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Annual Research Review: Digital health interventions for children and young people with mental health problems – a systematic and meta-review / Chris HOLLIS in Journal of Child Psychology and Psychiatry, 58-4 (April 2017)
[article]
Titre : Annual Research Review: Digital health interventions for children and young people with mental health problems – a systematic and meta-review Type de document : Texte imprimé et/ou numérique Auteurs : Chris HOLLIS, Auteur ; Caroline J. FALCONER, Auteur ; Jennifer L. MARTIN, Auteur ; Craig WHITTINGTON, Auteur ; Sarah STOCKTON, Auteur ; Cris GLAZEBROOK, Auteur ; E. Bethan DAVIES, Auteur Article en page(s) : p.474-503 Langues : Anglais (eng) Mots-clés : Digital health mental health eHealth methodology randomised controlled trials prevention Index. décimale : PER Périodiques Résumé : Background Digital health interventions (DHIs), including computer-assisted therapy, smartphone apps and wearable technologies, are heralded as having enormous potential to improve uptake and accessibility, efficiency, clinical effectiveness and personalisation of mental health interventions. It is generally assumed that DHIs will be preferred by children and young people (CYP) given their ubiquitous digital activity. However, it remains uncertain whether: DHIs for CYP are clinically and cost-effective, CYP prefer DHIs to traditional services, DHIs widen access and how they should be evaluated and adopted by mental health services. This review evaluates the evidence-base for DHIs and considers the key research questions and approaches to evaluation and implementation. Methods We conducted a meta-review of scoping, narrative, systematic or meta-analytical reviews investigating the effectiveness of DHIs for mental health problems in CYP. We also updated a systematic review of randomised controlled trials (RCTs) of DHIs for CYP published in the last 3 years. Results Twenty-one reviews were included in the meta-review. The findings provide some support for the clinical benefit of DHIs, particularly computerised cognitive behavioural therapy (cCBT), for depression and anxiety in adolescents and young adults. The systematic review identified 30 new RCTs evaluating DHIs for attention deficit/hyperactivity disorder (ADHD), autism, anxiety, depression, psychosis, eating disorders and PTSD. The benefits of DHIs in managing ADHD, autism, psychosis and eating disorders are uncertain, and evidence is lacking regarding the cost-effectiveness of DHIs. Conclusions Key methodological limitations make it difficult to draw definitive conclusions from existing clinical trials of DHIs. Issues include variable uptake and engagement with DHIs, lack of an agreed typology/taxonomy for DHIs, small sample sizes, lack of blinded outcome assessment, combining different comparators, short-term follow-up and poor specification of the level of human support. Research and practice recommendations are presented that address the key research questions and methodological issues for the evaluation and clinical implementation of DHIs for CYP. En ligne : http://dx.doi.org/10.1111/jcpp.12663 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=305
in Journal of Child Psychology and Psychiatry > 58-4 (April 2017) . - p.474-503[article] Annual Research Review: Digital health interventions for children and young people with mental health problems – a systematic and meta-review [Texte imprimé et/ou numérique] / Chris HOLLIS, Auteur ; Caroline J. FALCONER, Auteur ; Jennifer L. MARTIN, Auteur ; Craig WHITTINGTON, Auteur ; Sarah STOCKTON, Auteur ; Cris GLAZEBROOK, Auteur ; E. Bethan DAVIES, Auteur . - p.474-503.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 58-4 (April 2017) . - p.474-503
Mots-clés : Digital health mental health eHealth methodology randomised controlled trials prevention Index. décimale : PER Périodiques Résumé : Background Digital health interventions (DHIs), including computer-assisted therapy, smartphone apps and wearable technologies, are heralded as having enormous potential to improve uptake and accessibility, efficiency, clinical effectiveness and personalisation of mental health interventions. It is generally assumed that DHIs will be preferred by children and young people (CYP) given their ubiquitous digital activity. However, it remains uncertain whether: DHIs for CYP are clinically and cost-effective, CYP prefer DHIs to traditional services, DHIs widen access and how they should be evaluated and adopted by mental health services. This review evaluates the evidence-base for DHIs and considers the key research questions and approaches to evaluation and implementation. Methods We conducted a meta-review of scoping, narrative, systematic or meta-analytical reviews investigating the effectiveness of DHIs for mental health problems in CYP. We also updated a systematic review of randomised controlled trials (RCTs) of DHIs for CYP published in the last 3 years. Results Twenty-one reviews were included in the meta-review. The findings provide some support for the clinical benefit of DHIs, particularly computerised cognitive behavioural therapy (cCBT), for depression and anxiety in adolescents and young adults. The systematic review identified 30 new RCTs evaluating DHIs for attention deficit/hyperactivity disorder (ADHD), autism, anxiety, depression, psychosis, eating disorders and PTSD. The benefits of DHIs in managing ADHD, autism, psychosis and eating disorders are uncertain, and evidence is lacking regarding the cost-effectiveness of DHIs. Conclusions Key methodological limitations make it difficult to draw definitive conclusions from existing clinical trials of DHIs. Issues include variable uptake and engagement with DHIs, lack of an agreed typology/taxonomy for DHIs, small sample sizes, lack of blinded outcome assessment, combining different comparators, short-term follow-up and poor specification of the level of human support. Research and practice recommendations are presented that address the key research questions and methodological issues for the evaluation and clinical implementation of DHIs for CYP. En ligne : http://dx.doi.org/10.1111/jcpp.12663 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=305 Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap / Indu DUBEY in Autism, 28-3 (March 2024)
[article]
Titre : Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap Type de document : Texte imprimé et/ou numérique Auteurs : Indu DUBEY, Auteur ; Rahul BISHAIN, Auteur ; Jayashree DASGUPTA, Auteur ; Supriya BHAVNANI, Auteur ; Matthew K. BELMONTE, Auteur ; Teodora GLIGA, Auteur ; Debarati MUKHERJEE, Auteur ; Georgia LOCKWOOD ESTRIN, Auteur ; Mark H. JOHNSON, Auteur ; Sharat CHANDRAN, Auteur ; Vikram PATEL, Auteur ; Sheffali GULATI, Auteur ; Gauri DIVAN, Auteur ; Bhismadev CHAKRABARTI, Auteur Article en page(s) : p.755-769 Langues : Anglais (eng) Mots-clés : Autism digital health global LMIC Index. décimale : PER Périodiques Résumé : A diagnosis of autism typically depends on clinical assessments by highly trained professionals. This high resource demand poses a challenge in low-resource settings. Digital assessment of neurodevelopmental symptoms by non-specialists provides a potential avenue to address this challenge. This cross-sectional case-control field study establishes proof of principle for such a digital assessment. We developed and tested an app, START, that can be administered by non-specialists to assess autism phenotypic domains (social, sensory, motor) through child performance and parent reports. N = 131 children (2-7?years old; 48 autistic, 43 intellectually disabled and 40 non-autistic typically developing) from low-resource settings in Delhi-NCR, India were assessed using START in home settings by non-specialist health workers. The two groups of children with neurodevelopmental disorders manifested lower social preference, greater sensory interest and lower fine-motor accuracy compared to their typically developing counterparts. Parent report further distinguished autistic from non-autistic children. Machine-learning analysis combining all START-derived measures demonstrated 78% classification accuracy for the three groups. Qualitative analysis of the interviews with health workers and families of the participants demonstrated high acceptability and feasibility of the app. These results provide feasibility, acceptability and proof of principle for START, and demonstrate the potential of a scalable, mobile tool for assessing neurodevelopmental conditions in low-resource settings. Lay abstract Autism is diagnosed by highly trained professionals- but most autistic people live in parts of the world that harbour few or no such autism specialists and little autism awareness. So many autistic people go undiagnosed, misdiagnosed, and misunderstood. We designed an app (START) to identify autism and related conditions in such places, in an attempt to address this global gap in access to specialists. START uses computerised games and activities for children and a questionnaire for parents to measure social, sensory, and motor skills. To check whether START can flag undiagnosed children likely to have neurodevelopmental conditions, we tested START with children whose diagnoses already were known: Non-specialist health workers with just a high-school education took START to family homes in poor neighbourhoods of Delhi, India to work with 131 two-to-seven-year-olds. Differences between typically and atypically developing children were highlighted in all three types of skills that START assesses: children with neurodevelopmental conditions preferred looking at geometric patterns rather than social scenes, were fascinated by predictable, repetitive sensory stimuli, and had more trouble with precise hand movements. Parents' responses to surveys further distinguished autistic from non-autistic children. An artificial-intelligence technique combining all these measures demonstrated that START can fairly accurately flag atypically developing children. Health workers and families endorsed START as attractive to most children, understandable to health workers, and adaptable within sometimes chaotic home and family environments. This study provides a proof of principle for START in digital screening of autism and related conditions in community settings. En ligne : https://dx.doi.org/10.1177/13623613231182801 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=523
in Autism > 28-3 (March 2024) . - p.755-769[article] Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap [Texte imprimé et/ou numérique] / Indu DUBEY, Auteur ; Rahul BISHAIN, Auteur ; Jayashree DASGUPTA, Auteur ; Supriya BHAVNANI, Auteur ; Matthew K. BELMONTE, Auteur ; Teodora GLIGA, Auteur ; Debarati MUKHERJEE, Auteur ; Georgia LOCKWOOD ESTRIN, Auteur ; Mark H. JOHNSON, Auteur ; Sharat CHANDRAN, Auteur ; Vikram PATEL, Auteur ; Sheffali GULATI, Auteur ; Gauri DIVAN, Auteur ; Bhismadev CHAKRABARTI, Auteur . - p.755-769.
Langues : Anglais (eng)
in Autism > 28-3 (March 2024) . - p.755-769
Mots-clés : Autism digital health global LMIC Index. décimale : PER Périodiques Résumé : A diagnosis of autism typically depends on clinical assessments by highly trained professionals. This high resource demand poses a challenge in low-resource settings. Digital assessment of neurodevelopmental symptoms by non-specialists provides a potential avenue to address this challenge. This cross-sectional case-control field study establishes proof of principle for such a digital assessment. We developed and tested an app, START, that can be administered by non-specialists to assess autism phenotypic domains (social, sensory, motor) through child performance and parent reports. N = 131 children (2-7?years old; 48 autistic, 43 intellectually disabled and 40 non-autistic typically developing) from low-resource settings in Delhi-NCR, India were assessed using START in home settings by non-specialist health workers. The two groups of children with neurodevelopmental disorders manifested lower social preference, greater sensory interest and lower fine-motor accuracy compared to their typically developing counterparts. Parent report further distinguished autistic from non-autistic children. Machine-learning analysis combining all START-derived measures demonstrated 78% classification accuracy for the three groups. Qualitative analysis of the interviews with health workers and families of the participants demonstrated high acceptability and feasibility of the app. These results provide feasibility, acceptability and proof of principle for START, and demonstrate the potential of a scalable, mobile tool for assessing neurodevelopmental conditions in low-resource settings. Lay abstract Autism is diagnosed by highly trained professionals- but most autistic people live in parts of the world that harbour few or no such autism specialists and little autism awareness. So many autistic people go undiagnosed, misdiagnosed, and misunderstood. We designed an app (START) to identify autism and related conditions in such places, in an attempt to address this global gap in access to specialists. START uses computerised games and activities for children and a questionnaire for parents to measure social, sensory, and motor skills. To check whether START can flag undiagnosed children likely to have neurodevelopmental conditions, we tested START with children whose diagnoses already were known: Non-specialist health workers with just a high-school education took START to family homes in poor neighbourhoods of Delhi, India to work with 131 two-to-seven-year-olds. Differences between typically and atypically developing children were highlighted in all three types of skills that START assesses: children with neurodevelopmental conditions preferred looking at geometric patterns rather than social scenes, were fascinated by predictable, repetitive sensory stimuli, and had more trouble with precise hand movements. Parents' responses to surveys further distinguished autistic from non-autistic children. An artificial-intelligence technique combining all these measures demonstrated that START can fairly accurately flag atypically developing children. Health workers and families endorsed START as attractive to most children, understandable to health workers, and adaptable within sometimes chaotic home and family environments. This study provides a proof of principle for START in digital screening of autism and related conditions in community settings. En ligne : https://dx.doi.org/10.1177/13623613231182801 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=523