Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

Abstract

Objective

Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients.

Data Sources

PubMed.

Review Methods

A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review.

Conclusions

In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients’ engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care.

Implications for Practice

Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

Keywords

wearable technology precision medicine biometric data mobile health head and neck surgery

Wearable technology (“wearables”) represents a broad field of biosensor-based devices worn close to the skin to unobtrusively monitor an individual’s activities.¹ Modern consumer wearables, such as the Apple Watch Series 6 or Fitbit Charge 2, interpret and transmit these data to a smartphone or centralized database, allowing individuals to track unique parameters of health and wellness over extended periods. The market for consumer wearables has experienced significant levels of expansion: per the International Data Corporation, 444.7 million wearable units were shipped in 2020, representing a year-over-year increase of 27.2%.² As of 2020, approximately 30% of adults in the United States endorsed using a wearable, of whom half use the device daily.³

Though consumer wearables were widely adopted in lifestyle and fitness markets over the past decade, research into the application of wearable technology within health care has only recently accelerated.⁴ The interest among clinical researchers in these devices largely arises from their ability to provide passive monitoring of biometric data, defined as data relating to characteristics that are specific to an individual: physiologic (eg, heart rate, body temperature) and behavioral (eg, daily step count, number of hours of sleep).⁵ Many new-generation consumer wearables integrate multiple sources of biometric data to allow for detailed and comprehensive quantification of an individual’s health status. Consequently, health systems have begun to adopt consumer wearables as a tool to promote precision medicine.⁶ Several hospital systems have partnered with wearable technology companies to develop platforms that support the interoperability of wearable-derived data with electronic health records.⁷ These programs stand to potentially transform health care delivery by empowering patients’ self-management and improving universal accessibility to care.^8,9 Clinicians should consider how the adoption of consumer wearables into their practices might benefit their specific patient populations. Though prior studies across various surgical fields have employed consumer wearables for prediction of postoperative outcomes,^10-16 no research to date has explored the applications of these devices in otolaryngology. This review highlights the potential role of consumer wearables for patient monitoring in otolaryngology and briefly addresses the barriers and ethical considerations surrounding their adoption in health care.

Methods

An initial Google search was performed to identify all wearable devices approved by the Food and Drug Administration and/or commercially available for consumer purchase in the United States as of June 4, 2021. Key attributes of each device were documented (eg, form factor, sensors, and passively captured parameters). From this list of parameters, we identified the following as relevant to the delivery of patient care in otolaryngology: heart rate, respiratory rate, skin temperature, blood pressure, blood oxygenation levels, blood glucose levels, physical activity data (step count, energy expenditure, distance traveled, hours of active and sedentary time), and sleep data. After further discussion, we conceived the following list of theoretical parameters, which were not explicitly captured by any of the devices from the initial search but could nonetheless improve delivery of care: pain qualification, blood calcium levels, and gait/balance.

A search strategy was developed combining keyword and Medical Subject Headings terms related to wearable technology or clinically relevant parameters (Supplemental Materials 1, available online). Searches were conducted in the PubMed database June 4 to 11, 2021. Manuscripts were excluded for the following reasons: article is (1) not peer reviewed or (2) in English language; (3) wearable device is not designed for continuous long-term wear; (4) wearable device does not passively capture at least 1 biometric parameter relevant to otolaryngology; (5) wearable device is designed exclusively for therapeutic purpose; (6) wearable technology has been ubiquitous in clinical care prior to the past decade (eg, Holter monitors, pedometers); or (7) wearable technology is not available for consumer purchase, including devices that are exclusively built for use in research settings. As a caveat to this final exclusion criterion, articles were included if they described the use of a research-grade device with a biosensor that is also found in a consumer wearable. The PubMed search was complemented by scanning the references of articles that met inclusion criteria. Nonhuman subject research articles were sought to supplement the discussion and form the basis of the section on barriers and ethical considerations surrounding consumer wearables. Data from articles were synthesized into a final review.

The first 2 authors independently appraised the quality of articles that serve as primary evidence of the potential applications of consumer wearables or one of the listed biometric parameters to clinical care. Randomized controlled trials were assessed with the Cochrane Risk of Bias Tool for Randomized Trials (RoB 2.0). Systematic reviews were assessed via the Risk of Bias Assessment Tool for Systematic Reviews. To our knowledge, there is no established tool for evaluating the risk of bias in observational single-cohort studies. As such, we adapted the Quality Assessment Tool for Observational Cohort and Cross-sectional Studies (National Institutes of Health) to evaluate these studies. Final decisions were reached via consensus between the first 2 authors. Tables displaying the risk of bias for each tool-specific domain and overall risk of bias for each manuscript are presented in Supplemental Materials 2 (available online). Given the paucity of literature employing consumer wearables to measure the identified parameters in perioperative care, we did not categorically exclude articles based on the strength of the methodological quality. Instead, where appropriate, we have emphasized the areas where there is limited robust evidence to conclusively support the use of wearable technology for a given purpose.

Discussion

Table 1 highlights key attributes of select consumer wearables that have been previously applied to patient monitoring. Triaxial accelerometers and optical heart rate sensors are the most common biosensors within consumer wearables.⁶ Nonetheless, there remains large variability across consumer wearables regarding form factor and combination of biometric parameters captured. An in-depth discussion on the mechanism by which consumer wearables obtain and transmit biosensor data is beyond the scope of this review.

Table 1.

Key Features of Consumer Wearables Previously Applied to Patient Monitoring. ^a

Device	Form factor	Cost, $	Key sensors	Continuously captured parameters	Sample studies
Fitbit Charge	Smartwatch	130	TXA, optical heart rate sensor, PPG sensor, device temperature sensor	Activity tracker, ^b heart rate, sleep stage/timing, ^c SpO₂ levels, ^c skin temperature, ^c HRV, ^c RR ^c	Low,¹⁰ Daskivich,²⁸ Symer¹²⁷
Fitbit Flex	Smartwatch	100	TXA	Activity tracker	Reed,²⁷ Scheer¹²⁸
Apple Watch	Smartwatch	400	TXA, gyroscope, optical heart rate sensor, PPG sensor, ECG sensor ^d	Activity tracker, heart rate, sleep timing, ^c SpO₂ levels, ^e HRV, ^c RR ^c	Wu²⁴
Samsung Gear Smartwatch	Smartwatch	115	TXA, gyroscope, optical heart rate sensor	Activity tracker, heart rate, sleep stage/timing ^c	Abbadessa¹²⁹
Garmin VivoFit	Smartwatch	80	TXA	Activity tracker	Rossi,¹¹ Carmichael,¹³⁰ Sun¹³¹
Garmin Vivoactive	Smartwatch	330	TXA, gyroscope, optical heart rate sensor, PPG sensor, temperature sensor	Activity tracker, heart rate, sleep stage/timing, ^c SpO₂ levels, ^e RR	Finley¹³²
Xiaomi Mi Band	Smartwatch	43	TXA, gyroscope, optical heart rate sensor	Activity tracker, heart rate, sleep stage/timing ^c	Stienen¹³³
Oura Ring	Smart jewelry	300	TXA, gyroscope, optical heart rate sensor, NTC sensor	Activity tracker, heart rate, skin temperature, sleep stage/timing, ^c HRV, ^c RR ^c	Moshe¹³⁴
Dexcom CGM	Dermal implant	Prescription required; cost to individual varies by insurance coverage	Interstitial fluid glucose sensor	Continuous glucose monitoring	Nair¹⁰⁸
Fitbit Zip	Clip-on	60	TXA	Activity tracker	Appelboom,¹²¹ Mobbs¹³⁵

Abbreviations: CGM, continuous glucose monitor; ECG, electrocardiogram; HRV, heart rate variability; NTC, negative temperature coefficient; PPG, photoplethysmography; RR, respiratory rate; SpO₂, oxygen saturation; TXA, triaxial accelerometer.

Only devices that are commercially available in the United States as of June 2021 are listed. Given the rapid turnover in device models, version numbers are not listed. Data were gathered from device websites and news reports.

Activity trackers generally provide measurement of total daily step count, energy expenditure, distance traveled, and/or number of hours of active and sedentary time.

Parameter is captured only at night.

All listed consumer wearables with ECG sensors offer this feature only with spot checks rather than passive monitoring.

The Apple Watch Series 6 and Garmin Vivoactive measure SpO₂ continuously day and night and allow for spot checks.

There are numerous potential applications of consumer wearables for monitoring otolaryngology patients, such as assessment of mobilization, postoperative pain, sleep quality and timing, and ambulatory blood pressure. These devices may allow for enhanced detection of postoperative complications, such as surgical site infections, perioperative glucose abnormalities, and postoperative hypotension ( Figure 1 ). These applications are detailed in turn.

Figure 1.

Potential applications of consumer wearables for patient monitoring in otolaryngology. BP, blood pressure; HR, heart rate; HRV, heart rate variability; RR, respiratory rate; SpO₂, oxygen saturation.

Assessment and Encouragement of Mobilization

Postoperative ambulation has been suggested to reduce complications after major surgery, such as resection and free flap reconstruction of head and neck cancer (HNC).¹⁷ As such, early postoperative mobilization remains a core principle of all ERAS guidelines (enhanced recovery after surgery).¹⁸ Nonetheless, assessment of inpatient mobilization has traditionally relied on the imprecise recollections of the patient and hospital care team. Accurate measurement of inpatient physical activity level could allow for a better understanding of the degree to which postoperative mobilization may be considered safe and beneficial within distinct otolaryngology surgical populations.¹⁹ Many modern consumer wearables employ triaxial accelerometers to provide an estimate of select parameters of physical activity, such as step count, calorie expenditure, and distance traveled.^20,21 Emerging research across various surgical populations has identified that these parameters may predict the risk of readmission,^10-12,22,23 hospital length of stay,^13,24-28 postoperative complications,^11,12,14 and patient-reported outcome measures^15,16,29-33 (Supplemental Table S1, available online), though the strength of evidence supporting these findings is mixed. Inpatient monitoring via consumer wearables potentially permits indirect assessment of factors contributing to diminished mobilization, such as postoperative pain, thereby prompting tailored interventions.³⁴

Though prior research on consumer wearables in the inpatient postoperative setting remains limited, extensive literature over the past decade has evaluated the application of these devices in the outpatient setting, often as components of structured behavioral change programs.^35,36 These interventions tend to evaluate whether real-time biofeedback provided by consumer wearables, whether alone or in combination with other behavioral incentives, promotes increased physical activity and decreased sedentary behavior. Within the field of otolaryngology, poor preoperative functional performance has been associated with a significantly higher rate of unplanned 30-day readmissions after HNC surgery.³⁷ Behavioral interventions incorporating consumer wearables could optimize preoperative mobilization as a means of prehabilitation. Of note, there is conflicting evidence surrounding the efficacy of prehabilitation: though several studies across various surgical fields suggest that prehabilitation may shorten hospital length of stay^38,39 and reduce the risk of postoperative complications,^40,41 others report that prehabilitation does not improve postoperative outcomes.^42-47 Though there has been some focus on prehabilitation to reduce swallowing dysfunction among patients with HNC,⁴⁸ to our knowledge, no published literature has evaluated the effect of exercise prehabilitation on postoperative outcomes in this population.⁴⁹ Assessment of baseline preoperative physical activity level could also allow for the development of tailored activity goals in the immediate and extended postoperative setting.⁵⁰ This could be particularly beneficial for patients with HNC, as exercise is a known alleviating factor for cancer-related fatigue, which often lasts for months to years after curative treatment.^51,52 The success of these interventions rests on their ability to incentivize sustained patient engagement with prescribed behaviors, a notoriously difficult feat.⁵³ Nonetheless, the use of consumer wearables for assessment and promotion of physical activity among otolaryngology patients remains a clear avenue for future research.^37,54

Assessment of Postoperative Pain

Postoperative pain is a common phenomenon after major surgery that has been suggested to increase the risk for pulmonary complications and the development of chronic pain.^17,55,56 Though pain management is a core component of ERAS protocols, what constitutes appropriate perioperative pain management and how this is best assessed remains a topic of debate and public scrutiny.^57-59 Overprescribing of narcotics in the perioperative setting has been implicated as contributing to the nation’s opioid epidemic. Opioids were responsible for nearly 70,000 overdose deaths in 2020 alone.⁶⁰ Otolaryngologists and other surgeons face the challenge of adequately treating acute pain needs while recognizing their role in preventing broader deleterious health trends.

Pain is a complex biopsychosocial phenomenon unique to the individual, yet assessment of pain is traditionally restricted to unidimensional metrics, such as the Numeric Pain Rating Scale. However, these measures are subject to various cognitive biases and cannot be reliably captured in certain noncommunicative patient populations.⁶¹ The Joint Commission recognizes the limitations in metrics such as the Numeric Pain Rating Scale and encourages development of comprehensive assessments that incorporate the impact of pain on physical function.⁶² Given the known association between pain and delayed mobilization after major surgery, it is tempting to look to step count as measured by consumer wearables as a proxy indicator of postoperative pain levels.³⁴ However, the degree to which accelerometry data correlate with reported pain scores remains disputed.⁶¹ Thus, while accelerometry may provide some insight, these data alone are likely insufficient to characterize postoperative pain levels.

Alternatively, since pain is known to induce a sympathetic autonomic response, it may be possible to assess postoperative pain by assessing and trending physiologic markers that are continuously captured by many consumer wearables.⁶³ Specifically, increased pain has been associated with changes to resting heart rate and heart rate variability (HRV), a widely used metric of autonomic nervous system function.⁶⁴ Select consumer wearables with optical heart rate sensors deliver an approximation of heart rate and HRV by using reflective photoplethysmography, a technique in which changes in blood volume are measured by the amount of light reflected off illuminated skin.^65,66 Other devices report a direct estimate of HRV via single-lead electrocardiogram.^6,64 By continuously trending these parameters of autonomic function, consumer wearables could establish a patient’s preoperative baseline values and theoretically allow for better delineation of the timing and intensity of postoperative pain episodes.

Nonetheless, biometrics such as heart rate or HRV may be influenced by any source of sympathetic stimulation, such as inflammation, emotional stress, or physical activity.⁶⁷ Consequently, their specificity in identifying or quantifying postoperative pain remains low, especially in the outpatient setting.⁶⁸ The ability of artificial intelligence to predict subjective levels of pain from composite algorithms incorporating electrocardiogram data, skin temperature, accelerometry data, and/or galvanic skin response appears promising,^69-72 though the generalizability of these findings is questionable due to the use of simulated pain in healthy volunteers. These findings require validation in real-world assessments among patients with actual pain.⁷³ Thus, while the inclusion of objective biometric parameters to complement the evaluation of postoperative pain remains an area of significant interest, there exists no widely available tool to support clinicians to this end.

Assessment of Sleep Quality and Timing

Sleep is a vital function of life that is closely tied to physical health and quality of life.⁷⁴ Hospitalized patients are frequently subject to a variety of sleep disturbances, such as environmental noise and routine overnight measurement of vital signs. Sleep disturbances are especially common among patients with HNC, resulting in an elevated risk for postoperative delirium.^75,76 Furthermore, the stress from poor sleep when coupled with surgical stress may prolong postoperative recovery.⁷⁷ Though measurement of sleep parameters in all hospitalized patients by polysomnography would be cost inefficient, infeasible, and poorly tolerated, sleep quality is rarely assessed even by subjective metrics.⁷⁸ Consumer wearables present a novel method by which sleep quality and timing may be unobtrusively assessed in the perioperative setting. Many modern consumer wearables incorporate data from triaxial accelerometers and optical heart rate sensors into proprietary integrative algorithms to provide estimates of various sleep parameters, including total sleep time, wake after sleep onset, and sleep efficiency.⁷⁹ Though prior literature has suggested that consumer wearables tend to overestimate total sleep time and sleep efficiency as compared with polysomnography, recent studies on new-generation devices that incorporate multisensor data have demonstrated high validity that rivals many research-grade devices.^80,81 As such, consumer wearables remain a viable option for the assessment of perioperative sleep quality and could allow for tailored interventions in the inpatient setting to promote sleep hygiene.⁸²

Wearable-derived data on sleep quality and timing could be similarly used in the outpatient setting, especially in select populations that are known to suffer from poor sleep, such as patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) or chronic rhinosinusitis.⁸³ Identification of parameters suggestive of persistent poor sleep quality or increased cardiovascular risk could theoretically persuade patients to adhere to recommended treatment plans.^84,85 Importantly, real-time feedback delivered by consumer wearables must be coupled with patient education and may require additional behavioral incentives to promote patient engagement. Broadly, there is a significant need for prospective research evaluating behavioral interventions incorporating consumer wearables to promote patients’ adoption of healthy sleep habits.⁸⁶

There is ongoing research into the ability of consumer wearables to detect clinically relevant OSAHS parameters, such as sleep stage timing and number of nightly apneic episodes.^87-89 Though the application of artificial intelligence deep learning algorithms incorporating wearable-generated data appears promising, this is a growing area of research for which the evidence base remains limited. Modern consumer wearables remain limited in their ability to assess these parameters and should not yet be considered substitutes for gold standard polysomnography.^90,91

Assessment of Ambulatory Blood Pressure

Primary hypertension is seen in nearly half of adults in the United States and contributes to an elevated risk of stroke, myocardial infarction, and mortality, especially in the perioperative setting.^92-95 Select cuff-based wristwatches, such as the Omron HeartGuide, provide an intermittent reading of ambulatory blood pressure.⁹⁶ Other consumer wearables, such as the Biobeat wrist monitor, continuously track blood pressure via reflective photoplethysmography.^97,98 These devices have not been studied in the preoperative setting, where they could facilitate prehabilitation or prediction of clinical outcomes before major surgery. By trending patients’ blood pressure in the days to weeks prior to surgery, otolaryngologists and anesthesiologists may obtain a better impression of their baselines, potentially reducing the rate of same-day surgery cancellations due to uncontrolled hypertension.⁹⁹ Alternatively, given the known association between OSAHS and incident hypertension, ambulatory blood pressure readings gathered from consumer wearables could be integrated into metrics of clinical success after sleep surgery.¹⁰⁰ Finally, continuous blood pressure monitoring could be incorporated as part of a larger behavioral intervention to influence patients’ adherence to continuous positive airway pressure therapy, which has been demonstrated to mitigate the risk of hypertension in patients with OSAHS.¹⁰¹

Enhanced Detection of Postoperative Complications

Consumer wearables could serve to complement data collection among patients in the hospital general ward. Through their advantage of continuous passive observation, these devices may trend data that are otherwise captured at only select intervals, thereby allowing for early recognition of markers suggestive of postoperative complications. Prior literature has suggested that continuous inpatient monitoring with wearable devices, when coupled with automated alerts in the setting of clinical deterioration, may result in improved patient outcomes.^102,103

Postoperative surgical site infection is a common complication that significantly influences morbidity and mortality among patients with HNC.¹⁰⁴ Infection status in hospitalized patients on the general ward is traditionally assessed via intermittent physical examination and monitoring of vital signs. Yet, physiologic parameters suggestive of a postoperative infection may develop in only the interval between vital sign checks, resulting in a delay to treatment.¹⁰² A randomized controlled trial by Downey et al found that a nonconsumer wearable patch with continuous vital sign monitoring allowed for earlier administration of antibiotics after evidence of sepsis and lower 30-day readmission rates.¹⁰⁵ However, no studies to date have specifically applied consumer wearables for identification of infection in the postoperative setting. Therefore, the ability of consumer wearables to detect derangements in physiologic parameters suggestive of postoperative infection or sepsis is an area of potentially high-impact future research.

Perioperative hyperglycemia represents a significant predictor of surgical complications, medical complications, and surgical site infections among patients with HNC undergoing microvascular reconstruction.¹⁰⁶ Inpatient blood glucose monitoring is typically performed by taking a finger-prick blood sample at select intervals, though this allows for significant gaps during which glucose levels are not assessed. Moreover, these methods are invasive and inconvenient, especially for patients who require blood glucose monitoring several times daily.⁴ Though continuous glucose monitors (CGMs) such as the Dexcom G6 have been widely adopted in the ambulatory setting, the potential application of these devices within the inpatient setting has not been well explored.^107,108 In a recent study, Davis et al identified via pooled analysis that CGM technology may serve as a reliable tool for use among noncritically ill hospitalized patients.^109,110 Thus, the ability to transmit interstitial glucose levels from a patient’s CGM to the surgical team could allow for improved inpatient blood glucose management and optimization during postoperative recovery.

Postoperative hypotension is frequently seen among surgical patients and has been associated with increased risk of myocardial injury, acute kidney injury, and mortality.^111,112 Current data suggest that postoperative hypotension after major surgery is frequently undetected in the general care ward.¹¹¹ Therefore, continuous inpatient monitoring by consumer wearables coupled with automated alerts to the care team could permit early detection and intervention in the setting of critically low or elevated blood pressure readings.

Barriers and Ethical Considerations Surrounding Adoption of Consumer Wearables in Health Care

There remain various limitations and ethical considerations that must be addressed before the widespread integration of consumer wearables in clinical care. Notably, there are many concerns regarding the privacy and ownership of wearable-generated health data. Biometric data collected by consumer wearables currently fall in a gray area within the bounds of the Health Insurance Portability and Accountability Act (HIPAA).¹¹³ Wearable-derived data that are shared with health care organizations or integrated into electronic health records must be protected and stored in a HIPAA-compliant manner. As such, many wearable tech companies are building HIPAA-compliant platforms, such as Google Fit and Apple HealthKit, which support the interoperability of wearable-derived data with electronic health records.⁷ As these partnerships develop, wearable tech companies and health systems must ensure that the transfer of data across platforms remains secure against the risk of leakage and tampering.^114,115 Data that are solely gathered for consumer use do not fall under the purview of HIPAA. Consequently, many wearable tech companies have historically required users to give up their right to privacy over wearable-generated health data, allowing these companies to potentially take advantage of these data for commercial use or targeted advertising.^9,116 There is a pressing need for the development of a legal infrastructure that delineates the rights and responsibilities of these companies regarding patient-generated health data.¹¹⁷

The use of wearable-derived health data by insurance companies is another area of concern. Many health and life insurance companies are increasingly offering their customers discounts on consumer wearables or their insurance premiums if certain activity targets are met. While this could be viewed as a move to promote healthy behaviors and reduce future payouts, it is certainly possible that insurers could use data suggestive of poor health or lifestyle habits to penalize customers with risk-adjusted higher premiums or even coverage denials, as this is not currently covered under the Affordable Care Act clause on preexisting conditions.¹¹⁸ Insurance companies will undoubtedly play an increasing role over the next decade in promoting the accessibility and affordability of consumer wearables. As such, conversations among key stakeholders are necessary to ensure appropriate oversight on how insurance companies may use consumers’ wearable-derived data.

Many clinicians and patients may be skeptical of using wearable-derived data in health care since few consumer wearables have been cleared as medical devices through the Food and Drug Administration’s 510(k) program.¹¹⁹ The accuracy of data collected from consumer wearables is subject to several factors, such as device model, device nonuse, environmental factors, and motion artifact.^67,120 Device accuracy is additionally limited by decreased ambulation speed and stride length, which are often seen in the postoperative setting.^121,122 Prior research has suggested that reflective photoplethysmography may express variable degrees of accuracy when applied to individuals with darker skin tones.^123,124 This remains disputed⁶⁷ but, if true, carries major implications for the equitable and meaningful use of these devices in diverse populations. Validation of consumer wearables for the detection of clinically relevant end points requires the design of prospective studies in target populations and settings in which device accuracy is likely to remain high. For example, measurement of physiologic parameters reflective of sympathetic stimulation (eg, heart rate, HRV, respiratory rate) should occur only at rest, as these values are highly variable with physical activity.^67,125 Even if consumer wearables are validated for monitoring select populations, health care providers should not rely exclusively on wearable-derived data to guide decision making. Given the potential for misdiagnosis due to unreliable data or false alarms, the responsibilities of clinicians in responding to wearable-derived data need to be delineated.⁴

Furthermore, some patients may find the devices to be uncomfortable or perceive the process of continuous remote monitoring as intrusive.¹²⁶ Patients may consider remote monitoring to be a burden if they are tasked with interpreting and making decisions that they feel should be done by their health care team.⁹ Conversely, a care team may not be equipped to handle this potential deluge of data. Learning to interpret heterogeneously collected metrics and apply findings to clinical care, particularly in the absence of established best practices, may require considerable time, effort, and additional training.⁷

Finally, remote monitoring with consumer wearables requires broadband connectivity, technology literacy, and, in many cases, a compatible smartphone device. This may explain a lower rate of consumer wearable use among elderly, socioeconomically disadvantaged, and less educated populations.³ Underrepresentation of certain populations in training data and validation study cohorts would limit the generalizability of research findings. Clinicians and patients should employ shared decision making to thoroughly discuss the risks, benefits, and specific concerns surrounding consumer wearables for patient monitoring.¹²³ As each of these devices carries a cost ranging from $43 to $400, health systems or insurance companies should consider providing consumer wearables directly to patients at no cost. Doing otherwise risks promoting health disparities, as only patients with the means to provide their own wearables would receive the potential benefits. However, for these larger entities to justify offsetting the costs to patients, there must be sufficient evidence substantiating the benefits of incorporating these devices in clinical care. We recommend that future studies conduct cost-effectiveness analyses to estimate the long-term expenses and returns of uniformly providing consumer wearables to specific patient populations.

Implications for Practice

Consumer wearables may significantly transform health care delivery as tools to promote precision medicine. Limited literature from other surgical fields supports the ability of wearable-derived data to predict postoperative outcomes and promote the adoption of healthy behaviors. Nonetheless, research on the role of consumer wearables for monitoring otolaryngology patients is lacking. Interventions employing consumer wearables to assess and encourage physical activity and sleep in the perioperative period represent an actionable and high-impact area of research. Otolaryngologists’ understanding of the potential role and limitations of consumer wearables could allow for well-designed prospective studies that validate the utility of these devices in our field.

Supplemental Material

sj-docx-1-oto-10.1177_01945998211061681 – Supplemental material for Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

Supplemental material, sj-docx-1-oto-10.1177_01945998211061681 for Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review by Shaan N. Somani, Katherine M. Yu, Alexander G. Chiu, Kevin J. Sykes and Jennifer A. Villwock in Otolaryngology–Head and Neck Surgery

Supplemental Material

sj-docx-2-oto-10.1177_01945998211061681 – Supplemental material for Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

Supplemental material, sj-docx-2-oto-10.1177_01945998211061681 for Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review by Shaan N. Somani, Katherine M. Yu, Alexander G. Chiu, Kevin J. Sykes and Jennifer A. Villwock in Otolaryngology–Head and Neck Surgery

Supplemental Material

sj-docx-3-oto-10.1177_01945998211061681 – Supplemental material for Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

Supplemental material, sj-docx-3-oto-10.1177_01945998211061681 for Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review by Shaan N. Somani, Katherine M. Yu, Alexander G. Chiu, Kevin J. Sykes and Jennifer A. Villwock in Otolaryngology–Head and Neck Surgery

Footnotes

Acknowledgements

We thank Jacob White for his assistance in designing search queries and conducting the initial literature search.

Authorship Contributions

Shaan N. Somani, conception and design, drafting and critically revising manuscript, final approval; Katherine M. Yu, conception and design, critically revising manuscript, final approval; Alexander G. Chiu, conception and design, critically revising manuscript, final approval; Kevin J. Sykes, conception and design, critically revising manuscript, final approval; Jennifer A. Villwock, conception and design, critically revising manuscript, final approval.

Disclosures

Competing interests: None.

Sponsorships: None.

Funding source: None.

ORCID iD

Shaan N. Somani

Supplemental Material

Additional supporting information is available in the online version of the article.

References

Gao

Emaminejad

Nyein

HYY

, et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature. 2016;529(7587):509-514. doi:10.1038/nature16521

International Data Corporation. Consumer enthusiasm for wearable devices drives the market to 28.4% growth in 2020, according to IDC. Published 2021. Accessed June 4, 2021. https://www.idc.com/getdoc.jsp?containerId=prUS47534521

Chandrasekaran

Katthula

Moustakas

. Patterns of use and key predictors for the use of wearable health care devices by US adults: insights from a national survey. J Med Internet Res. 2020;22(10):e22443. doi:10.2196/22443

Zhang

Xie

, et al. Wearable health devices in health care: narrative systematic review. JMIR Mhealth Uhealth. 2020;8(11):e18907. doi:10.2196/18907

Koong

Yang

Tseng

. A user authentication scheme using physiological and behavioral biometrics for multitouch devices. Sci World J. 2014;2014:781234. doi:10.1155/2014/781234

Jeong

Bychkov

Searson

. Wearable devices for precision medicine and health state monitoring. IEEE Trans Biomed Eng. 2019;66(5):1242-1258. doi:10.1109/TBME.2018.2871638

Dinh-Le

Chuang

Chokshi

Mann

. Wearable health technology and electronic health record integration: scoping review and future directions. JMIR Mhealth Uhealth. 2019;7(9):e12861. doi:10.2196/12861

Weiss

Rydland

Øversveen

Jensen

Solhaug

Krokstad

. Innovative technologies and social inequalities in health: a scoping review of the literature. PLoS One. 2018;13(4):e0195447. doi:10.1371/journal.pone.0195447

Lucivero

Jongsma

. A mobile revolution for healthcare? Setting the agenda for bioethics. J Med Ethics. 2018;44(10):685-689. doi:10.1136/medethics-2017-104741

10.

Low

Bovbjerg

Ahrendt

, et al. Fitbit step counts during inpatient recovery from cancer surgery as a predictor of readmission. Ann Behav Med. 2018;52(1):88-92. doi:10.1093/abm/kax022

11.

Rossi

Melstrom

Fong

Sun

. Predicting post-discharge cancer surgery complications via telemonitoring of patient-reported outcomes and patient-generated health data. J Surg Oncol. 2021;123(5):1345-1352. doi:10.1002/jso.26413

12.

Cos

Williams

, et al. Predicting outcomes in patients undergoing pancreatectomy using wearable technology and machine learning: prospective cohort study. J Med Internet Res. 2021;23(3):e23595. doi:10.2196/23595

13.

Sossenheimer

Erondu

, et al. Using wearable biosensors to predict length of stay for patients with IBD after bowel surgery. Dig Dis Sci. Published online March 24, 2021. doi:10.1007/s10620-021-06910-w

14.

Hedrick

Hassinger

Myers

, et al. Wearable technology in the perioperative period: predicting risk of postoperative complications in patients undergoing elective colorectal surgery. Dis Colon Rectum. 2020;63(4):538-544. doi:10.1097/DCR.0000000000001580

15.

Bini

Shah

Bendich

Patterson

Hwang

Zaid

. Machine learning algorithms can use wearable sensor data to accurately predict six-week patient-reported outcome scores following joint replacement in a prospective trial. J Arthroplast. 2019;34(10):2242-2247. doi:10.1016/j.arth.2019.07.024

16.

Karas

Marinsek

Goldhahn

Foschini

Ramirez

Clay

. Predicting subjective recovery from lower limb surgery using consumer wearables. Digit Biomark. 2020;4(suppl 1):73-86. doi:10.1159/000511531

17.

Twomey

Matthews

Nakoneshny

, et al. Impact of early mobilization on recovery after major head and neck surgery with free flap reconstruction. Cancers. 2021;13(12):2852. doi:10.3390/cancers13122852

18.

Coyle

Main

Hughes

, et al. Enhanced recovery after surgery (ERAS) for head and neck oncology patients. Clin Otolaryngol. 2016;41(2):118-126. doi:10.1111/coa.12482

19.

Twomey

Culos-Reed

Daun

Ferber

Dort

. Wearable activity trackers and mobilization after major head and neck cancer surgery: you can’t improve what you don’t measure. Int J Surg. 2020;84:120-124. doi:10.1016/j.ijsu.2020.10.032

20.

Bassett

Toth

LaMunion

Crouter

. Step counting: a review of measurement considerations and health-related applications. Sports Med. 2017;47(7):1303-1315. doi:10.1007/s40279-016-0663-1

21.

Jones

Kang

Welk

Lee

. How valid are wearable physical activity trackers for measuring steps? Eur J Sport Sci. 2017;17(3):360-368. doi:10.1080/17461391.2016.1255261

22.

Kane

Hassinger

Myers

, et al. Wearable technology and the association of perioperative activity level with 30-day readmission among patients undergoing major colorectal surgery. Surg Endosc. Published online March 29, 2021. doi:10.1007/s00464-021-08449-3

23.

Doryab

Dey

Kao

Low

. Modeling biobehavioral rhythms with passive sensing in the wild: a case study to predict readmission risk after pancreatic surgery. Proc ACM Interact Mob Wearable Ubiquitous Technol. 2019;3(1):1-21. doi:10.1145/3314395

24.

Chang

, et al. Wearable-based mobile health app in gastric cancer patients for postoperative physical activity monitoring: Focus group study. JMIR Mhealth Uhealth. 2019;7(4):1-12. doi:10.2196/11989

25.

Wolk

Linke

Bogner

, et al. Use of activity tracking in major visceral surgery—the Enhanced Perioperative Mobilization Trial: a randomized controlled trial. J Gastrointest Surg. 2019;23(6):1218-1226. doi:10.1007/s11605-018-3998-0

26.

Cook

Thompson

Prinsen

Dearani

Deschamps

. Functional recovery in the elderly after major surgery: assessment of mobility recovery using wireless technology. Ann Thorac Surg. 2013;96(3):1057-1061. doi:10.1016/j.athoracsur.2013.05.092

27.

Reed

Tabone

Szoka

Abunnaja

Bailey

. The use of an activity tracker to objectively measure inpatient activity after bariatric surgery. Surg Obes Relat Dis. 2021;17(1):90-95. doi:10.1016/j.soard.2020.08.033

28.

Daskivich

Houman

Lopez

, et al. Association of wearable activity monitors with assessment of daily ambulation and length of stay among patients undergoing major surgery. JAMA Netw Open. 2019;2(2):e187673. doi:10.1001/jamanetworkopen.2018.7673

29.

Kim

Nam

Choi

Han

Jeon

Park

. The usefulness of a wearable device in daily physical activity monitoring for the hospitalized patients undergoing lumbar surgery. J Korean Neurosurg Soc. 2019;62(5):561-566. doi:10.3340/jkns.2018.0131

30.

Massouh

Martin

Chan

, et al. Is activity tracker-measured ambulation an accurate and reliable determinant of postoperative quality of recovery? A prospective cohort validation study. Anesth Analg. 2019;129(4):1144-1152. doi:10.1213/ANE.000 0000000003913

31.

Ghent

Mobbs

Betteridge

Choy

. Assessment and post-intervention recovery after surgery for lumbar disk herniation based on objective gait metrics from wearable devices using the gait posture index. World Neurosurg. 2020;142:e111-e116. doi:10.1016/j.wneu.2020.06.104

32.

Patterson

Chung

Bendich

Barry

Bini

. Wearable activity sensors and early pain after total joint arthroplasty. Arthroplast Today. 2020;6(1):68-70. doi:10.1016/j.artd.2019.12.006

33.

Low

Vega

, et al. Digital biomarkers of symptom burden self-reported by perioperative patients undergoing pancreatic surgery: prospective longitudinal study. JMIR Cancer. 2021;7(2):e27975. doi:10.2196/27975

34.

Morrison

Magaziner

McLaughlin

, et al. The impact of post-operative pain on outcomes following hip fracture. Pain. 2003;103(3):303-311. doi:10.1016/S0304-3959(02)00458-X

35.

Düking

Tafler

Wallmann-Sperlich

Sperlich

Kleih

. Behavior change techniques in wrist-worn wearables to promote physical activity: content analysis. JMIR Mhealth Uhealth. 2020;8(11):e20820. doi:10.2196/20820

36.

Chen

. Wearable activity trackers for promoting physical activity: a systematic meta-analytic review. Int J Med Inform. 2021;152:104487. doi:10.1016/j.ijmedinf.2021.104487

37.

Sindhar

Kallogjeri

Wildes

Avidan

Piccirillo

. Association of preoperative functional performance with outcomes after surgical treatment of head and neck cancer: a clinical severity staging system. JAMA Otolaryngol Head Neck Surg. 2019;145(12):1128-1136. doi:10.1001/jamaoto.2019.1035

38.

Santa Mina

Clarke

Ritvo

, et al. Effect of total-body prehabilitation on postoperative outcomes: a systematic review and meta-analysis. Physiotherapy. 2014;100(3):196-207. doi:10.1016/j.physio.2013.08.008

39.

Halliday

Doganay

Wynter-Blyth

Hanna

Moorthy

. The impact of prehabilitation on post-operative outcomes in oesophageal cancer surgery: a propensity score matched comparison. J Gastrointest Surg. Published online December 2, 2020. doi:10.1007/s11605-020-04881-3

40.

Moran

Guinan

McCormick

, et al. The ability of prehabilitation to influence postoperative outcome after intra-abdominal operation: a systematic review and meta-analysis. Surgery. 2016;160(5):1189-1201. doi:10.1016/j.surg.2016.05.014

41.

Barberan-Garcia

Ubré

Roca

, et al. Personalised prehabilitation in high-risk patients undergoing elective major abdominal surgery: a randomized blinded controlled trial. Ann Surg. 2018;267(1):50-56. doi:10.1097/SLA.0000000000002293

42.

Carli

Bousquet-Dion

Awasthi

, et al. Effect of multimodal prehabilitation vs postoperative rehabilitation on 30-day postoperative complications for frail patients undergoing resection of colorectal cancer: a randomized clinical trial. JAMA Surg. 2020;155(3):233-242. doi:10.1001/jamasurg.2019.5474

43.

Ferreira

Lawson

Ekmekjian

Carli

Scheede-Bergdahl

Chevalier

. Effects of preoperative nutrition and multimodal prehabilitation on functional capacity and postoperative complications in surgical lung cancer patients: a systematic review. Support Care Cancer. 2021Oct;29(10):5597-5610. doi:10.1007/s00520-021-06161-5

44.

Matthew

Lopez

, et al. Prehabilitation and acute postoperative physical activity in patients undergoing radical prostatectomy: a secondary analysis from an RCT. Sports Med Open. 2019;5(1):18. doi:10.1186/s40798-019-0191-2

45.

Ausania

Senra

Meléndez

Caballeiro

Ouviña

Casal-Núñez

. Prehabilitation in patients undergoing pancreaticoduodenectomy: a randomized controlled trial. Rev Esp Enferm Dig. 2019;111(8):603-608. doi:10.17235/REED.2019.6182/2019

46.

Luther

Gabriel

Watson

Francis

. The impact of total body prehabilitation on post-operative outcomes after major abdominal surgery: a systematic review. World J Surg. 2018;42(9):2781-2791. doi:10.1007/s00268-018-4569-y

47.

Janssen

ERC

Punt

Clemens

Staal

Hoogeboom

Willems

. Current prehabilitation programs do not improve the postoperative outcomes of patients scheduled for lumbar spine surgery: a systematic review with meta-analysis. J Orthop Sports Phys Ther. 2021;51(3):103-114. doi:10.2519/JOSPT.2021.9748

48.

Loewen

Jeffery

Rieger

Constantinescu

. Prehabilitation in head and neck cancer patients: a literature review. J Otolaryngol Head Neck Surg. 2021;50(1):2. doi:10.1186/s40463-020-00486-7

49.

Dort

Twomey

Culos-Reed

. Exercise prehabilitation—supporting recovery from major head and neck cancer surgery. JAMA Otolaryngol Head Neck Surg. 2020;146(8):689-690. doi:10.1001/jamaoto.2020.1346

50.

Kim

, et al. Effects of an activity tracker with feedback on physical activity in women after midline laparotomy: a randomized controlled trial. J Obstet Gynaecol Res. 2021;47(7):2544-2550. doi:10.1111/jog.14807

51.

Twomey

Yeung

Wrightson

Millet

Culos-Reed

. Post-exertional malaise in people with chronic cancer-related fatigue. J Pain Symptom Manage. 2020;60(2):407-416. doi:10.1016/j.jpainsymman.2020.02.012

52.

Blount

McDonough

Gao

. Effect of wearable technology-based physical activity interventions on breast cancer survivors’ physiological, cognitive, and emotional outcomes: a systematic review. J Clin Med. 2021;10(9):2015. doi:10.3390/jcm10092015

53.

Grimes

Outtrim

Griffin

Ercole

. Accelerometery as a measure of modifiable physical activity in high-risk elderly preoperative patients: a prospective observational pilot study. BMJ Open. 2019;9(11):e032346. doi:10.1136/bmjopen-2019-032346

54.

Baldwin

Parry

Norton

Williams

Lewis

. A scoping review of interventions using accelerometers to measure physical activity or sedentary behaviour during hospitalization. Clin Rehabil. 2020;34(9):1157-1172. doi:10.1177/0269215520932965

55.

Ballantyne

Carr

DeFerranti

, et al. The comparative effects of postoperative analgesic therapies on pulmonary outcome: cumulative meta-analyses of randomized, controlled trials. Anesth Analg. 1998;86(3):598-612. doi:10.1097/00000539-199803000-00032

56.

Peters

Sommer

de Rijke

, et al. Somatic and psychologic predictors of long-term unfavorable outcome after surgical intervention. Ann Surg. 2007;245(3):487-494. doi:10.1097/01.sla.0000245495.79781.65

57.

Brummett

Waljee

Goesling

, et al. New persistent opioid use after minor and major surgical procedures in US adults. JAMA Surg. 2017;152(6):e170504. doi:10.1001/jamasurg.2017.0504

58.

Bicket

Long

Pronovost

Alexander

. Prescription opioid analgesics commonly unused after surgery: a systematic review. JAMA Surg. 2017;152(11):1066-1071. doi:10.1001/jamasurg.2017.0831

59.

Serrell

Greenberg

Borza

. Surgeons and perioperative opioid prescribing: an underappreciated contributor to the opioid epidemic. Cancer. 2021;127(2):184-187. doi:10.1002/cncr.33199

60.

Centers for Disease Control and Prevention. Provisional drug overdose death counts. Published 2021. Accessed July 21, 2021. https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm

61.

Leroux

Rzasa-Lynn

Crainiceanu

Sharma

. Wearable devices: current status and opportunities in pain assessment and management. Digit Biomark. 2021;5(1):89-102. doi:10.1159/000515576

62.

Baker

. History of the Joint Commission’s pain standards: lessons for today’s prescription opioid epidemic. JAMA. 2017;317(11):1117-1118. doi:10.1001/jama.2017.0935

63.

Schlereth

Birklein

. The sympathetic nervous system and pain. Neuromolecular Med. 2008;10(3):141-147. doi:10.1007/s12017-007-8018-6

64.

Lee

Yoon

Park

. Smart ECG monitoring patch with built-in R-peak detection for long-term HRV analysis. Ann Biomed Eng. 2016;44(7):2292-2301. doi:10.1007/s10439-015-1502-5

65.

Mejía-Mejía

Budidha

Abay

May

Kyriacou

. Heart rate variability (HRV) and pulse rate variability (PRV) for the assessment of autonomic responses. Front Physiol. 2020;11:779. doi:10.3389/fphys.2020.00779

66.

Stone

Ulman

Tran

, et al. Assessing the accuracy of popular commercial technologies that measure resting heart rate and heart rate variability. Front Sports Act Living. 2021;3:585870. doi:10.3389/fspor.2021.585870

67.

Bent

Goldstein

Kibbe

Dunn

. Investigating sources of inaccuracy in wearable optical heart rate sensors. NPJ Digit Med. 2020;3:18. doi:10.1038/s41746-020-0226-6

68.

Cowen

Stasiowska

Laycock

Bantel

. Assessing pain objectively: the use of physiological markers. Anaesthesia. 2015;70(7):828-847. doi:10.1111/anae.13018

69.

Johnson

Yang

Gollarahalli

, et al. Use of mobile health apps and wearable technology to assess changes and predict pain during treatment of acute pain in sickle cell disease: feasibility study. JMIR Mhealth Uhealth. 2019;7(12):e13671. doi:10.2196/13671

70.

Jiang

Mieronkoski

Syrjälä

, et al. Acute pain intensity monitoring with the classification of multiple physiological parameters. J Clin Monit Comput. 2019;33(3):493-507. doi:10.1007/s10877-018-0174-8

71.

Naeini

Subramanian

Calderon

, et al. Pain recognition with electrocardiographic features in postoperative patients: method validation study. J Med Internet Res. 2021;23(5):e25079. doi:10.2196/25079

72.

Aqajari

SAH

Cao

Naeini

, et al. Pain assessment tool with electrodermal activity for postoperative patients: method validation study. JMIR Mhealth Uhealth. 2021;9(5):e25258. doi:10.2196/25258

73.

Naeini

Jiang

Syrjälä

, et al. Prospective study evaluating a pain assessment tool in a postoperative environment: protocol for algorithm testing and enhancement. JMIR Res Protoc. 2020;9(7):e17783. doi:10.2196/17783

74.

Worley

. The extraordinary importance of sleep: the detrimental effects of inadequate sleep on health and public safety drive an explosion of sleep research. P T. 2018;43(12):758-763.

75.

Santoso

AMM

Jansen

de Vries

Leemans

van Straten

Verdonck-de Leeuw

. Prevalence of sleep disturbances among head and neck cancer patients: a systematic review and meta-analysis. Sleep Med Rev. 2019;47:62-73. doi:10.1016/j.smrv.2019.06.003

76.

Zhu

Wang

Liu

, et al. Risk factors for postoperative delirium in patients undergoing major head and neck cancer surgery: a meta-analysis. Jpn J Clin Oncol. 2017;47(6):505-511. doi:10.1093/jjco/hyx029

77.

Dolan

Huh

Tiwari

Sproat

Camilleri-Brennan

. A prospective analysis of sleep deprivation and disturbance in surgical patients. Ann Med Surg (Lond). 2016;6:1-5. doi:10.1016/j.amsu.2015.12.046

78.

Hoey

Fulbrook

Douglas

. Sleep assessment of hospitalised patients: a literature review. Int J Nurs Stud. 2014;51(9):1281-1288. doi:10.1016/j.ijnurstu.2014.02.001

79.

De Zambotti

Cellini

Goldstone

Colrain

Baker

. Wearable sleep technology in clinical and research settings. Med Sci Sports Exerc. 2019;51(7):1538-1557. doi:10.1249/MSS.0000000000001947

80.

Evenson

Goto

Furberg

. Systematic review of the validity and reliability of consumer-wearable activity trackers. Int J Behav Nutr Phys Act. 2015;12:159. doi:10.1186/s12966-015-0314-1

81.

Moreno-Pino

Porras-Segovia

López-Esteban

Artés

Baca-García

. Validation of Fitbit Charge 2 and Fitbit Alta HR against polysomnography for assessing sleep in adults with obstructive sleep apnea. J Clin Sleep Med. 2019;15(11):1645-1653. doi:10.5664/jcsm.8032

82.

Dubose

Hadi

. Improving inpatient environments to support patient sleep. Int J Qual Health Care. 2016;28(5):540-553. doi:10.1093/intqhc/mzw079

83.

Mahdavinia

Schleimer

Keshavarzian

. Sleep disruption in chronic rhinosinusitis. Expert Rev Anti Infect Ther. 2017;15(5):457-465. doi:10.1080/14787210.2017.1294063

84.

Ucak

Dissanayake

Sutherland

de Chazal

Cistulli

. Heart rate variability and obstructive sleep apnea: current perspectives and novel technologies. J Sleep Res. 2021;30(4):e13274. doi:10.1111/jsr.13274

85.

Dedhia

Shah

Bliwise

, et al. Hypoglossal nerve stimulation and heart rate variability: analysis of STAR Trial responders. Otolaryngol Head Neck Surg. 2019;160(1):165-171. doi:10.1177/0194599818800284

86.

Baron

Duffecy

Berendsen

Cheung Mason

Lattie

Manalo

. Feeling validated yet? A scoping review of the use of consumer-targeted wearable and mobile technology to measure and improve sleep. Sleep Med Rev. 2018;40:151-159. doi:10.1016/j.smrv.2017.12.002

87.

Papini

Fonseca

van Gilst

Bergmans

JWM

Vullings

Overeem

. Wearable monitoring of sleep-disordered breathing: estimation of the apnea-hypopnea index using wrist-worn reflective photoplethysmography. Sci Rep. 2020;10(1):13512. doi:10.1038/s41598-020-69935-7

88.

Altini

Kinnunen

. The promise of sleep: a multi-sensor approach to accurate sleep stage detection using the Oura ring. Sensors. 2021;21(13):4302. doi:10.3390/s21134302

89.

Fonseca

Weysen

Goelema

, et al. Validation of photoplethysmography-based sleep staging compared with polysomnography in health middle-aged adults. Sleep. 2017;40(7). doi:10.1093/sleep/zsx097

90.

Haghayegh

Khoshnevis

Smolensky

Diller

Castriotta

. Deep neural network sleep scoring using combined motion and heart rate variability data. Sensors. 2021;21(1):25. doi:10.3390/s21010025

91.

Korkalainen

Aakko

Duce

, et al. Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea. Sleep. 2020;43(11):zsaa098. doi:10.1093/sleep/zsaa098

92.

Aronow

. Management of hypertension in patients undergoing surgery. Ann Transl Med. 2017;5(10):227. doi:10.21037/atm.2017.03.54

93.

Nagele

Rao

Penta

, et al. Postoperative myocardial injury after major head and neck cancer surgery. Head Neck. 2011;33(8):1085-1091. doi:10.1002/hed.21577

94.

Staessen

Yang

Melgarejo

, et al. Association of office and ambulatory blood pressure with mortality and cardiovascular outcomes. JAMA. 2019;322(5):409-420. doi:10.1001/jama.2019.9811

95.

Centers for Disease Control and Prevention. Facts about hypertension. Published 2021. Accessed July 21, 2021. https://www.cdc.gov/bloodpressure/facts.htm

96.

Kario

. Management of hypertension in the digital era: small wearable monitoring devices for remote blood pressure monitoring. Hypertension. 2020;76(3):640-650. doi:10.1161/HYPERTENSIONAHA.120.14742

97.

Nachman

Gilan

Goldstein

, et al. 24-Hour ambulatory blood pressure measurement using a novel non-invasive, cuff-less, wireless device. Am J Hypertens. Published online June 18, 2021. doi:10.1093/ajh/hpab095

98.

Hale

. FDA clears “cuffless” blood pressure tracking smartwatch from Biobeat. Fierce BioTech. Published 2019. Accessed August 4, 2021. https://www.fiercebiotech.com/medtech/fda-clears-cuffless-blood-pressure-tracking-smartwatch-from-biobeat

99.

Dix

Howell

. Survey of cancellation rate of hypertensive patients undergoing anaesthesia and elective surgery. Br J Anaesth. 2001;86(6):789-793. doi:10.1093/bja/86.6.789

100.

Pang

Baptista

PMJ

Olszewska

, et al. SLEEP-GOAL: a multicenter success criteria outcome study on 302 obstructive sleep apnoea (OSA) patients. Med J Malaysia. 2020;75(2):117-123.

101.

Marin

Agusti

Villar

, et al. Association between treated and untreated obstructive sleep apnea and risk of hypertension. JAMA. 2012;307(20):2169-1276. doi:10.1001/jama.2012.3418

102.

Leenen

JPL

Leerentveld

van Dijk

van Westreenen

Schoonhoven

Patijn

. Current evidence for continuous vital signs monitoring by wearable wireless devices in hospitalized adults: systematic review. J Med Internet Res. 2020;22(6):e18636. doi:10.2196/18636

103.

Joshi

Archer

Morbi

, et al. Short-term wearable sensors for in-hospital medical and surgical patients: mixed methods analysis of patient perspectives. JMIR Perioper Med. 2021;4(1):e18836. doi:10.2196/18836

104.

Bourget

Chang

JTC

DBS

Chang

Wei

. Free flap reconstruction in the head and neck region following radiotherapy: a cohort study identifying negative outcome predictors. Plast Reconstr Surg. 2011;127(5):1901-1908. doi:10.1097/PRS.0b013e31820cf216

105.

Downey

Randell

Brown

Jayne

. Continuous versus intermittent vital signs monitoring using a wearable, wireless patch in patients admitted to surgical wards: pilot cluster randomized controlled trial. J Med Internet Res. 2018;20(12):e10802. doi:10.2196/10802

106.

Bollig

Spradling

Dooley

Galloway

Jorgensen

. Impact of perioperative hyperglycemia in patients undergoing microvascular reconstruction. Head Neck. 2018;40(6):1196-1206. doi:10.1002/hed.25097

107.

Galindo

Aleppo

Klonoff

, et al. Implementation of continuous glucose monitoring in the hospital: emergent considerations for remote glucose monitoring during the COVID-19 pandemic. J Diabetes Sci Technol. 2020;14(4):822-832. doi:10.1177/1932296820932903

108.

Nair

Dellinger

Flum

Rooke

Hirsch

. A pilot study of the feasibility and accuracy of inpatient continuous glucose monitoring. Diabetes Care. 2020;43(11):e168-e169. doi:10.2337/dc20-0670

109.

Davis

Spanakis

Migdal

, et al. Accuracy of Dexcom G6 continuous glucose monitoring in non–critically ill hospitalized patients with diabetes. Diabetes Care. 2021;44(7):1641-1646. doi:10.2337/dc20-2856

110.

Levitt

Silver

Spanakis

. Inpatient continuous glucose monitoring and glycemic outcomes. J Diabetes Sci Technol. 2017;11(5):1028-1035. doi:10.1177/1932296817698499

111.

Hoppe

Kouz

Saugel

. Perioperative hypotension: clinical impact, diagnosis, and therapeutic approaches. J Emerg Crit Care Med. 2020;4:8. doi:10.21037/jeccm.2019.10.12

112.

Khanna

Shaw

Stapelfeldt

, et al. Postoperative hypotension and adverse clinical outcomes in patients without intraoperative hypotension, after noncardiac surgery. Anesth Analg. 2021;132(5):1410-1420. doi:10.1213/ANE.00000000 00005374

113.

Donovan

. How does HIPAA apply to wearable health technology? Health IT Security. Published 2018. Accessed July 4, 2021. https://healthitsecurity.com/news/how-does-hipaa-apply-to-wearable-health-technology

114.

Guk

Han

Lim

, et al. Evolution of wearable devices with real-time disease monitoring for personalized healthcare. Nanomaterials. 2019;9(6):813. doi:10.3390/nano9060813

115.

Cilliers

. Wearable devices in healthcare: privacy and information security issues. Health Inf Manag. 2020;49(2-3):150-156. doi:10.1177/1833358319851684

116.

Predel

Steger

. Ethical challenges with smartwatch-based screening for atrial fibrillation: putting users at risk for marketing purposes? Front Cardiovasc Med. 2021;7:615927. doi:10.3389/fcvm.2020.615927

117.

Yetisen

Martinez-Hurtado

Ünal

Khademhosseini

Butt

. Wearables in medicine. Adv Mater. 2018;30(33):e1706910. doi:10.1002/adma.201706910

118.

Raber

McCarthy

Yeh

. Health insurance and mobile health devices: opportunities and concerns. JAMA. 2019;321(18):1767-1768. doi:10.1001/jama.2019.3353

119.

US Food and Drug Administration. Digital health innovation action plan. Published 2020. Accessed July 4, 2021. https://www.fda.gov/media/106331/download

120.

Zhang

Song

Vullings

, et al. Motion artifact reduction for wrist-worn photoplethysmograph sensors based on different wavelengths. Sensors. 2019;19(3):673. doi:10.3390/s19030673

121.

Appelboom

Taylor

Bruce

, et al. Mobile phone-connected wearable motion sensors to assess postoperative mobilization. JMIR Mhealth Uhealth. 2015;3(3):e78. doi:10.2196/mhealth.3785

122.

Feehan

Geldman

Sayre

, et al. Accuracy of fitbit devices: systematic review and narrative syntheses of quantitative data. JMIR Mhealth Uhealth. 2018;6(8):e10527. doi:10.2196/10527

123.

Colvonen

DeYoung

Bosompra

NOA

Owens

. Limiting racial disparities and bias for wearable devices in health science research. Sleep. 2020;43(10):zsaa159. doi:10.1093/sleep/zsaa159

124.

Ray

Liaqat

Gabel

De Lara

. Skin tone, confidence, and data quality of heart rate sensing in WearOS smartwatches. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events, PerCom Workshops 2021. IEEE; 2021:213-219. doi:10.1109/PerComWorkshops51409.2021.9431120

125.

Lewis

Directo

Kim

MJY

Dolezal

. Validation of biofeedback wearables for photoplethysmographic heart rate tracking. J Sports Sci Med. 2016;15(3):540-547.

126.

Nangalia

Prytherch

Smith

. Health technology assessment review: remote monitoring of vital signs—current status and future challenges. Crit Care. 2010;14(5):233. doi:10.1186/cc9208

127.

Symer

Abelson

Milsom

McClure

Yeo

. A mobile health application to track patients after gastrointestinal surgery: results from a pilot study. J Gastrointest Surg. 2017;21(9):1500-1505. doi:10.1007/s11605-017-3482-2

128.

Scheer

Bakhsheshian

Keefe

, et al. Initial experience with real-time continuous physical activity monitoring in patients undergoing spine surgery. Clin Spine Surg. 2017;30(10):E1434-E1443. doi:10.1097/BSD.0000000000000521

129.

Abbadessa

Lavorgna

Miele

, et al. Assessment of multiple sclerosis disability progression using a wearable biosensor: a pilot study. J Clin Med. 2021;10(6):1160. doi:10.3390/jcm10061160

130.

Carmichael

Overbey

Hosokawa

, et al. Wearable technology—a pilot study to define “normal” postoperative recovery trajectories. J Surg Res. 2019;244:368-373. doi:10.1016/j.jss.2019.06.057

131.

Sun

Dumitra

Ruel

, et al. Wireless monitoring program of patient-centered outcomes and recovery before and after major abdominal cancer surgery. JAMA Surg. 2017;152(9):852-859. doi:10.1001/jamasurg.2017.1519

132.

Finley

Fay

Batsis

, et al. A feasibility study of an unsupervised, pre-operative exercise program for adults with lung cancer. Euro J Cancer Care. 2020;29(4):e13254. doi:10.1111/ecc.13254

133.

Stienen

Rezaii

, et al. Objective activity tracking in spine surgery: a prospective feasibility study with a low-cost consumer grade wearable accelerometer. Sci Rep. 2020;10(1):4939. doi:10.1038/s41598-020-61893-4

134.

Moshe

Terhorst

Opoku Asare

, et al. Predicting symptoms of depression and anxiety using smartphone and wearable data. Front Psychiatry. 2021;12:625247. doi:10.3389/fpsyt.2021.625247

135.

Mobbs

Phan

Maharaj

Rao

. Physical activity measured with accelerometer and self-rated disability in lumbar spine surgery: a prospective study. Global Spine J. 2016;6(5):459-464. doi:10.1055/s-0035-1565259

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.06 MB

0.01 MB

0.03 MB