This study is the first to report preferences for overnight at-home sleep testing in a large sample of patients suffering from sleep disorders. As diagnostic assessments of sleep disorders are increasingly shifting to the patients’ home, the findings of this study can help clinicians and policy makers to optimize patient pathways. As shown in earlier studies, patient perceptions and knowledge of sleep measurements are important drivers of adherence to sleep testing protocols, and can hence impact the effectiveness of medical interventions for sleep disorders [18].

While a broad range of different care models exists, most healthcare systems require a prescription from a sleep physician for coverage of an overnight sleep study. Though HSAT are theoretically limited to diagnosis of sleep-related breathing disorders, they are commonly used as a first-line test to rule out sleep apnea before a more comprehensive sleep assessment is performed. This is not only related to coverage constraints, but increasingly also due to significant limitations of in-laboratory PSG capacity, which can delay access and definitive diagnosis. From that perspective, the DCE designed for this study required patients to make decisions they could encounter in real-life situations.

All attributes presented in this study were statistically significant, except for Diagnostic accuracy. This was not expected a priori since this attribute is closely related to receiving a precise diagnosis. As visible from the coefficients of this attribute, the differences in the levels presented were probably not designed large enough to create a stronger response. This is likely because the worst level still had an acceptable diagnostic accuracy. This may have participants conclude that a test yielding this accuracy level would be sufficient if used under direction of a physician. This also underlines the relevance of the physicians’ role in the diagnostic assessment of sleep disorders and the need for adequate education of the patient on this important topic. Especially with the emergence of consumer healthcare devices and smartphone applications that claim to assess sleep, the value of a high level of diagnostic accuracy of a sleep test, which is properly validated in clinical trials, should be made clear to the patient. This is not only relevant for the assessment of sleep disorders itself, but furthermore to inform the patient about potential negative long-term consequences on their cardiovascular risk profile. With current scientific advancements that demonstrate the benefits of advanced diagnostic tools with greater prognostic value, the relevance of diagnostic accuracy should not be underestimated.

Among the attributes that reached statistical significance, Waiting time to discuss results had the largest coefficients and hence the greatest relative importance for participants. This finding underlines the medical need from the patient perspective and the utility derived from a direct personal consultation without delay. The results presented here are in line with a study conducted in Australia that assessed preferences of OSA-care pathways, in which patients expressed strong preferences for short waiting times [19]. Sleep quality during test had the second most influence on the choice decisions and was especially important to patients with a preference for overnight assessment in the sleep laboratory. In light of a recent study by Colleli et al., that reported worse sleep for some patients depending on their individual chronotype, clinicians may evaluate both preferences and chronotype to identify the ideal overnight assessment for each patient to ensure that the needs are properly met [20].

Given the complexity of some measurement devices, it is not surprising that patients prefer support over self-application, though Effort to apply device was only of moderate relevance in the choice decisions. Interestingly though, the results were independent of prior experience, a parameter that can influence choice behavior significantly as shown previously on sleep therapeutics [21]. Here again, previous studies have shown that usability of the device plays an important role and influences adherence to prescribed overnight sleep testing [18].

In general, a relatively high heterogeneity was found in response behavior, which led to various subgroup analyses. Previous preference studies in the field of sleep disorders observed comparable differences in choice decisions among patients [22,23,24]. As increasingly recognized, sleep disorders are rarely homogenous and patients with a common diagnosis such as OSA often have different underlying endo- or phenotypes causing the disease [25, 26]. For this study in particular, participants reported a wide range of symptoms and will have received various diagnoses after the sleep study, which may explain the observed heterogeneity in responses and thus preferences. The variances that were found after stratification for age, gender, prior experience and preferred sleep study location, underline the need for an individual approach to diagnosis of sleep disorders. With the increasing prevalence of sleep disorders, balancing the requirements of individual patients on the one hand, and the provision of care on population level on the other hand, represents a major challenge to sleep physicians, policy makers and payers though, which is gaining in importance with innovative and expensive treatments for sleep disorders being developed and marketed.

Even though preferences in this study were heterogeneous, the attributes Waiting time to discuss results and Waiting time to test stood out as highly relevant across all subgroups. Given that sleep medical services are a scarce resource in most countries, it underlines the need for a more efficient provision of care and urges institutions in this field to provide the right incentives to ensure attractiveness of sleep medicine for younger physicians [27]. Considering the rising demand for sleep diagnostic testing, this is paramount to ensure sustainable access for patients suffering from sleep disorders to enable timely diagnosis and treatment without delay.

Given the burden of in-laboratory sleep testing, which often requires admission to a hospital, it was not expected that a bit less than half of all participants preferred this type of assessment over an at-home sleep study. Though the average age of patients preferring in-laboratory PSG was significantly higher, stratification of DCE data by age did not reveal any significant differences in the attributes that addressed practical considerations of the sleep study, such as Effort to apply device. Stratification by preferred sleep study location though revealed some important differences in choice behavior, with strong preferences for short waiting times among patients that preferred in-laboratory test over home. Patients may have expected that the entire diagnostic process is shorter when admitted to a hospital with daily presence of physicians directly in the sleep laboratory.

Overall, the WTP for out-of-pocket spending for sleep testing was high, which underscores the medical need experienced by patients due to symptoms of sleep disorders. Especially considering the relatively good coverage of sleep medical services in Germany, which usually requires no or rather low co-payments, these findings emphasize the value patients assign to sleep testing and consequently good sleep itself. This is also supported by the correlation that was observed between the number of symptoms and WTP, in which patients with more symptoms, and presumably higher need for treatment, reported higher inclination for out-of-pocket payments for sleep testing.

Some limitations should be noted for this study, which are largely inherent to the methodology of choice analysis with DCE. First, the attributes of overnight sleep testing that were presented to participants are not exhaustive, and other attributes might will likely also be important for patients. To not overwhelm patients by creating highly complex choice tasks, the list of attributes had to be limited to those seven presented, which is already on the upper end of what is considered appropriate for a DCE. Furthermore, the results of a DCE should be interpreted in the context of the local healthcare system, values as well as cultural background of the participants. By recruiting patients from two sleep centers, based in two states with different sociodemographic backgrounds, the study tried to optimize potential influence from these factors.

Another aspect is that all participants had previous experiences with healthcare services, the majority also with sleep diagnostic procedures. These experiences will have influenced choice behavior and thus the results from the DCE. It was attempted to mitigate this aspect by gathering data on their diagnostic experiences. Due to the length of the questionnaire, and in order not to overwhelm participants, certain information had to be excluded, such as previous or actual waiting times for healthcare services that patients might have experienced. The same holds true for additional medical or demographic information, such as socio-economic status, type of health insurance or education, which may influence decision making. The influence of these factors shall be subject of future studies on this topic.

A further limitation arises from using a stated-preferences over a revealed-preferences approach, which would have been able to evaluate actual choices of patients in need of overnight sleep testing. As patients cannot choose freely in most regulated healthcare systems, due to constraints of for example reimbursement fees, coverage policies and limited availability due to prescription requirements, this approach is commonly not practical in eliciting preferences. Different studies, also in the context of sleep medicine, have shown though, that the stated-preference approach as used in DCE, has a high external validity and can predict choice behavior in real-world situations [11, 12, 28].