Procedure for administering the assessment tools

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Part II : Interpersonal Coordination

Chapter 4: Is nonverbal interpersonal coordination reduced in social interactions involving

4.5 Conclusion

5.2.6 Procedure for administering the assessment tools

On completion of the interaction task, each participant was asked to complete the Manchester short assessment of quality of life (MANSA), the objective social outcomes Index (SIX), the Standard Progressive Matrices Raven spatial test of IQ (SPMR) and the

Mill Hill Vocabulary scale of IQ (MHV). Participants completed these assessments as they sat separately at three desks in the interaction space. In order to complete the social cognitive tests, a projector was set up in the room and the videos for the SCRT and PONS were played for the individuals as they were seated at their desks. Although all three participants watched the short video clips at the same time, they were asked not to speak to each other during this task and completed their assessments using a pen and paper individually.

All participants took part in a short one-to-one interview with the researcher, during which, they completed the tests of executive functioning; Brixton Spatial Anticipation Task, (BAST) and the Hayling Sentence Completion Task (HSCT). Participants also completed the three assessments of theory of mind (the hinting task, the Ice-Cream Van (ICV) task and the Sally Anne (SA) task) and the rapport questionnaire.

5.2.7 Data Analysis

5.2.7.1 Preparation of interpersonal coordination data

The coordination data (r and delay) corresponds to a ‘pair’ of individuals. Therefore, in order to compare coordinated movement with the assessment scores on an individual basis the coordination when following (i.e. producing the correlated movement second) was derived for each individual. The coordination when following was used as this provides a measure of an individual’s coordination when responding to the head movement of another, thus allowing associations to be made between an individual’s responsiveness to others with their own assessment performance. For the remainder of this chapter, an individual’s coordination refers to their mean r and delay when they are following the movements of their interacting partners. Healthy participants in the patient groups interact with one patient and one healthy participant, therefore for these participants two measures of coordination was derived; coordination when following the patient (i.e. following in the P-HPp pair) and coordination when following the other healthy participant (i.e. following in the HPp-HPp pair).

5.2.7.2 Dealing with missing assessment data

Some participants did not complete all of the assessments. This was for a number of reasons; firstly, assessments of rapport, anxiety (BAI) and IQ (SPMR and MHV) were not conducted for the first ten groups recruited. These assessments were only introduced in the final thirty groups. Secondly, a number of healthy participants who participated in the interaction tasks did not complete the full set of questionnaires due to limitations on their time. Importantly all patients did complete the full set of questionnaires.

Missing values were imputed using a multiple imputation (MI) method conducted in SPSS 19. Traditional ad-hoc deletion or replacement methods were avoided as they may cause lowered sample sizes or biased results due to deletion of missing values or artificially reduce the variance of the variable due to data replacement using mean values (Wayman, 2003). The MI method has been shown to perform better than such ad-hoc measures (Schafer & Graham, 2002). It has been shown to produce unbiased parameter estimates which reflect the uncertainty associated with estimating missing data and is capable of handling variables that deviate from normality or have a high rate of missing data (Wayman, 2003). In this study the MI method used a fully conditional specification model, using 5 imputations as recommended by SPSS. The missing values were modeled using the relationship between the existing variables, thus preserving the characteristics of the dataset including means, variances, correlations and data parameters whilst retaining the sample size (McCleary, 2002). The MI method provided a full data set that was then analysed in the usual manner using the SPSS 19 software. The results reported below correspond with those recommended by Sterne et al. (2009) and are inline with the STROBE initiative to strengthen observational studies (von Elm et al., 2007). The percentages and frequencies of missing data can be found in Appendix C.

Although the mean rapport score for each individual was imputed using MI, the rapport score that individuals gave to their partner was not. Rapport score is unlike other measures in the study, in that it is relates to two individuals; the rater and the rated. The MI method cannot calculate scores between cases, therefore the decision was taken to be

conservative with this data. All statistical analysis assessing healthy participants’ rapport score to the patient will only included cases that are complete in the original data set.

5.2.8 Statistical Analysis

All statistical analysis were conducted in SPSS with p-values of p<.05 considered significant, and p-values between p=.05 and p<.1 noted as a trend

5.2.8.1 Comparison of patient and healthy participants’ anxiety, cognitive and social features

An independent samples t-test compared patients and healthy participants’ scores on measures of anxiety (BAI), executive functioning (BSAT & HSCT), IQ (SPMR and MHV), social cognition (SCRT, PONS, Hinting task, 1st and 2nd Order ToM Tasks), quality of life (MANSA), and social functioning (SIX) and rapport.

5.2.8.2 Investigating the impact of antipsychotic medication on patients’ coordination.

In order to unpick the impact of antipsychotic medication on patients’ coordination, the mean r and delay displayed by patients on each medication type (i.e. (i) medication free, (ii) taking newer atypical antipsychotic mediation, (iii) taking older typical antipsychotic mediation) was compared with two separate generalized linear analysis of variance models (GLM) using a Bonferroni pair-wise comparison.

In each model the dependent variable was the coordination (r or delay) and the independent variable was medication type (medication free or atypical antipsychotic medication or typical antipsychotic medication). Patients’ symptoms (PANSS positive, negative and general), medication dose (CPZE), age, gender and the length of the interaction (seconds) were adjusted for by including them as covariates in the analysis. The model analyzing r used a linear response model and the delay model used a gamma response model, to account for the different data distributions.

5.2.8.3 Association between patients’ coordination and clinical, cognitive and social features

Bivariate correlations assessed the relationship between patients’ coordination (r and delay) and their scores on PANSS symptoms (PANSS positive, PANSS negative, PANSS general), medication dose (CPZE), symptoms of anxiety (BAI), executive functioning (Brixton Spatial Anticipation Test, Hayling Sentence Completion Task), IQ (Standard Progressive Matrices of Ravens and Mill Hill Vocabulary test), social cognitive assessments (Social Cue Recognition Test, Profile of Nonverbal Sensitivity test, the hinting task, and the 1st and 2nd order Theory of Mind tests), quality of life (MANSA), social functioning (SIX), social networks (SNS) and their rapport score.

5.2.8.4 Association between patients’ symptoms, rapport and their partners’ coordination

Bivariate correlations assessed the relationship between healthy participants’ coordination (r and delay), both in the patient pair (P-HPp) and in the healthy participant pair (HPp-HPp), and patients’ symptoms (PANSS positive, PANSS negative, PANSS general) and the rapport score they give to the patient .

A multiple linear regression model was used to further analyse significant correlations (p<.1) across all associations.

5.3 Results

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