Guided self-help interventions for mental health disorders in children with neurological conditions: study protocol for a pilot randomised controlled trial

Bennett, Sophie; Heyman, Isobel; Coughtrey, Anna; Simmonds, Jess; Varadkar, Sophia; Stephenson, Terence; DeJong, Margaret; Shafran, Roz

doi:10.1186/s13063-016-1663-z

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…The published protocol for this pilot study is available 17 . The study was registered with the Current Controlled Trials register (ISRCTN21184717).…”

Section: Methodsmentioning

confidence: 99%

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Telephone-guided self-help for mental health difficulties in neurological conditions: a randomised pilot trial

et al. 2021

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ObjectiveThis study aimed to conduct a randomised pilot trial to assess the feasibility of a randomised controlled trial (RCT) to investigate the effect of telephone-guided self-help for the treatment of mental health difficulties in children with neurological conditions.DesignPreliminary RCT. The primary outcome measure was the Strengths and Difficulties Questionnaire.SettingNeurology clinics in a national tertiary paediatric hospital.PatientsYoung people attending neurology clinics who met criteria for mental health difficulties according to the Development and Wellbeing Assessment.Interventions12 weeks of telephone-guided self-help based on a modular approach to psychological therapy for children delivered to children and/or their parents (n=17; eight males; mean age 12.04 years, SD=3.34) or a waiting list for telephone-guided self-help with no additional intervention over 12 weeks (n=17; nine males; mean age 10.53 years, SD=3.14).Results124 participants completed the DAWBA, and 34 children and young people were entered into the trial. 65% of those randomised to the intervention arm completed the full intervention, and the intervention was acceptable to those completing it. However, there were significant problems related to lack of data completion (38% data loss for primary outcome measure), choice of control comparator and outcome measures. Due to significant loss of data at follow-up, the effect size findings are considered unreliable.ConclusionsFurther feasibility work should be conducted to improve data completeness before progression to a definitive trial of guided self-help for mental health problems in children with neurological conditions can be recommended.Trial registration numberISRCTN21184717.

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“…The published protocol for this pilot study is available 17 . The study was registered with the Current Controlled Trials register (ISRCTN21184717).…”

Section: Methodsmentioning

confidence: 99%

“…The associated identification study was funded for 12 months and we aimed to recruit at least 18 participants based on estimated screening rates and retention rates in this time period 25 .…”

Section: Methodsmentioning

confidence: 99%

Telephone-guided self-help for mental health difficulties in neurological conditions: a randomised pilot trial

et al. 2021

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“…Certain web-based alternatives including guided self-help, chatbots, and other web-based interventions can be used to increase affordable access to psychological therapies (either as an accompaniment or as stand-alone interventions). There is a lack of larger studies and clinical evaluation for evidence-based guided self-help interventions, although positive findings in reducing psychological symptoms in children [39] were expanded upon in the context of neurological illness [40]. The rarity and dissimilarity of evidence-based studies with chatbots limits discussions on their effectiveness.…”

Section: Other Emerging Aspects Of Digital Mental Healthmentioning

confidence: 99%

Digital Mental Health Challenges and the Horizon Ahead for Solutions

Balcombe¹,

Leo²

2021

JMIR Ment Health

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The demand outstripping supply of mental health resources during the COVID-19 pandemic presents opportunities for digital technology tools to fill this new gap and, in the process, demonstrate capabilities to increase their effectiveness and efficiency. However, technology-enabled services have faced challenges in being sustainably implemented despite showing promising outcomes in efficacy trials since the early 2000s. The ongoing failure of these implementations has been addressed in reconceptualized models and frameworks, along with various efforts to branch out among disparate developers and clinical researchers to provide them with a key for furthering evaluative research. However, the limitations of traditional research methods in dealing with the complexities of mental health care warrant a diversified approach. The crux of the challenges of digital mental health implementation is the efficacy and evaluation of existing studies. Web-based interventions are increasingly used during the pandemic, allowing for affordable access to psychological therapies. However, a lagging infrastructure and skill base has limited the application of digital solutions in mental health care. Methodologies need to be converged owing to the rapid development of digital technologies that have outpaced the evaluation of rigorous digital mental health interventions and strategies to prevent mental illness. The functions and implications of human-computer interaction require a better understanding to overcome engagement barriers, especially with predictive technologies. Explainable artificial intelligence is being incorporated into digital mental health implementation to obtain positive and responsible outcomes. Investment in digital platforms and associated apps for real-time screening, tracking, and treatment offer the promise of cost-effectiveness in vulnerable populations. Although machine learning has been limited by study conduct and reporting methods, the increasing use of unstructured data has strengthened its potential. Early evidence suggests that the advantages outweigh the disadvantages of incrementing such technology. The limitations of an evidence-based approach require better integration of decision support tools to guide policymakers with digital mental health implementation. There is a complex range of issues with effectiveness, equity, access, and ethics (eg, privacy, confidentiality, fairness, transparency, reproducibility, and accountability), which warrant resolution. Evidence-informed policies, development of eminent digital products and services, and skills to use and maintain these solutions are required. Studies need to focus on developing digital platforms with explainable artificial intelligence–based apps to enhance resilience and guide the treatment decisions of mental health practitioners. Investments in digital mental health should ensure their safety and workability. End users should encourage the use of innovative methods to encourage developers to effectively evaluate their products and services and to render them a worthwhile investment. Technology-enabled services in a hybrid model of care are most likely to be effective (eg, specialists using these services among vulnerable, at-risk populations but not severe cases of mental ill health).

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