Consumer sleep technologies (CSTs), such as Fitbit, Apple Watch, Oura Ring, and Garmin wearables, are becoming more prevalent in mainstream life, offering users new ways to monitor sleep. As their popularity grows, more patients are bringing CST-generated data into the clinic, often accompanied by months of information. While these devices are, in most cases, not intended for identifying sleep disorders, their presence in clinical encounters is unavoidable.
CSTs offer something traditional assessments rarely do: longitudinal, ecologically valid sleep data. By tracking metrics across days or weeks, these devices can reveal behavioral patterns relevant to a patient’s sleep health, especially in those with irregular schedules or circadian rhythm disturbances. When integrated thoughtfully into care, CSTs may boost patient engagement, support behavioral interventions, and extend treatment into the home through telehealth or remote monitoring.
Still, caution is necessary. These tools vary in technical accuracy, lack universal performance evaluation, and perform differently across populations and devices.
What CSTs actually measure
Most consumer sleep devices estimate sleep using a combination of accelerometry and photoplethysmography-derived data. From these inputs, they report common metrics such as total sleep time (TST), time in bed (TIB), and sleep efficiency. Some models also estimate sleep stages. While sleep staging is typically the least reliable output, multiple validation studies suggest that CSTs produce moderately accurate estimates of TST and TIB relative to polysomnography, particularly among healthy adults in controlled settings.1,2
Devices that pair movement and heart rate data, such as the Fitbit, Apple Watch, and Oura Ring, tend to outperform older models that rely solely on motion. However, limitations persist. Most CSTs struggle with detecting wake after sleep onset (WASO), and their sleep staging accuracy remains highly variable. Complicating matters, CSTs rely on proprietary algorithms, and these differ across manufacturers. The Oura Ring, for example, places heavier emphasis on heart rate variability, while the Fitbit uses a combination of pulse rate and movement through methods not publicly disclosed. As a result, the same person wearing two devices on the same night might receive very different results, potentially leading to confusion or mistrust in the data.
Population-specific considerations: Insomnia, sleep apnea, and comorbid conditions
CSTs tend to overestimate total sleep time and underestimate WASO, particularly in individuals with insomnia or fragmented sleep patterns. This mismatch between device-generated estimates and subjective experience may cause confusion or anxiety. Some patients may mistakenly believe their sleep has improved based on tracker outputs despite ongoing symptoms, while others may grow preoccupied with the data, potentially complicating clinical care. Baron and colleagues introduced the term “orthosomnia” to describe sleep-related anxiety and hypervigilance that arise in response to wearable sleep feedback.3 Even among patients who do not meet diagnostic criteria for insomnia, CST data may contribute to rumination or reinforce maladaptive sleep behaviors in some patients.
There is a limited role of CSTs in identification of OSA. For instance, the Apple Watch received US Food and Drug Administration (FDA) clearance in 2024 for detecting breathing irregularities consistent with moderate to severe OSA, but it is not approved as a diagnostic tool and cannot replace formal screening, and the Fitbit offers feedback on oxygen variation in the night. Another concern is that the sleep metrics measured by devices tend to be less accurate among individuals with disrupted sleep (eg, insomnia). Additionally, individuals with chronic diseases, pain, or mood disorders all may have disrupted sleep patterns that compromise device accuracy. While poor sleep is common in these groups, interpreting CST outputs requires clinical caution to avoid misdiagnosis, premature reassurance, or missed opportunities for appropriate treatment.
When consumer data can help
Despite these limitations, CSTs offer a window into longitudinal sleep behavior—a view that is difficult to obtain through in-clinic assessments or brief sleep questionnaires. Most devices provide multiweek summaries of sleep timing, duration, and regularity, which can be especially helpful in cases of circadian rhythm disorders, delayed sleep phase, or inconsistent sleep schedules. These patterns may not surface during a single clinic visit but often emerge clearly in CST reports.
For instance, population data show that weekday sleep is 30 to 45 minutes shorter than weekend sleep, and sleep schedules shift later during summer months.4 Tracking data can also enhance behavioral sleep treatments. A usability study by Pulantara and colleagues found that CST integration with a digital cognitive behavioral therapy for insomnia (CBT-I) platform increased treatment engagement, adherence, and personalization.5 By providing behavioral feedback, CSTs can reinforce consistent bedtimes, support sleep restriction therapy, and help track progress over time.
That said, clinicians can help patients focus on the big picture. When patients are guided to view CSTs as behavioral tools rather than diagnostic instruments, they may become more engaged and less anxious about individual fluctuations.
Our lab at the University of Utah has developed and tested a coaching program for individuals with short sleep duration that utilizes brief coaching, a CST, and education to set goals and encourage behavior change.6 We have utilized this technique in several studies, and patients have found the CST to be engaging and helpful for extending their sleep.7
Clinical validation and regulatory limitations
Importantly, most CSTs are marketed as wellness devices and do not have FDA approval. The exception is the Apple Watch, which recently received FDA clearance for sleep apnea detection in 2024. Proprietary algorithms are rarely disclosed or standardized, and software updates, often unannounced, can alter sleep scoring, undermining consistency and clinical reliability. Performance evaluation studies to date have primarily involved young, healthy, and predominantly White participants in lab settings, limiting generalizability.
The accuracy of CSTs remains unclear in older adults, those with comorbid conditions, and racially or ethnically diverse populations. Medications that alter heart rate or movement may further distort outputs, complicating interpretation. These limitations highlight the need for real-world validation. Future studies should assess CST performance in naturalistic settings and high-risk populations and clarify whether they can augment, though not replace, remote monitoring, behavioral therapies, or clinical evaluation.
Integrating CSTs into clinical practice
To help clinicians navigate the growing role of CSTs in clinical settings, the World Sleep Society issued a set of consensus recommendations in April 2025.8 These guidelines encourage empathetic, informed engagement with CST data and recommend the following best practices:
- Ask patients about their sleep tracker use and what information they find helpful or concerning.
- Acknowledge the interest while educating them on the benefits and limitations of tracking.
- Avoid overinterpreting sleep staging data, which are less reliable and not clinically important for treating sleep disorders.
- Emphasize behavioral trends and multiday averages instead of nightly readings.
- Do not use CSTs to diagnose sleep disorders. Insomnia is a self-reported sleep disorder. OSA risk may be identified by some devices, but formal sleep testing is necessary for diagnosis.
- Use CSTs cautiously to support behavioral treatments such as CBT-I. They may help with engagement but are not necessary for the intervention.
Some health care systems, including Kaiser Permanente and Ochsner, have already started integrating wearable data into care models, pairing tracking tools with health coaching, electronic health record integration, and trained support teams.9 These models show that CSTs can support patient-centered care when used thoughtfully and interpreted within the context of clinical expertise.
Communicating effectively with patients
Clinicians do not need to become wearable tech experts to address CST concerns effectively. Patients may misinterpret nightly variability as a sign of pathology or dysfunction. Helping patients reframe CST data as behavioral feedback—rather than a sleep “grade”—might prevent unnecessary worry and redirect attention to core treatment goals.
Framing the conversation around long-term patterns, daily functioning, and symptom changes provides context and supports better outcomes. Patients are more likely to benefit when providers ground the discussion in consistency, routine, and sleep-related behaviors rather than nightly scores. This approach also reinforces the ideology of helping patients feel understood while also potentially reducing distress around misunderstood metrics.
Be prepared for CSTs
Consumer sleep trackers are likely to remain a fixture in modern sleep care. Although not designed or validated for clinical diagnosis, they can support treatment adherence, promote behavior change, and deepen patient engagement when integrated thoughtfully and with scientific transparency. Clinicians should be prepared to discuss CST data, clarify their limitations, and interpret findings within the broader context of each patient’s symptoms and history. By anchoring these conversations in evidence and applying frameworks such as the 2025 World Sleep Society guidelines, providers can engage with CSTs in ways that enhance care without compromising clinical integrity.
This article was originally published in the Fall 2025 issue of CHEST Physician.
References
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