30 HOW TO TREAT : DIGITAL HEALTH INTERVENTIONS IN SLEEP DISORDERS
30 HOW TO TREAT : DIGITAL HEALTH INTERVENTIONS IN SLEEP DISORDERS
4 AUGUST 2023 ausdoc . com . au
Figure 3 . Continuous positive airway pressure ( CPAP ).
PAGE 28 on their overall quality of life . 17 RLS is more common in older adults and has a higher prevalence in women . 31
ROLE OF THE GP
DESPITE the ubiquity of sleep disorders in primary care , several factors at different levels of the healthcare system contribute to the challenges in the identification and effective management of sleep disorders . Foremost , few patients seek help from a GP with an overt sleep complaint . Instead , many will present with non-specific symptoms ( for example , fatigue , excessive sleepiness , impaired daytime function and poor mood ). The GP must thus be vigilant in assessing sleep health as
32 , 33 part of routine patient check-ups .
Additional factors to consider include the time constraints / workload and funding structure of general practice , which may contribute to primary care being better supported to manage acute rather than chronic conditions .
The historical lack of sleep health education within medical curricula , and limited sleep health awareness in the community , may also contribute to an under-detection of sleep disorders in primary care . 34
GPs are typically the first point of contact for patients seeking treatment for these problems and have an important role in identifying and triaging patients to appropriate care . However , the shortage of specialist sleep physicians and psychologists , and the uneven geographical distribution of specialists , may further limit referral and timely patient access to care . In considering these practice constraints , digital health interventions can potentially facilitate GP management of sleep health problems , overcome several of these issues , and improve sleep health management in primary care .
Evaluation in primary care
Take a detailed history , including the variables listed in box 2 . 35 Also take a history from the patient ’ s partner , if they are present . This can be important in the assessment process , as the
Box 2 . Information to ask for when taking history
• Sleep duration / total sleep time .
• Sleep quality / sleep fragmentation .
• Regularity in sleep patterns from night to night .
• Sleep behaviours , such as bed and waking times , caffeine and alcohol consumption .
• Nocturnal awakenings .
• Daytime symptoms , such as daytime sleepiness , fatigue and poor mood .
• Physical symptoms .
• Duration of symptoms .
• Severity of symptoms .
• Psychosocial impact of the complaints .
• Coexisting disease : — Hypertension ( especially in younger patients ). — Cardiovascular disease ( especially AF and heart failure ). — Cerebrovascular disease ( stroke and TIA ). — Diabetes mellitus . — Thyroid disease .
• Family history of sleep disorders , or continuous positive airway pressure ( see figure 3 ) use for OSA .
patient may not be aware of some of their own symptoms experienced during sleep . Daytime sleepiness , which may be a feature in some patients with sleep disorders , is common in the general population . Box 3 lists the differential diagnoses of daytime sleepiness , and the causes to exclude . 35
DIGITAL HEALTH IN PRIMARY CARE
THERE has been an enormous increase in digital health applications since the early 2000s . For instance , there are more than one billion wearable devices and more than 325,000 mobile health ( mHealth ) applications worldwide . 36 There are more than 500 sleep mHealth apps alone ; however , evidence supporting efficacy is available in less than 2 % of these . 37
Many consumer technologies have also been developed that focus on health , fitness and wellbeing . Patients are increasingly using these devices ( see figure 4 ) to track their general health and , in many instances , their sleep duration and quality , although evidence supporting the accuracy of these devices is limited . 38
A 2020 scoping review proposed that several existing mHealth applications ( see figure 5 ) could aid primary care through medical history taking , clinical decision-support , health promotion and the provision of information about disease-specific care . 39 However , clinical support models integrating mHealth apps with direct clinical care have yet to be established in Australia .
At present , the use of mHealth apps is typically ad hoc , and GPs lack an evidence-based mHealth database to support their practice . 40
The RACGP is acutely aware of the need for managing mHealth apps in primary care and has developed a toolkit with systematic information about how to incorporate these technologies into general practice . 41
DIGITAL SLEEP HEALTH APPROACHES
DIGITAL sleep health interventions are a burgeoning market and topic of curiosity for patients and physicians alike . Importantly , digital sleep health interventions are not just medical devices ;
Box 3 . Differential diagnoses of daytime sleepiness
• OSA .
• Poor quality ( fragmented sleep ) and quantity of sleep ( sleep restriction ).
• Insomnia .
• Circadian rhythm disorders ( including shift work sleep disorder ).
• Depression .
• Narcolepsy .
• Hypothyroidism .
• Restless legs syndrome / periodic limb movement disorder .
• Medication related : — Sedatives . — A side effect of medication not used for sedation ; eg , some antidepressants , anti-epileptics and antipsychotics . — Stimulants such as caffeine , theophylline , amphetamines , betablockers , SSRIs .
• Excess alcohol intake .
• Neurological disorders : — Dystrophia myotonica . — Previous encephalitis . — Previous head injury . — Parkinsonism . — Idiopathic hypersomnolence .
they also include software . This broad term encapsulates a range of products , including smartphone apps , web-based programs , computer applications , and wearable and ‘ nearable ’ ( placed near
42 , 43 the patient ) devices .
The common features of all interventions are that their purpose is to monitor , assess / diagnose , treat , manage , and / or prevent sleep disorders .
A broad range of digital sleep health interventions is currently available for patients and physicians . Some specific examples include web-based programs where patients self-administer brief , generalised versions of CBT for insomnia ( the recommended first-line treatment for insomnia ); apps that use the smartphone ’ s microphone to monitor breathing sounds to detect snoring , breathing irregularities , and apnoeic events ; wearable devices that monitor sleep stages and breathing events ; under-the-mattress devices that monitor sleep , heart rate and breathing ; and software that monitors adherence to sleep disorder treatments such as tracking continuous positive airway pressure ( CPAP ) usage and efficacy metrics .
The digital sleep health intervention market is growing at an exponential rate ; many new technologies emerge every year , each potentially with their own particular sensors , algorithms and capabilities .
Many interventions are also updated ( for example , as new model versions ) or discontinued each year . As the capabilities of new and current digital sleep health interventions evolve rapidly over time , so too will the categories depicted in figure 6 . The definition of digital sleep health interventions is further complicated by the distinction between consumer and medical devices . With respect to the regulation of digital sleep health interventions , the TGA has adopted the term ‘ software as a medical device ’ ( SaMD ), which includes digital health interventions , defining SaMD as software “ that has an intended purpose consistent with the definitions of a medical device ”. 44
Accordingly , wearable fitness trackers that incidentally monitor sleep are not classed as SaMDs because their intended purpose is not to track sleep disorder symptoms .