Heart and Respiration Rate Variability Analysis to Estimate the Apnoea-Hypopnoea Index in People With Intellectual Disabilities.
A simple chest band plus a smart rule can screen sleep apnoea in adults with ID.
01Research in Context
What this study did
van den Broek et al. (2026) tested a new computer rule that reads heart and breathing data.
The rule turns one night of chest-band recordings into a sleep-apnoea score.
Seventy-three adults with intellectual disability wore the bands while they slept.
What they found
The rule matched the hospital sleep-lab score most of the time.
Agreement was strong enough to trust for screening, not perfect enough to replace labs.
How this fits with other research
Meier et al. (2012) tried wrist-watch actigraphy in older adults with ID. Only one in three could wear it all night.
The new chest-band method worked in almost every adult, so it fixes the wear-time problem actigraphy had.
de Leeuw et al. (2024) showed resting ECG is easy to collect in two-thirds of older clients with ID. Naomi’s team proves the same signals can also run overnight, giving two tests for one setup.
Why it matters
You now have a low-cost, low-burden way to spot sleep apnoea in clients who hate wires. Run the chest band for one night, feed the file to the algorithm, and flag severe cases for the sleep doctor. No extra staff, no lab wait list, no lost nights.
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02At a glance
03Original abstract
BACKGROUND: Obstructive sleep apnoea (OSA) is highly prevalent in people with intellectual disabilities, and when left untreated, negatively influences daily activities and social interactions. Polysomnography (PSG) remains the diagnostic gold standard but can be an obtrusive and strenuous endeavour in people with intellectual disabilities, related to factors such as communicative impairment, anxiety, challenging behaviour and sensory hypersensitivity. Alternative methods to assess OSA severity by estimating the apnoea-hypopnoea index (AHI) have been proposed, based on heart and respiration rate variability signals. These signals could potentially be obtained with less obtrusive monitoring devices. We investigated whether this approach is also suitable in people with intellectual disabilities. METHODS: We analysed overnight PSG data from 73 participants with intellectual disabilities. AHI was predicted by an algorithm trained to use cardiorespiratory inputs (from electrocardiogram and respiratory induction plethysmography) to detect the occurrence of sleep-disordered breathing events and total sleep time. It was compared to the PSG-derived AHI by means of Spearman's correlation and intraclass correlation coefficients (ICC). The diagnostic capacity of the algorithm to differentiate between OSA severity groups was evaluated using Cohen's κ coefficient of agreement and accuracy, using near-boundary double labelling, with the following boundaries: 'no OR mild OSA' 2.4 ≤ AHI < 7.0, 'mild OR moderate OSA' 12.4 ≤ AHI < 17.4 and 'moderate OR severe OSA', 26.6 ≤ AHI < 35.2. RESULTS: The algorithm achieved a strong Spearman's correlation between the predicted and PSG-derived AHI of 0.76 (p < 0.001) and a moderate ICC of 0.74 (p < 0.001). Differentiation in OSA severity classes was done with a κ of 0.58 and accuracy of 68.5%, indicating a moderate level of agreement. CONCLUSIONS: We show the potential of determining the severity of OSA in people with intellectual disabilities by estimating AHI using an algorithm based on surrogate cardiorespiratory signals. This allows the development of less obtrusive diagnostic modalities focusing only on cardiorespiratory inputs to assess OSA severity.
Journal of intellectual disability research : JIDR, 2026 · doi:10.1111/jir.70077