In applied psychology, ambulatory sleep assessment is increasingly used to investigate how psychosocial conditions such as shift work, work-home crossover/spillover, and socioeconomic status impact on sleep/wake patterns and circadian rhythms (e.g., Baek et al., Reference Baek, Han and Choi-Kwon2020; Barber et al., Reference Barber, Taylor, Burton and Bailey2017; Etindele Sosso et al., Reference Etindele Sosso, Holmes and Weinstein2021; Feng et al., Reference Feng, Booth, Baldwin-Rodríguez, Osorno and Narayanan2021; Zhang et al., Reference Zhang, Murphy, Lammers‐van der Holst, Barger, Lai and Duffy2023). While self-reports have traditionally dominated the field, with consequent limitations in terms of reliability, adherence to long-term monitoring, and ability to capture multifaceted sleep features, the recent advancements in wearable technology have open up new avenues for expanding the horizons of applied psychology research and practice. Increasingly accessible, user-friendly, and multi-functional devices allow for unprecedented large-scale and extensive long-term monitoring of sleep patterns and related physiology (Baron et al., Reference Baron, Duffecy, Berendsen, Cheung Mason, Lattie and Manalo2018; de Zambotti et al., Reference de Zambotti, Cellini, Menghini, Sarlo and Baker2020). Yet, while such devices are ideal methods for monitoring sleep in free-living conditions, their use implies numerous challenges that need to be considered to fully exploit their potential.
Here, we review selected works highlighting the main advantages and pitfalls of using wearable sleep trackers in applied psychology. Building from a limited number of recent studies that we selected among representative applications of ambulatory sleep assessment in applied psychology, we aim at answering the question: “Is it time to include wearable sleep trackers in the applied psychologists’ toolbox?” We conducted a narrative review aiming at highlighting the potential of novel vs. older wearable sleep technology to be used in applied psychology research and practice. After introducing the relevance of ambulatory assessment and sleep monitoring in the field, we briefly review key device profiles and what we consider the main opportunities and challenges of integrating these methods in the applied psychologist’ toolbox.
Ambulatory Biobehavioral Assessment in Applied Psychology
Ambulatory assessment in free-living conditions has drawn the attention of applied psychologists since the first half of the 20th century (see Klumb et al., Reference Klumb, Elfering and Herre2009). For instance, early applications focused on the effects of unemployment on daily life, everyday communication flows, and other problems in fields such as industry, business, and education (e.g., Hinrichs, Reference Hinrichs1964; Jahoda, Reference Jahoda1992; Münsterberg, Reference Münsterberg1913). Particularly, biobehavioral monitoring has been employed by applied psychologists since World War II and increasingly used since the 1980s (Akinola, Reference Akinola2010; Boucsein & Backs, Reference Boucsein, Backs, Backs and Boucsein2000; Ganster et al., Reference Ganster, Crain and Brossoit2018), with recent trends including cardiovascular load assessment, stress and creativity at work, and the promotion of worker sleep quality (e.g., Akinola et al., Reference Akinola, Kapadia, Lu and Mason2019; Dias et al., Reference Dias, Silva, Folgado, Nunes, Cepeda, Cheetham and Gamboa2023; Zhang et al., Reference Zhang, Murphy, Lammers‐van der Holst, Barger, Lai and Duffy2023).
Sleep as a Key Biobehavioral Factor in Applied Psychology
In a recent diary study conducted with 101 supervisor-subordinate dyads over five working days, Tariq et al. (Reference Tariq, Weng, Garavan, Obaid and Hassan2020) reported a spillover effect of supervisors’ poor sleep on next-day abusive supervisory behavior, which in turn showed a crossover effect on subordinates’ sleep quality. Sleep is a fundamental aspect of individual functioning and health that is connected to virtually all psychological processes (Buysse, Reference Buysse2014; Grandner, Reference Grandner2017). It is also a highly complex phenomenon manifesting at multiple levels (physiological, behavioral, subjective) that do not always take the same direction. For instance, paradoxical insomnia has been described as a prevalent condition where individuals perceive sleep disturbances that are not corroborated by objective methods (Rezaie et al., Reference Rezaie, Fobian, McCall and Khazaie2018). Moreover, sleep manifests as a dynamic multidimensional process whose temporal fluctuations (e.g., timing, quality, efficiency, and regularity over time) are critical to capture its health and psychosocial correlates (Buysse, Reference Buysse2018).
Ambulatory Sleep Assessment
Ambulatory sleep assessment originated with the first applications of sleep diaries and portable polysomnography (PSG) (Schulz, Reference Schulz2022; van de Water et al., Reference van de Water, Holmes and Hurley2011), and the first attempts to monitor sleep/wake patterns with small-sized devices attached to the human body, dating back to the 1970s (Foster et al., Reference Foster, Kupfer, Weiss, Lipponen, McPartland and Delgado1972; Kupfer et al., Reference Kupfer, Detre, Foster, Tucker and Delgado1972). While such pioneering applications drove outstanding progress in sleep research, sleep tracking has become mainstream only in recent years thanks to the push of the wearable industry targeting the consumer market (Kolla et al., Reference Kolla, Mansukhani and Mansukhani2016).
Portable Polysomnography
PSG is the recognized gold-standard for measuring sleep and diagnosing sleep disorders. It implies the multichannel recording of cortical, muscular, and eye-movement activity into 30-second epochs to be manually expert scored based on international norms (Kryger et al., Reference Kryger, Roth and Dement2005). While PSG is largely restricted to laboratory settings, ambulatory PSG has the advantage of reaching participants’ home and reducing the burden of laboratory testing. However, even portable PSG devices can be poorly suitable for long-term monitoring due to equipment size, obtrusiveness, and still relatively high costs (van de Water et al., Reference van de Water, Holmes and Hurley2011). Moreover, the required technical expertise is a great obstacle for its utilization in fields such as applied psychology, which only counts a few PSG-based studies (e.g., Åkerstedt et al., Reference Åkerstedt, Lekander, Peterson, Kecklund and Axelsson2014).
Actigraphy
As the accepted alternative to PSG in non-laboratory settings, actigraphy uses piezoelectric sensors to quantify body movements (acceleration or ‘activities’) and characterize sleep/wake patterns by defining sleep as the absence of motion (Kripke et al., Reference Kripke, Mullaney, Messin and Wyborney1978; Sadeh et al., Reference Sadeh, Sharkey and Carskadon1994). Most actigraphy devices (e.g., Philips Actiwatch) are wrist-based and have been repeatedly tested to evaluate their accuracy in estimating PSG-like sleep parameters such as total sleep time (TST), sleep onset latency (SOL), and wake after sleep onset (WASO) (see Sadeh, Reference Sadeh2011). The reduced size and obtrusiveness of wrist-worn devices are key determinants of their suitability for long-term monitoring and their widespread use in applied psychology. For instance, Etindele Sosso et al. (Reference Etindele Sosso, Holmes and Weinstein2021) recently reviewed 19 actigraphy-based studies on social inequities, reporting shorter TST and longer SOL for individuals with lower socioeconomic status. Actigraphy is also widely used in occupational health psychology, where the detrimental effects of job demands and related perseverative cognitions on sleep duration and quality have been repeatedly reported (e.g., Dorrian et al., Reference Dorrian, Baulk and Dawson2011; Melo et al., Reference Melo, Campanini, Souza, Andrade, Durán González, Jiménez-López and Mesas2021; von Gall et al., Reference von Gall, Muth and Angerer2023). Yet, while being overall simpler than PSG, the use of actigraphy still requires technical expertise and relatively high research budgets. This, together with its frequently highlighted limitation in wake detection (i.e., low sensitivity to motionless wake), are among the main limitations of this technique (Sadeh, Reference Sadeh2011; Scott et al., Reference Scott, Lack and Lovato2019).
Consumer-Grade Wearable Sleep Trackers
In the present days, encountering relatives, friends, and even strangers wearing smart watches, armbands, or clothing to monitor their steps, heart rate, and sleep cycles has become increasingly common. The consumer wearable industry is indeed bringing the most recent innovations in ambulatory sleep assessment. The possibility of monitoring sleep passively, continuously, and unobtrusively through simple objects of everyday usage (chest, wristband, rings, etc.) opened the way to a new era of sleep research characterized by longer-term and larger-scale research designs (e.g., Clark et al., Reference Clark, Stucky, Azza, Schwab, Müller, Weibel, Button, Karlen, Seifritz, Kleim and Landolt2021; Stucky et al., Reference Stucky, Clark, Azza, Karlen, Achermann, Kleim and Landolt2021). For instance, Willoughby et al. (Reference Willoughby, Alikhani, Karsikas, Chua and Chee2023) were able to investigate sleep differences (e.g., duration, timing, variability, and social jetlag) across 35 countries by accessing and analyzing over 50 million night’s sleep data from over 200,000 unique Oura Ring users (242 nights per user, on average). Consumer-oriented features such as higher memory capacity and longer battery life, together with their lower costs and required expertise, make these devices ideal for such extensive data collections, providing unprecedented knowledge on the numerous factors (e.g., geographical, cultural, environmental) affecting sleep.
Beyond Sleep Tracking
An important advancement in the newer-generation sleep trackers (both research- and consumer-grade) is the integration of additional sensors enabling the quantification of sleep macrostructure (staging), 24h activity, and cardio-respiratory function (see de Zambotti et al., Reference de Zambotti, Cellini, Menghini, Sarlo and Baker2020). The most popular of these additional sensors is photoplethysmography (PPG), which uses infrared or LED light to estimate heart rate and heart rate variability (HRV) from peripheral blood volume pulse fluctuations. Due to the expected changes in cardiac activity between wake and sleep, and across sleep stages, such feature has been suggested to improve sleep classification accuracy (Chinoy et al., Reference Chinoy, Cuellar, Huwa, Jameson, Watson, Bessman, Hirsch, Cooper, Drummond and Markwald2021; Haghayegh et al., Reference Haghayegh, Khoshnevis, Smolensky, Diller and Castriotta2019) while providing estimates of time in ‘light’, ‘deep’, and rapid-eye-movement (REM) sleep (e.g., Wulterkens et al., Reference Wulterkens, Fonseca, Hermans, Ross, Cerny, Anderer, Long, van Dijk, Vandenbussche, Pillen, van Gilst and Overeem2021).
Wearable Sleep Trackers in Applied Psychology
From the overview reported above, it is evident that wearable sleep trackers imply several potential advantages for the advancement of applied psychology research and practice. Yet, due to the novel and rapidly evolving nature of such technologies, we believe that providing specific recommendations on their use in the field is somehow premature. While some more general recommendations have been recently provided by de Zambotti et al. (Reference de Zambotti, Goldstein, Cook, Menghini, Altini, Cheng and Robillard2023), here we summarize what we consider the main opportunities and challenges of using these devices in applied psychology.
Opportunities for Applied Psychologists
The main advantage of wearable sleep tackers for applied psychologists is disposing of multi-source measurements that mitigate common method bias (Eatough et al., Reference Eatough, Shockley and Yu2016) and allow considering specific sleep profiles such as paradoxical insomnia (Rezaie et al., Reference Rezaie, Fobian, McCall and Khazaie2018). On the one hand, experience sampling methods (ESM) are the ideal tools to contextualize objective sleep data with complementary information on behaviors, experiences, and environmental factors immediately preceding or following sleep episodes. ESM are well-established in applied psychology (Beal, Reference Beal2015; Ohly et al., Reference Ohly, Sonnentag, Niessen and Zapf2010) and increasingly implemented within dedicated or third-party mobile applications (Pejovic et al., Reference Pejovic, Lathia, Mascolo, Musolesi, Tkalčič, De Carolis, De Gemmis, Odić and Košir2016). For instance, by using Fitbit Charge 3 devices and the SurveySparrow mobile app, we were able to continuously track sleep parameters (e.g., TST, sleep stages, cardiac activity) over two months from a sample of 93 adolescents, and to analyze their within-individual relationships with pre-sleep stress, worry, and mood (Menghini et al., Reference Menghini, Yuksel, Prouty, Baker, King and de Zambotti2023).
On the other hand, while the reduced costs of consumer-grade devices can increase the scalability of sleep monitoring, the passive nature of wearable recording (not requiring any action from the user) is a key feature to extend assessment durations beyond what can be usually done with ESM (e.g., multiple weeks, months, and even years). Moreover, several wearables can be synchronized with cloud services (e.g., Empatica Health Monitoring Platform), third-party platforms (e.g., Small Steps Labs Fitabase), and dedicated SDKs/APIs to allow researchers and practitioners accessing more granular sensor data (sometimes including raw data) from multiple devices. Although consumer-grade wearables are not optimized for being used in research settings, several solutions can be adopted to adapt these devices for specific research goals.
From the practitioner side, it is worth mentioning that wearable sleep trackers can also be useful to implement ecological momentary interventions (Balaskas et al., Reference Balaskas, Schueller, Cox and Doherty2021; Nahum-Shani et al., Reference Nahum-Shani, Smith, Spring, Collins, Witkiewitz, Tewari and Murphy2018) and to assess the impact of psychological interventions such as counseling and psychotherapy (Chellappa & Aeschbach, Reference Chellappa and Aeschbach2022). For instance, Torres and Zhang (Reference Torres and Zhang2021) implemented an employee wellness program where 30 hotel managers tracked their diurnal activity and sleep patterns over 14 days using Fitbit Charge 2 devices, showing positive outcomes such as reduced caloric intake and increased work engagement. This and other applications such as improving work efficiency and reduce work-related injuries (see Khakurel et al., Reference Khakurel, Pöysä, Porras, Akan, Bellavista, Cao, Coulson, Dressler, Ferrari, Gerla, Kobayashi, Palazzo, Sahni, Shen, Stan, Xiaohua and Zomaya2017) highlight the great potential of using wearable sleep trackers in applied psychology research and practice.
Challenges for Applied Psychologists
Despite such promising opportunities, wearable sleep trackers pose several challenges that should be carefully considered. First, their validity is uncertain and can vary across different devices and populations, with method comparison studies being increasingly needed as these technologies evolve (Benedetti et al., Reference Benedetti, Menghini, Vallat, Mallett, Kiss, Faraguna, Baker and de Zambotti2023; Depner et al., Reference Depner, Cheng, Devine, Khosla, de Zambotti, Robillard, Vakulin and Drummond2020; Menghini, Cellini, et al., Reference Menghini, Cellini, Goldstone, Baker and de Zambotti2021). The term ‘performance evaluation’ has been recently proposed instead of ‘validation’ precisely because the continuous update of device features prevents the research community from definitely establishing their validity (de Zambotti et al., Reference de Zambotti, Menghini, Grandner, Redline, Zhang, Wallace and Buxton2022). Depending on the research focus (e.g., overnight composite indicators vs. within-night changes in sleep macrostructure or physiology), the device output should demonstrate adequate agreement with reference (e.g., PSG-based) measurements before being applied to a specific population (for an overview on how to interpret performance evaluation metrics, see Menghini, Cellini, et al., Reference Menghini, Cellini, Goldstone, Baker and de Zambotti2021). For instance, we recently evaluated Fitbit Charge 3 performance in a sample of 39 adolescents that simultaneously undertook laboratory-based PSG recording (Menghini, Yuksel, et al., Reference Menghini, Yuksel, Goldstone, Baker and de Zambotti2021). Our results indicated systematic underestimations of TST by 11 ± 15.6 minutes, with no substantial differences between healthy sleepers and participants with insomnia symptomatology. Whether such discrepancies are excessively large for a specific utilization of the device is something that researchers and practitioners should think about before integrating a wearable tracker in their monitoring protocols.
Second, the black-box nature of proprietary and undisclosed algorithms is regarded with suspicion by the scientific community. While this applies to both research-grade (including actigraphy) and consumer-grade devices, the use of consumer technology for diagnosing and treating sleep problems has been discouraged so far (Khosla et al., Reference Khosla, Deak, Gault, Goldstein, Hwang, Kwon, O’Hearn, Schutte-Rodin, Yurcheshen, Rosen, Kirsch, Chervin, Carden, Ramar, Aurora, Kristo, Malhotra, Martin, Olson and Rowley2018). Again, it is the researcher/practitioner’s responsibility to search for available evidence justifying the use of a given device to record specific sleep parameters in a specific population. Third, raw data (e.g., epoch-by-epoch sleep classifications, light, acceleration, and PPG signal) can be only accessed from a minority of devices, constraining the derivable range of output parameters, and threatening the reproducibility of the measurement procedures (Baron et al., Reference Baron, Duffecy, Berendsen, Cheung Mason, Lattie and Manalo2018; de Zambotti et al., Reference de Zambotti, Cellini, Menghini, Sarlo and Baker2020). While we recommend relying on data exported at the maximum possible resolution (e.g., minute-level heart rate), being able to inspect data quality and identify unreliable observations is a necessary skill to ascertain the validity of the collected measures. More generally, the large number of features to be considered (size, cost, battery life, memory capacity, range of sensors, available evidence on device performance, etc.) can pose great challenges to applied psychologists approaching these methods for the first time. In the state of science review recently requested by the Sleep Research Society (de Zambotti et al., Reference de Zambotti, Goldstein, Cook, Menghini, Altini, Cheng and Robillard2023), we systematically address these and other issues while providing some guidance on how to evaluate, choose, and use these new methods.
Further challenges are common to intensive longitudinal designs not involving wearables, such as dealing with participant burden and missing data. For instance, missing wearable data can be due to device malfunctioning or lack of participant compliance (e.g., participants not wearing the device). As in other ambulatory assessment techniques, it is important to keep in touch with participants/clients to monitor their adherence with the research protocol and the occurrence of technical errors. Finally, adopting wearable trackers in applied settings might pose ethical and privacy concerns related to the potential misuse of individual sensitive information (Akinola, Reference Akinola2010; Moore & Piwek, Reference Moore and Piwek2017). Using anonymized user accounts and removing personal and identifying information are among the fundamental practices that should be considered to prevent unethical uses of these technologies.
Conclusions
Wearable sleep trackers are increasingly considered as valuable tools to objectively measure sleep in an ecologically valid, large-scale, and temporally extensive way. This opportunity should not be missed by applied psychologists to improve the quantity and quality of the data collected in both research and professional practice. Overall, the increasing intuitiveness and automaticity of wearable sleep tracking (particularly in the consumer-oriented space) can strongly facilitate the inclusion of these methods in the applied psychologist’s toolbox. Yet, a degree of awareness on the methodological pitfalls of using wearable trackers and interpreting their output is highly recommended. Here, we concisely summarized some of the opportunities and challenges of using wearable sleep trackers in applied psychology, and we provided key references to master the use of wearable sleep monitoring.
Conflicts of interest
MdZ has received research funding unrelated to this work from Noctrix Health Inc. and Verily Life Science LLC. MdZ is a co-founder and Chief Scientific Officer at Lisa Health Inc., and has ownership of shares in Lisa Health Inc.
Data sharing
Not applicable.
Authorship credit
Luca Menghini played a lead role in conceptualization, investigation, methodology, writing–original draft, writing–review & editing; Cristian Balducci played a supporting role in conceptualization and writing–review & editing; Massimiliano de Zambotti played a supporting role in conceptualization, investigation, methodology, writing–original draft, writing–review & editing.
Open access
