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The comfort of a smartwatch is recognized as a pivotal determinant affecting users’ engagement with the device. This study delved into the intricate interplay between smartwatch comfort, users’ behavioral intention, and their actual usage behavior within the specific context of sleep. Through the lens of a structural equation model, we find that the appearance and movement dimensions of comfort significantly influence users’ intention to wear the smartwatch during sleep, while other dimensions like pressure, harm, attachment, and perceived change do not show significant effects. Furthermore, this intention significantly translated into smartwatch usage behavior. Building on these insights, we subsequently embarked on an exploration of how personality traits interplay with comfort and intention to impact smartwatch usage during sleep. The moderated mediation models revealed that the personality trait of openness acts as a moderator, amplifying the relationship between smartwatch comfort and behavioral intention. Individuals with higher levels of openness exhibit increased inclination to adopt the smartwatch during sleep, even when comfort is compromised. These insights contribute to a nuanced understanding of smartwatch usage behavior and offer guidance for personalized design strategies, especially within the sleep context.
Introduction
In contemporary times, smartwatches have evolved into versatile wearable devices, seamlessly integrating functions like communication, timekeeping, texting, and health monitoring (Bölen, 2020). The integration of innovative health tracking features has expanded their utility, enabling continuous monitoring irrespective of time and place (Kamal Basha et al., 2022). Notably, the incorporation of sleep detection functionality has positioned sleep monitoring as a crucial application for smartwatches.
Undoubtedly, sleep quality profoundly influences well-being (Su and He, 2023; Zhu et al., 2023). With sleep monitoring capabilities, smartwatch users can actively monitor sleep patterns and gain insights into their sleep behaviors (Guillodo et al., 2020). Beyond data provision, this monitoring can also reveal an individual’s mental state, as inadequate sleep associates with reduced performance, low energy, and emotional issues (Lui et al., 2022). Smartwatches provide sleep-related data like duration, nocturnal awakenings, and sleep stages, enabling users to proactively manage sleep habits, enhance awareness of potential issues, and ultimately refine sleep quality (Liang and Ploderer, 2016). However, it is imperative to acknowledge that users place a premium on comfort when it comes to the sleep environment (Hashimoto et al., 2015). This reality has engendered a scenario where only a subset of users is amenable to wearing smartwatches while asleep.
Certainly, the utilization patterns of smartwatches within sleep settings are influenced by a multitude of factors. Existing research underscores the pivotal role of usage intention as the prime determinant of usage behavior (Buenaflor and Kim, 2013; Spagnolli et al., 2014). Notably, comfort emerges as a central element impacting the usage intention of wearable devices (Spagnolli et al., 2014; Xu et al., 2024). However, despite a meticulous analysis of prior literature, it becomes apparent that investigations into the interplay between smartwatch comfort, usage intention, and usage behavior, particularly in sleep scenes, remain absent. Furthermore, earlier inquiries predominantly appraise smartwatch comfort through singular dimensions, such as vibration strength comfort (Chang, Qin, and Wang, 2023) or tactile comfort (Gil, Kim, and Oakley, 2018). Notably absent is a comprehensive exploration of the collective impact of diverse comfort dimensions on user behavior. As such, there exists a critical gap in our understanding of how comfort attributes holistically contribute to user interactions with smartwatches, warranting further exploration.
Moreover, extant studies have unveiled pronounced individual disparities in the usage of smartwatches (Choi and Kim, 2016). Varied individual factors, encompassing aspects like gender, needs, and attitudes (Kim and Shin, 2015; Wu, Wu, and Chang, 2016), exert discernible impacts on the adoption and usage intentions surrounding smartwatches. However, the exploration of personality’s influence on smartwatch use remains a relatively uncharted territory. Previous studies have illuminated the impact of personality traits on diverse technological domains, such as mobile application usage (Xu et al., 2016), smartphone utilization (Chittaranjan et al., 2013), perceived system accessibility (Kortum and Oswald, 2017), and the embrace of wearable smart devices (Chittaranjan et al., 2013). It is a reasonable conjecture that the intricate interplay between personality traits and smartwatch engagement yields significant insights into user behavior.
With the intention of bridging these aforementioned gaps, the present study is driven by two principal objectives. First, we aim to identify pivotal dimensions of comfort that significantly influence the inclination to utilize smartwatches during sleep. Second, this research endeavors to unravel the interplay between personality traits and the impact of comfort on users’ patterns of usage. As we look ahead, wearable devices are poised to emerge as the most intimately woven information and communication technologies in the Internet of Things era. Central to this domain are smartwatches, serving as one of the cornerstones of wearable technology (Hong et al., 2017). By delving into the nexus between comfort and usage behavior, this exploration holds the potential to yield insights crucial for advancing the smartwatch industry. Additionally, insights gleaned from the study of personality dynamics can offer developers a heightened comprehension of smartwatch user behaviors (Buettner, 2017; Huseynov, 2020), thereby empowering the development of innovative functionalities tailored to users with diverse personality traits.
Literature review and hypotheses
Smartwatch use in sleep scenarios
A smartwatch, resembling a wristwatch, is equipped with an integrated system that amplifies its functionalities beyond mere timekeeping (Bölen, 2020). In addition to its primary function as a time indicator, a smartwatch boasts a repertoire of features encompassing reminders, navigation assistance, calibration, monitoring, and interactive capabilities. Evolving dynamically, smartwatches now encompass an expanding array of functionalities, including fitness tracking, health monitoring, and location tracing, which have captured the fascination and engagement of a multitude of users (McIntyre, 2014). Recent investigations underscore that the surge in smartwatch shipments can be attributed to the augmentation of health monitoring capabilities, which lends critical support to individual healthcare (Lee, Choi, and Lee, 2023).
In the realm of health monitoring, sleep surveillance emerges as a pivotal function for assessing sleep quality. Sleep, an indispensable aspect of human existence, profoundly influences overall well-being (Mukherjee et al., 2015), exerting a profound impact on mental, cerebral, and physical restoration (Grandner, 2017). The mounting prevalence of chronic sleep disorders, encompassing insomnia and sleep apnea, underscores a contemporary challenge, with a significant portion of the population grappling with sleep disturbances (Guillodo et al., 2020; Huynh et al., 2021). Comparisons drawn by certain researchers between smartwatch sleep monitoring capabilities and traditional methodologies like sleep diaries, polysomnography, and wrist-worn actigraphy reveal that the precision of smartwatch measurements is constrained (Cook et al., 2019; Gaiduk et al., 2023). Indeed, the accuracy of sleep monitoring remains a limitation, impinging upon the clinical viability of smartwatches (Kolla et al., 2016). Nevertheless, as posited by Liang and Ploderer (2016), insights provided by smartwatches, encompassing metrics like sleep duration, instances of nocturnal awakenings, and sleep stages, prompt a significant demographic of smartwatch users to proactively oversee their sleep behaviors. This, in turn, fosters heightened awareness of potential sleep-related issues and prompts modifications to sleep habits, culminating in elevated sleep quality.
Conversely, in the context of sleep scenarios, users exhibit heightened demands for comfort (Hashimoto et al., 2015). This dichotomy prompts users to navigate a decision-making juncture: whether to don a smartwatch for sleep quality monitoring or to forgo its use to preserve sleep quality. In this light, delving into the array of comfort-related factors influencing smartwatch usage within sleep settings holds the potential to offer valuable insights for the refinement of forthcoming smartwatch designs. Consequently, such insights can serve as a catalyst for bolstering the integration of smartwatches within sleep scenarios, aligning with user preferences and enhancing their utility in this domain.
The comfort dimensions for smartwatch
Comfort stands out as one of the most critical attributes for devices that have direct contact with the human skin (Tadesse et al., 2021). Prior research has introduced diverse comfort dimensions for various wearable electronic devices, such as thermal comfort (Nilsson, 2007), tactile comfort (Das and Alagirusamy, 2011), and physiological comfort (Bartels, 2005). However, an existing research gap remains in the exploration of sub-dimensions of comfort specific to smartwatches. Finding inspiration from past studies on wearable device comfort could provide valuable insights for addressing this gap.
The landscape of comfort assessment has witnessed considerable effort, with numerous studies dedicated to comprehensively evaluating the comfort of wearable devices. Among the well-recognized tools is the Comfort Rating Scales (CRS), frequently employed to evaluate Wearable Computers (Knight and Baber, 2005). These scales incorporate six distinct dimensions: emotion, attachment, harm, perceived change, movement, and anxiety. This comprehensive framework captures a multitude of factors that contribute to an individual’s overall comfort experience while interacting with wearable devices. Extending the scope of the CRS framework, recent research by Lee et al. (2022) introduced an innovative dimension to the comfort evaluation, addressing the significance of a device’s appearance. This enhancement recognizes the potential impact of visual design and aesthetics on shaping the overall comfort perception. Furthermore, the International Organization for Standardization (ISO) has established standards (ISO 9241-410, 2008) that encompass a comfort questionnaire. This tool systematically assesses subjective comfort, considering aspects such as upper limb comfort, ease of use, and work efficiency (Kim et al., 2014). Remarkably, the dimensions of Pressure and Harm within the ISO framework hold particular relevance to the broader comfort experience, particularly within the realm of wearable technology.
Combining this diverse array of comfort dimensions, it becomes evident that evaluating comfort in the context of wearable devices requires a multidimensional approach. Given the impact of distinct comfort dimensions on usability and overall experience, it is logical to incorporate these dimensions when investigating the comfort of smartwatches. Guided by this understanding, we put forth the proposition that the six dimensions outlined in Table 1 serve as pivotal sub-dimensions influencing the comfort of smartwatches. With this foundation, our study seeks to delve into the intricate connections between these six comfort dimensions and patterns of behavior in smartwatch usage. By doing so, our objective is to contribute to a holistic comprehension of the comfort factors that shape the adoption and sustained usage of smartwatches.
Table 1. Description for comfort dimensions in this study.
Comfort dimension | Description |
|---|---|
Appearance | The suitability of the device’s form (Lee et al., 2022), such as the size of a smartwatch. |
Pressure | Discomfort caused by the pressure exerted on the wrist by the device (Kim et al., 2014). |
Harm | Physical impact, harm to the body (Knight and Baber, 2005). |
Attachment | Sensory perception of the device on the body, attachment (Knight and Baber, 2005). |
Perceived change | Experiencing physical discomfort, distress (Knight and Baber, 2005). |
Movement | The device has a tangible impact on movement (Knight and Baber, 2005). |
Usage intention
Comfort is not a direct determinant of user behavioral differences; rather, a preexisting factor precedes user usage behavior - usage intentions (Venkatesh et al., 2003). The impact of usage intentions on usage behavior has been substantiated through various theories, such as the Technology Acceptance Model (TAM) (Davis et al., 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Davis et al., 1989).
TAM, widely applied to investigate determinants of technology acceptance (Pikkarainen et al., 2004; Wu and Wang, 2005), including studies on wearable device adoption (Buenaflor and Kim, 2013; Spagnolli et al., 2014), posits that ultimate usage behavior is determined by usage intentions. Furthermore, usage intentions are influenced by attitude and perceived usefulness (Davis et al., 1989). UTAUT, on the other hand, asserts that behavioral intentions are determined by performance expectancy, effort expectancy, and social influence, while also being influenced by factors like gender, age, experience, and voluntariness of use. Usage behavior is shaped by both intentions and facilitating conditions, influenced by different variables (Davis et al., 1989). Both theories underscore usage intentions as a precursor to usage behavior, encapsulating various external motivational factors. Hence, it is evident that users’ usage intentions directly impact subsequent usage behavior.
Recognizing that comfort functions as an extrinsic incentive, potentially shaping users’ inclinations to interact with wearable devices during sleep scenarios, we propose that the comfort level directly impacts users’ intentions to use smartwatches. This intention may subsequently exert an influence on user behavior within the realm of sleep-related contexts.
The influence of personality on usage behavior
While comfort can indeed influence users’ usage behavior, the impact of discomfort is not absolute, as individual personality traits can also lead to varied behavioral outcomes (Kortum and Oswald, 2017). In characterizing users’ individual traits, this study employed the Big Five personality traits, encompassing five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism (Huseynov, 2020). The Big Five personality model has been widely employed to elucidate the roles of different personality traits across various scenarios (Sprotles and Kendall, 1986).
Each dimension of the Big Five model exhibits distinct characteristics. Openness is associated with curiosity, openness to change, a penchant for learning, and embracing new experiences (McCrae and Costa, 1999). Conscientiousness is linked to responsibility, persistence, prudence, and thoroughness (McCrae and John, 1992). High scores in extraversion are often associated with talkativeness, energy, sociability, courage, and enthusiasm (Costa and McCrae, 1992). Individuals scoring high in agreeableness tend to be more flexible, polite, trustworthy, cooperative, and tolerant (Judge and Ilies, 2002). Neuroticism correlates with elevated levels of anxiety, discontent, shyness, and pessimism (Sanders, 2008).
Past studies have explored the influence of personality traits on perceived system usability (Kortum and Oswald, 2017), mobile app usage (Xu et al., 2016), smartphone usage (Chittaranjan et al., 2013), and wearable device adoption (Chittaranjan et al., 2013), consistently observing significant effects of personality traits on usage behavior. Among the five dimensions of personality, openness, and agreeableness are closely linked to subjective usability assessment (Kortum and Oswald, 2017). Therefore, in this study, we continue to investigate how users’ personality traits impact their ultimate usage behavior of smartwatches.
Hypotheses
The impact of comfort in usage intention and behavior
The comfort of smartwatches has a significant impact on their usage patterns (Tadesse et al., 2021). Given the diverse array of smartwatch choices available in the market, variations in comfort levels are bound to occur. These comfort disparities can subsequently lead to differences in users’ intentions to use smartwatches, thereby exerting an influence on their actual usage behaviors. In the scope of this study, the assessment of smartwatch comfort encompasses six distinct dimensions: appearance, pressure, harm, attachment, perceived change, and movement (Kim et al., 2014; Knight and Baber, 2005; Lee et al., 2022). The central objective of this research is to explore the predictive roles of various comfort dimensions in shaping users’ intentions to use smartwatches. To address this, we posit the following hypotheses (see Fig. 1):
Fig. 1 [Images not available. See PDF.]
The conceptual model.
H1a–H1f represent Hypotheses 1a through 1f, and H2 represents Hypothesis 2. Source: Own elaboration.
H1a: The appearance dimension positively predicts users’ intentions to use smartwatches in the context of sleep.
H1b: The pressure dimension negatively predicts users’ intentions to use smartwatches in the context of sleep.
H1c: The harm dimension negatively predicts users’ intentions to use smartwatches in the context of sleep.
H1d: The attachment dimension positively predicts users’ intentions to use smartwatches in the context of sleep.
H1e: The perceived change dimension negatively predicts users’ intentions to use smartwatches in the context of sleep.
H1f: The movement dimension positively predicts users’ intentions to use smartwatches in the context of sleep.
Furthermore, behavioral intention stands as a crucial antecedent to usage behavior. Behavioral intention can be defined as an individual’s likelihood of utilizing a specific technology (Venkatesh et al., 2003). Aligned with the TAM (Davis et al., 1989) and the UTAUT model (Davis et al., 1989), behavioral intention is recognized as a precursor to usage behavior, with a positive correlation existing between these two constructs (Schierz et al., 2010). Therefore, our research model posits that the intention to use smartwatches will impact the actual usage behavior of these devices:
H2: The usage intention of smartwatches positively predicts the usage behavior of smartwatches in the context of sleep.
The moderating role of personality
The comfort of smartwatches can lead to variations in users’ usage behavior, but these differences are not absolute. Previous literature has revealed the moderating role of personality on usage behavior (Chittaranjan et al., 2013; Kortum and Oswald, 2017; Xu et al., 2016). Building upon this foundation, we first attempt to establish a mediation model, positing that usage intentions mediate the relationship between comfort and usage behavior. Thus, we propose:
H3: Usage intentions mediate the impact of comfort on usage behavior.
Expanding on the mediation model, we introduce personality as a moderating variable. Not all dimensions of personality have been found to exert an influence in previous literature, with Openness and Agreeableness emerging as the most influential dimensions (Kortum and Oswald, 2017). Hence, we propose:
H4a: The Openness personality trait moderates the relationship between comfort and usage intentions.
H4b: The Agreeableness personality trait moderates the relationship between comfort and usage intentions.
Methods
Participants
Following the lower bound formula (n ≥ 50r2 - 450r + 1100; r is the ratio of indicators to latent variables) for the sample size in structural equation modeling (Christopher Westland, 2010; Hau et al., 1996), the minimum sample size for this study is 200. This study has collected a total of 500 sample data, far exceeding 200.
In the preparation phase, a list of inclusion and exclusion criteria was developed for data collection. Participants meeting the following inclusion criteria were considered eligible:
Wearers of smartwatches or fitness trackers.
Individuals who, following an explanation of the research process and objectives, provided informed consent to participate in the study (in compliance with ethical and legal requirements).
Exclusion criteria were defined as follows:
Participants with excessively short response times, less than 300 s.
Instances automatically rejected by the platform (e.g., failure to correctly answer attention-check questions, used to assess the sincerity of participant responses).
Participants who exhibited incomplete or logically inconsistent questionnaire responses (e.g., reporting a monthly living expense of 1 unit).
Based on the above Inclusion and Exclusion criteria, we recruited smartwatch users for our survey through the Credamo platform (www.credamo.com) until we obtained valid data from 500 participants. The samples comprised 59.6% (n = 298) females and the average age was 30.90 years (SD = 6.40). Table 2 presents the demographic information of all participants.
Table 2. Demographic information of participants.
Measures | Items | Number | Percentage (%) |
|---|---|---|---|
Sex | Male | 202 | 40.4 |
Female | 298 | 59.6 | |
Age (M = 30.901) (SD = 6.40) | 18–27 | 142 | 28.4 |
28–37 | 300 | 60.0 | |
38–47 | 46 | 9.2 | |
48–57 | 10 | 2.0 | |
58–67 | 2 | 0.4 | |
BMI (M = 21.46) (SD = 2.65) | Light weight | 43 | 8.6 |
Normal | 387 | 77.4 | |
Overweight | 63 | 12.6 | |
Obesity | 7 | 1.4 | |
Education level | Postgraduate and above | 90 | 18.0 |
Bachelor’s degree | 361 | 72.2 | |
Associate degree | 39 | 7.8 | |
High school/vocational school | 9 | 1.8 | |
Junior high school and below | 1 | 0.2 |
Measures
Behavioral intention and use behavior
For measuring behavioral intention, we referred to items from previous literature (Chao, 2019; Ding and Yang, 2023; Osswald et al., 2012) and adapted them for smartwatch use. Participants were asked to rate their agreement with statements on a 7-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree). The statements included: “I intend to continue wearing my smartwatch while sleeping in the future,” “I plan to use my smartwatch while sleeping in the future,” and “I would like to use my smartwatch while sleeping in the future.” The Cronbach’s α coefficient was 0.868 in this study, and the Omega coefficient (Hayes and Coutts, 2020) is 0.868. For measuring use behavior, participants were asked to recall and report the frequency of wearing the smartwatch while sleeping over the past week, using a rating scale from 0 (0 days) to 7 (7 days).
Smartwatch comfort
For measuring comfort, a portion of the items were adapted from a general scale for wearable device comfort (Knight et al., 2006). Following Brislin’s translation model (Brislin, 1970), we initially translated and back-translated the aforementioned scales. Subsequently, the translated items were further modified to be applicable to smartwatches. Other items were derived from interviews with users conducted in the preliminary phase. The scale consists of six dimensions: appearance, pressure, harm, attachment, perceived change, and movement. Each dimension includes four items, and responses were collected using a 7-point Likert scale (see Supplementary Material Table S1). The Cronbach’s α coefficient for the overall scale in this study was 0.897, and the Omega coefficient is 0.887. The individual α and Omega coefficients for each dimension were as follows: appearance (α = 0.654, ω = 0.652), pressure (α = 0.764, ω = 0.773), harm (α = 0.683, ω = 0.686), attachment (α = 0.594, ω = 0.442), perceived change (α = 0.525, ω = 0.536), and movement (α = 0.785, ω = 0.795). The goodness-of-fit indices from the confirmatory factor analysis were satisfactory, with χ2/df = 2.615, GFI = 0.906, CFI = 0.907, NFI = 0.858, TLI = 0.891, RMSEA = 0.057, and SRMR = 0.066.
Big five personality traits
For measuring personality, we employed the open-source 44-item Simplified Chinese version of the Big Five Personality Inventory. This inventory has been demonstrated to have good reliability and validity and is suitable for assessing participants’ personality traits (Carciofo, Yang, Song, Du, and Zhang, 2016). The Cronbach’s α coefficient for the overall scale in this study was 0.868, and the Omega coefficient is 0.809. The individual α and Omega coefficients for each personality dimension were as follows: extraversion (α = 0.886, ω = 0.889), agreeableness (α = 0.736, ω = 0.769), conscientiousness (α = 0.639, ω = 0.744), neuroticism (α = 0.865, ω = 0.871), and openness (α = 0.832, ω = 0.841).
Statistical analysis
First, to examine the effects of various dimensions of comfort on behavioral intentions and use behavior, and to validate hypotheses H1a-1f and H2, we conducted a structural equation modeling (SEM) analysis using Amos. We assessed the fit of the hypothesized model (see Fig. 1) using several fit indices, including χ2/df, the goodness-of-fit index (GFI), the comparative fit index (CFI), the normed fit index (NFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR). These fit indices collectively evaluated the goodness of fit of the hypothesized model.
Second, the PROCESS plugin in SPSS 26.0 was employed to analyze the hypothesized mediation model (H3) and moderated mediation models (H4a, H4b) of this study (see Fig. 2). Bootstrap resampling with replacement was used on the original dataset, generating 5000 samples, with each sample subjected to indirect and moderated mediation effect tests. During these analyses, participants’ age, sex, BMI, and education level were included as control variables. If the bias-corrected (BC) 95% confidence interval (CI) did not encompass zero, statistical significance was considered at the 0.05 level (Hayes, 2013).
Fig. 2 [Images not available. See PDF.]
The moderated mediation model. Source: Own elaboration.
Results
Correlation analysis
First, zero-order correlations were conducted to examine direct linear relationships among all variables. As illustrated in Table 3, use behavior exhibited positive correlations with behavioral intention (r(499) = 0.671, p < 0.01), pressure (r(499) = 0.177, p < 0.01), attachment (r(499) = 0.185, p < 0.01), perceived change (r(499) = 0.127, p < 0.01), and movement (r(499) = 0.296, p < 0.01). Furthermore, all dimensions of comfort were positively correlated with behavioral intention (all r > 0.201, p < 0.01). Various personality dimensions also displayed connections with use behavior, where extraversion, agreeableness, openness, and conscientiousness demonstrated positive correlations (all r > 0.228, all p < 0.05), while neuroticism exhibited a negative correlation with usage behavior (r(499) = −0.155, p < 0.01). The linear correlations among the variables formed the foundation for subsequent model construction. Employing more intricate structural equation models can enhance our understanding of the relationships among variables and validate the underlying assumptions.
Table 3. Zero-order correlations among variables.
Variables | M | SD | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 Use behavior | 5.034 | 2.301 | 1 | |||||||||||||||
2 Behavioral intention | 17.186 | 3.383 | 0.671** | 1 | ||||||||||||||
3 Appearance | 24.654 | 2.145 | 0.086 | 0.235** | 1 | |||||||||||||
4 Pressure | 23.348 | 3.251 | 0.177** | 0.207** | 0.231** | 1 | ||||||||||||
5 Harm | 24.390 | 2.768 | 0.064 | 0.201** | 0.308** | 0.641** | 1 | |||||||||||
6 Attachment | 21.884 | 3.131 | 0.185** | 0.274** | 0.369** | 0.407** | 0.398** | 1 | ||||||||||
7 Perceived change | 21.798 | 3.101 | 0.127** | 0.223** | 0.290** | 0.480** | 0.484** | 0.456** | 1 | |||||||||
8 Movement | 22.400 | 3.892 | 0.296** | 0.364** | 0.340** | 0.642** | 0.557** | 0.487** | 0.538** | 1 | ||||||||
9 Extraversion | 30.588 | 5.939 | 0.228** | 0.283** | 0.194** | 0.394** | 0.294** | 0.316** | 0.301** | 0.485** | 1 | |||||||
10 Agreeableness | 39.234 | 3.858 | 0.145** | 0.283** | 0.270** | 0.422** | 0.368** | 0.333** | 0.289** | 0.408** | 0.441** | 1 | ||||||
11 Conscientiousness | 38.178 | 4.777 | 0.112* | 0.228** | 0.253** | 0.469** | 0.346** | 0.401** | 0.359** | 0.511** | 0.566** | 0.613** | 1 | |||||
12 Neuroticism | 15.904 | 5.543 | −0.155** | −0.254** | −0.221** | −0.462** | −0.348** | −0.412** | −0.381** | −0.516** | −0.744** | −0.534** | −0.681** | 1 | ||||
13 Openness | 38.408 | 5.714 | 0.228** | 0.290** | 0.173** | 0.325** | 0.268** | 0.391** | 0.270** | 0.415** | 0.667** | 0.479** | 0.581** | −0.586** | 1 | |||
14 BMI | 21.459 | 2.646 | −0.034 | −0.069 | 0.046 | 0.016 | 0.028 | 0.027 | 0.023 | 0.002 | −0.034 | 0.004 | −0.015 | −0.056 | −0.055 | 1 | ||
15 Age | 30.900 | 6.399 | −0.007 | 0.021 | 0.072 | 0.040 | 0.021 | 0.026 | −0.020 | 0.108* | 0.019 | 0.124** | 0.214** | −0.160** | 0.035 | 0.245** | 1 | |
16 Education | 4.060 | 0.587 | −0.034 | 0.019 | −0.093* | −0.016 | −0.061 | 0.071 | 0.015 | 0.012 | 0.115* | 0.033 | 0.049 | −0.056 | 0.148** | −0.107* | −0.166** | 1 |
*p < 0.05, **p < 0.01,
Results from structural equation modeling
Subsequently, we proceeded to construct structural equation models to assess the validity of Hypotheses H1a-1f and H2. As depicted in Fig. 3, the dimension of appearance emerged as a noteworthy predictor of behavioral intention (β = 0.677, p < 0.01). Additionally, the movement dimension demonstrated substantial predictive power for behavioral intention (β = 0.739, p < 0.01). Notably, behavioral intention also exhibited a significant predictive relationship with use behavior (β = 0.739, p < 0.01). The fitness indicators indicated a favorable overall model fit: χ2/df = 2.324, GFI = 0.903, CFl = 0.917, NFI = 0.865, TLI = 0.905, RMSEA = 0.052, SRMR = 0.069. In light of these findings, our results substantiated the validity of H1a and H1f, confirming that both the appearance and movement dimensions exert a significant positive predictive influence on usage intention. Furthermore, under the assumption of H2’s validity, it was established that behavioral intention significantly and positively predicts use behavior. Our findings indicate that both the appearance of a smartwatch and its impact on individuals’ activities play a role in influencing their intention to wear the smartwatch during sleep. Moreover, these factors ultimately have an impact on individuals’ actual behavior of wearing the smartwatch while sleeping.
Fig. 3 [Images not available. See PDF.]
Path of the structural equation model.
Path coefficients were standardized estimates. The larger the value of the path coefficient, the greater the influence on the endogenous variables. Source: Own elaboration.
Testing for the mediation effect of behavioral intention
We then proceeded to validate Hypothesis 3, which posits the mediating role of behavioral intention between comfort and use behavior. As shown in Fig. 4, after controlling for participants’ sex, age, BMI, and education level, the results of the mediation model reveal that comfort significantly predicts behavioral intention (β = 0.339, p < 0.001) and usage behavior (β = 0.292, p < 0.001). Upon introducing the mediating variable of behavioral intention, the predictive effect of comfort on usage behavior becomes non-significant (β = 0.069, p = 0.051), while behavioral intention exhibits a significant positive predictive effect on usage behavior (β = 0.654, p < 0.001). The direct effect of comfort on use behavior is 0.070, accounting for 23.97% of the total effect; the indirect effect of comfort on use behavior through behavioral intention is 0.222, constituting 76.03% of the total effect. Notably, with the inclusion of behavioral intention, the confidence interval for the direct effect of comfort on usage behavior, ranging from 0.051 to −0.0002, encompasses zero. Therefore, behavioral intention fully mediates the relationship between comfort and usage behavior (Baron and Kenny, 1986). With the confirmation of Hypothesis H3, it is established that behavioral intention plays a mediating role between smartwatch’s comfort and the use of smartwatch while sleeping.
Fig. 4 [Images not available. See PDF.]
Results for the mediation model. Source: Own elaboration.
Testing for the moderated mediation model
Lastly, we examined the moderating role of personality traits (H4a: openness; H4b: agreeableness). As indicated in Table 4, the predictive effect of comfort on use behavior was not significant (β = 0.070, p = 0.051), while behavioral intention exhibited a significant predictive effect on use behavior (β = 0.654, p < 0.001). Comfort significantly predicted behavioral intention (β = 0.273, p < 0.001), and openness also showed a significant predictive effect on behavioral intention (β = 0.126, p < 0.01). Moreover, the interaction between openness and behavioral intention was significant (β = −0.112, p < 0.01), indicating that openness plays a moderating role in the first half of the mediation process. Therefore, the validation of Hypothesis H4a suggests that openness has the capacity to moderate the mediating effect of usage intention between comfort and usage behavior. Regrettably, it is worth noting that we did not identify any evidence in support of Hypothesis H4b (see Supplementary Material Table S2).
Table 4. Moderated mediation analysis (openness).
Variables | Outcome: use behavior | Outcome: behavioral intention | ||
|---|---|---|---|---|
β (SE) [CI] | t | β (SE) [CI] | t | |
Sex | 0.126 (0.076) [−0.024, 0.276] | 1.646 | −0.139 (0.095) [−0.326, 0.047] | −1.466 |
BMI | 0.018 (0.014) [−0.010, 0.047] | 1.248* | −0.039 (0.018) [−0.074, −0.003] | −2.145* |
Age | −0.004 (0.005) [−0.015, 0.006] | −0.814 | 0.007 (0.007) [−0.007, 0.020] | 0.958 |
Education | 0.000 (0.006) [−0.012, 0.011] | −0.072 | 0.007 (0.008) [−0.008, 0.022] | 0.897 |
Comfort | 0.070 (0.036) [0.000, 0.139] | 1.959 | 0.273 (0.046) [0.183, 0.362] | 5.995*** |
Behavior intention | 0.654 (0.036) [0.585, 0.724] | 18.402** | ||
Openness | 0.126 (0.048) [0.031, 0.221] | 2.605** | ||
Openness*Behavioral intention | −0.112 (0.042) [−0.194, −0.030] | −2.672** | ||
R2 | 0.677 | 0.406 | ||
△R2 | 0.458 | 0.165 | ||
F | 69.387 | 13.895 | ||
Sex was coded as 1 = male, 2 = female. *p < 0.05. **p < 0.01, ***p < 0.001.
Subsequently, a simple main effects analysis was conducted to examine the moderating impact of openness on use behavior. The results, as depicted in Fig. 5, reveal that as the comfort level of the smartwatch decreases, individuals with high openness (β = 0.161, p < 0.01) exhibit a higher frequency of use behavior compared to individuals with low openness (β = 0.384, p < 0.001). This outcome provides evidence for the moderating role of openness within the mediation model. It underscores the notion that even when a smartwatch is uncomfortable, individuals with high openness are still more inclined to wear it during sleep scenarios.
Fig. 5 [Images not available. See PDF.]
Moderating effects of openness in the relationship between comfort and smartwatch usage behavior.
M denotes the mean, and SD denotes the standard deviation. Source: Own elaboration.
Discussion
This study delved into the influence of smartwatch comfort on users’ behavioral intention and actual usage behavior, specifically within the context of sleep. Initially, we investigated how different dimensions of comfort in the smartwatch impact its usage. The results obtained from the structural equation model highlighted the significant influence of the appearance and movement dimensions of comfort on users’ intention to wear the smartwatch during sleep. Moreover, this intention was found to have a significant impact on their subsequent smartwatch usage behavior. Subsequently, our inquiry extended to explore the role of personality traits in shaping smartwatch usage during sleep. Through the construction of models that encompassed moderation and mediation, with behavioral intention as the mediating factor, we demonstrated the mediating role of behavioral intention between smartwatch comfort and smartwatch usage. Additionally, the personality trait of openness emerged as a significant moderator, exerting considerable influence on the relationship between smartwatch comfort and behavioral intention. These findings offer pivotal insights for comprehending patterns of smartwatch usage and provide valuable direction for formulating personalized strategies moving forward.
In this study, the structural equation model confirmed the significant predictive role of smartwatch usage intention on its usage behavior within the sleep context. This aligns with previous findings (Schierz et al., 2010), indicating that behavioral intention serves as a precursor to usage behavior, and the two are positively correlated. Additionally, partial hypotheses of H1 were supported, indicating that comfort significantly influences the usage intention of smartwatches during sleep. This outcome further bolsters the evidence for “comfort being a pivotal factor in the adoption of wearable technologies” (Alduaij, 2022). However, only the appearance and movement dimensions of comfort within H1 exhibited significant predictive power for users’ usage intention. This suggests that in the sleep context, only specific dimensions of comfort impact users’ intention to use the smartwatch. This finding is consistent with prior research (Hegde et al., 2018), which has shown that different activities and wearing locations of wearable devices can lead to varying effects of comfort dimensions. These findings suggest that future smartwatch designs should place greater emphasis on both their appearance and their impact on human activities.
The limited influence of the other four dimensions (pressure, harm, attachment, perceived change) on smartwatch usage intention during sleep may stem from a combination of factors. Firstly, the dimensions of pressure and harm concern the potential wrist discomfort linked with smartwatch usage (Knight and Baber, 2005). Contemporary usability studies consistently reflect a substantial proportion of users perceiving smartwatches as comfortable for everyday use (Laborde et al., 2021; Prémont et al., 2020). Ongoing design improvements in the smartwatch market have notably reduced the discomfort associated with pressure and harm, resulting in a diminished impact. Secondly, attachment pertains to the physical sensation of the device on the body (Knight and Baber, 2005). Wrist-worn wearable devices, being prominent, demonstrate minimal discomfort in this attachment aspect (Hegde et al., 2018), further contributing to their muted influence. Additionally, the dimension of perceived change, involving feelings of unease or distinctness upon wearing the device (Knight and Baber, 2005), likely exerts a more significant effect in social contexts. Given the unique nature of sleep settings, the correlation between the discomfort from the perceived change dimension and smartwatch usage intention remains constrained. In sum, we believe that the limited discomfort posed by pressure, harm, attachment, and perceived change dimension may contribute to the observed limited impact of these dimensions on smartwatch usage intention during sleep.
In alignment with previous research (Davis et al., 1989; Venkatesh et al., 2003), our study confirms that the mediating role of usage intention persists in the relationship between smartwatch comfort and smartwatch usage behavior. Building on this foundation, we extended our investigation to encompass the moderating impact of personality traits. A body of earlier studies has convincingly demonstrated the profound influence of personality on user behavior (Kortum and Oswald, 2017; Sprotles and Kendall, 1986), particularly highlighting the pivotal role of openness in shaping perceived product usability (Kortum and Oswald, 2017). Through a comprehensive analysis utilizing a moderated mediation model, our study uncovers that the personality trait of openness serves as a mediator in connecting smartwatch comfort with usage intention. Given that openness encapsulates an individual’s inquisitiveness, willingness to adapt to change, and eagerness for novel experiences (Costa & McCrae, 1999), we assume that individuals with high levels of openness, predisposed to embracing smartwatches, are more likely to employ them during sleep despite potential comfort issues. Moreover, research has consistently demonstrated a notable negative correlation between openness and daily pain sensitivity (Bar-Shalita and Cermak, 2020; Pud et al., 2014), implying that individuals with elevated openness might exhibit reduced sensitivity to discomfort, possibly extending to their experiences with smartwatches.
From a theoretical perspective, our discovery of the impact of comfort (particularly aesthetics and activity) on user engagement in sleep scenarios contributes to the burgeoning field of wearable technology. This insight enriches the theoretical models of wearable technology adoption (Buenaflor and Kim, 2013; Spagnolli et al., 2014) by emphasizing the importance of aesthetic and ergonomic design elements. In addition, the mediating role of behavioral intentions and the moderating role of openness personality traits provide a more comprehensive framework for predicting user behavior in interactions with health-related wearable devices. However, the investigation of continuous usage intention, which is more meaningful for smartwatch manufacturers (Sharma et al., 2023), is not included in the framework of this study and can be explored in future work.
From a practical standpoint, smartwatches offer practicality, affordability, and portability in sleep detection compared to medical-grade monitors (Concheiro-Moscoso et al., 2023). Researchers have found them effective in detecting obstructive sleep apnea syndrome, hinting at their potential as medical aids (Cinar Bilge et al., 2024). Thus, considering comfort alongside accuracy in smartwatch use during sleep scenarios opens avenues for further exploration and promotion in healthcare (Masoumian Hosseini et al., 2023). Furthermore, sleep problems are particularly prevalent among the elderly population (Li and Gooneratne, 2019). Leveraging the monitoring capabilities of smartwatches can facilitate self-management interventions for addressing sleep issues in this demographic. This, in turn, empowers individuals to better regulate their health and enhance their overall quality of life (LeBlanc et al., 2019; Torossian et al., 2021). Collaboration between government agencies and communities can utilize smartwatches to provide targeted support for the elderly. Finally, for smartwatch manufacturers, the insights gleaned from this study underscore the importance of prioritizing the aesthetic design and activity features of smartwatches. Aesthetics and form factors significantly influence adoption and sustained usage, yet they are often overlooked in research (Pateman et al., (2018)). The outcomes of this study can furnish manufacturers with precise optimization suggestions, including adjustments to the watch face size to better suit the target demographic and refining the watch dimensions for enhanced wrist compatibility.
This study also has several limitations. First, the collection of smartwatch usage behavior data relied on subjective reports, introducing potential biases compared to actual usage data. Future research focusing on usage behavior could consider incorporating objective smartwatch usage data to replace subjective reports. Second, the explored dimensions of comfort in this study were centered around wearing comfort, but in reality, comfort encompasses more than just wearability. For instance, emotional factors like anxiety (Knight and Baber, 2005) were not considered. Finally, the investigation of individual differences in this study was confined to personality traits, potentially neglecting other individual-level and societal factors that might influence smartwatch usage. Consequently, future research could delve deeper into the impact of diverse individual characteristics on smartwatch usage behavior.
Conclusion
In conclusion, this study sheds light on the impact of smartwatch comfort on users’ behavioral intention and usage behavior during sleep. Our findings highlight the significant influence of appearance and movement comfort on users’ intention to wear smartwatches during sleep, subsequently affecting their actual usage behavior. The study also reveals the mediating role of behavioral intention and the moderating effect of openness personality trait in this relationship. Our findings underscore the importance of aesthetics and ergonomic design in wearable technology adoption, providing valuable insights for smartwatch manufacturers. However, this study has limitations, including reliance on subjective reports for data collection and the limited exploration of comfort dimensions. Future research should address these limitations and explore additional factors influencing smartwatch usage behavior.
Author contributions
Conceptualization: [Hongting Li], [Liang Xu]; Methodology: [Bingfei Xu], [Keyuan Zhou], [Rui Yan], [Yingchao Wu], [Haimo Zhang]; Formal analysis and investigation: [Bingfei Xu]; Writing - original draft preparation: [Lihong Ting], [Bingfei Xu]; Writing - review and editing: [Zaoyi Sun], [Liang Xu]; Funding acquisition: [Hongting Li], [Keyuan Zhou], [Rui Yan], [Yingchao Wu], [Haimo Zhang]. Supervision: [Keyuan Zhou], [Liang Xu].
Funding
This work was supported by the National Natural Science Foundation of the People’s Republic of China (Grant number 72371228) and the Human Factors Research Fund of OPPO (Grant number SKY-HX-20220281).
Data availability
The data is available at https://osf.io/7mtf5.
Competing interests
The authors declare no competing interests.
Ethical approval
In accordance with ethical standards and guidelines for research involving human subjects, all necessary ethics approvals have been obtained for the conduct of this study. The research protocol was reviewed and approved by Zhejiang University of Technology’s Research Ethics Committee (IRB Number: 2023D003).
Informed consent
All participants provided informed consent prior to their participation in the study.
Supplementary information
The online version contains supplementary material available at https://doi.org/10.1057/s41599-024-03214-y.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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