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Organisational aspects in sleep clinics

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Organization (WHO) Classiﬁcation of Digital Health Interventions of 2018, it includes:

1) consultations between a remote client and healthcare provider, i.e. real-time consultation; 2) remote monitoring of client health or diagnostic data by a provider, i.e. telemonitoring; 3) transmission of medical data to healthcare providers, i.e. asynchronous telemedicine; and 4) consultations for case management between healthcare providers, i.e. health worker to health worker communication. Until now, telemonitoring has been the most largely used option in sleep medicine, but there is room to extend the use of telemedicine to teleconsultation or the diagnostic process. Although the technology is readily available, it is nevertheless necessary to develop organisational models to apply it broadly, and educational material for the health professionals involved.

Suarez-Giron et al. (2019) proposed strategies for renewing the way SDB clinics are managed. Figure 1 illustrates a management strategy for patients with probable sleep disturbance; it focuses on the importance of the general practitioner, who plays a pivotal role in early diagnosis. It is important to have not only a multidisciplinary team, including various specialties such as pulmonologists, cardiologists and ENT specialists, but also nursing sta who are specialised in management of these patients. Sleep breathing disorders vary from uncomplicated OSA, which can easily be managed by a general practitioner, to less frequent disturbances, such as hypoventilation syndromes, restless legs syndrome, CSA and other conditions that need mechanical ventilation and all the resources that only a fully equipped reference hospital can oer. This model is very similar to the ‘hub–spoke model’, with management of sleep disorders diversiﬁed according to clinical features and to disease burden.

OSA suspicion

Primary care medicine

Anthropometric features and clinic symptoms Dierential diagnosis If OSA is ﬁnally suspected, initiate general measures

Low compliance: sleep unit

Good compliance/no symptoms: primary care

Future of sleep breathing disorders: Better knowledge (medical school, residency) Primary care role: new devices will allow diagnosis Patient–centred medicine concept Other diseases: control sleep/nonsleep disorders/healthy sleep Basic management: networking and telemedicine (personalisation, understanding and simpliﬁcation) NIV

Refer in case of: No response to general measures Moderate-to-severe symptoms Other sleep or notorious non-sleep disorders

Telemedicine options

Administration

Management

Hospital during ﬁrst weeks

Non-reference hospital

Simple devices/CPAP if needed

Reference hospital

Complicated cases or other

sleep disorders

Fully equipped

Multidisciplinary team

Diagnosis: home/hospital

Clinical decision

Telemedicine options

Follow-up

Telemedicine options

Figure 1. Proposed use of telemedicine (purple boxes) at dierent steps of SDB management. Reproduced and modiﬁed from Suarez-Giron et al. (2019) with permission.

344

ERS Handbook: Respiratory Sleep Medicine

Organisational aspects in sleep clinics

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Table 1. Evolution of the management of SDB from the traditional model (pre-pandemic atpresent diagnosis) to the near-future model (post-SARS-CoV-2)

Diagnosis at present Near future In-hospital or home PSG/HRP Home PSG/HRP remote transmission

Smartphone with dierent sensors Devices collecting data over several days Contactless devices with data transmission

Titration

PSG hospital Remote titration Home titration Automatic devices with data transmission Auto-CPAP treatment Improvement of auto-CPAP devices with oximetry

Reproduced and modiﬁed from Suarez-Giron et al. (2019) with permission.

The future goal is to guarantee a diagnostic–therapeutic path that minimises patient visits to the hospital and healthcare costs, and gives doctors real-time and shareable data. This goal can be achieved by using new tools, such as WiFi transmission of PSG/ home sleep tests (table 1). These data should be available using common devices such as smartphones and accessible by all the professional ﬁgures involved. In addition, use of telemedicine technologies could also make communications between members of the multidisciplinary team easier; for example, to discuss cases with the ENT specialist, indications for MADs with the dentist or lifestyle change programmes with the rehabilitation team.

The use of telemedicine and artiﬁcial intelligence for screening and early diagnosis of respiratory sleep disorders has led to the generation of computerised algorithms on standardised questionnaires (such as the ESS and the Berlin score), which stratify the risk of OSA in the patients examined.

In addition to these diagnostic methods, a strategy should be developed for home titration of PAP devices, in which the physician works in close cooperation with the home care provider, before the phase of established treatment with telemonitoring follow-up. Examples include remote titration using devices with automatic data transmission, or use of auto-CPAP devices, including an oximetry signal. Whichever model is eventually chosen by the physician, a patient-centred approach is highly recommended (ﬁgure 2), with attention to the patient’s global health, including monitoring of lifestyle factors, BP and medications. In future, telemonitoring could also be used in patients receiving MAD or positional treatment.

There are some problematic aspects that are still awaiting suitable solutions. A major problem is that manufacturers of PAP devices have developed their own commercial platforms for telemonitoring, and use dierent criteria for detection of respiratory events during treatment. These platforms are ‘black boxes’, and the physician needs to order sleep studies to check possible treatment problems. Another problem is that records of telemedicine interventions should be kept, especially in light of reimbursement procedures. Protocols should be developed to list the exact activities of each of the health professionals involved, with detailed descriptions of the tasks of physician, nurses and home care providers, to avoid potentially dangerous actions being undertaken by dierent ﬁgures. In particular, prescribing the type of device being used, as well as setting pressures, are medical tasks; the sleep physician should take such decisions, clearly documenting them. In this respect, rigorous

345ERS Handbook: Respiratory Sleep Medicine

Organisational aspects in sleep clinics

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Clinical units

Primary, hospital, networking

Administration

Healthy sleep

Wellbeing services

Commercial

companies

Implementation and objectives

1. Sleep as a source of disease and health

2. Management and circuits cost

3. Customisation

4. Information and communication technologies between the dierent actors

Figure 2. Patient-centred approach in the management of SDB. Reproduced and modiﬁed from Suarez-Giron et al. (2019) with permission.

certiﬁcation of competence in sleep disorders should be a major requisite for health professionals. Finally, more sleep clinic personnel are needed; they should be trained in telemonitoring technologies, since, while the patients may save time, the workload of the clinic might actually increase.

Currently, we are experiencing a mixed model, in which the usual sleep clinic activities are still in place, and an increasing number of patients are followed by telemonitoring and teleconsultations. While the sleep clinic will probably remain the cornerstone for initial patient access, the number of patients on home treatment will become very large in the near future, raising potential diculties and pitfalls in patient management. A solution might be represented by a central structure monitoring several patients, run by home care providers and forwarding alerts to the physician, should any problem arise. This or similar solutions may considerably relieve the workload of sleep clinics at a reasonable cost. It is likely that each public health system will implement the most appropriate solution at the national level, but some agreement in Europe would be highly desirable, to reach a standardised protocol in all European countries.

Finally, personal data protection and legal issues concerning data transmission and storage are important issues to be considered.

Conclusion

The SARS-CoV-2 pandemic has been a major ‘stress test’ for public health systems, and its lesson will remain for future global infectious emergencies. A major progressive change in the organisational aspects of sleep medicine can be foreseen in the next few years. New technological developments, such as self-applied PSG and new diagnostic paradigms, will profoundly change the traditional organisation of sleep clinics and laboratories. While the need to obtain in-laboratory full PSG will remain, especially for neurological disorders and complicated cases, the majority of diagnostic and therapeutic interventions will certainly be performed at the patient’s home. Nevertheless, it is essential to retain a patient-centred management approach (ﬁgure

2), as developed in recent years, to ensure the most comprehensive and eective model of care.

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Organisational aspects in sleep clinics

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Further reading

• Ackrivo J, et al. (2021). Telemonitoring for home-assisted ventilation: a narrative review. Ann

Am Thorac Soc; 18: 1761–1772.

• Ayas NT, et al. (2021). Revisiting level II sleep studies in the era of COVID-19: a theoretical

economic decision model in patients with suspected obstructive sleep apnea. Sleep Sci Pract; 5: 11.

• Cistulli PA, et al. (2019). Short-term CPAP adherence in obstructive sleep apnea: a big data

analysis using real world data. Sleep Med; 59: 114–116.

• Collop NA, et al. (2007). Clinical guidelines for the use of unattended portable monitors in the

diagnosis of obstructive sleep apnea in adult patients. J Clin Sleep Med; 3: 737–747.

• Fields BG, et al. (2020). Sleep telemedicine training in fellowship programs: a survey of

program directors. J Clin Sleep Med; 16: 575–581.

• Fischer J, et al. (2012). Standard procedures for adults in accredited sleep medicine centres in

Europe. J Sleep Res; 21: 357–368.

• Grote L, et al. (2020). Sleep apnoea management in Europe during the COVID-19 pandemic:

data from the European Sleep Apnoea Database (ESADA). Eur Respir J; 55: 2001323.

• Katyayan A, et al. (2019). Computer algorithms in assessment of obstructive sleep apnoea

syndrome and its application in estimating prevalence of sleep related disorders in population. Indian J Otolaryngol Head Neck Surg; 71: 352–359.

• Patel SR, et al. (2021). Age and sex disparities in adherence to CPAP. Chest; 159: 382–389.

• Pépin JL, et al. (2017). Does remote monitoring change OSA management and CPAP

adherence? Respirology; 22: 1508–1517.

• Pépin J-L, et al. (2021). Adherence to continuous positive airway pressure hugely improved

during COVID-19 lockdown in France. Am J Respir Crit Care Med; 204: 1103–1106.

• Schiza S, et al. (2021). Sleep laboratories reopening and COVID-19: a European perspective.

Eur Respir J; 57: 2002722.

• Suarez-Giron M, et al. (2019). New organisation for follow-up and assessment of treatment

ecacy in sleep apnoea. Eur Respir Rev; 28: 190059.

• World Health Organization (2018). Classiﬁcation of Digital Health Interventions v1.0: a Shared

Language to Describe the Uses of Digital Technology for Health. https://apps.who.int/iris/ handle/10665/260480

347ERS Handbook: Respiratory Sleep Medicine

Emerging technologies

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to monitor sleep and circadian rhythms

Renaud Tamisier, Sébastien Baillieul and Jean-Louis Pépin

Sleep is a major driver of health through its association with physical, mental and neurobehavioural well-being, and was recently reported as one component of Life’s Essential 8 for cardiovascular health. Sleep is directly controlled by our internal chronobiology and disrupted by diseases. External factors, like lifestyle and physical environment, but also societal and cultural constraints or events, may mitigate sleep structure and quantity. Multiple aspects of sleep have been associated with poor health outcomes. Both the quantity and quality of sleep are inﬂuenced by diseases, in particular sleep apnoea and insomnia. Poor sleep quality induces daytime fatigue, impaired alertness (causing work and trac accidents), and lack of productivity. In addition, insucient sleep and/or sleep fragmentation have been linked to impaired cardiometabolic health, with greater weight gain in those with habitual sleep deprivation, and increased incidence of several chronic diseases and conditions, including type 2 diabetes, CVD and depression. Sleep apnoea (which aects nearly one billion people worldwide) is one of the most prevalent chronic diseases and a risk factor for other chronic diseases. Therefore, to improve wellbeing, screening for sleep apnoea and insomnia, and monitoring sleep and circadian rhythms represent important tasks that should be available to a large proportion of the world’s population.

PSG: a gold standard measurement that is unable to fulﬁl all needs

Since its introduction, PSG technology and knowledge have improved. Sleep researchers and physicians use PSG as the method of choice to explore sleep and circadian rhythms. The realisation and analysis of polysomnographic data to explore

Key points

• Sleep and circadian rhythms impact both day-to-day life and many diseases.

• Due to the enormous demand, exploring sleep using standard overnight PSG is obviously an impossible task.

• Emerging tools for exploring sleep and circadian rhythms are divided in three major groups based on the technology used and its ability to detect sleep.

• All these emerging tools propose connection with a phone application that oers interesting sleep-supporting programmes with continuous feedback to the user.

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Emerging technologies: sleep and circadian rhythms

Finger ring

Headband

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pathophysiology or carry out accurate diagnoses are subject to well-established guidelines. However, performing PSG to monitor sleep and circadian rhythms in the huge population aected by sleep or circadian rhythm disorders, in a short period of time, during a speciﬁc event or over multiple nights, is an impossible task.

Digital tools to monitor sleep and circadian rhythms

Various technologies have been proposed as alternatives for monitoring sleep (ﬁgure 1 and table 1). Most of these technologies aim to use the eect of sleep stage on several physiological parameters to recreate a representation of sleep structure and fragmentation. They use:

• The detection of body movements by accelerometers, sensors under the mattress

or sheets, or even phones or other devices that have acoustic sensors.

• Ventilation, as an accurate analysis of ventilation can provide a good indication of

waking from sleep, as well as the opportunity to detect sleep apnoea or monitor its treatment.

• Cerebral electrical activity using dry electrodes and automatic analysis with an

approach modiﬁed from that used for EEG analysis, to retrieve sleep structure.

Movement detection

The detection of movements during the day and at night is the main technology used to detect sleep. The principle is based on the fact that across sleep stages, muscle tone and obviously movements decrease as one progresses from wakefulness to slow-wave sleep and are totally suppressed during REM sleep. Connected wristbands, rings, watches, connected mattresses and some mobile phone applications use

Directed to subject

under bedside table

Under mattress devices

Figure 1. Illustration of the dierent devices that have been tested against PSG, using dierent positioning strategies and technologies.

Forehead patch

4:34 13:39

Chin patch

Wrist device

349ERS Handbook: Respiratory Sleep Medicine

Emerging technologies: sleep and circadian rhythms

https://t.me/medicina_free

Miller et al. (2022)

Grandner et al. (2023)

Miller et al. (2022)

Grandner et al. (2023),

Miller et al. (2022)

Le-Dong et al. (2021)

Edouard et al. (2021)

expenditure and sleep

Steps, HR, energy

temperature sensors

and sleep duration

HR, calories burnt, step count

sleep duration

temperature sensors

and sleep duration

HR, calories burnt, step count

temperature, HR, stress

Sleep, activity, recovery,

Sleep, HR Miller et al. (2022)

temperature sensors

body position

temperature sensors,

dry electrodes

body movements

Device Position Technology Feature measured References

Table 1. Commercially available devices that have been tested and compared with PSG

WHOOP 3.0 Wrist Accelerometer Sleep Grandner et al. (2023),

FitBit Charge 4 Wrist Accelerometer, optical and

350

Actiwatch 2 Wrist Accelerometer Sleep Chinoy et al. (2021),

Apple Watch Wrist Accelerometer, optical and

Polar Vantage V Wrist Accelerometer, optical sensors HR, step count and

Readiband Wrist Accelerometer Sleep Chinoy et al. (2021)

Garmin Forerunner 245 Wrist Accelerometer, optical and

Oura gen 3 Finger ring Accelerometer, optical and

ERS Handbook: Respiratory Sleep Medicine

Somﬁt Forehead patch Accelerometer, optical and

Sunrise Chin Accelerometer Sleep, ventilation and

Dreem Headband Head EEG dry electrodes Sleep Arnal et al. (2020)

Earlysense Under mattress Movement detection Sleep, HR, body movements Chinoy et al. (2021)

Withings Under mattress Movement detection Sleep, HR, ventilation and

Resmed S+ Under bedside table Radiofrequency waves Sleep Chinoy et al. (2021)

HR: heart rate.

Emerging technologies: sleep and circadian rhythms

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this physiological principle to monitor sleep. However, to dierentiate between certain sleep stages some progress is still required. These technologies are reliable in assessing sleep state (sleeping versus no sleep). However, detecting awakening is much more problematic; in particular, the detection of wakeful episodes during sleep.

Ventilation monitoring

Because wakefulness and sleep stages impact ventilation and respiratory eort, interesting studies have been undertaken to identify sleep from wakefulness using detectors of mandibular movements or connected mattresses, the latter monitoring both ventilation and body movements. For example, a recent study using mandibular movements demonstrated reasonably good reliability in detecting sleep stages. Since ventilation, upper airway and orofacial muscle activity reveal the changes in trigeminal motor nucleus activity driven by brainstem centres involved in sleep and wake transitions, mandibular movements can reﬂect transitions in sleep phases. Using an Extreme Gradient Boosting classiﬁer as the core algorithm, four states of vigilance (awake, stage 2, slow-wave sleep and REM sleep) were detected with good conﬁdence by artiﬁcial intelligence.

Electrophysiological signals

In a contrasting approach, other devices have been developed to obtain electrophysiological signals related to brain activity, eye movements, or muscle tone from dry electrodes. An example of this type of device is the forehead patch Somﬁt system that also incorporates blood volume (from a LED paired with a photodiode) and heart rate measurements. Although this needs to be cautiously evaluated, the system is supposed to assess various sleep metrics, heart rate and heart rate variability via the manufacturer’s dedicated phone applications. Therefore, a hypnogram with evaluation of wake, N1, N2, N3 and/or REM stages will be proposed along with several sleep metrics. Data provided by this forehead patch could be precise to within 30-s epochs and provide the possibility to calculate the amount of time spent in any stage of sleep (total sleep time) and the amount of wake, N1, N2, N3 and REM for each period of ‘time in bed’. This device is designed to perform sleep recordings to assist medical professionals in the diagnosis of sleep disorders.

An easy-to-use headband with ﬁve dry electroencephalographic electrodes has been developed for signal acquisition and automatic sleep staging. This device, which attempts to approximate a real EEG, demonstrates high reliability in detecting sleep, wake-aer-sleep-onset and sleep staging. In fact, it was initially designed to measure the EEG signal and completes this measurement by assessing heart rate and heart rate variability plus breathing frequency and variability. Using a phone application, the headband communicates with a server that downloads data and thus can process the data and provide the expected metrics in real time or at the end of the night. These devices are used by many individuals in a wellbeing context and are not yet approved for medical diagnosis. However, its accuracy and high power of analysis has allowed compilation of a large dataset from a broad group of users and analysis of, for example, how dierent external events may impair sleep structure and duration in the general population. This technology was used to demonstrate how the ﬁrst lockdown during the coronavirus disease 2019 (COVID-19) pandemic altered sleep by increasing sleep duration, particularly for subjects with an ‘eveningness’ chronobiology.

All these devices are associated with applications that help users to manage their sleep. These applications provide metrics that are measured and calculated by the device. Some of them attempt to improve sleep lifestyle by providing counselling

351ERS Handbook: Respiratory Sleep Medicine

Emerging technologies: sleep and circadian rhythms

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and tips related to the user’s sleep habits and physical activity. By design these solutions are able to give direct feedback to the user depending on their behaviour. These applications may also help in providing dedicated training programmes or even electronic cognitive behavioural therapy in order to improve sleep quality.

Conclusion

There is a need to develop new tools to assess and monitor sleep quality and circadian rhythms. This is essentially driven by the quest for wellbeing and the need to better understand and treat sleep disorders. There is increasing recognition that diseases aecting sleep are highly prevalent in the general population making it problematic to use PSG to assess all these patients.

Overall, the consumer sleep-tracking devices have high sensitivity but relatively low speciﬁcity, indicating a tendency for the devices to accurately detect sleep but to be less accurate in detecting wake. By contrast, some sleep measurement devices are able to detect sleep stages with sucient reliability and artiﬁcial intelligence may open up new ways of detecting sleep structure.