Subtyping of Insomnia

Chronic insomnia is a widespread problem, affecting about ten percent of the
adult population. Apart from an unsatisfactory sleep pattern, insomnia
additionally involves daytime complaints such as fatigue, attentional
disturbances and mood disturbances, causing serious problems for quality of
life, general health and labor productivity.

A generic and effective treatment for insomnia that targets hyperarousal is
Cognitive Behavioural Therapy for insomnia (CBT-I). However, part of the
insomnia patients does not respond to CBT-I and patients who do respond have
variable outcomes. Scientific literature is increasingly becoming aware of the
idea that insomnia is a general term for a number of subtypes consisting of
different sleep complaints and having different causes. As a consequence, the
effectiveness of (non-pharmacological) treatment of a patient is likely to be
strongly dependent of the patient*s individual characteristics. To date, no
validated stratification method to subtype insomnia is available.

In this study we aim to better understand underlying mechanisms of insomnia and
the heterogeneity in treatment response in a broad patient population, using
cluster analysis as a tool for detecting subtypes. Subtypes will be detected
with primary use of physiological parameters. Eventually, this study could lead
towards finding clinically relevant subtypes, personalizing treatment and
eventually even predicting if a non-pharmacological intervention for insomnia
will be effective for an individual patient.

The primary objective of this study is to identify clinically relevant subtypes
of insomnia using mainly physiological parameters. Mainly macro and
microstructural parameters of the EEG will be used, as well as parameters from
ECG and finger PPG.

Secondary objectives:
- to evaluate if subtypes differ regarding other demographic and clinical
variables such as age, gender, educational level, neuropsychological test
results, co-morbidities and CBT-I treatment result.
- to validate the HF measures obtained by the Philips ELAN logging device in
insomnia subjects.

This study will be a cross-sectional study with a clinical follow-up.

Measurements will be done at the patient*s home, while patients are on the
waiting list for their intake at Kempenhaeghe. The measurements will consist of
one week of keeping a sleep diary, one night of ambulatory PSG and
neuropsychological tests (a time estimation task and an emotional priming task)
and questionnaires. Additionally, wrist-worn PPG measurements will be done
during the same week of the sleep diary.

During the analysis, which is exploratory, important parameters for the
subtyping of insomnia will be identified. For the identification of subtypes we
will use cluster analysis. Macro- and microstructural characteristics of the
EEG will primarily be reviewed, as well as cardiovascular characteristics
measured by ECG and finger PPG.
Important parameters are:
- Total sleep time (TST)
- Wake-time After Sleep Onset (WASO)
- Sleep Onset Latency (SOL)
- HRV measured by ECG
- Ratio of low frequent to high frequent EEG power
- Amount of microarousals during REM and non-REM sleep
- Number of K-complexes

Additionally, the ELAN logging device will be validated.

Participating in the study does not result in large health risks, nor is it
likely to cause major physical discomfort. Taking part in ambulatory PSG might
result in a night of poorer-than-normal sleep. Furthermore, wearing the Philips
wearable optical sensing platform for two weeks might cause minor discomfort.
Due to the psychological aspects of insomnia, great emphasis will be given to
the fact that the measurements are not part of the treatment. This way, the
influence of the research participation on the treatment of insomnia will be
minimalized.