A novel home video behaviour analysis algorithm to diagnose childhood chronic insomnia
A novel home video behaviour analysis algorithm to diagnose childhood chronic insomnia
Objectives/Introduction: Chronic insomnia (CI) is the most common sleep disorder in childhood, categorised as limit setting or sleep onset association type. In both, parent‐child interaction is significant in the development and persistence of the problem. CI is currently diagnosed by clinical interview with the parent, however parental recall of their own behaviour may be inaccurate.
Objective observation may aid diagnosis and treatment. We aimed to trial home video technology in childhood brain tumour survivors who have a high prevalence of CI. Specifically, we aimed to develop a novel scoring algorithm of behavioural observations for future clinical applications.
Methods: Participants were recruited from neuro‐oncology clinic sat Southampton Children's Hospital, UK. Inclusion criteria were: aged 3–12 years, completed brain tumour treatment at least 6 months previously, and at risk of CI according to a screening questionnaire (Sleep Disturbance Scale for Children: Disorders of Initiating and Maintaining Sleep subscale). Families completed a home video sleep study using an infra‐red camera with actigraphy (SOMNO medics), followed by a clinical sleep history taken from the parent (audio recorded to allow peer review). Video analysis was undertaken using BORIS v6.1, a software programme designed by researchers at University of Turin for animal behaviour observation. An analysis algorithm was designed and iteratively developed which categorised behaviours into the following domains: sleep schedule, bedtime routine,lights out, night wakings, parent and child speech and behaviour(sleep promoting and sleep inhibiting). These domains could both be visually plotted and quantitatively computed (e.g. total and mean duration of activities) to aid interpretation by the clinician.
Results: Five children have been recruited. Inter‐rater reliability for scoring of sleep‐related behaviours will be presented. Preliminary video analysis has indicated that parents of childhood brain tumour survivors commonly demonstrate overly soothing behaviour at bedtime and night wakings (e.g. co‐sleeping and extended close physical contact), which is likely to contribute to CI in the child.
Conclusions: Video behavioural analysis has the potential to be a useful tool in aiding diagnosis of CI and, particularly with the further development of our scoring algorithm, could be used to give parents specific feedback regarding their overnight interactions.
345-346
Galbraith, Lorna
a12520ee-ecb1-4ede-bca6-85ddb09f38a8
Bull, Kim
751f8b25-29ba-4d4f-96e2-6c339a83a47f
Hill, Catherine
867cd0a0-dabc-4152-b4bf-8e9fbc0edf8d
11 September 2018
Galbraith, Lorna
a12520ee-ecb1-4ede-bca6-85ddb09f38a8
Bull, Kim
751f8b25-29ba-4d4f-96e2-6c339a83a47f
Hill, Catherine
867cd0a0-dabc-4152-b4bf-8e9fbc0edf8d
Galbraith, Lorna, Bull, Kim and Hill, Catherine
(2018)
A novel home video behaviour analysis algorithm to diagnose childhood chronic insomnia.
Journal of Sleep Research, 27 (S1), , [P556].
(doi:10.1111/jsr.12751).
Record type:
Meeting abstract
Abstract
Objectives/Introduction: Chronic insomnia (CI) is the most common sleep disorder in childhood, categorised as limit setting or sleep onset association type. In both, parent‐child interaction is significant in the development and persistence of the problem. CI is currently diagnosed by clinical interview with the parent, however parental recall of their own behaviour may be inaccurate.
Objective observation may aid diagnosis and treatment. We aimed to trial home video technology in childhood brain tumour survivors who have a high prevalence of CI. Specifically, we aimed to develop a novel scoring algorithm of behavioural observations for future clinical applications.
Methods: Participants were recruited from neuro‐oncology clinic sat Southampton Children's Hospital, UK. Inclusion criteria were: aged 3–12 years, completed brain tumour treatment at least 6 months previously, and at risk of CI according to a screening questionnaire (Sleep Disturbance Scale for Children: Disorders of Initiating and Maintaining Sleep subscale). Families completed a home video sleep study using an infra‐red camera with actigraphy (SOMNO medics), followed by a clinical sleep history taken from the parent (audio recorded to allow peer review). Video analysis was undertaken using BORIS v6.1, a software programme designed by researchers at University of Turin for animal behaviour observation. An analysis algorithm was designed and iteratively developed which categorised behaviours into the following domains: sleep schedule, bedtime routine,lights out, night wakings, parent and child speech and behaviour(sleep promoting and sleep inhibiting). These domains could both be visually plotted and quantitatively computed (e.g. total and mean duration of activities) to aid interpretation by the clinician.
Results: Five children have been recruited. Inter‐rater reliability for scoring of sleep‐related behaviours will be presented. Preliminary video analysis has indicated that parents of childhood brain tumour survivors commonly demonstrate overly soothing behaviour at bedtime and night wakings (e.g. co‐sleeping and extended close physical contact), which is likely to contribute to CI in the child.
Conclusions: Video behavioural analysis has the potential to be a useful tool in aiding diagnosis of CI and, particularly with the further development of our scoring algorithm, could be used to give parents specific feedback regarding their overnight interactions.
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Published date: 11 September 2018
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Local EPrints ID: 430533
URI: http://eprints.soton.ac.uk/id/eprint/430533
ISSN: 0962-1105
PURE UUID: 582fbd62-e305-4df6-bb86-eb238deb7b00
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Date deposited: 03 May 2019 16:30
Last modified: 16 Mar 2024 03:06
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Author:
Lorna Galbraith
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