The Interaction Between Gender, Health and Smartwatch use on Health Anxiety

The Interaction Between Gender, Health and Smartwatch use on Health Anxiety | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Interaction Between Gender, Health and Smartwatch use on Health Anxiety Amy L. Perry, Richard P. Steel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7758416/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract This study investigates the relationship between gender, smartwatch use, diagnosed health conditions, and health anxiety. Previous research has shown health anxiety is more common among woman and those with chronic conditions, but little is known about how these factors interact with the use of wearable health technology. This study employed a quasi-experimental cross-sectional design with 252 participants, controlling for age and trait anxiety levels. The study found that men who have a diagnosed health condition and use a smartwatch experience significantly higher levels of health anxiety. In contrast, woman who have a diagnosed health condition and use a smartwatch report lower health anxiety. This suggests that smartwatches may contribute to increased anxiety in men and may provide reassurance for women. These findings underscore the need for personalised approaches to wearable health technology that consider gender differences and the potential psychological impacts on users. Future research should explore how wearables impact health anxiety across other populations and examine whether different types of smartwatches have distinct effects on health anxiety. health anxiety smartwatch use wearable health technology gender differences diagnosed health conditions Figures Figure 1 Figure 2 Introduction Health anxiety is a relatively new concept, evolving from what was traditionally known as hypochondria ( 1 ). It is characterised by an excessive and often irrational fear of having or developing a serious illness ( 2 ). This condition typically manifests in a cyclical pattern, beginning with checking the body for any signs of illness ( 3 , 4 ). In health anxious individuals, this behaviour is often followed by seeking reassurance from medical professionals or through the internet, which paradoxically tends to increase anxiety rather than alleviate it ( 5 , 6 ). This heightened anxiety often triggers more frequent and obsessive body checks ( 1 ), which, in turn, perpetuates the cycle of health anxiety ( 7 ). Researchers have speculated that the COVID-19 pandemic has further amplified health anxiety ( 8 ). The fear of infection and heightened awareness of health risks has led to an increase in health anxiety-related behaviours, such as excessive hygiene practices and extreme social isolation. While these behaviours have been protective, for some they have become maladaptive, leading to the development of health anxiety ( 9 ). Consequently, the prevalence of clinical levels of health anxiety is rising, particularly among young children and middle-aged adults ( 10 , 11 ). Clinical levels of health anxiety can lead individuals to undergo unnecessary medical consultations, examinations and tests, which places undue strain on the healthcare system ( 12 ). Additionally, health anxiety can significantly impact an individual’s mental and physical well-being ( 13 ). If this necessitates treatment, it places further strain on healthcare resources ( 5 ). Individuals with health conditions are more likely to experience health anxiety, particularly since the context of the COVID-19 pandemic. Heinen, Varghese ( 9 ) found that both physical and mental health conditions were significant predictors of higher scores on the Short Health Anxiety Inventory (SHAI) ( 14 ), even when controlling for demographic variables such as gender. Jeffers, Benotsch ( 15 ) found that individuals who misuse prescription medications are more likely to have both a medical condition and health anxiety, indicating some relationship between health conditions and health anxiety. A systematic review by Storer, Holden ( 16 ) identified health anxiety as the most prevalent form of anxiety among gastroenterology and hepatology outpatients, surpassing generalised and social anxiety. Similarly, women with early-stage breast cancer have been shown to experience elevated levels of health anxiety following diagnosis and treatment ( 17 ). In a study focusing specifically on COVID-related anxiety, King, McQuaid ( 18 ) found that while individuals with at-risk medical conditions did not report significantly higher levels of COVID-related anxiety, they did report poorer quality of life and more extreme protective behaviours, such as staying home excessively and compulsively disinfecting. Taken together, the literature indicates a bidirectional and multifaceted relationship between health conditions and health anxiety, where the presence of health conditions may not always heighten anxiety levels directly, but does appear to shape how that anxiety is experienced and expressed, warranting further exploration. A part of the broader category of consumer health wearables, smartwatches have become increasingly popular in recent years. These devices offer a range of health monitoring capabilities, including heart rate tracking, sleep analysis, and activity logging ( 19 ). By providing real-time data, the technology has revolutionised health management as users can use it to make informed decisions, such as increasing physical activity or adjusting sleep patterns, to enhance overall well-being ( 20 ). With the potential to lower healthcare costs, smartwatches are being investigated and trailed as replacements for traditional, in-person healthcare visits ( 21 ). Despite the positive impact smartwatches may have on helping patients and healthcare professionals to monitor health and healthy behaviour, there is increasing concern that these devices may contribute to the development of health anxiety ( 22 ). Interestingly, regulatory bodies, such as the American Food and Drug Administration (FDA), have recognised the potential risks associated with some functions of wearables ( 23 ). For example, Dhruva, Shah ( 24 ) discussed the FDA’s clearance of Apple Watch features for detecting irregular heart rhythms and generating electrocardiograms (ECGs). The study identified several risks, including the potential for misinterpretation and over-reliance on the devices. These risks highlight the importance of considering the psychological impacts of smartwatch use as the potential for misinterpretation could cause or exacerbate health anxiety. Demonstrating this risk, Rosman, Gehi and Lampert ( 25 ) conducted a case study following a patient diagnosed with atrial fibrillation, who purchased a commercially available smartwatch for cardiac monitoring. Certain notifications from the smartwatch, such as elevations in heart rate during exercise and inconclusive ECGs, were misinterpreted as indicators of the patient’s health condition worsening. This led to a significant increase in the number of ECGs performed over time, suggesting that the notifications were wrongly perceived as health threats. Rosman and colleagues noted that this misinterpretation fuelled the continuous cycle of excessive worry and compulsive body checking behaviours, ultimately leading to increased health anxiety. As a further example of the potential for smartwatches to increase health anxiety, Filippaios, Tran ( 26 ) explored the psychological effects of receiving health alerts among poststroke adults. Their analysis revealed a significant reduction in self-rated physical health status among users who received alerts. Qualitative research has also found evidence of health anxiety that may have been exacerbated by wearable activity tracker use. Andersen, Langstrup and Lomborg ( 27 ), who explored wearable activity trackers in patients with chronic heart disease, found mixed experiences among participants. While some gained greater insight into their health and made positive health-related improvements, others experienced new health-related anxieties, such as doubts about their health and feelings of failure in monitoring it. It is important to acknowledge the evidence for smartwatch related increases in health anxiety is not equivocal. Echoing the positive experiences noted by Andersen, Langstrup and Lomborg ( 27 ), Paul et al. (2023) reported no significant difference in anxiety levels between the smartwatch and control groups. Although this study examined general anxiety, the health-focused context of their study allows its findings to be relevant and extendable to the ream of health anxiety. The evidence suggests that wearable health technologies, though helpful for some, may contribute to or worsen health anxiety in others, particularly when users lack medical literacy or support in interpreting the data they receive. Beyond the influence of underlying health conditions, evidence points to gender as another key factor shaping experiences of health anxiety, with women consistently showing greater vulnerability. It is reported that women are more likely to develop anxiety disorders in general (McLean & Anderson, 2009), and this trend extends to health anxiety specifically. During the COVID-19 pandemic, women reported significantly higher health anxiety than men, with gender emerging as a significant predictor in regression analyses ( 28 ). Similarly, Liu, Zhang ( 29 ) found that women experienced higher levels of post-traumatic stress symptoms during the pandemic, including hyperarousal and intrusive thoughts, features that closely align with cognitive and physiological aspects of health anxiety. It is possible that women’s greater experiences of health anxiety stems from how they perceive and interpret bodily sensations. Grabauskaite, Baranauskas and Griskova-Bulanova ( 30 ) found that women report greater interoceptive awareness, including noticing and emotionally responding to internal sensations. However, they also tend to have lower trust in these sensations and are more likely to worry more about them in comparison to men, which are patterns closely aligned with the core features of health anxiety. This aligns with findings from Heinen, Varghese ( 9 ), who also noted greater sensitivity to bodily symptoms among women. Although O’Bryan and McLeish ( 31 ), did not identify gender as a direct predictor of health anxiety, they observed that women scored higher in anxiety sensitivity and negative affect, both of which are associated with elevated health anxiety through indirect pathways. Moreover, cultural and psychological factors, such as gender role socialisation and reinforcement of somatic concern in girls may further contribute to increased illness-related worry in women ( 32 ). Taken together, the literature supports the notion that women are more likely to experience higher levels of health anxiety than men, and that this difference may be shaped by a complex interaction of biological, cognitive, and sociocultural factors. The advent of wearable activity trackers and smartwatches has seen them employed by researchers and clinicians to help motivate and monitor patients’ physical activity. However, there has been very little research investigating maladaptive effects of smartwatch use, particularly in clinical populations. The primary aim of this research will be to investigate whether smartwatch use can increase health anxiety when comparing both clinical and non-clinical populations. Furthermore, as there are known gender differences in health anxiety between males and females, this study will also investigate gender differences in health anxiety. Given the aims of the present research, the following predictions were made. H1 Previous research has documented that real-time health data is sometimes inaccurate, can exacerbate anxiety by inflicting worry and perceptions of ill-health. It is therefore predicted that participants who use smartwatches will report higher health anxiety scores compared to non-users. H2 The cognitive behavioural model of health anxiety posits that individuals with chronic illnesses are more likely to focus on potential health threats. Therefore, participants with diagnosed health conditions will report higher health anxiety scores compared to those without diagnosed health conditions. H3 Previous research has demonstrated consistent evidence showing that women are generally more vigilant about health-related risks and are more prone to anxiety than men. It is therefore predicted that female participants will report higher health anxiety scores compared to male participants. H4 It is expected that the combination of these factors will reveal nuanced differences in anxiety levels, reflecting the complex interplay highlighted in the literature. There will be a significant interaction between smartwatch use, health condition, and gender on health anxiety scores. Due to the exploratory nature of this prediction, we did not predict the anticipated direction of these effects. Method Design The study employed a quasi-experimental between-participant cross-sectional design, analysed using a 2 x 2 x 2 ANCOVA. The design involved three independent variables and one dependent variable. The first independent variable, 'Smartwatch use’, had two levels: 'smartwatch user' and 'non-user'. The second independent variable, 'health condition', had two levels: 'diagnosed’ and 'no condition'. The third independent variable, ‘gender’, had two levels: ‘male’ and ‘female’. The dependent variable measured was health anxiety. Age and trait anxiety were included as covariates to control for their potential confounding effects. Participants self-selected into the conditions based on their current use of smartwatches, if they had any diagnosed health conditions and whether they identified as male or female. Participants Criteria required participants to be aged 18 years or older, identify as either male or female and be capable of understanding and completing the study materials in English. Participants were recruited using convenience and volunteer sampling methods through the SONA system, social media platforms, and word-of-mouth referrals. All participants engaged in the study online. A total of 261 participants were recruited. After removal of two participants who did not identify as either male or female, 259 participants were included in the analysis. The mean age of the participants was 35.86 years (SD = 15.56), with a mode of 23 years. To treat age as a continuous variable, midpoints of the age categories were assigned to each participant. The gender distribution consisted of 70 males and 188 females. Participants were predominantly of white ethnic background ( n = 194 participants). Materials The study utilised an online survey administered via Qualtrics, which comprised both a researcher-designed section and standardised psychometric scales to assess various constructs. Participant and Demographic Information Demographic information was collected from participants, including age and gender. At the end of the survey, participants were asked if they owned a smartwatch and if they had a diagnosed health condition. These questions were asked at the end of the survey to reduce the potential for response bias. Participants reported cardiovascular conditions (n = 23), respiratory conditions (n = 20), metabolic conditions (n = 10), musculoskeletal conditions (n = 22), neurological conditions (n = 16), mental health conditions (n = 37), gastrointestinal conditions (n = 27), autoimmune conditions (n = 6), endocrine disorders (n = 23), skin conditions (n = 16), chronic pain (n = 3 ) and cancer (n = 9). An a priori power analysis was conducted using GPower 3.1 ( 33 ) for a three-way ANCOVA with two covariates, assuming a small-to-medium effect size (η² = .05), α = .05, and power (1–β) = .80. The analysis indicated that a sample size of N = 250 would be required. The obtained sample (N = 259) was therefore adequate in meeting this requirement. Behavioural Inhibition System Scale The Behavioural Inhibition System (BIS; Carver and White ( 34 ), was used to assess trait anxiety levels. The BIS is a measure of anxiety that assesses an individual's sensitivity to potential punishment cues, specifically designed to measure anxiety related to sensitivity to potential punishment - a distinct approach compared to other general anxiety measures. Therefore, it was chosen for this study as it facilitates the exploration of anxiety in the context of fear of negative outcomes. Participants respond to items on a 4-point Likert scale that ranges from 1 (strongly agree) to 4 (strongly disagree). Participants are required to rate statements such as, ‘I worry about making mistakes.’. Lower total scores indicate greater sensitivity to punishment cues, which correlates with higher levels of anxiety. This scale has been shown to have good reliability (α = .84) ( 35 ) and high construct reliability (α > .80) ( 36 ), suggesting that the scale reliably measures anxiety. Short Health Anxiety Inventory Health anxiety was measured using the SHAI ( 14 ), an abbreviated version of the Health Anxiety Inventory ( 37 ). The SHAI is a 14-item measure that screens for general health anxiety in the past six months. Participants respond to each item by indicating which of four statements best describes them (e.g., 0 = ‘I do not worry about my health’ to 3 = ‘I spend most of my time worrying about my health’). The scale’s instructions specify that participants can select more than one statement if they identify with more than one. If participants select more than one statement, the highest selected was be taken as their response. Scores are summed for a total composite score ranging between 0 and 42, with higher scores indicating a higher level of health anxiety. It is common for studies to define prevalence of clinical health anxiety by scoring greater than or equal to 18 ( 4 , 9 ). This cut off has been shown to reliably identify individuals with clinically significant levels of health anxiety ( 37 ). Low levels of health anxiety are defined as scoring 10 or below ( 38 ). The scale has been shown to have good internal reliability in both non-clinical samples (α = .89) ( 14 ) and for patients with existing medical conditions (α = .84) ( 39 ). Diamond, Dysch and Daniels ( 4 ) found this same level of internal reliability (α = .87). Procedure The study was conducted online, meaning the participants could complete the survey at their convenience, while maintaining consistent methodological standards. Upon expressing interest, potential participants were directed to a secure online platform hosted by Qualtrics. The first page of the survey provided detailed information about the study, including its purpose, the nature of the tasks, potential risks, benefits of participating, and confidentiality measures. Participants were required to provide electronic informed consent before proceeding with the survey. They were informed that participation was voluntary, they could withdraw at any time without penalty, and all responses would be anonymised to protect their privacy. Once consent was obtained, participants completed the survey, which included the demographic information section and the BIS and SHAI scales. Instructions were provided at the start of each section to ensure participants understood how to respond to each question. The survey was designed to be intuitive and user-friendly to encourage complete and accurate responses. Upon completing the survey, participants were directed to a debriefing page which provided additional resources about mental health. Participants were thanked for their contribution and provided with contact information should they have any questions about the study, wish to withdraw or to receive information about the results of the study. The study protocol was approved by Nottingham Trent University’s ethics committee prior to initiation. All data were collected and stored in accordance the university’s guidelines to ensure confidentiality and security of participant information. The dataset supporting the conclusions of this article is available in the Figshare repository, [ 10.6084/m9.figshare.30196978 ]. Results Preliminary Analysis and Assumptions The internal consistency of the scales used was assessed using Cronbach's alpha. The BIS scale had a Cronbach’s alpha of .734 (M = 14.14, SD = 3.14) and the SHAI had a Cronbach’s alpha of .867 (M = 13.47, SD = 5.86). These values confirm the internal consistency of these measures in this study. The assumption of normality for the residuals of the dependent variable (SHAI scores) was evaluated. Skewness and kurtosis were normal across groups when examining both the descriptive statistics and visual examination of the normal distribution. The assumption of homogeneity of variance was evaluated using Levene's test. The results indicated that the assumption was met, as evidenced by a non-significant result (F(7, 251) = 2.02, p = .053). This suggests that the variances of SHAI scores are equal across the groups, fulfilling the requirement for conducting ANCOVA. The assumption of linearity was evaluated to ensure a linear relationship between the dependent variable (SHAI scores) and the covariates (age, and trait anxiety (BIS scores)). The visual spread in a boxplot indicate that the linearity assumption was adequately met, meeting the assumptions for ANCOVA. The assumption of homogeneity of regression slopes was tested by including the interaction terms between each covariate (age and BIS scores) and each independent variable (gender, smartwatch ownership, diagnosed health condition) in the ANCOVA model. The interaction terms were non-significant, all p > .05 (e.g., Gender × Age2: F(1, 247) = 0.33, p = .567), indicating that the relationship between the covariates and SHAI scores did not differ significantly across groups. Thus, the assumption was met, supporting the appropriateness of conducting ANCOVA. Descriptive Statistics Table 1 presents the demographic distribution of participants across the eight experimental conditions as well as the mean SHAI score, where higher scores indicate higher levels of health anxiety. It is important to note that due to the larger proportion of female participants in the study, the groups differed in size. Table 1 Descriptive Statistics of Participants by Group Group n Age (M) SHAI Min Max Range 1: Smartwatch user, Diagnosed, Male 13 39 15.46(6.90) 2 27 25 2: Smartwatch user, Diagnosed, Female 44 37.20 15.43(5.54) 5 31 26 3: Smartwatch user, No condition, Male 21 35.43 8.62(4.43) 0 18 18 4: Smartwatch user, No condition, Female 53 32.08 14.85(7.74) 1 36 35 5: Non-user, Diagnosed, Male 21 44.19 11.95(5.23) 2 23 21 6: Non-user, Diagnosed, Female 37 39.24 16.03(6.36) 2 30 28 7: Non-user, No condition, Male 16 39.13 10.31(3.09) 5 16 11 8: Non-user, No condition, Female 54 31.37 12.50(5.97) 0 29 29 Key: n = number of participants, SD = standard deviation, min = minimum score, max = maximum score Main analysis Table 1 An ANCOVA was conducted to examine the effect of smartwatch ownership, diagnosed health condition, and gender on health anxiety (SHAI scores), controlling for age and trait anxiety (BIS scores). Age was a significant covariate (F(1, 249) = 12.567, p < .001, η2 = .048), implying that age had a significant effect on health anxiety scores, with descriptive statistics showing that younger participants reported higher health anxiety compared to older participants. Trait anxiety was also significant (F(1, 249) = 34.168, p < .001, η2 = .121). A Pearson correlation showed a significant negative association between trait anxiety (BIS) and health anxiety (SHAI) (r(259) = − .429, p < .001), indicating that higher trait anxiety (lower BIS scores) was associated with higher health anxiety (higher SHAI scores). Therefore, trait anxiety was also retained as a covariate. H1 The first hypothesis predicted that individuals who used a smartwatch would experience greater health anxiety than those who did not use a smartwatch. However, when testing this assumption, no significant effect was found for smartwatch use (F(1, 249) = 0.891, p = .346, η² = .004). H2 The second hypothesis predicted that individuals with a health condition would experience greater health anxiety than those without a health condition. Supporting this prediction, health condition had a significant main effect on health anxiety (F1, 249) = 19.951, p < .001, η² = .074), with individuals with a diagnosed health condition displaying higher levels of health anxiety. H3 The third hypothesis was that females would experience higher levels of health anxiety than males. However, the results of this test were non-significant (F1, 249) = 2.131, p = .146, η² = .008). Interaction Effects The final hypothesis was that there would be an interaction between health status, gender and smartwatch use. The interaction between smartwatch use, health condition and gender was significant (F(1, 249) = 6.396, p = .012, η² = .025), suggesting a combined effect of these factors on health anxiety. The interaction between smartwatch use, diagnosed health condition, and gender on health anxiety is illustrated in Figs. 1 and 2 . Follow-up analyses examined the two-way interaction between smartwatch use and gender within each health condition group. For participants with a diagnosed health condition (Fig. 1 ), the smartwatch use × gender interaction trended towards significance (F(1, 109) = 2.91, p = .091, η² = .026). The pattern suggests that males who used smartwatches reported higher health anxiety, whereas females who did not use smartwatches reported higher anxiety. For participants without a diagnosed health condition (Fig. 2 ), the smartwatch use × gender interaction also trended towards significant (F(1, 138) = 3.47, p = .065, η² = .025) but showed an opposite trend: males who used smartwatches reported lower health anxiety, while females who used smartwatches reported higher anxiety. These non-significant trends help to explain the significant three-way interaction observed in the overall model. Figure 1 Figure 2 Discussion The aim of the present research was to examine the effect of health condition, gender and smartwatch use on health anxiety. In line with current knowledge, this study found that individuals with diagnosed health conditions experience significantly higher levels of health anxiety compared to those without such conditions ( 1 , 11 , 40 ). The hypothesis that gender and smartwatch use would independently affect health anxiety was not supported. However, the study revealed that the use of smartwatches affects males and females differently depending on their health condition. The prediction that health anxiety would be higher in individuals with health conditions, was supported. This finding is consistent with a growing body of research demonstrating a clear link between health conditions and elevated health anxiety ( 9 , 15 , 16 ). Notably, this result challenges the findings of King, McQuaid ( 18 ), who reported no significant difference in COVID-related anxiety between individuals with and without at-risk health conditions. One possible explanation for this discrepancy is that King et al. focused specifically on anxiety related to COVID-19, which may be influenced by additional factors such as perceived risk or public health messaging, rather than generalised health anxiety. In contrast, the present study measured health anxiety more broadly, offering a more comprehensive understanding of how living with a health condition may affect health-related fears. Therefore, while King et al. highlighted differences in behavioural outcomes and quality of life, the current findings suggest that the presence of a health condition itself may be more directly linked to heightened health anxiety than their results indicated. When considered together, the findings from both the current study and King et al. suggest that health conditions may both elevate health anxiety and influence how it is expressed, with the extent of each depending on the individual's context and the nature of the health threat. A novel finding revealed that there was an interaction between gender and smartwatch use for individuals who have a health condition. Specifically, men who use smartwatches and have a diagnosed health condition displayed higher health anxiety than women. In contrast, women who did not use smartwatches had higher health anxiety than men. This finding challenges the conventional understanding of gender differences in anxiety ( 2 , 10 , 28 , 32 ). These findings also reflect the mixed results observed by Andersen, Langstrup and Lomborg ( 27 ), where some individuals used their smartwatches without developing health anxiety, while others experienced heightened anxiety associated with smartwatch use. The findings imply that men with health conditions are particularly vulnerable to the effects of smartwatches on health anxiety. Since it is well established that men are generally less aware of bodily sensations than women ( 9 ), these men may over-rely on smartwatch data to compensate for this reduced bodily awareness. When data from their watch is interpreted as indicating that their health condition is worsening, it may lead to the development of health anxieties as they struggle to reconcile the data with how they physically feel. This issue is likely exacerbated when the data is inaccurate or unclear ( 23 ). However, an alternative explanation comes from Lupton et al.’s ( 41 ) concept of 'data sense,' which posits that experiences with sensor data are not only cognitive but also involve sensory and emotional dimensions. It could be that smartwatches are teaching men to become more aware of bodily sensations. Initially this might seem beneficial but over time, this increased awareness may lead to similar anxiety levels observed in women, as men begin to worry about sensations they were previously unaware of. If this were the case, however, you would expect the health anxiety scores for this group to be similar to those of women. Yet, the anxiety scores for these men were higher, perhaps lending more support to the first explanation. In contrast, among men without diagnosed health conditions, those who did not use smartwatches reported higher levels of health anxiety compared to smartwatch users. Among females, the pattern differed: women with a health condition experienced increased health anxiety when they did not use a smartwatch, whereas those without a health condition showed higher health anxiety when they did use a smartwatch. Overall, this suggests that Rosman et al.’s ( 25 ) report may represent an isolated case; the findings indicate that women with health conditions can use a smartwatch without increasing health anxiety levels. The smartwatch may provide reassurance by confirming the sensations they are already experiencing. Andersen, Langstrup and Lomborg ( 27 ) support this notion, finding that some patients used their smartwatches for reassurance about heart function, thus avoiding unnecessary anxiety. The findings of Lomborg and Frandsen ( 42 ) provide insight into why females without health conditions exhibit higher health anxiety scores when using smartwatches compared to non-users. Lomborg and Frandsen emphasise the communicative nature of self-tracking, where users often engage with their health data in a social context. This engagement can amplify anxiety through social comparison or the pressure to meet perceived health standards. Women are generally more prone to social comparison and have a stronger desire for social acceptance compared to men ( 43 ). As a result, the constant monitoring and sharing of health data through smartwatches might exacerbate health anxiety in women, as they may feel a greater need to conform to health norms or expectations. In contrast, men may be less affected by these social dynamics, which could explain why their health anxiety remains relatively lower when using smartwatches in the absence of a health condition. Implications The study extends current knowledge by surveying the general population, rather than focusing solely on clinical populations. This broader application enhances our understanding of health anxiety in a wider context and makes the findings more applicable to everyday smartwatch users. The findings of this study suggest that the development of health technology, particularly smartwatches, could benefit from a more personalised approach that considers individual user characteristics. For men with health conditions, the technology might need to provide contextual information to accompany the data. This would leave less room for misinterpretation and potentially mitigate health anxiety. For women, particularly those without health conditions, there might need to be considerations around how the data is presented and how it interacts with social pressures. The potential for misinterpretation of health data highlights the need for better education around the use of smartwatches. Users may need guidance on accurate interpretation and measures to prevent over-reliance on the technology. However, there is a need to consider how such personalisation could influence health disparities, particularly if certain groups (e.g., older adults, lower-income individuals) are less able to access or effectively use these tailored technologies. Furthermore, healthcare providers should be aware of the interaction between gender and wearable health technologies, and the effect it can have on health anxiety. This awareness can guide more personalised recommendations about the appropriateness of smartwatches for individuals and ensuring they are given appropriate instruction regarding the interpretation of health-related data. Strengths and limitations The study recruited 252 participants, which is a robust sample size for the analyses conducted. The power sensitivity analysis confirms that the study had sufficient power to detect significant effects, strengthening the findings. The study also controlled for important covariates – age and trait anxiety – which helps to isolate the effects of the primary independent variables on health anxiety. Since participants self-reported their health conditions and smartwatch use, there is a possibility of response bias, particularly social desirability bias. The researcher placed the questions most likely to provoke response bias at the end, so although this particular confound is unlikely, it cannot be entirely ruled out. While the sample size of 252 participants is robust, the demographic composition – predominantly female and white – limits the generalisability of the findings to more diverse populations. Future research should aim to include a more diverse participant pool to ensure that the findings are applicable across different cultural, racial, and socioeconomic groups. Additionally, we had no control over the type of devices participants used. It may be that some users used fairly simple devices that has limited health data, while others used devices capable of producing more advanced health-related data. Therefore, we cannot make claims about how smartwatches may most influence health anxiety. Finally, as Andersen, Langstrup and Lomborg ( 27 ) noted, there is a distinction between clinically validated self-monitoring technologies and the lower-cost sensors available in the consumer market. The current study did not control for or collect data on the specific type of smartwatch used. Consequently, the varying reliability and accuracy of these devices could contribute to different levels of health anxiety, particularly if users place excessive trust in data from less reliable wearables. This highlights the potential for technology type to significantly influence the psychological outcomes associated with health monitoring devices. Future research The findings underscore the complexity of the relationship between gender, health technology use, and health anxiety, highlighting the need for further research to explore these dynamics in greater depth. Future studies could examine how different types of wearables, such as higher-quality devices that may meet clinical standards versus lower-quality consumer-grade products, influence health anxiety across these diverse groups. It would also be useful to understand if it is specific health metrics recorded by smartwatches that induced health anxiety, or whether this effect is related to having a wide range of health information via the smartwatch. Future research could also utilise qualitative methods to explore the underlying factors contributing to this discrepancy, offering deeper insights into how individuals with and without health conditions engage with smartwatches and how these interactions influence their health anxiety. Conclusion This study offers insights into the complex interactions between smartwatch use, health conditions, gender, and health anxiety. Consistent with previous research, individuals with diagnosed health conditions were found to experience significantly higher levels of health anxiety compared to those without such conditions. Notably, the study highlights the dual potential of smartwatches to both alleviate and exacerbate health anxiety, depending on the user's characteristics and health status. For men with diagnosed health conditions, smartwatch use was associated with the highest levels of health anxiety, suggesting that these individuals may over-rely on wearable health technologies, leading to misinterpretation of data and increased anxiety. In contrast, women with health conditions appeared to benefit from smartwatch use, suggesting it provides reassurance rather than exacerbating their anxiety. However, for women without health conditions, the use of smartwatches was linked to higher health anxiety, potentially due to social comparison pressures and the desire for acceptance. These findings underscore the importance of considering individual differences when developing and recommending the use of health technologies. The psychological effects of smartwatches are not uniform; they are influenced by factors such as gender, health status, and the way individuals interact with and interpret health data. This suggests a need for personalised approaches to the use of wearable health technologies, particularly for populations at risk of heightened anxiety. Abbreviations FDA American Food and Drug Administration ECGs Electrocardiograms BIS The Behavioural Inhibition System (scale) SHAI Short Health Anxiety Inventory Declarations Human Ethics and Consent to Participate Informed consent was obtained from all individual participants included in the study. Ethical Approval Ethical approval to conduct this research was provided by the Nottingham Trent University Social Science Research Ethics Committee (S3REC) Funding Declaration No funding was received for conducting this study. Author Contribution A.P wrote the main manuscript. The methods were devised by A.P and R.S, and both authors contributed to the data analysis. Both authors reviewed the manuscript Data Availability The datasets generated during and/or analysed during the current study are available in the Figshare repository: Steel, Richard (2025). Perry_Steel_HealthAnxiety_Smartwatch. figshare. Dataset. https://doi.org/10.6084/m9.figshare.30196978.v1 References Tyrer P. COVID-19 health anxiety. World Psychiatry. 2020;19(3):307–8. Svestkova A, Kvardova N, Smahel D. Health Anxiety in Adolescents: The Roles of Online Health Information Seeking and Parental Health Anxiety. J Child Fam stud. 2023;33(4):1083–94. Sonuga-Barke EJS, Fearon P, Commentary et al. Health anxiety in youth during 'COVID' - some thoughts prompted by Rask. (2024). J Child Psychol Psychiatry. 2024;65(4):431-4. Diamond PR, Dysch L, Daniels J. Health anxiety in stroke survivors: a cross-sectional study on the prevalence of health anxiety in stroke survivors and its impact on quality of life. Disabil Rehabil. 2023;45(1):27–33. Hedman-Lagerlof E, Tyrer P, Hague J, Tyrer H. Health anxiety. BMJ. 2019;364:l774. Peng RX. How online searches fuel health anxiety: Investigating the link between health-related searches, health anxiety, and future intention. Comput Hum Behav. 2022;136. Mathes BM, Norr AM, Allan NP, Albanese BJ, Schmidt NB. Cyberchondria: Overlap with health anxiety and unique relations with impairment, quality of life, and service utilization. Psychiatry Res. 2018;261:204–11. Dennis D, Radnitz C, Wheaton MG. A Perfect Storm? Health Anxiety, Contamination Fears, and COVID-19: Lessons Learned from Past Pandemics and Current Challenges. Int J Cogn Ther. 2021;14(3):497–513. Heinen A, Varghese S, Krayem A, Molodynski A. Understanding health anxiety in the COVID-19 pandemic. Int J Soc Psychiatry. 2022;68(8):1756–63. Rask CU, Duholm CS, Poulsen CM, Rimvall MK, Wright KD. Annual Research Review: Health anxiety in children and adolescents-developmental aspects and cross-generational influences. J Child Psychol Psychiatry. 2024;65(4):413–30. Sunderland M, Newby JM, Andrews G. Health anxiety in Australia: prevalence, comorbidity, disability and service use. Br J Psychiatry. 2013;202(1):56–61. Norbye AD, Abelsen B, Forde OH, Ringberg U. Health anxiety is an important driver of healthcare use. BMC Health Serv Res. 2022;22(1):138. Byrne GJ. Health anxiety in older people. Int Psychogeriatr. 2022;34(8):687–8. Abramowitz JS, Deacon BJ, Valentiner DP. The Short Health Anxiety Inventory: Psychometric Properties and Construct Validity in a Non-clinical Sample. Cognit Ther Res. 2007;31(6):871–83. Jeffers AJ, Benotsch EG, Green BA, Bannerman D, Darby M, Kelley T, Martin AM. Health anxiety and the non-medical use of prescription drugs in young adults: A cross-sectional study. Addict Behav. 2015;50:74–7. Storer B, Holden M, Kershaw KA, Braund TA, Chakouch C, Coleshill MJ, et al. Global Prevalence of Anxiety in Gastroenterology and Hepatology Outpatients: A Systematic Review and Meta-Analysis. Curr Gastroenterol Rep. 2025;27(1):17. Jones SL, Hadjistavropoulos HD, Sherry SB. Health anxiety in women with early-stage breast cancer: What is the relationship to social support? Can J Behav Sci / Revue canadienne des Sci du comportement. 2012;44(2):108–16. King JD, McQuaid A, Leeson VC, Samuel O, Grant J, Imran Azeem MS, et al. The association of severe COVID anxiety with poor social functioning, quality of life, and protective behaviours among adults in United Kingdom: a cross-sectional study. BMC Psychiatry. 2023;23(1):117. Choi H-Y, Keil M, Baird AM. Intention to use smartwatch health applications: A regulatory fit and locus of control perspective. Inf Manag. 2022;59(6). Reeder B, David A. Health at hand: A systematic review of smart watch uses for health and wellness. J Biomed Inf. 2016;63:269–76. Varma N, Marrouche NF, Aguinaga L, Albert CM, Arbelo E, Choi JI, HRS/EHRA/APHRS/LAHRS, et al. /ACC/AHA Worldwide Practice Update for Telehealth and Arrhythmia Monitoring During and After a Pandemic. J Am Coll Cardiol. 2020;76(11):1363–74. Kaplan DM, Greenleaf M, Lam WA. Wear With Care: A Call for Empirical Investigations of Adverse Outcomes of Consumer Health Wearables. Mayo Clin Proc Digit Health. 2023;1(3):413–8. Shuren J, Patel B, Gottlieb S. FDA Regulation of Mobile Medical Apps. JAMA. 2018;320(4):337–8. Dhruva SS, Shah ND, Vemulapalli S, Deshmukh A, Beatty AL, Gamble GM, et al. Heart Watch Study: protocol for a pragmatic randomised controlled trial. BMJ Open. 2021;11(12):e054550. Rosman L, Gehi A, Lampert R. When smartwatches contribute to health anxiety in patients with atrial fibrillation. Cardiovasc Digit Health J. 2020;1(1):9–10. Filippaios A, Tran KT, Mehawej J, Ding E, Paul T, Lessard D, et al. Psychosocial measures in relation to smartwatch alerts for atrial fibrillation detection. Cardiovasc Digit Health J. 2022;3(5):198–200. Andersen TO, Langstrup H, Lomborg S. Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study. J Med Internet Res. 2020;22(7):e15873. Ozdin S, Bayrak Ozdin S. Levels and predictors of anxiety, depression and health anxiety during COVID-19 pandemic in Turkish society: The importance of gender. Int J Soc Psychiatry. 2020;66(5):504–11. Liu N, Zhang F, Wei C, Jia Y, Shang Z, Sun L, et al. Prevalence and predictors of PTSS during COVID-19 outbreak in China hardest-hit areas: Gender differences matter. Psychiatry Res. 2020;287:112921. Grabauskaite A, Baranauskas M, Griskova-Bulanova I. Interoception and gender: What aspects should we pay attention to? Conscious Cogn. 2017;48:129–37. O’Bryan EM, McLeish AC. An Examination of the Indirect Effect of Intolerance of Uncertainty on Health Anxiety Through Anxiety Sensitivity Physical Concerns. J Psychopathol Behav Assess. 2017;39(4):715–22. McLean CP, Anderson ER. Brave men and timid women? A review of the gender differences in fear and anxiety. Clin Psychol Rev. 2009;29(6):496–505. Faul F, Erdfelder E, Lang A-G, Buchner A. G Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175–91. Carver CS, White TL, Behavioral, Inhibition. Behavioral Activation, and Affective Responses to Impending Reward and Punishment: The BIS/BAS Scales. J Personal Soc Psychol. 1994;67(2):319–33. Chen Y-L, Sherwood SN, Freeman AJ. Multidimensional Item Response Theory of the BIS/BAS Scales: Evidence for a Bifactor Model Structure. J Psychopathol Behav Assess. 2023;45(4):1059–67. Rodriguez A, Reise SP, Haviland MG. Applying Bifactor Statistical Indices in the Evaluation of Psychological Measures. J Pers Assess. 2016;98(3):223–37. Salkovskis PM, Rimes KA, Warwick HM, Clark DM. The Health Anxiety Inventory: development and validation of scales for the measurement of health anxiety and hypochondriasis. Psychol Med. 2002;32(5):843–53. Hayter AL, Salkovskis PM, Silber E, Morris RG. The impact of health anxiety in patients with relapsing remitting multiple sclerosis: Misperception, misattribution and quality of life. Br J Clin Psychol. 2016;55(4):371–86. Kehler MD, Hadjistavropoulos HD. Is health anxiety a significant problem for individuals with multiple sclerosis? J Behav Med. 2009;32(2):150–61. Hadjistavropoulos HD, Janzen JA, Kehler MD, Leclerc JA, Sharpe D, Bourgault-Fagnou MD. Core cognitions related to health anxiety in self-reported medical and non-medical samples. J Behav Med. 2012;35(2):167–78. Lupton D, Pink S, Labond CH, Sumartojo S. Personal data contexts, data sense, and self-tracking cycling. Int J communication. 2018;12:647–66. Lomborg S, Frandsen K. Self-tracking as communication. Inform Communication Soc. 2015;19(7):1015–27. Guimond S, Branscombe NR, Brunot S, Buunk AP, Chatard A, Desert M, et al. Culture, gender, and the self: variations and impact of social comparison processes. J Pers Soc Psychol. 2007;92(6):1118–34. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 18 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor invited by journal 07 Oct, 2025 Editor assigned by journal 04 Oct, 2025 Submission checks completed at journal 04 Oct, 2025 First submitted to journal 01 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7758416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533618485,"identity":"1136c88e-d6fc-4534-8b46-b375b1e857ef","order_by":0,"name":"Amy L. Perry","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"L.","lastName":"Perry","suffix":""},{"id":533618486,"identity":"0458e05f-d747-4c1c-8ebf-3f18c5dd12b3","order_by":1,"name":"Richard P. Steel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBAC+2YGNiDFzCDfAKQeVMAlEnBqMTgM1WJwDKTuDIMEYS0HYFpAVGIbMVqOMz978HOPNYOBfPPBB4nz7tQZHGB++IGxLQ2PX9jMDXuepTPIt7ElGyRueyZhcIDNWIKxLQenFjtmHjYJngOHGRiO8ZhJJG47DNTCYMbA2FaBU4sxUIvkH7AW/u8/EueAtLB/w6vFsJmHTRpqCxtDYgNICw/IFtwOMzjMZiYtcyCdx+BYmrFEwrHDkjMP8xRLJJzD7X2D84efSb45YC0n33z44YcPNYf5+Y63b/zwoSwZpxYY4EEwmRnwxcooGAWjYBSMAmIAABSxUP49FtMOAAAAAElFTkSuQmCC","orcid":"","institution":"Nottingham Trent University","correspondingAuthor":true,"prefix":"","firstName":"Richard","middleName":"P.","lastName":"Steel","suffix":""}],"badges":[],"createdAt":"2025-10-01 10:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7758416/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7758416/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94223348,"identity":"2e13dd8a-f4d3-4df5-8542-d00d1b36aa23","added_by":"auto","created_at":"2025-10-23 19:08:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163263,"visible":true,"origin":"","legend":"","description":"","filename":"SteelPerryVancouver1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/d178ab3dc812c89f5c73d029.docx"},{"id":94223345,"identity":"3a5338db-c817-4015-83de-72aa512d062e","added_by":"auto","created_at":"2025-10-23 19:08:17","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4117,"visible":true,"origin":"","legend":"","description":"","filename":"f79becea995341c1a9d8e79dfc201279.json","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/ece17433880cbd65366a6d25.json"},{"id":94223360,"identity":"9ac77c97-cf03-489a-9aba-178d9e28af76","added_by":"auto","created_at":"2025-10-23 19:08:18","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109669,"visible":true,"origin":"","legend":"","description":"","filename":"f79becea995341c1a9d8e79dfc2012791enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/c567d4496938819d250039c6.xml"},{"id":94223373,"identity":"b7a90f91-2019-471b-b20f-e3846238f15e","added_by":"auto","created_at":"2025-10-23 19:08:20","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108771,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/8911c8ae9042623adf69b26e.png"},{"id":94223337,"identity":"28c16617-8ae7-4efb-8aae-5a58079ef85b","added_by":"auto","created_at":"2025-10-23 19:08:16","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10936,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/82de66a225ea66c2a044187c.png"},{"id":94223355,"identity":"b0ac6fbc-47c8-4b47-9d8a-ebe3f6724edb","added_by":"auto","created_at":"2025-10-23 19:08:17","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":109026,"visible":true,"origin":"","legend":"","description":"","filename":"f79becea995341c1a9d8e79dfc2012791structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/c072e646ad05f07c05e2955e.xml"},{"id":94223392,"identity":"32ad0c32-3e55-4395-a58f-036b964c3d91","added_by":"auto","created_at":"2025-10-23 19:08:20","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117207,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/59d91b9ca9ce67d4b950ba88.html"},{"id":94223328,"identity":"adbf7903-b1df-46cf-862b-9a8872a0a2e7","added_by":"auto","created_at":"2025-10-23 19:08:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":170485,"visible":true,"origin":"","legend":"\u003cp\u003eThe Interaction between Gender and Smartwatch Use for Individuals without a Health Condition.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/2589bbb297b22bd68977b19a.jpeg"},{"id":94223343,"identity":"e25cc5b2-6ae0-4f05-84b0-e54114f3339a","added_by":"auto","created_at":"2025-10-23 19:08:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20109,"visible":true,"origin":"","legend":"\u003cp\u003eThe Interaction between Gender and Smartwatch Use for Individuals with a Health Condition.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/a0e4154897697f4c34f9fe70.png"},{"id":94223461,"identity":"c116df5a-2b3b-4026-b180-c5461a227442","added_by":"auto","created_at":"2025-10-23 19:08:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":770397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7758416/v1/40303825-d073-4bd9-82a8-11969790facd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Interaction Between Gender, Health and Smartwatch use on Health Anxiety","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth anxiety is a relatively new concept, evolving from what was traditionally known as hypochondria (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is characterised by an excessive and often irrational fear of having or developing a serious illness (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). This condition typically manifests in a cyclical pattern, beginning with checking the body for any signs of illness (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In health anxious individuals, this behaviour is often followed by seeking reassurance from medical professionals or through the internet, which paradoxically tends to increase anxiety rather than alleviate it (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This heightened anxiety often triggers more frequent and obsessive body checks (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), which, in turn, perpetuates the cycle of health anxiety (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eResearchers have speculated that the COVID-19 pandemic has further amplified health anxiety (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The fear of infection and heightened awareness of health risks has led to an increase in health anxiety-related behaviours, such as excessive hygiene practices and extreme social isolation. While these behaviours have been protective, for some they have become maladaptive, leading to the development of health anxiety (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Consequently, the prevalence of clinical levels of health anxiety is rising, particularly among young children and middle-aged adults (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eClinical levels of health anxiety can lead individuals to undergo unnecessary medical consultations, examinations and tests, which places undue strain on the healthcare system (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, health anxiety can significantly impact an individual\u0026rsquo;s mental and physical well-being (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). If this necessitates treatment, it places further strain on healthcare resources (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIndividuals with health conditions are more likely to experience health anxiety, particularly since the context of the COVID-19 pandemic. Heinen, Varghese (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) found that both physical and mental health conditions were significant predictors of higher scores on the Short Health Anxiety Inventory (SHAI) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), even when controlling for demographic variables such as gender. Jeffers, Benotsch (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) found that individuals who misuse prescription medications are more likely to have both a medical condition and health anxiety, indicating some relationship between health conditions and health anxiety. A systematic review by Storer, Holden (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) identified health anxiety as the most prevalent form of anxiety among gastroenterology and hepatology outpatients, surpassing generalised and social anxiety. Similarly, women with early-stage breast cancer have been shown to experience elevated levels of health anxiety following diagnosis and treatment (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). In a study focusing specifically on COVID-related anxiety, King, McQuaid (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) found that while individuals with at-risk medical conditions did not report significantly higher levels of COVID-related anxiety, they did report poorer quality of life and more extreme protective behaviours, such as staying home excessively and compulsively disinfecting. Taken together, the literature indicates a bidirectional and multifaceted relationship between health conditions and health anxiety, where the presence of health conditions may not always heighten anxiety levels directly, but does appear to shape how that anxiety is experienced and expressed, warranting further exploration.\u003c/p\u003e\u003cp\u003eA part of the broader category of consumer health wearables, smartwatches have become increasingly popular in recent years. These devices offer a range of health monitoring capabilities, including heart rate tracking, sleep analysis, and activity logging (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). By providing real-time data, the technology has revolutionised health management as users can use it to make informed decisions, such as increasing physical activity or adjusting sleep patterns, to enhance overall well-being (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). With the potential to lower healthcare costs, smartwatches are being investigated and trailed as replacements for traditional, in-person healthcare visits (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Despite the positive impact smartwatches may have on helping patients and healthcare professionals to monitor health and healthy behaviour, there is increasing concern that these devices may contribute to the development of health anxiety (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, regulatory bodies, such as the American Food and Drug Administration (FDA), have recognised the potential risks associated with some functions of wearables (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). For example, Dhruva, Shah (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) discussed the FDA\u0026rsquo;s clearance of Apple Watch features for detecting irregular heart rhythms and generating electrocardiograms (ECGs). The study identified several risks, including the potential for misinterpretation and over-reliance on the devices. These risks highlight the importance of considering the psychological impacts of smartwatch use as the potential for misinterpretation could cause or exacerbate health anxiety.\u003c/p\u003e\u003cp\u003eDemonstrating this risk, Rosman, Gehi and Lampert (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) conducted a case study following a patient diagnosed with atrial fibrillation, who purchased a commercially available smartwatch for cardiac monitoring. Certain notifications from the smartwatch, such as elevations in heart rate during exercise and inconclusive ECGs, were misinterpreted as indicators of the patient\u0026rsquo;s health condition worsening. This led to a significant increase in the number of ECGs performed over time, suggesting that the notifications were wrongly perceived as health threats. Rosman and colleagues noted that this misinterpretation fuelled the continuous cycle of excessive worry and compulsive body checking behaviours, ultimately leading to increased health anxiety.\u003c/p\u003e\u003cp\u003eAs a further example of the potential for smartwatches to increase health anxiety, Filippaios, Tran (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) explored the psychological effects of receiving health alerts among poststroke adults. Their analysis revealed a significant reduction in self-rated physical health status among users who received alerts. Qualitative research has also found evidence of health anxiety that may have been exacerbated by wearable activity tracker use. Andersen, Langstrup and Lomborg (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), who explored wearable activity trackers in patients with chronic heart disease, found mixed experiences among participants. While some gained greater insight into their health and made positive health-related improvements, others experienced new health-related anxieties, such as doubts about their health and feelings of failure in monitoring it. It is important to acknowledge the evidence for smartwatch related increases in health anxiety is not equivocal. Echoing the positive experiences noted by Andersen, Langstrup and Lomborg (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), Paul et al. (2023) reported no significant difference in anxiety levels between the smartwatch and control groups. Although this study examined general anxiety, the health-focused context of their study allows its findings to be relevant and extendable to the ream of health anxiety. The evidence suggests that wearable health technologies, though helpful for some, may contribute to or worsen health anxiety in others, particularly when users lack medical literacy or support in interpreting the data they receive.\u003c/p\u003e\u003cp\u003eBeyond the influence of underlying health conditions, evidence points to gender as another key factor shaping experiences of health anxiety, with women consistently showing greater vulnerability. It is reported that women are more likely to develop anxiety disorders in general (McLean \u0026amp; Anderson, 2009), and this trend extends to health anxiety specifically. During the COVID-19 pandemic, women reported significantly higher health anxiety than men, with gender emerging as a significant predictor in regression analyses (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Similarly, Liu, Zhang (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) found that women experienced higher levels of post-traumatic stress symptoms during the pandemic, including hyperarousal and intrusive thoughts, features that closely align with cognitive and physiological aspects of health anxiety.\u003c/p\u003e\u003cp\u003eIt is possible that women\u0026rsquo;s greater experiences of health anxiety stems from how they perceive and interpret bodily sensations. Grabauskaite, Baranauskas and Griskova-Bulanova (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) found that women report greater interoceptive awareness, including noticing and emotionally responding to internal sensations. However, they also tend to have lower trust in these sensations and are more likely to worry more about them in comparison to men, which are patterns closely aligned with the core features of health anxiety. This aligns with findings from Heinen, Varghese (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), who also noted greater sensitivity to bodily symptoms among women. Although O\u0026rsquo;Bryan and McLeish (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), did not identify gender as a direct predictor of health anxiety, they observed that women scored higher in anxiety sensitivity and negative affect, both of which are associated with elevated health anxiety through indirect pathways. Moreover, cultural and psychological factors, such as gender role socialisation and reinforcement of somatic concern in girls may further contribute to increased illness-related worry in women (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Taken together, the literature supports the notion that women are more likely to experience higher levels of health anxiety than men, and that this difference may be shaped by a complex interaction of biological, cognitive, and sociocultural factors.\u003c/p\u003e\u003cp\u003eThe advent of wearable activity trackers and smartwatches has seen them employed by researchers and clinicians to help motivate and monitor patients\u0026rsquo; physical activity. However, there has been very little research investigating maladaptive effects of smartwatch use, particularly in clinical populations. The primary aim of this research will be to investigate whether smartwatch use can increase health anxiety when comparing both clinical and non-clinical populations. Furthermore, as there are known gender differences in health anxiety between males and females, this study will also investigate gender differences in health anxiety. Given the aims of the present research, the following predictions were made.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003ePrevious research has documented that real-time health data is sometimes inaccurate, can exacerbate anxiety by inflicting worry and perceptions of ill-health. It is therefore predicted that participants who use smartwatches will report higher health anxiety scores compared to non-users.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003eThe cognitive behavioural model of health anxiety posits that individuals with chronic illnesses are more likely to focus on potential health threats. Therefore, participants with diagnosed health conditions will report higher health anxiety scores compared to those without diagnosed health conditions.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003ePrevious research has demonstrated consistent evidence showing that women are generally more vigilant about health-related risks and are more prone to anxiety than men. It is therefore predicted that female participants will report higher health anxiety scores compared to male participants.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cp\u003eIt is expected that the combination of these factors will reveal nuanced differences in anxiety levels, reflecting the complex interplay highlighted in the literature. There will be a significant interaction between smartwatch use, health condition, and gender on health anxiety scores. Due to the exploratory nature of this prediction, we did not predict the anticipated direction of these effects.\u003c/p\u003e\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDesign\u003c/h2\u003e\u003cp\u003eThe study employed a quasi-experimental between-participant cross-sectional design, analysed using a 2 x 2 x 2 ANCOVA. The design involved three independent variables and one dependent variable. The first independent variable, 'Smartwatch use\u0026rsquo;, had two levels: 'smartwatch user' and 'non-user'. The second independent variable, 'health condition', had two levels: 'diagnosed\u0026rsquo; and 'no condition'. The third independent variable, \u0026lsquo;gender\u0026rsquo;, had two levels: \u0026lsquo;male\u0026rsquo; and \u0026lsquo;female\u0026rsquo;. The dependent variable measured was health anxiety. Age and trait anxiety were included as covariates to control for their potential confounding effects. Participants self-selected into the conditions based on their current use of smartwatches, if they had any diagnosed health conditions and whether they identified as male or female.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eCriteria required participants to be aged 18 years or older, identify as either male or female and be capable of understanding and completing the study materials in English. Participants were recruited using convenience and volunteer sampling methods through the SONA system, social media platforms, and word-of-mouth referrals. All participants engaged in the study online.\u003c/p\u003e\u003cp\u003eA total of 261 participants were recruited. After removal of two participants who did not identify as either male or female, 259 participants were included in the analysis. The mean age of the participants was 35.86 years (SD\u0026thinsp;=\u0026thinsp;15.56), with a mode of 23 years. To treat age as a continuous variable, midpoints of the age categories were assigned to each participant. The gender distribution consisted of 70 males and 188 females. Participants were predominantly of white ethnic background (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;194 participants).\u003c/p\u003e\n\u003ch3\u003eMaterials\u003c/h3\u003e\n\u003cp\u003eThe study utilised an online survey administered via Qualtrics, which comprised both a researcher-designed section and standardised psychometric scales to assess various constructs.\u003c/p\u003e\n\u003ch3\u003eParticipant and Demographic Information\u003c/h3\u003e\n\u003cp\u003eDemographic information was collected from participants, including age and gender. At the end of the survey, participants were asked if they owned a smartwatch and if they had a diagnosed health condition. These questions were asked at the end of the survey to reduce the potential for response bias. Participants reported cardiovascular conditions (n\u0026thinsp;=\u0026thinsp;23), respiratory conditions (n\u0026thinsp;=\u0026thinsp;20), metabolic conditions (n\u0026thinsp;=\u0026thinsp;10), musculoskeletal conditions (n\u0026thinsp;=\u0026thinsp;22), neurological conditions (n\u0026thinsp;=\u0026thinsp;16), mental health conditions (n\u0026thinsp;=\u0026thinsp;37), gastrointestinal conditions (n\u0026thinsp;=\u0026thinsp;27), autoimmune conditions (n\u0026thinsp;=\u0026thinsp;6), endocrine disorders (n\u0026thinsp;=\u0026thinsp;23), skin conditions (n\u0026thinsp;=\u0026thinsp;16), chronic pain (n\u0026thinsp;=\u0026thinsp;3 ) and cancer (n\u0026thinsp;=\u0026thinsp;9).\u003c/p\u003e\u003cp\u003eAn a priori power analysis was conducted using GPower 3.1 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) for a three-way ANCOVA with two covariates, assuming a small-to-medium effect size (η\u0026sup2; = .05), α\u0026thinsp;=\u0026thinsp;.05, and power (1\u0026ndash;β)\u0026thinsp;=\u0026thinsp;.80. The analysis indicated that a sample size of N\u0026thinsp;=\u0026thinsp;250 would be required. The obtained sample (N\u0026thinsp;=\u0026thinsp;259) was therefore adequate in meeting this requirement.\u003c/p\u003e\n\u003ch3\u003eBehavioural Inhibition System Scale\u003c/h3\u003e\n\u003cp\u003eThe Behavioural Inhibition System (BIS; Carver and White (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), was used to assess trait anxiety levels. The BIS is a measure of anxiety that assesses an individual's sensitivity to potential punishment cues, specifically designed to measure anxiety related to sensitivity to potential punishment - a distinct approach compared to other general anxiety measures. Therefore, it was chosen for this study as it facilitates the exploration of anxiety in the context of fear of negative outcomes. Participants respond to items on a 4-point Likert scale that ranges from 1 (strongly agree) to 4 (strongly disagree). Participants are required to rate statements such as, \u0026lsquo;I worry about making mistakes.\u0026rsquo;. Lower total scores indicate greater sensitivity to punishment cues, which correlates with higher levels of anxiety. This scale has been shown to have good reliability (α\u0026thinsp;=\u0026thinsp;.84) (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and high construct reliability (α\u0026thinsp;\u0026gt;\u0026thinsp;.80) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), suggesting that the scale reliably measures anxiety.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eShort Health Anxiety Inventory\u003c/h2\u003e\u003cp\u003eHealth anxiety was measured using the SHAI (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), an abbreviated version of the Health Anxiety Inventory (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The SHAI is a 14-item measure that screens for general health anxiety in the past six months. Participants respond to each item by indicating which of four statements best describes them (e.g., 0 = \u0026lsquo;I do not worry about my health\u0026rsquo; to 3 = \u0026lsquo;I spend most of my time worrying about my health\u0026rsquo;). The scale\u0026rsquo;s instructions specify that participants can select more than one statement if they identify with more than one. If participants select more than one statement, the highest selected was be taken as their response. Scores are summed for a total composite score ranging between 0 and 42, with higher scores indicating a higher level of health anxiety. It is common for studies to define prevalence of clinical health anxiety by scoring greater than or equal to 18 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This cut off has been shown to reliably identify individuals with clinically significant levels of health anxiety (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Low levels of health anxiety are defined as scoring 10 or below (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The scale has been shown to have good internal reliability in both non-clinical samples (α\u0026thinsp;=\u0026thinsp;.89) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and for patients with existing medical conditions (α\u0026thinsp;=\u0026thinsp;.84) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Diamond, Dysch and Daniels (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) found this same level of internal reliability (α\u0026thinsp;=\u0026thinsp;.87).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe study was conducted online, meaning the participants could complete the survey at their convenience, while maintaining consistent methodological standards. Upon expressing interest, potential participants were directed to a secure online platform hosted by Qualtrics. The first page of the survey provided detailed information about the study, including its purpose, the nature of the tasks, potential risks, benefits of participating, and confidentiality measures. Participants were required to provide electronic informed consent before proceeding with the survey. They were informed that participation was voluntary, they could withdraw at any time without penalty, and all responses would be anonymised to protect their privacy.\u003c/p\u003e\u003cp\u003eOnce consent was obtained, participants completed the survey, which included the demographic information section and the BIS and SHAI scales. Instructions were provided at the start of each section to ensure participants understood how to respond to each question. The survey was designed to be intuitive and user-friendly to encourage complete and accurate responses. Upon completing the survey, participants were directed to a debriefing page which provided additional resources about mental health. Participants were thanked for their contribution and provided with contact information should they have any questions about the study, wish to withdraw or to receive information about the results of the study.\u003c/p\u003e\u003cp\u003e The study protocol was approved by Nottingham Trent University\u0026rsquo;s ethics committee prior to initiation. All data were collected and stored in accordance the university\u0026rsquo;s guidelines to ensure confidentiality and security of participant information. The dataset supporting the conclusions of this article is available in the Figshare repository, [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6084/m9.figshare.30196978\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.30196978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePreliminary Analysis and Assumptions\u003c/h2\u003e\u003cp\u003eThe internal consistency of the scales used was assessed using Cronbach's alpha. The BIS scale had a Cronbach\u0026rsquo;s alpha of .734 (M\u0026thinsp;=\u0026thinsp;14.14, SD\u0026thinsp;=\u0026thinsp;3.14) and the SHAI had a Cronbach\u0026rsquo;s alpha of .867 (M\u0026thinsp;=\u0026thinsp;13.47, SD\u0026thinsp;=\u0026thinsp;5.86). These values confirm the internal consistency of these measures in this study.\u003c/p\u003e\u003cp\u003eThe assumption of normality for the residuals of the dependent variable (SHAI scores) was evaluated. Skewness and kurtosis were normal across groups when examining both the descriptive statistics and visual examination of the normal distribution. The assumption of homogeneity of variance was evaluated using Levene's test. The results indicated that the assumption was met, as evidenced by a non-significant result (F(7, 251)\u0026thinsp;=\u0026thinsp;2.02, p\u0026thinsp;=\u0026thinsp;.053). This suggests that the variances of SHAI scores are equal across the groups, fulfilling the requirement for conducting ANCOVA. The assumption of linearity was evaluated to ensure a linear relationship between the dependent variable (SHAI scores) and the covariates (age, and trait anxiety (BIS scores)). The visual spread in a boxplot indicate that the linearity assumption was adequately met, meeting the assumptions for ANCOVA.\u003c/p\u003e\u003cp\u003eThe assumption of homogeneity of regression slopes was tested by including the interaction terms between each covariate (age and BIS scores) and each independent variable (gender, smartwatch ownership, diagnosed health condition) in the ANCOVA model. The interaction terms were non-significant, all p\u0026thinsp;\u0026gt;\u0026thinsp;.05 (e.g., Gender \u0026times; Age2: F(1, 247)\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;=\u0026thinsp;.567), indicating that the relationship between the covariates and SHAI scores did not differ significantly across groups. Thus, the assumption was met, supporting the appropriateness of conducting ANCOVA.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive Statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic distribution of participants across the eight experimental conditions as well as the mean SHAI score, where higher scores indicate higher levels of health anxiety. It is important to note that due to the larger proportion of female participants in the study, the groups differed in size.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Participants by Group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAge (M)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSHAI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1: Smartwatch user, Diagnosed, Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.46(6.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2: Smartwatch user, Diagnosed, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.43(5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3: Smartwatch user, No condition, Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.62(4.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4: Smartwatch user, No condition, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.85(7.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5: Non-user, Diagnosed, Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.95(5.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6: Non-user, Diagnosed, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.03(6.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7: Non-user, No condition, Male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.31(3.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8: Non-user, No condition, Female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.50(5.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eKey: n\u0026thinsp;=\u0026thinsp;number of participants, SD\u0026thinsp;=\u0026thinsp;standard deviation, min\u0026thinsp;=\u0026thinsp;minimum score, max\u0026thinsp;=\u0026thinsp;maximum score\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eMain analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003eAn ANCOVA was conducted to examine the effect of smartwatch ownership, diagnosed health condition, and gender on health anxiety (SHAI scores), controlling for age and trait anxiety (BIS scores).\u003c/p\u003e\u003cp\u003eAge was a significant covariate (F(1, 249)\u0026thinsp;=\u0026thinsp;12.567, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, η2\u0026thinsp;=\u0026thinsp;.048), implying that age had a significant effect on health anxiety scores, with descriptive statistics showing that younger participants reported higher health anxiety compared to older participants. Trait anxiety was also significant (F(1, 249)\u0026thinsp;=\u0026thinsp;34.168, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, η2\u0026thinsp;=\u0026thinsp;.121). A Pearson correlation showed a significant negative association between trait anxiety (BIS) and health anxiety (SHAI) (r(259)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.429, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that higher trait anxiety (lower BIS scores) was associated with higher health anxiety (higher SHAI scores). Therefore, trait anxiety was also retained as a covariate.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH1\u003c/strong\u003e\u003cp\u003eThe first hypothesis predicted that individuals who used a smartwatch would experience greater health anxiety than those who did not use a smartwatch. However, when testing this assumption, no significant effect was found for smartwatch use (F(1, 249)\u0026thinsp;=\u0026thinsp;0.891, p\u0026thinsp;=\u0026thinsp;.346, η\u0026sup2; = .004).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH2\u003c/strong\u003e\u003cp\u003eThe second hypothesis predicted that individuals with a health condition would experience greater health anxiety than those without a health condition. Supporting this prediction, health condition had a significant main effect on health anxiety (F1, 249)\u0026thinsp;=\u0026thinsp;19.951, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, η\u0026sup2; = .074), with individuals with a diagnosed health condition displaying higher levels of health anxiety.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH3\u003c/strong\u003e\u003cp\u003eThe third hypothesis was that females would experience higher levels of health anxiety than males. However, the results of this test were non-significant (F1, 249)\u0026thinsp;=\u0026thinsp;2.131, p\u0026thinsp;=\u0026thinsp;.146, η\u0026sup2; = .008).\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInteraction Effects\u003c/h2\u003e\u003cp\u003eThe final hypothesis was that there would be an interaction between health status, gender and smartwatch use. The interaction between smartwatch use, health condition and gender was significant (F(1, 249)\u0026thinsp;=\u0026thinsp;6.396, p\u0026thinsp;=\u0026thinsp;.012, η\u0026sup2; = .025), suggesting a combined effect of these factors on health anxiety.\u003c/p\u003e\u003cp\u003eThe interaction between smartwatch use, diagnosed health condition, and gender on health anxiety is illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Follow-up analyses examined the two-way interaction between smartwatch use and gender within each health condition group. For participants with a diagnosed health condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the smartwatch use \u0026times; gender interaction trended towards significance (F(1, 109)\u0026thinsp;=\u0026thinsp;2.91, p\u0026thinsp;=\u0026thinsp;.091, η\u0026sup2; = .026). The pattern suggests that males who used smartwatches reported higher health anxiety, whereas females who did not use smartwatches reported higher anxiety. For participants without a diagnosed health condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the smartwatch use \u0026times; gender interaction also trended towards significant (F(1, 138)\u0026thinsp;=\u0026thinsp;3.47, p\u0026thinsp;=\u0026thinsp;.065, η\u0026sup2; = .025) but showed an opposite trend: males who used smartwatches reported lower health anxiety, while females who used smartwatches reported higher anxiety. These non-significant trends help to explain the significant three-way interaction observed in the overall model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe aim of the present research was to examine the effect of health condition, gender and smartwatch use on health anxiety. In line with current knowledge, this study found that individuals with diagnosed health conditions experience significantly higher levels of health anxiety compared to those without such conditions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The hypothesis that gender and smartwatch use would independently affect health anxiety was not supported. However, the study revealed that the use of smartwatches affects males and females differently depending on their health condition.\u003c/p\u003e\u003cp\u003eThe prediction that health anxiety would be higher in individuals with health conditions, was supported. This finding is consistent with a growing body of research demonstrating a clear link between health conditions and elevated health anxiety (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Notably, this result challenges the findings of King, McQuaid (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), who reported no significant difference in COVID-related anxiety between individuals with and without at-risk health conditions. One possible explanation for this discrepancy is that King et al. focused specifically on anxiety related to COVID-19, which may be influenced by additional factors such as perceived risk or public health messaging, rather than generalised health anxiety. In contrast, the present study measured health anxiety more broadly, offering a more comprehensive understanding of how living with a health condition may affect health-related fears. Therefore, while King et al. highlighted differences in behavioural outcomes and quality of life, the current findings suggest that the presence of a health condition itself may be more directly linked to heightened health anxiety than their results indicated. When considered together, the findings from both the current study and King et al. suggest that health conditions may both elevate health anxiety and influence how it is expressed, with the extent of each depending on the individual's context and the nature of the health threat.\u003c/p\u003e\u003cp\u003eA novel finding revealed that there was an interaction between gender and smartwatch use for individuals who have a health condition. Specifically, men who use smartwatches and have a diagnosed health condition displayed higher health anxiety than women. In contrast, women who did not use smartwatches had higher health anxiety than men. This finding challenges the conventional understanding of gender differences in anxiety (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). These findings also reflect the mixed results observed by Andersen, Langstrup and Lomborg (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), where some individuals used their smartwatches without developing health anxiety, while others experienced heightened anxiety associated with smartwatch use.\u003c/p\u003e\u003cp\u003eThe findings imply that men with health conditions are particularly vulnerable to the effects of smartwatches on health anxiety. Since it is well established that men are generally less aware of bodily sensations than women (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), these men may over-rely on smartwatch data to compensate for this reduced bodily awareness. When data from their watch is interpreted as indicating that their health condition is worsening, it may lead to the development of health anxieties as they struggle to reconcile the data with how they physically feel. This issue is likely exacerbated when the data is inaccurate or unclear (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, an alternative explanation comes from Lupton et al.\u0026rsquo;s (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) concept of 'data sense,' which posits that experiences with sensor data are not only cognitive but also involve sensory and emotional dimensions. It could be that smartwatches are teaching men to become more aware of bodily sensations. Initially this might seem beneficial but over time, this increased awareness may lead to similar anxiety levels observed in women, as men begin to worry about sensations they were previously unaware of. If this were the case, however, you would expect the health anxiety scores for this group to be similar to those of women. Yet, the anxiety scores for these men were higher, perhaps lending more support to the first explanation.\u003c/p\u003e\u003cp\u003eIn contrast, among men without diagnosed health conditions, those who did not use smartwatches reported higher levels of health anxiety compared to smartwatch users. Among females, the pattern differed: women with a health condition experienced increased health anxiety when they did not use a smartwatch, whereas those without a health condition showed higher health anxiety when they did use a smartwatch. Overall, this suggests that Rosman et al.\u0026rsquo;s (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) report may represent an isolated case; the findings indicate that women with health conditions can use a smartwatch without increasing health anxiety levels. The smartwatch may provide reassurance by confirming the sensations they are already experiencing. Andersen, Langstrup and Lomborg (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) support this notion, finding that some patients used their smartwatches for reassurance about heart function, thus avoiding unnecessary anxiety.\u003c/p\u003e\u003cp\u003eThe findings of Lomborg and Frandsen (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) provide insight into why females without health conditions exhibit higher health anxiety scores when using smartwatches compared to non-users. Lomborg and Frandsen emphasise the communicative nature of self-tracking, where users often engage with their health data in a social context. This engagement can amplify anxiety through social comparison or the pressure to meet perceived health standards. Women are generally more prone to social comparison and have a stronger desire for social acceptance compared to men (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). As a result, the constant monitoring and sharing of health data through smartwatches might exacerbate health anxiety in women, as they may feel a greater need to conform to health norms or expectations. In contrast, men may be less affected by these social dynamics, which could explain why their health anxiety remains relatively lower when using smartwatches in the absence of a health condition.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImplications\u003c/h2\u003e\u003cp\u003eThe study extends current knowledge by surveying the general population, rather than focusing solely on clinical populations. This broader application enhances our understanding of health anxiety in a wider context and makes the findings more applicable to everyday smartwatch users. The findings of this study suggest that the development of health technology, particularly smartwatches, could benefit from a more personalised approach that considers individual user characteristics. For men with health conditions, the technology might need to provide contextual information to accompany the data. This would leave less room for misinterpretation and potentially mitigate health anxiety. For women, particularly those without health conditions, there might need to be considerations around how the data is presented and how it interacts with social pressures. The potential for misinterpretation of health data highlights the need for better education around the use of smartwatches. Users may need guidance on accurate interpretation and measures to prevent over-reliance on the technology. However, there is a need to consider how such personalisation could influence health disparities, particularly if certain groups (e.g., older adults, lower-income individuals) are less able to access or effectively use these tailored technologies. Furthermore, healthcare providers should be aware of the interaction between gender and wearable health technologies, and the effect it can have on health anxiety. This awareness can guide more personalised recommendations about the appropriateness of smartwatches for individuals and ensuring they are given appropriate instruction regarding the interpretation of health-related data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations\u003c/h2\u003e\u003cp\u003eThe study recruited 252 participants, which is a robust sample size for the analyses conducted. The power sensitivity analysis confirms that the study had sufficient power to detect significant effects, strengthening the findings. The study also controlled for important covariates \u0026ndash; age and trait anxiety \u0026ndash; which helps to isolate the effects of the primary independent variables on health anxiety.\u003c/p\u003e\u003cp\u003eSince participants self-reported their health conditions and smartwatch use, there is a possibility of response bias, particularly social desirability bias. The researcher placed the questions most likely to provoke response bias at the end, so although this particular confound is unlikely, it cannot be entirely ruled out. While the sample size of 252 participants is robust, the demographic composition \u0026ndash; predominantly female and white \u0026ndash; limits the generalisability of the findings to more diverse populations. Future research should aim to include a more diverse participant pool to ensure that the findings are applicable across different cultural, racial, and socioeconomic groups. Additionally, we had no control over the type of devices participants used. It may be that some users used fairly simple devices that has limited health data, while others used devices capable of producing more advanced health-related data. Therefore, we cannot make claims about how smartwatches may most influence health anxiety.\u003c/p\u003e\u003cp\u003eFinally, as Andersen, Langstrup and Lomborg (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) noted, there is a distinction between clinically validated self-monitoring technologies and the lower-cost sensors available in the consumer market. The current study did not control for or collect data on the specific type of smartwatch used. Consequently, the varying reliability and accuracy of these devices could contribute to different levels of health anxiety, particularly if users place excessive trust in data from less reliable wearables. This highlights the potential for technology type to significantly influence the psychological outcomes associated with health monitoring devices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eFuture research\u003c/h2\u003e\u003cp\u003eThe findings underscore the complexity of the relationship between gender, health technology use, and health anxiety, highlighting the need for further research to explore these dynamics in greater depth. Future studies could examine how different types of wearables, such as higher-quality devices that may meet clinical standards versus lower-quality consumer-grade products, influence health anxiety across these diverse groups. It would also be useful to understand if it is specific health metrics recorded by smartwatches that induced health anxiety, or whether this effect is related to having a wide range of health information via the smartwatch. Future research could also utilise qualitative methods to explore the underlying factors contributing to this discrepancy, offering deeper insights into how individuals with and without health conditions engage with smartwatches and how these interactions influence their health anxiety.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study offers insights into the complex interactions between smartwatch use, health conditions, gender, and health anxiety. Consistent with previous research, individuals with diagnosed health conditions were found to experience significantly higher levels of health anxiety compared to those without such conditions. Notably, the study highlights the dual potential of smartwatches to both alleviate and exacerbate health anxiety, depending on the user's characteristics and health status.\u003c/p\u003e\u003cp\u003eFor men with diagnosed health conditions, smartwatch use was associated with the highest levels of health anxiety, suggesting that these individuals may over-rely on wearable health technologies, leading to misinterpretation of data and increased anxiety. In contrast, women with health conditions appeared to benefit from smartwatch use, suggesting it provides reassurance rather than exacerbating their anxiety. However, for women without health conditions, the use of smartwatches was linked to higher health anxiety, potentially due to social comparison pressures and the desire for acceptance.\u003c/p\u003e\u003cp\u003eThese findings underscore the importance of considering individual differences when developing and recommending the use of health technologies. The psychological effects of smartwatches are not uniform; they are influenced by factors such as gender, health status, and the way individuals interact with and interpret health data. This suggests a need for personalised approaches to the use of wearable health technologies, particularly for populations at risk of heightened anxiety.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFDA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Food and Drug Administration\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eECGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectrocardiograms\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Behavioural Inhibition System (scale)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSHAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eShort Health Anxiety Inventory\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eHuman Ethics and Consent to Participate\u003c/b\u003e\u003c/p\u003e\u003cp\u003eInformed consent\u003c/strong\u003e was obtained from all individual participants included in the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003cp\u003e Ethical approval to conduct this research was provided by the Nottingham Trent University Social Science Research Ethics Committee (S3REC)\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eDeclaration\u003c/p\u003e\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.P wrote the main manuscript. The methods were devised by A.P and R.S, and both authors contributed to the data analysis. Both authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available in the Figshare repository: Steel, Richard (2025). Perry_Steel_HealthAnxiety_Smartwatch. figshare. Dataset. https://doi.org/10.6084/m9.figshare.30196978.v1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTyrer P. COVID-19 health anxiety. World Psychiatry. 2020;19(3):307\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSvestkova A, Kvardova N, Smahel D. Health Anxiety in Adolescents: The Roles of Online Health Information Seeking and Parental Health Anxiety. J Child Fam stud. 2023;33(4):1083\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSonuga-Barke EJS, Fearon P, Commentary et al. Health anxiety in youth during 'COVID' - some thoughts prompted by Rask. (2024). J Child Psychol Psychiatry. 2024;65(4):431-4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiamond PR, Dysch L, Daniels J. Health anxiety in stroke survivors: a cross-sectional study on the prevalence of health anxiety in stroke survivors and its impact on quality of life. Disabil Rehabil. 2023;45(1):27\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHedman-Lagerlof E, Tyrer P, Hague J, Tyrer H. Health anxiety. BMJ. 2019;364:l774.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng RX. How online searches fuel health anxiety: Investigating the link between health-related searches, health anxiety, and future intention. Comput Hum Behav. 2022;136.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMathes BM, Norr AM, Allan NP, Albanese BJ, Schmidt NB. Cyberchondria: Overlap with health anxiety and unique relations with impairment, quality of life, and service utilization. Psychiatry Res. 2018;261:204\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDennis D, Radnitz C, Wheaton MG. A Perfect Storm? Health Anxiety, Contamination Fears, and COVID-19: Lessons Learned from Past Pandemics and Current Challenges. Int J Cogn Ther. 2021;14(3):497\u0026ndash;513.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHeinen A, Varghese S, Krayem A, Molodynski A. Understanding health anxiety in the COVID-19 pandemic. Int J Soc Psychiatry. 2022;68(8):1756\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRask CU, Duholm CS, Poulsen CM, Rimvall MK, Wright KD. Annual Research Review: Health anxiety in children and adolescents-developmental aspects and cross-generational influences. J Child Psychol Psychiatry. 2024;65(4):413\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSunderland M, Newby JM, Andrews G. Health anxiety in Australia: prevalence, comorbidity, disability and service use. Br J Psychiatry. 2013;202(1):56\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorbye AD, Abelsen B, Forde OH, Ringberg U. Health anxiety is an important driver of healthcare use. BMC Health Serv Res. 2022;22(1):138.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eByrne GJ. Health anxiety in older people. Int Psychogeriatr. 2022;34(8):687\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbramowitz JS, Deacon BJ, Valentiner DP. The Short Health Anxiety Inventory: Psychometric Properties and Construct Validity in a Non-clinical Sample. Cognit Ther Res. 2007;31(6):871\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeffers AJ, Benotsch EG, Green BA, Bannerman D, Darby M, Kelley T, Martin AM. Health anxiety and the non-medical use of prescription drugs in young adults: A cross-sectional study. Addict Behav. 2015;50:74\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStorer B, Holden M, Kershaw KA, Braund TA, Chakouch C, Coleshill MJ, et al. Global Prevalence of Anxiety in Gastroenterology and Hepatology Outpatients: A Systematic Review and Meta-Analysis. Curr Gastroenterol Rep. 2025;27(1):17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJones SL, Hadjistavropoulos HD, Sherry SB. Health anxiety in women with early-stage breast cancer: What is the relationship to social support? Can J Behav Sci / Revue canadienne des Sci du comportement. 2012;44(2):108\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKing JD, McQuaid A, Leeson VC, Samuel O, Grant J, Imran Azeem MS, et al. The association of severe COVID anxiety with poor social functioning, quality of life, and protective behaviours among adults in United Kingdom: a cross-sectional study. BMC Psychiatry. 2023;23(1):117.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoi H-Y, Keil M, Baird AM. Intention to use smartwatch health applications: A regulatory fit and locus of control perspective. Inf Manag. 2022;59(6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eReeder B, David A. Health at hand: A systematic review of smart watch uses for health and wellness. J Biomed Inf. 2016;63:269\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVarma N, Marrouche NF, Aguinaga L, Albert CM, Arbelo E, Choi JI, HRS/EHRA/APHRS/LAHRS, et al. /ACC/AHA Worldwide Practice Update for Telehealth and Arrhythmia Monitoring During and After a Pandemic. J Am Coll Cardiol. 2020;76(11):1363\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKaplan DM, Greenleaf M, Lam WA. Wear With Care: A Call for Empirical Investigations of Adverse Outcomes of Consumer Health Wearables. Mayo Clin Proc Digit Health. 2023;1(3):413\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShuren J, Patel B, Gottlieb S. FDA Regulation of Mobile Medical Apps. JAMA. 2018;320(4):337\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDhruva SS, Shah ND, Vemulapalli S, Deshmukh A, Beatty AL, Gamble GM, et al. Heart Watch Study: protocol for a pragmatic randomised controlled trial. BMJ Open. 2021;11(12):e054550.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRosman L, Gehi A, Lampert R. When smartwatches contribute to health anxiety in patients with atrial fibrillation. Cardiovasc Digit Health J. 2020;1(1):9\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFilippaios A, Tran KT, Mehawej J, Ding E, Paul T, Lessard D, et al. Psychosocial measures in relation to smartwatch alerts for atrial fibrillation detection. Cardiovasc Digit Health J. 2022;3(5):198\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndersen TO, Langstrup H, Lomborg S. Experiences With Wearable Activity Data During Self-Care by Chronic Heart Patients: Qualitative Study. J Med Internet Res. 2020;22(7):e15873.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOzdin S, Bayrak Ozdin S. Levels and predictors of anxiety, depression and health anxiety during COVID-19 pandemic in Turkish society: The importance of gender. Int J Soc Psychiatry. 2020;66(5):504\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu N, Zhang F, Wei C, Jia Y, Shang Z, Sun L, et al. Prevalence and predictors of PTSS during COVID-19 outbreak in China hardest-hit areas: Gender differences matter. Psychiatry Res. 2020;287:112921.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrabauskaite A, Baranauskas M, Griskova-Bulanova I. Interoception and gender: What aspects should we pay attention to? Conscious Cogn. 2017;48:129\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Bryan EM, McLeish AC. An Examination of the Indirect Effect of Intolerance of Uncertainty on Health Anxiety Through Anxiety Sensitivity Physical Concerns. J Psychopathol Behav Assess. 2017;39(4):715\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcLean CP, Anderson ER. Brave men and timid women? A review of the gender differences in fear and anxiety. Clin Psychol Rev. 2009;29(6):496\u0026ndash;505.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFaul F, Erdfelder E, Lang A-G, Buchner A. G Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCarver CS, White TL, Behavioral, Inhibition. Behavioral Activation, and Affective Responses to Impending Reward and Punishment: The BIS/BAS Scales. J Personal Soc Psychol. 1994;67(2):319\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Y-L, Sherwood SN, Freeman AJ. Multidimensional Item Response Theory of the BIS/BAS Scales: Evidence for a Bifactor Model Structure. J Psychopathol Behav Assess. 2023;45(4):1059\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodriguez A, Reise SP, Haviland MG. Applying Bifactor Statistical Indices in the Evaluation of Psychological Measures. J Pers Assess. 2016;98(3):223\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalkovskis PM, Rimes KA, Warwick HM, Clark DM. The Health Anxiety Inventory: development and validation of scales for the measurement of health anxiety and hypochondriasis. Psychol Med. 2002;32(5):843\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHayter AL, Salkovskis PM, Silber E, Morris RG. The impact of health anxiety in patients with relapsing remitting multiple sclerosis: Misperception, misattribution and quality of life. Br J Clin Psychol. 2016;55(4):371\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKehler MD, Hadjistavropoulos HD. Is health anxiety a significant problem for individuals with multiple sclerosis? J Behav Med. 2009;32(2):150\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHadjistavropoulos HD, Janzen JA, Kehler MD, Leclerc JA, Sharpe D, Bourgault-Fagnou MD. Core cognitions related to health anxiety in self-reported medical and non-medical samples. J Behav Med. 2012;35(2):167\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLupton D, Pink S, Labond CH, Sumartojo S. Personal data contexts, data sense, and self-tracking cycling. Int J communication. 2018;12:647\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLomborg S, Frandsen K. Self-tracking as communication. Inform Communication Soc. 2015;19(7):1015\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuimond S, Branscombe NR, Brunot S, Buunk AP, Chatard A, Desert M, et al. Culture, gender, and the self: variations and impact of social comparison processes. J Pers Soc Psychol. 2007;92(6):1118\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"health anxiety, smartwatch use, wearable health technology, gender differences, diagnosed health conditions","lastPublishedDoi":"10.21203/rs.3.rs-7758416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7758416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the relationship between gender, smartwatch use, diagnosed health conditions, and health anxiety. Previous research has shown health anxiety is more common among woman and those with chronic conditions, but little is known about how these factors interact with the use of wearable health technology. This study employed a quasi-experimental cross-sectional design with 252 participants, controlling for age and trait anxiety levels. The study found that men who have a diagnosed health condition and use a smartwatch experience significantly higher levels of health anxiety. In contrast, woman who have a diagnosed health condition and use a smartwatch report lower health anxiety. This suggests that smartwatches may contribute to increased anxiety in men and may provide reassurance for women. These findings underscore the need for personalised approaches to wearable health technology that consider gender differences and the potential psychological impacts on users. Future research should explore how wearables impact health anxiety across other populations and examine whether different types of smartwatches have distinct effects on health anxiety.\u003c/p\u003e","manuscriptTitle":"The Interaction Between Gender, Health and Smartwatch use on Health Anxiety","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 19:08:10","doi":"10.21203/rs.3.rs-7758416/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T08:21:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T21:51:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259898420059063839694496522337352888721","date":"2026-02-15T18:43:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235522125939939106283152887084935686931","date":"2026-02-14T23:32:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T02:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265574017863809536727365218206018370656","date":"2026-01-30T02:14:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T02:07:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48743560621072720055465949640955159435","date":"2025-10-19T01:16:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T00:48:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T05:53:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-04T10:31:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-04T10:31:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-10-01T10:02:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8c297a2f-ac91-4d7a-913d-aecac08b4f87","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T03:24:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 19:08:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7758416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7758416","identity":"rs-7758416","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}