best friends between the groups (table 6.4). Teachers smoking behaviour was again significantly higher in the control group. As the programme targeted the students themselves and not their surroundings no big differences were expected here. The slight differences between baseline and follow-up might be the result of more awareness on the topics among the students, making them watch their parents more closely.


Table 6.4 Observed smoking- and drinking behaviour of direct environments respondents on baseline (Q1) and follow-up (Q2) by studygroup (N=204)




E-Learning

(86≥n≤90)

Q1 Q2


Control

(113≥n≤114)

Q1 Q2


Total

(200≥n≤204)



Q1 Q2

Significance¹


Smoking













Father (n=202)

48.9% 47.2%

59.1% 54.0%

54.5% 51.0%

n.s.

Mother (n=202)

42.7% 48.3%

53.6 % 45.1%

48.8% 46.5%

n.s.

Brother and/or sister (n=200)

23.3% 24.4%

35.2% 36.0%

29.8% 31.0%

n.s.

Best friend (n=204)

30.6% 41.1%

48.6% 45.6%

40.6% 43.6%

n.s.

Teacher (n=200)

12.6% 18.4%

42.3% 46.9%

29.3% 34.5%

p=.00²
















Drinks Alcohol













Father (n=204)

77.8% 75.6%

70.5% 78.1%

73.8% 77.0%

n.s.

Mother (n=203)

67.4% 59.6%

54.0% 57.0%

59.9% 58.1%

n.s.

Brother and/or sister (n=202)

45.5% 44.9%

53.7% 54.0%

50.0% 50.0%

n.s.

Best friend (n=203)

39.8% 52.8%

54.5% 54.4%

48.0% 53.7%

n.s.

Teacher (n=203)

66.3% 65.2%

63.7% 62.3%

64.9% 63.5%

n.s.

¹ Differences between groups tested with Chi-Square, keepin p< .01 because of multiple comparisons

² Significant difference between groups, more smoking teachers in control group



6.3 Effects on behaviour

In comparison with the baseline results there are no great differences in smoking and drinking alcohol in the last 4 weeks (table 6.5). Working with “Prepared in time” did not change the behaviour of the students. We do see a significant difference between the experiment group and control group. More respondents in the control group smoked over the last 4 weeks in comparison to the experiment group. The difference might be caused by the program, with less students starting smoking in the experiment group, but might also be caused by a different reason. There is also a significant difference between the experimental group and control group on drinking alcohol in the last 4 weeks. More students in the control group have been drinking. Again it is hard to say if this effect can be claimed by the e-learningprogram only or if other influences caused this difference (table 6.5).


Table 6.5 Behaviour on smoking and drinking alcohol on follow up by studygroup (N=205)




E-Learning

Control

Total

Significance¹

Did you smoke since filling out baseline? (n=205)













Yes

26.7%

43.5%

36.1%

.001²

No

73.3%

56.5%

63.9%



















Did you smoke in the last 4 weeks? (n=204)













Yes

25.3%

27.4%

26.5%

n.s.

No

74.7%

72.6%

73.5%



















Did you drink alcohol since filling out baseline? (n=185)













Yes

43.8%

49.5%

47.0%

n.s.

No

56.2%

50.5%

53.0%



















Did you drink alcohol in last 4 weeks? (n=128)













Yes

54.2%

78.3%

67.2%

.016³

No

45.8%

21.7%

32.8%




¹ Differences testen with ANOVA

² Significantly more respondents in the controlgroup smoked since filling out Q1

³ Significantly more respondents in the controlgroup drank alcohol in the last 4 weeks.
When we look at the alcoholfrequency in the last 4 weeks and the amount of drinks there are no significant differences between the experiment- and controlgroep. However we do see an increase in the drinking compared to the baseline in both groups. The percentage of respondents drinking 1-3 times in the last 4 weeks nearly doubled compared to the baseline. The amount of drinks they drink on such an occasion however has not really increased. Most respondents still drinking 1 drink or less (table 6.6).

Table 6.6 Alcoholfrequency in last 4 weeks & amount of alcohol on occasion on baseline (Q1) and follow-up (Q2) for respondents who indicated drinking alcohol more then once on Q1 (N=128)




E-Learning

Q1 Q2


Control

Q1 Q2


Total

Q1 Q2


Significance¹

Alcoholfrequency in last 4 weeks (n=128)













0

64%

45.8%

55.8%

21.7%

59.4%

32.8%

n.s.

1-3

19.1%

32.2%

19.5%

40.6%

19.3%

36.7%




4-6

10.1%

11.9%

10.6%

20.3%

10.4%

16.4%




7-10

5.6%

8.5%

4.4%

4.3%

5.0%

6.2%




11 times or more

1.1%

1.7%

9.7%

13.0%

5.9%

7.8%



















How many drinks on occasion? (n=111)













Never had a drink

31.2%

29.0%

35.2%

36.5%

33.6%

33.1%




Less then 1

20.3%

-

10.2%

-

14.5%

-

n.s.

1

12.5%

30.4%

13.6%

23.5%

13.2%

26.6%




2-3

14.1%

17.4%

17.0%

17.6%

15.8%

17.5%




4-6

12.5%

14.5%

14.8%

11.8%

13.8%

13.0%




7-10

6.1%

7.2%

4.5%

9.4%

5.3%

8.4%




More then 10

3.1%

1.4%

4.5%

1.2%

3.9%

1.3%




¹ Differences between groups were tested with Unianova variance analyses

Working with “Prepared in time” does not seem to have had any effect on being allowed to smoke or drink at home (table 6.7). Neither did it prompt students to make agreements with their parents on smoking and drinking. There are no significant differences between the groups. There are no great differences between the baseline and follow-up either. As we already saw that most students did not tell their parents about the baseline questionnaire and hardly talk about smoking and alcohol with their parents and friends this is not surprising and lies in line with expectation.


Table 6.7 Results baseline (Q1) and follow-up (Q2) allowed to smoke/drink alcohol at home & non-smoking and non-drinking agreements by studygroup (N=205)




E-Learning (88≥n≤92)

Q1 Q2


Control (110≥n≤115)

Q1 Q2


Total (201≥n≤205)

Q1 Q2


Significance¹

Total 2006²

(n=703)


Are you allowed to smoke at home? (n=205)










n.s.




Yes, I already smoke at home

10.9% 12.2%

15.0% 12.7%

13.2% 12.5%




<1%

Yes, I’m allowed but I don’t smoke at home

15.2% 18.9%

17.7% 15.5%

16.6% 17.0%




13%

No, I’m not allowed to smoke at home

51.1% 37.8%

45.1% 42.7%

47.8% 40.5%




48%

I don’t know

22.8% 31.1%

22.1% 29.1%

22.4% 30.0%




39%



















Are you allowed to drink alcohol at home? (n=205)










n.s.




Yes, I drink alcohol at home

27.2% 23.3%

32.7% 38.9%

30.2% 32.0%




38%

Yes, I’m allowed but don’t drink alcohol at home

15.2% 22.2%

12.4% 11.5%

13.7% 16.3%




19%

No, I’m not allowed to drink alcohol at home

34.8% 22.2%

34.5% 17.7%

34.6% 19.7%




15%

I don’t know

22.8% 32.2%

20.4% 31.9%

21.0% 31.5%




29%



















Do you have an agreement with someone to not start smoking (until a certain age)? (n=203)










n.s.




Yes, with my parents

22.7% 23.9%

28.7% 24.1%

26.1% 24.0%




30%

Yes, at school

5.7% 3.4%

- 0.9%

2.5% 2.0%




<1%

Yes, with someone else

1.1% -

1.7% 3.6%

1.5% 2.0%




1%

No, but I’d like an agreement

4.5% 2.3%

9.6% 4.5%

7.4% 3.5%




18%

No, I don’t want an agreement

65.9% 70.5%

60.0% 67.0%

62.6% 68.5%




50%



















Do you have an agreement with someone to not start drinking alcohol (until a certain age)? (n=201)










n.s.




Yes, with my parents

29.5% 20.0%

23.9% 19.3%

26.4% 19.6%




19%

Yes, at school

- 1.1%

0.9% 0.9%

0.5% 1.0%




-

Yes, with someone else

1.1% 3.3%

- 2.8%

0.5% 3.0%




-

No, but I’d like an agreement

5.7% 3.3%

8.0% 7.3%

7.0% 5.5%




16%

No, I don’t want an agreement

63.6% 72.2%

67.3% 69.7%

65.7% 70.9%




65%

¹Differences between groups were tested with Unianova variance analyses

²Results from original study by Ter Huurne (2006); normal primaryschool, agegroup 9-13 years old



Chapter 7 - Discussion & Recommendations




7.1 Prevalence of cigarette smoking and alcohol drinking among adolescents with an intellectual disability

This study gives a first impression into the smoking and alcohol drinking behaviour of adolescents with a borderline or mild intellectual disability in the Netherlands. Looking at the lifetime prevalence of smoking we saw that nearly 50% of the respondents already had their first smoke. Considering the average age of the studygroup was 13½ years old, this percentage is scaringly high. With a monthly prevalence of 28% and daily use at nearly 20% this studygroup scores significantly higher then their peers in the general population who have a lifetime prevalence of 39%, a monthly prevalence of 19% and a daily use of 7% (Monschouwer et.al. 2007). We do see however that it is not just the adolescents in the studygroup that score high above average, the smoking behaviour of their parents is also concerning. Around 50% of fathers and mothers smoke, which is also high above the national average of 28% (Monschouwer et.al. 2007).


When we look at alcohol drinking behaviour we do not see significant differences with the general population (Monschouwer et.al. 2007; CBS, 2009). With a lifetime prevalence of 81% among the men and 67% among the females however, the alcohol drinking behaviour of these adolescents seems problematic. For most of these students the drinking happened more then once, with 15% even drinking before the age of 10. It is not just the prevalence that is problematic, also the amount most respondents drink is dangerously high with nearly 20% binge drinking on a drinking occasion, damaging their health and enlarging the risk of becoming an alcoholic later on in life (McGillicuddy, 2006). As the drinking behaviour of the general adolescent population is seen as problematic by the Dutch government it is safe to say it should also be seen as a big problem among the intellectually disabled adolescents. Even more so as the risks of problematic use among this group in the future are bigger (Kress et.al., 1993; McGillicuddy et.al. 1999; Beitchman et.al. 2001).
The national survey by Monschouwer et.al. (2007) was done in the agegroup 12-16 years old, which is slighlty older then our studygroup and was done within mainstream school levels. It is known that lower level education people smoke more and at an earlier age then higher level education people. (CBS, 2008) Since this study was done with special needs students only it is likely that the higher percentage of ever smoke can be acclaimed to this fact. If we look at the nationwide VMBO-B level, the lowest mainstream +education level and closest to special needs schools, we see that they score higher than the national average as well and are closer to the studygroup (Monschouwer et.al., 2007).

This study was a pilot study with a small N. All schools were located in the east of the Netherlands, comparable among each other but not necessarily comparable with other parts in the Netherlands. Measurements were done with self-answering questionnaires which means all is seen from the perspective of the respondents. This might influence the accuracy as they might have tried to make themselves look better by reducing their smoking/drinking behavior or the other way around, by thinking giving high answers makes them ‘cool’. All questionnaires however were anonymous, only identifiable by numbers, in order to try and downsize this effect.


7.2 Prepared in time


This study shows that e-learning is a useful method for adolescents with an ID. The participants were well capable of working with the computerprogram, even complaining about it being to slow. While working with the program some teachers felt that students were more willing to cooperate. They also noticed that it seemed that students were able to concentrate longer than in ‘normal’ classes and they seemed to show a longer attentionspan while working with the program. The students graded the “Prepared in time” with an average of 6 out of 10. They did not mind working with it but found that it sometimes was a little childish and slow. They also suffered from problems with the sound-system, preventing them from watching the movies and hearing the explenation given by Professor Proficatco. This problem definitely influences the students interest in the program as they afterwards indicated that they would have enjoyed the program more if the movies could have been watched properly.

Looking at the effectiveness we a significant increase in knowledge on alcohol was found, when comparing the experiment group with the control group. There was no significant increase in knowledge on smoking or in total knowledge. Also on the behavioural determinants attitude, intention, subjective norm, social support, peer pressure and self-efficicay no significant increases were found. However, attitude proved to be a big indicator in use of alcohol and tobacco. This is not in line with Theory of Planned Behaviour (TPB) by Ajzen (Conner & Norman, 2005), which says that all behaviour comes from intention and that intention is influenced by attitude, perceived behavioural control and subjective norm. According to the TPB someones attitude has no direct influence on a persons behaviour (Conner et.al., 2005). The results of this study show otherwise which might indicate that this theory does not fit with the ID population. However, as said before, this study only had a small number of participant. On larger scale these effects might turn out differently.

While evaluation the scores on the behavioural determintants of the baseline questionnaire and the follow-up questionnaire a strange phenomena was seen on alcohol drinking; students felt more peer pressure but also more social support while their feelings of self-efficacy went down. An explanation for this strange development could be the schoolcamps the students went on the week before answering the follow-up questionnaire. All participating schools went on schoolcamps which, in general, are moments in a adolescents life where they are exposed to substances like alcohol. They might have felt more pressure and also support from classmates to drink or not drink. As there was also an increase in drinking over the last 4 weeks on the follow-up questionnaire it is likely that students gave in and had a drink, experiencing lower self-efficacy. However this raises the question on the effectiveness of “Prepared in time” as a prevention intervention programme. As mentioned before, this development happened in both the experimental group and control group. It appears that the students in the experimental group went through the same process as the other students, not feeling supported or more adequate to deal with this kind of high pressure situations. As the goals of “Prepared in time” include raising self-efficacy, changing attitudes and lowering intention to start smoking and drinking alcohol is seems to have failed on this occasion.

Influencing the outcome in this study was the fact that the sound system on the computers in the largest experimental school did not work properly. Because of this the movies in the program could not be watched properly. To keep the students focussed the decision was made to only do half of the smoking part and to do the full alcohol part. However, this problem does not seem to explain the problems in achieving the set goals of “Prepared in time”. The largest differences on lower self-efficacy and higher peer-pressure where seen on drinking alcohol. The students all finished the alcohol part in “Prepared in time” and only did half of the smoking part. Not being able to do the full smoking part probably does explain the lack of significant results on the knowledge on smoking, as we did see a significant increase in knowledge on alcohol within the experimentgroup.

All together though not many results were found on effectiveness, which raises the question whether it is a better way of teaching in comparison to ‘normal’ classroom/guestteacher programmes. It is known that repetition is very important in the education of ID people (Annand et.al. 1998). For this study they worked with “Prepared in time” only once and there was no follow-up in the classroom or at home.

As this study was done towards the end of the school year the pressure to get all data before the summer holidays was on. This meant there was exactly 6 weeks between baseline and follow-up questionnaire. Quite a few students did not feel like answering a questionnaire again, finding them boring and too long. This might have influenced the outcome of all parts, also explaining why the scores on knowledge went down. It also caused more missing values as students skipped questions and sometimes (unintentionally) full pages as a result of lack of interest and attention and a urge to finish quickly.

What this study does show however is that the use of alcohol and tobacco among ID adolescents is comparable to (alcohol) or higher then (smoking) their peers in the general population. As there were no numbers available on this subject beforehand there were no real expectations but the results were seen as shocking. There is a definite need of proper, well developed prevention intervention programmes for this targetgroup. E-learning proofed to be a well workable program for ID students and is seen as a good component in a larger scale prevention intervention programme.

7.3 Topics for future research


The first recommendation for future research is a larger scale epidemiological study into the use of alcohol, tobacco and drugs among intellectually disabled adolescents. If the results of this study are a reliable indicator of use among this group in all parts of the Netherlands, the substance use and misuse problems are bigger and more worrying then most people expect.

This study also showed that the parents of the students in special needs schools smoke more then would be expected based on national averages. This is in line with research done by Fidler, Mitchell, Raab & Charleston (1992) who found similar results. Their research also showed a relationship between the smoking behaviour of parents en the smoking behaviour of their children (Fidler et.al., 1992). However there is not much research in this topic among the intellectual disabled population and it is unknown if ID adolescents are more or less influences by parents, familymembers and friends.

To protect the ID adolescents from healthdamage and future addiction problems a prevention intervention programme should be developed and tested in the hope of not only increasing their knowledge but also change intention and, more importantly, the attitudes on alcohol and tobacco. As this study shows e-learning could be a good component in this programme. Also skills training and role play are seen as a good addition as these are parts in treatment programmes that work for ID adults (Kelman et.al. 1997; Degenhardt, 2000; McGillicuddy, 2006). It is known that most students in special needs schools come from a low social-economical status background. A lot of parents did not have much education themselves and are not fully aware of the dangers of smoking and alcohol and the influence their behaviour has on their children. Developing a programme to teach them about these subjects and help them support their children might help decrease the use of tobacco and alcohol among both parents and their children.

As some teachers felt that students were more willing to cooperate, seemed to concentrate longer than in ‘normal’ classes and seemed to show a longer attentionspan it might be interesting to do a study into the concentration levels and intake of knowledge with e-learning in comparison to normal teaching and guest-teacher classes. If there observations were correct it might provide a great way of supporting the teaching system in special needs schools.


Acknowledgements

After working on this study for nearly a year there are a few people I would like to thank for making this research project possible. First of all the 5 schools and their students that participated in this study: OSG Erasmus Almelo, Bonhoeffer Praktijkonderwijs Enschede, De Maat Ommen, Praktijkonderwijs Zutphen and Thomas a Kempiscollege Zwolle. Without their enthousiasm and willingness to cooperate none of this would have been possible. Thanks again for all your time and efforts!


Secondly I would like to thank my supervisors at the University of Twente, Stans and Marcel. They had some doubts at the start of this project about the achievability but have always been supportive and helpfull. Your help on the statistical analysis was very needed and welcome and the general feedback always usefull.
I would also like to thank Mieke Platenkamp at Tactus, for her help during this last year and making it possible to use the wonderful ‘Prepared in time’ program and to Marike van Dijk for helping me out on the SPSS department Another great help at Tactus has been Joanneke van der Nagel who’s feedback has been of great value and who’s enthousiasm and knowledge inspired me to try and better myself. Together with Marion Kiewik, she gave me the oppurtunity to present at a European Congress which was amazing and a lot of fun. Thank you for making this possible!
Another thank you goes to the collegues at departement Zorgondersteuning en Behandeling at AveleijnSDT who, throughout the year, have always been interested in and supportive of this project and helpful during my placement. I would also like to thank my family for their support, for graciously giving up computertime and for helping with all the small bits and bobs that needed to be done throughout the year.
And last but certainly not least I would like to thank Marion Kiewik. From the moment I asked ‘Do you think the topics e-learning and intellectual disabillity are interesting for my final research project?’ she has been nothing but enthousiatic and supportive. Her belief in me and this project made working on it a lot easier and definitly helped in times when things were not running as smoothly as planned. All the conversations and feedback kept me sharp and helped me to write this paper. Thank you for all your help!
Louise

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Appendix


Appendix 1 List of Tables

Appendix 2 Baseline questionnaire

Appendix 3 Proces-evaluation questionnaire

Appendix 4 Follow-up questionniare experiment group

Appendix 5 Follow-up questionnaire control group

Appendix 1 – List of Tables




Table 3.1: Participating schools, number of students, gender & class..........................................................14

Table 3.2: Topics in questionnaires...............................................................................................................16

Table 4.1: Participating school, classes and respons.....................................................................................23

Table 4.2: Demographic variables gender, age, origin and living situation of respondents by studygroup..24

Table 4.3: Smoking status, smoking bevahiour in last 4 weeks & smoking behaviour daily by gender & studygroup....................................................................................................................................25

Table 4.4: Age of first cigarette of respondents by study group..................................................................25

Table 4.5: Alcohol status by gender & studygroup and & age first drink.....................................................26

Table 4.6: Alcohol frequencies in life by studygroup and gender................................................................27

Table 4.7: Alcohol frequencies in last 4 weeks, amount of alcohol on occasion and drinking places of respondents who drank alcohol more then once by study group................................................28

Table 4.8: Frequency of smoking and drinking alcohol by persons in environment of respondent by study

group............................................................................................................................................29



Table 4.9: Correct answered knowledge questions on avarage by theme and studygroup.........................30

Table 4.10: Behavioural determinants on smoking and alcohol by study group............................................32

Table 4.11: Correlation Smoking behaviour, knowledge & behavioural determinants at baseline................33

Table 4.12: Correlation Alcohol behaviour, knowledge & behavioural determinants at baseline..................33

Table 4.13: Multiple regression analyses on smoking status, smoking last 4 weeks, intention to start

smoking, alcohol status, drinking in last 4 weeks and intention to start drinking.......................34



Table 5.1: Appreciation and average grades of e-learning program............................................................36

Table 5.2: Appreciation different items on e-learning program...................................................................36

Table 5.3: Appreciation of Professor Profitacto...........................................................................................37

Table 5.4: What respondents learned about smoking and alcohol...............................................................37

Table 5.5: Improvement tips for programme according to respondents......................................................38

Table 6.1: Indirect behavioural effects of intervention by studygroup.........................................................40

Table 6.2: Knowledge scores on smoking and alcohol on baseline (Q1) andfollow-up (Q2) by studygroup 41

Table 6.3: Behaviourdeterminants from ASE-Model on smoking and alcohol on baseline (Q1) and

follow-up (Q2) by studygroup.......................................................................................................41



Table 6.4: Observed smoking- and drinking behaviour of direct environments respondents on baseline

(Q1) and follow-up (Q2) by studygroup........................................................................................42



Table 6.5: Behaviour on smoking and drinking alcohol on follow up by studygroup....................................43

Table 6.6: Alcoholfrequency in last 4 weeks & amount of alcohol on occasion on baseline (Q1) and follow-up (Q2) for respondents who indicated drinking alcohol more then once on Q1........................43

Table 6.7: Results baseline (Q1) and follow-up (Q2) allowed to smoke/drink alcohol at home & non-smoking and non-drinking agreements by studygroup................................................................44

Appendix 2 – Baseline Questionnaire




Appendix 3 – Proces-Evaluation Questionnaire




Appendix 4 - Follow-up Questionniare Experiment Group




Appendix 5 - Follow-up Questionnaire Control Group


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