Use of GFCF Diets in Children with ASD. An Investigation into Parents’ Beliefs Using the Theory of Planned Behaviour

Marsden, Rachel; Francis, John; Garner, Iain

doi:10.1007/s10803-019-04035-8

Cited by 6 publications

(5 citation statements)

References 57 publications

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“…ese mental nonadherence phenotypes highlighted explanatory factors such as health literacy, education, socioeconomic status, cognitive abilities, and reasoning patterns of nonadhering patients [8][9][10][11]. By contrast, researchers studying medication adherence presented evidence which demonstrates the impact of a variety of factors found to be positively associated with medication adherence: health locus of control (belief that health is in one's own control), health literacy, language, cultural backgrounds, and so on [12][13][14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers

Xie

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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Background. Medication nonadherence represents a major burden on national health systems. According to the World Health Organization, increasing medication adherence may have a greater impact on public health than any improvement in specific medical treatments. More research is needed to better predict populations at risk of medication nonadherence. Objective. To develop clinically informative, easy-to-interpret machine learning classifiers to predict people with psychiatric disorders at risk of medication nonadherence based on the syntactic and structural features of written posts on health forums. Methods. All data were collected from posts between 2016 and 2021 on mental health forum, administered by Together 4 Change, a long-running not-for-profit organisation based in Oxford, UK. The original social media data were annotated using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) system. Through applying multiple feature optimisation techniques, we developed a best-performing model using relevance vector machine (RVM) for the probabilistic prediction of medication nonadherence among online mental health forum discussants. Results. The best-performing RVM model reached a mean AUC of 0.762, accuracy of 0.763, sensitivity of 0.779, and specificity of 0.742 on the testing dataset. It outperformed competing classifiers with more complex feature sets with statistically significant improvement in sensitivity and specificity, after adjusting the alpha levels with Benjamini–Hochberg correction procedure. Discussion. We used the forest plot of multiple logistic regression to explore the association between written post features in the best-performing RVM model and the binary outcome of medication adherence among online post contributors with psychiatric disorders. We found that increased quantities of 3 syntactic complexity features were negatively associated with psychiatric medication adherence: “dobj_stdev” (standard deviation of dependents per direct object of nonpronouns) (OR, 1.486, 95% CI, 1.202–1.838, P < 0.001 ), “cl_av_deps” (dependents per clause) (OR, 1.597, 95% CI, 1.202–2.122, P , 0.001), and “VP_T” (verb phrases per T-unit) (OR, 2.23, 95% CI, 1.211–4.104, P , 0.010). Finally, we illustrated the clinical use of the classifier with Bayes’ monograph which gives the posterior odds and their 95% CI of positive (nonadherence) versus negative (adherence) cases as predicted by the best-performing classifier. The odds ratio of the posterior probability of positive cases was 3.9, which means that around 10 in every 13 psychiatric patients with a positive result as predicted by our model were following their medication regime. The odds ratio of the posterior probability of true negative cases was 0.4, meaning that around 10 in every 14 psychiatric patients with a negative test result after screening by our classifier were not adhering to their medications. Conclusion. Psychiatric medication nonadherence is a large and increasing burden on national health systems. Using Bayesian machine learning techniques and publicly accessible online health forum data, our study illustrates the viability of developing cost-effective, informative decision aids to support the monitoring and prediction of patients at risk of medication nonadherence.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers

Xie

Zhao

et al. 2022

Computational Intelligence and Neuroscience

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“…This literature predominantly explores perceptions of equine interventions (Boyd & Le Roux, 2017 ; Malcolm et al, 2018 ; Tan & Simmonds, 2018 ), therefore the present study adds an underrepresented perspective on including dogs. Furthermore, this finding is valuable given the influence of parental perceptions on treatment efficacy (Andrews et al, 2010 ; Marsden et al, 2019 ).…”

Section: Discussionmentioning

confidence: 96%

“…A relationship between parental perceptions and treatment efficacy has been found in paediatric healthcare, where factors including attitude, perceived behaviour control, and perceived norm predicted behaviours related to both obesity monitoring (Andrews et al, 2010 ) and gluten-free diet (Marsden et al, 2019 ). In both cases, a positive parental attitude was found to significantly contribute towards behaviour, namely actively undertaking the health behaviour being studied.…”

Section: Introductionmentioning

confidence: 99%

Dogs at school: a quantitative analysis of parental perceptions of canine-assisted activities in schools mediated by child anxiety score and use case

Fynn

Runacres

2022

ICEP

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Canine-assisted activities in schools can benefit students’ educational, emotional, and social needs. Furthermore, they could be an effective form of non-clinical mental health treatment for children and adolescents. In the United Kingdom, school dogs are growing in popularity, however, little is known about how parents perceive canine-assisted activities as a treatment option. This is important as parental perceptions can influence engagement, whilst lack of awareness can become a barrier to treatment. This study uses a cross-sectional design to quantitatively explore the acceptability of canine-assisted activities amongst UK-based parents (n = 318) of children aged six to 16 (M = 10.12, SD = 3.22). An online survey used a treatment evaluation to determine acceptability across three use-cases. These included a child reading to dogs to improve literacy skills, a child interacting one-to-one to foster greater self-esteem and social skills, and a classroom dog to improve student behaviour and motivation. Additionally, the scale for generalised anxiety disorder was used to rank child anxiety as high or low, where high was a score equal to or above the UK clinical borderline threshold. The results found canine-assisted activities were less acceptable for the behavioural than the reading and social use-cases. Furthermore, parents of children with high anxiety had higher acceptability scores than parents of children with low anxiety for the reading and social use-cases but not for the behavioural use case. These findings suggest that UK parents' acceptability of canine-assisted activities in schools is mediated by child anxiety score. Furthermore, that parents may be less aware of the benefits of classroom dogs than other types of school-based canine-assisted activities.

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“…Some research has also focused more on the motivation in parents of children with ASD to adopt a GFD or GCFD. Marsden et al [ 103 ] noted that parents who adopted these diets for their children with ASD were most influenced by “anticipated regret, positive outcomes and attitude”. Perceived control was also relevant as a factor (with more predicting use of the diet).…”

Section: Adoption Of the Gfd And Gcfd In Asdmentioning

confidence: 99%

Gluten and Autism Spectrum Disorder

2021

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An expanding body of literature is examining connections between Autism Spectrum Disorder (ASD) and dietary interventions. While a number of specialist diets have been suggested as beneficial in ASD, gluten has received particularly close attention as a potentially exacerbating factor. Reports exist suggesting a beneficial effect of the gluten-free diet (GFD) in ameliorating behavioural and intellectual problems associated with ASD, while epidemiological research has also shown a comorbidity between ASD and coeliac disease. However, both caregivers and clinicians have expressed an uncertainty of the value of people with ASD going gluten-free, and as the GFD otherwise receives considerable public attention a discussion which focuses specifically on the interaction between ASD and gluten is warranted. In this review we discuss the historical context of ASD and gluten-related studies, and expand this to include an overview of epidemiological links, hypotheses of shared pathological mechanisms, and ultimately the evidence around the use and adoption of the GFD in people with ASD.

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Use of GFCF Diets in Children with ASD. An Investigation into Parents’ Beliefs Using the Theory of Planned Behaviour

Cited by 6 publications

References 57 publications

Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers

Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers

Dogs at school: a quantitative analysis of parental perceptions of canine-assisted activities in schools mediated by child anxiety score and use case

Gluten and Autism Spectrum Disorder

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