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A School-Based Comparison of Positive Search Training to Enhance Adaptive Attention Regulation with a Cognitive-Behavioural Intervention for Reducing Anxiety Symptoms in Children

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Abstract

Many children experience anxiety but have limited access to empirically-supported interventions. School-based interventions using brief, computer-assisted training provide a viable way of reaching children. Recent evidence suggests that computer-delivered ‘positive search training’ (PST) reduces anxiety in children. This multi-informant, randomised controlled trial compared classroom-based, computer-delivered PST (N = 116) to a classroom-based, therapist-delivered cognitive-behavioural intervention (CBI) (N = 127) and a curriculum-as-usual control condition (CAU) (N = 60) in 7–11 year old children. Primary outcomes were child and parent report of child anxiety symptoms. Secondary outcomes were child and parent report of child depressive symptoms and child attention biases. Outcomes were assessed before and after the interventions, and six- and 12-months post-intervention. Teacher report of children’s social-emotional functioning was assessed at pre- and post-intervention. As expected, compared to CAU, children receiving PST and the CBI reported greater anxiety reductions by post-intervention and six-month follow-up but, unexpectedly, not at 12-month follow-up. Partially consistent with hypotheses, compared to CAU, parents reported greater anxiety reductions in children receiving PST, but not the CBI, at 12-month follow-up. Contrary to expectation, there was a pre- to post-intervention increase in threat attention bias in PST compared to the other conditions, with no significant differences at follow-up. In support of hypotheses, teachers reported higher post-intervention social-emotional functioning in Year 5 students receiving the CBI but, unexpectedly, lower post-intervention functioning in students receiving PST. There were no effects on depressive symptoms. Further research is needed on strategies to maintain long-term benefits and determine preventative versus early intervention effects.

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Notes

  1. CONSORT diagram depicts participant numbers based on the primary outcome measure of SCAS_C.

  2. MAR requires that the probability of being missing, considering only the case of missing values in the dependent variable (DV), is not related to the underlying missing value of the DV. Since testing directly whether the data is MAR is not possible since the missing values are obviously unobserved, some support from the MAR for this study can be gained if the proportion of missing values does not vary significantly across Intervention factor by Time factor combinations. This was tested using a binomial/logit generalized linear model (GLM) for proportion of missing values using the test of independence for a three-way (i.e. “present vs missing” x Intervention x Time) contingency table.

  3. For the four time-points, this base LMM assumes spherical covariance between TIME with 3 variance parameters estimated by Residual Maximum Likelihood (REML) (i.e. the “REML” option of lme) of Class, Participant, and Residual variance parameters. In addition, two additional LMMs with the above fixed effects but with more general error models were fitted. First, the most general error model, the full unstructured between-TIME covariance matrix, was fitted in addition to the Class random effect and residual variances and gives an additional 5 covariance parameters to be estimated compared to the base LMM. Second, a continuous-time autoregressive (CAR) error term (Diggle et al. 2002; Pinheiro and Bates 2004) was used to replace the 6 parameters in the general repeated measures variance structure (i.e. “unstructured” in lme) with a single CAR parameter (phi). The REML log-likelihood was used for likelihood-ratio tests (REMLRT) of the improvement in fit for each of these more general error model terms compared to the base model. Visual inspection of plots of standardised residuals for the base model against values of the dependent variable were used to determine if the assumption of homogeneity of variance was reasonable and whether a transformation such as square root or log transform was required to stabilise the residual variance. Also, standardised residuals greater than 4 or less than −4 were considered for exclusion as outliers.

  4. Supplementary analyses were performed to determine whether PST and the CBI had a preventative effect (i.e., prevented increasing symptoms) or an early intervention effect (i.e., prevented growth of symptoms in those experiencing symptoms). This included re-fitting the LMM to the post-intervention, 6-month, and 12-month SCAS-C data using differences in the outcome variable of post- minus pre-intervention as was carried out for the DV_Pre covariate analysis (see below in main text) except that this covariate was dropped as a predictor variable. The resultant random participant effect estimates were modelled for trend with DV_Pre using cubic smoothing splines. Further, a “growth curve” approach was fitted to the above data with random participant-level intercept and slope LMM using Time_i as a continuous variable combined with treatment main effect and Time_i by treatment interaction as additional terms. Trends in random effect slope estimates with DV_Pre were also modelled using cubic smoothing splines. Using these analyses, an “intervention” hypothesis was tested by the detection of a significant and decreasing trend with DV_Pre for higher levels of this variable (i.e., “intervention”), however, rejection of the null hypothesis did not obtain an adequate level of statistical support. The “prevention” hypothesis is more difficult to test since prevention is indicated by a lack of a significant positive trend with DV_Pre for low values of this variable, and as a result, acceptance of this hypothesis could be due to low power to detect a positive trend.

  5. Since DV_Pre is continuous, to test if its effect was linear or not, the model was fitted as a Generalized Additive Mixed Model (GAMM) (Wood 2006) with cubic smoothing splines in DV_Pre fitted for each level of the INT factor. The mgcv R-package (Wood 2004, 2006, 2011) function gamm was used with the same error structure as the base LMM used for the non-differenced DV. If any of the three smoothing splines (i.e one for each level of INT) were significantly different from a no-trend model component and in addition, the spline was close to linear, the GAMM was replaced with the LMM described above. To compare the results of this model directly with the Default contrasts in the LMM for the non-differenced DV, the estimated mean from the fitted LMM for each time point and INT factor level was constructed with adjustment for the overall average value of DV_Pre.

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Acknowledgements

This project was supported by funding awarded to the authors by Australian Rotary Health.

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Authors and Affiliations

  1. School of Applied Psychology, Griffith University, Queensland, 4122, Australia

    Allison M. Waters, Melanie J. Zimmer-Gembeck & Trisha A. Groth

  2. Scandy Statistical Modelling Pty Ltd, Blackmans Bay, Australia

    Steven G. Candy

  3. Department of Psychology, University of California, Los Angeles, CA, USA

    Michelle G. Craske

  4. Psychology, University of Southampton, Southampton, UK

    Brendan P. Bradley & Karin Mogg

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  1. Allison M. Waters
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Correspondence to Allison M. Waters.

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Waters, A.M., Candy, S.G., Zimmer-Gembeck, M.J. et al. A School-Based Comparison of Positive Search Training to Enhance Adaptive Attention Regulation with a Cognitive-Behavioural Intervention for Reducing Anxiety Symptoms in Children. J Abnorm Child Psychol 47, 1821–1840 (2019). https://doi.org/10.1007/s10802-019-00551-4

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