Research Use, trustworthiness and willingness to self-monitor health and personal wellness: a UK population survey

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This study investigated public attitudes towards self-monitoring of health and personal wellness. Methods 4270 UK adults selected to produce a nationally representative sample (by age, gender, ethnicity, social grade/socioeconomic group, geographic region) completed an online survey. Self-monitoring questions addressed experiences (use, motivation, trust, actions taken), benefits, problems, willingness (to use, to have results sent to GP) and the preferred action if a problem is identified. Questions concerned 12 physiologic and behavioural health aspects (blood pressure, blood sugar, body composition, cholesterol, diet, eyesight, fitness/exercise, hearing, menstrual cycles/periods, mental and cognitive health, pulse/heart rate, and skin conditions). Results Most respondents had self-monitored (79%) and were willing, if recommended by the NHS, to self-monitor (86%) at least one health aspect. The proportion of respondents who had self-monitored was similar for health and personal wellness; however, the proportion willing to self-monitor was higher for health than personal wellness. Self-monitoring was higher among younger adults and those with more years of education. Most (70–94%) respondents trusted the results; 78% were willing to have results sent automatically to their GP. If a problem were identified, discussing the results with a GP was a common and preferred action, but 14–38% of respondents (depending on health aspect) took no action. Males, older adults, those living in areas of high deprivation, and those with lower education were less likely to be willing to monitor their health. Main benefits were enhanced awareness of health, greater control, easier access to information, more balanced engagement with providers, and psychological support. Main problems included anxiety, obsessive testing, low self-efficacy for monitoring, poor motivation, post-monitoring inaction, misinterpretation of results, and technical concerns. Conclusions A high proportion of the UK public use/are willing to use and trust self-monitoring of health and wellness. It has the potential to transform care through proactive remote health management and preventative action. However, the findings also highlight socioeconomic differences in self-monitoring. Future research is needed to address how engagement of self-monitoring can be enhanced equitably and used effectively alongside clinical care to promote better health. Trial registration https://osf.io/96mzt Self-monitoring public attitudes online survey experiences willingness trust health and personal wellness Figures Figure 1 Figure 2 Figure 3 Contributions to the literature The UK Government has outlined aspirations for health monitoring technologies to be part of routine care to support proactive health monitoring and preventative action. Public opinion about self-monitoring is important because this has the potential to change how the public will engage with future NHS initiatives on self-monitoring of health. Our study suggests the UK public use, trust, and are willing to self-monitor their health and personal wellness, and have results sent to their GP. Socioeconomic differences in willingness to self-monitor were found highlighting the need to address how engagement in self-monitoring can be enhanced equitably and used effectively alongside clinical care. Background The revolution in interest in new technologies and adoption of personal health self-monitoring has the potential to transform health care. 1 Health can be tracked using tools such as wearable technologies, biosensors, smart medical devices, apps and over-the-counter diagnostic kits. 2 The motivation for self-monitoring of health may be for medical reasons (e.g., blood glucose monitoring for managing diabetes), or for wellness purposes (e.g., monitoring exercise for fitness). Reported benefits to public health include: Increased self-awareness and promotion of preventative behaviours, early detection of health issues, enhanced self-efficacy and management of chronic conditions, lifestyle changes, improved treatment adherence, reduced pressure on healthcare systems, better communication with healthcare providers, and improved outcomes. 3,4 The UK government has expressed considerable interest in self-monitoring of health 5 and there is an appetite amongst the public to use technology to take greater responsibility for their health. 6 While users generally show positive attitudes toward health monitoring technologies, concerns have been expressed over accuracy, cost, and burden (to the public and healthcare system). 1 There are also concerns about the current market and regulatory oversight for some direct-to-consumer tests. 7,8 Healthcare providers have recognised the potential benefits of self-monitoring for patient health 9 but also concerns (e.g. workload). 10 Public opinion about self-monitoring of health and wellness is important because, by nature, self-monitoring requires voluntary active engagement if future NHS initiatives including preventative approaches are to be successful. Previous UK surveys 6,11 have examined use of self-monitoring technology, but not whether the public trusts the results or what actions are taken (or preferred) following self-monitoring. We undertook a cross-sectional survey of UK adults that investigated attitudes and experiences towards self-monitoring of health and wellness over a broad range of health aspects. The survey included questions related to willingness to pay and share data, within the context of the NHS. Methods Respondents Respondents were adults living in the UK selected by YouGov PLC UK (an internet-based research service) from members signed up to the survey platform. A target sample size of 4000 allowed us to estimate key prevalences to a precision of ± 1.5%. Online survey Nine survey questions (Supplementary Table S1) were embedded into a YouGov survey completed between 31 st July and August 13 th , 2025. Questions addressed: Self-monitoring experience, trust in results, actions taken and whether monitoring was recommended by a health professional. Willingness to self-monitor, and, of those willing, the proportion willing to have results automatically sent to their GP or other health professional, willingness to pay for self-monitoring (assuming a similar cost to an NHS prescription) and preferred next steps if a problem were identified. Main benefits of, and problems with, self-monitoring. For (a) and (b), participants indicated their experience and willingness to self-monitor a range of medical health and wellness health aspects: blood pressure, blood sugar, body composition, cholesterol, diet, eyesight, fitness/exercise, hearing, menstrual cycles/periods, mental and cognitive health, pulse/heart rate, and skin conditions. An ‘Other’ option was available for respondents to indicate if they had monitored any other health aspect not listed. Patient and Public involvement Twelve members of the public (age range: 20-69 years) provided feedback on the draft survey. While none thought it was burdensome or time consuming, they recommended that we provide a definition of ‘self-monitoring’, examples of what this might entail, and to add additional health conditions to our list (e.g., skin conditions and blood glucose). Procedure YouGov invited individuals signed up to their platform to complete the survey via a link in an email. YouGov set quotas and monitored completion rates by key demographics (age, gender, ethnicity, geographical region, and social grade/socio-economic group) using information held on their panel member database. Once the desired number of respondents was reached, the survey link became inactive. Post-weighting was then applied by YouGov to ensure the final dataset was representative of the UK population according to the key demographics. Highest level of education, gender, age, and index of multiple deprivation (IMD) data were provided by YouGov from data held on their panel member database. Analysis Analyses were pre-registered on Open Science Framework (https://osf.io/96mzt/overview). YouGov provided summary weighted and unweighted (raw) data for 4341 respondents. There were no major differences between the weighted and unweighted data, so unweighted data were used for all analyses. Two authors independently reviewed open-ended responses and noted any seemingly generated by AI (e.g., ChatGPT). All data from these respondents were removed from analyses (n=71, 1.64%). This resulted in an analytic sample of 4270 respondents. Statistical analyses were performed using Stata 19.0. Categorial responses were summarised in number and percentages. To explore potential differences between self-monitoring experiences and willingness to monitor for medical and personal wellness purposes the research team selected (post-hoc) two health aspects that commonly relate to physiologic monitoring for medical purposes (blood pressure and blood sugar) and two behavioural health aspects that relate more to personal wellness (fitness/exercise and diet) for comparison. Whilst the reasons for monitoring were not gathered in the present study, similar distinctions in use have been considered previously 13 . Logistic regression analyses examined the relationship between key outcomes: (i) Has self-monitored, (ii) Would be willing to monitor, and (iii) Would be willing to pay for an app or equipment) and explanatory variables (preregistered as likely covariates: health condition, highest level of education, gender, age, and IMD). Multilevel logistic regression with a random effect term for ‘person’ was used for outcomes (i) and (ii). Any respondent answering ‘ I don’t know ’ for outcome (iii) was excluded from analyses as were two participants with missing IMD data. For monitoring of ‘menstrual cycle/periods’ only data from respondents identifying as female were included. Conclusions remained unchanged after sensitivity analyses that (a) removed IMD and education from analyses in turn due to multicollinearity concerns; (b) reinstated suspicious cases; (c) replaced ‘I don’t know’ responses with a negative response. We used 95% confidence intervals (CI) throughout. Exact confidence intervals were included for key prevalence statistics. Open-ended question responses were analysed using inductive content analysis 14 achieved in a two-step process using NVivo 14. 15 First, the data were reduced by assigning codes to each response. Where multiple concepts were described within a response, multiple codes were assigned. Second, similar codes were grouped together to form categories. Codes within each category were then further grouped into sub-categories, as considered appropriate. Results Respondents were broadly representative of the UK population in age structure and gender (e.g., slightly more females than males, Table 1 ). Table 1 Summary of respondent characteristics. Total n (%) 4,270 Gender Male 2,023 (47.4%) Female 2,247 (52.6%) Age 18–24 443 (10.4%) 25–34 722 (16.9%) 35–44 701 (16.4%) 45–54 778 (18.2%) 55–64 652 (15.3%) 65+ 974 (22.8%) Index of Multiple Deprivation (IMD) Decile 1 to 3 (most deprived) 1,102 (25.8%) Decile 4 to 7 (middle) 1,767 (41.4%) Decile 8 to 10 (least deprived) 1,399 (32.8%) Highest education level Secondary 815 (19.1%) Further 1,593 (37.3%) Higher 1,862 (43.6%) Self-monitoring experience Among the 4270 respondents in the analytic sample, 3354 (79%, 95% CI: 77, 80) indicated they had monitored at least one of the 12 listed aspects of health, 2540 (59%) had monitored at least two, 1757 (41%) had monitored at least three, and 1101 (26%) had monitored at least four. 1858 (44%) had monitored at least one of blood pressure and blood sugar compared to 2009 (47%) who had monitored at least one of fitness/exercise and diet. Figure 1 shows blood pressure was the health aspect with the highest proportion of respondents (41%) who had self-monitored, followed by fitness/exercise (37%). Self-monitoring of blood pressure was more common among older individuals, whereas the converse was true for monitoring of fitness/exercise. The least monitored health aspects were cholesterol (3%) and sensory health (eye/hearing, 5%). Although provided with the opportunity, few respondents (< 2%) indicated they had self-monitored any other health aspect. Most of the self-monitoring was self-motivated; blood pressure and blood sugar monitoring were most often recommended by a health professional (Fig. 2 A). For all health aspects, most respondents indicated they trusted (either a little or a lot) the results (Fig. 2 B). Trust was highest for blood pressure (94%) and blood sugar (92%), and lowest for skin conditions (70%) and eyesight (70%). Trust was largely similar when self-monitoring had been recommended by a health professional such as a GP than when it hadn’t, with biggest discrepancies for eyes (82% vs 70%) hearing (85% vs 72%) and skin conditions (79% vs 66%). Among respondents who had self-monitored, between 15% and 56% (skin conditions and pulse/heart rate, respectively) indicated that monitoring did not show a problem (Supplementary Table S2). Respondents were more likely to indicate that monitoring did not show a problem when this was self-initiated than when it had been recommended by a health professional. The highest discrepancies in monitoring not showing a problem between health professional recommended and self-initiated monitoring were for menstrual (17% vs 60%), heart (24% vs 60%) and hearing (5% vs 40%). Discussing the findings with a GP was the most preferred action if a problem were identified for blood pressure, blood sugar, cholesterol, hearing, mental/cognitive health, menstrual cycle, and skin conditions (Table 2 ). Between 14 and 38% of respondents reported taking no action following identification of a problem, with the highest rates of inaction being for pulse/heart rate (38%) and hearing (34%). When monitoring had been recommended by a health professional, there was a lower proportion of respondents saying that ‘no action’ was taken than when it hadn’t been recommended by a health professional. Largest differences in inactivity between monitoring recommended by a health professional versus not were seen for blood pressure (9% vs 30%), menstrual (12% vs 32%) and hearing (11% vs 37%). Table 2 Actions taken by respondents after self-monitoring indicated a problem or concern among respondents who had self-monitored the health aspect. Table excludes those who indicated that monitoring did not show a problem. N(%). Blood pressure (n = 1011) Discussed with GP Discussed with another HP Sought advice online Changed my lifestyle Did not take action Don't know Prefer not to say 616 (61%) 123 (12%) 68 (7%) 136 (13%) 152 (15%) 11 (1%) 15 (1%) Blood sugar (n = 285) 126 (44%) 62 (22%) 28 (10%) 72 (25%) 39 (14%) 11 (4%) 8 (3%) Body composition (n = 849) 114 (13%) 57 (7%) 90 (11%) 467 (55%) 195 (23%) 15 (2%) 19 (2%) Cholesterol (n = 109) 63 (58%) 11 (10%) 7 (6%) 28 (26%) 16 (15%) 4 (4%) 0 (0%) Diet (n = 791) 73 (9%) 52 (7%) 120 (15%) 487 (62%) 120 (15%) 30 (4%) 20 (3%) Eyesight (n = 133) 29 (22%) 61 (46%) 8 (6%) 16 (12%) 29 (22%) 6 (5%) 2 (2%) Fitness/ exercise (n = 771) 48 (6%) 34 (4%) 111 (14%) 413 (54%) 204 (26%) 22 (3%) 22 (3%) Hearing (n = 134) 48 (36%) 29 (22%) 15 (11%) 6 (4%) 45 (34%) 1 (1%) 3 (2%) Mental/ cognitive health (n = 582) 246 (42%) 112 (19%) 91 (16%) 134 (23%) 100 (17%) 24 (4%) 19 (3%) Menstrual cycle (n = 342) 162 (47%) 16 (5%) 58 (17%) 42 (12%) 91 (27%) 7 (2%) 12 (4%) Pulse/heart rate (n = 601) 219 (36%) 53 (9%) 56 (9%) 79 (13%) 230 (38%) 12 (2%) 13 (2%) Skin conditions (n = 200) 96 (48%) 35 (18%) 38 (19%) 41 (20%) 30 (15%) 3 (2%) 4 (2%) GP = General practitioner; HP = health professional GP = General practitioner; HP = health professional Figure 3 shows that those with greater levels of education were more likely to have self-monitored compared to those with secondary education (further education OR: 1.30, 95% CI: 1.15, 1.48; higher OR: 1.82; 95% CI: 1.60, 2.07), and compared to respondents aged 55–64, respondents aged ≤ 44 were more likely to have self-monitored (18–24 OR: 1.21, 95% CI: 1.01, 1.44; 25–34 OR: 1.36, 95% CI: 1.16, 1.59; 35–44 OR: 1.26, 95% CI: 1.08, 1.47). Confidence intervals included 1 for gender (OR: 1.09, 95% CI: 1.00, 1.19). Self-monitoring benefits and problems Table 3 displays the main reported benefits and problems. Benefits fell into five broad categories: enhanced awareness of health, greater control over health and symptoms, changed engagement with healthcare providers, easy access to health information, and psychologically beneficial. Main problems identified were psychological (e.g., anxiety, obsessive behaviours); behavioural (e.g., around adherence, inaction); potential health outcomes being impacted (e.g., by a lack of guidance and professional support); and technical concerns (e.g., accuracy/need of equipment). Table 3 Categories and subcategories for main benefits of, and problems with, self-monitoring of health. Categories Subcategories Benefits Enhanced awareness of health status, symptoms and changes in health status and/or symptoms Earlier identification of a problem Earlier diagnosis Alert to take action sooner An understanding of whether health was changing A way to observe trends in health over time Ability to track the progress of interventions Insights into the impact of lifestyle on health Tools for mapping symptoms to behaviours Greater control over health and symptoms Allows for regular testing Identifying and reducing risks Preventing problems Providing a record of health status over time Ability to maintain better health Changed engagement with healthcare providers Sharing of data Avoiding an unnecessary medical appointment Knowing when it is and is not necessary to seek medical care Saving NHS resources Easy access to health information Home-based Convenient Does not require an appointment Immediate results Psychologically beneficial Providing reassurance and peace of mind A motivation to make change and meet goals Empowering of self-care Encouraging of self-discipline Improving self-confidence Fostering self-accountability Less anxiety provoking than seeing a medical professional Problems Psychological impacts Cause of anxiety, stress, and hypochondriasis The individual becoming overly reliant on and obsessed with self-monitoring Self-monitoring being demotivating if goals are not met Undesirable results and implications are disregarded Results are not trusted Individuals lack self-confidence when using equipment and interpreting results Behavioural factors Adherence to monitoring Monitoring incorrectly and/or inconsistently Finding it annoying Finding time to do it Inaction following monitoring Finding it physically uncomfortable Potential health outcomes being impacted Lack of guidance and professional support Misinterpretation of results Missing something important Taking incorrect actions following monitoring Incorrectly recording data Creating a delay in help seeking or diagnosis of a condition Doctors don’t take the data seriously Technical concerns Accuracy of the equipment and the data Equipment failures Data privacy/security Need for, and cost of, monitoring equipment Willingness to self-monitor If recommended by the NHS, 3678 (86%, 95% CI: 85, 87) were willing to monitor at least one of the 12 health aspects. 3045 (71%) were willing to monitor at least one of blood pressure and blood sugar compared to 2488 (58%) who were willing to monitor at least one of fitness/exercise and diet. Figure 1 shows that blood pressure had the highest proportion of respondents (66%) willing to self-monitor, followed by blood sugar and pulse/heart rate (both 53%). Health aspects with the lowest proportion of respondents willing to self-monitor were skin conditions (34%), hearing (37%) and eyesight (38%). Among females, willingness to self-monitor menstrual cycles was high across pre-menopausal ages. Females were more likely than males to be willing to self-monitor (OR: 1.25, 95% CI: 1.08, 1.45, Fig. 3 ). Compared to respondents with secondary education, respondents with further and higher education were more likely to be willing to self-monitor (further education OR: 1.44, 95% CI: 1.17, 1.78; higher OR: 2.43, 95% CI: 1.96, 3.01). The youngest (18–24) and oldest (65+) respondents were less likely to be willing to self-monitor compared to respondents aged 55–64 (18–24 OR: 0.72, 95% CI: 0.54, 0.97; 65 + OR:0.54, 95% CI: 0.42, 0.69). Respondents in the most deprived neighbourhoods were less likely to be willing, and respondents in the least deprived areas were more likely to be willing to self-monitor compared to those in areas with middle levels of deprivation (most deprived OR: 0.79, 95% CI: 0.66, 0.95; least deprived OR: 1.27, 95% CI: 1.07, 1.51). The majority (78%) of respondents were willing for results to be automatically sent to a GP, whereas only one third were willing to pay for an app or other equipment if required (Supplementary Fig. 1). Compared to males, females were less likely to be willing to pay (OR: 0.82, 95% CI: 0.72, 0.95, Supplementary Fig. 2) and younger adults were less likely to be willing to pay compared to older adults (18–24 OR: 0.66, 95% CI: 0.50, 0.87; 25–34 OR: 0.75, 95% CI: 0.58, 0.96). Respondents with further and higher levels of education were more likely to be willing to pay compared to those with secondary education (further OR: 1.25, 95% CI: 1.02, 1.53; higher OR: 1.57, 95% CI: 1.29, 1.93). If self-monitoring identified a problem, the most preferred action for all age groups was contacting one’s GP followed by making changes to one’s lifestyle (Supplementary Table S3). The proportion of respondents preferring to contact one’s GP increased with age and the proportion preferring to seek online advice decreased with age. Discussion Main finding of this study This study examined the experiences and opinions of a broadly representative sample of 4270 UK adults concerning self-monitoring of health and personal wellness. The majority of respondents had self-monitored and/or would be willing to do so if recommended by the NHS and were willing for results to be sent to their GP automatically. The proportion of respondents with experience of self-monitoring was generally similar for health as it was for personal wellness; however, the proportion who would be willing to self-monitor if recommended by the NHS was higher for medical than personal wellness. Main benefits included enhanced awareness and control of health; easier access to health information; more balanced engagement with healthcare providers; and psychological support (e.g., reassurance, motivation). Main problems included psychological factors (e.g., anxiety, obsessive behaviours); behavioural factors (e.g., non-adherence, inaction); health outcomes being impacted by a lack of guidance and professional support; and technical concerns around accuracy/needing equipment). Despite a willingness to self-monitor, only one third of respondents were willing to pay for equipment to do so. There were sociodemographic differences in willingness to self-monitor with males, older adults, those living in areas of high deprivation, and those with lower education less likely to be willing to monitor their health. What is already known on this topic Consistent with previous research, 11 the current findings demonstrate: (i) a high interest among the general population to monitor their health if recommended by the NHS, and (ii) the public’s openness to have results automatically sent to their GP. The observed sociodemographic differences may be due to differential access to technologies limiting opportunities to engage with self-monitoring and differences in digital health literacy restricting the ability to successfully use self-monitoring tools; indeed, digital health literacy is greater for those with higher education and lower for older adults. 16 Self-monitoring was perceived to be beneficial not only because of the information it provided, but also because it motivated behaviour change, and empowered individuals to maintain healthy behaviours, which aligns with benefits previously identified by GPs. 17 The importance of these aspects must not be underestimated when it comes to health promotion and disease prevention. Many open-ended responses reported the benefit of having ‘evidence’ to take to the doctor; however, a sizeable proportion of respondents indicated they did not act when a problem was identified. It is possible that some members of the public do not consider problems identified to be of sufficient severity to warrant action. 18 Additionally, challenges in accessing health services post-monitoring 19 and limited opportunities for lifestyle changes (e.g., associated with social determinants of health) 20 could hinder action being taken. This study found trust in results was similar for healthcare provider recommended and self-initiated monitoring, but proportionally more respondents took action when monitoring was recommended by a health professional. Reasons for this difference are unknown, but it is possible that self-monitoring recommended by a health professional included follow-up contact and additional tailored advice to support action being taken. Previous systematic reviews (e.g., for diet, 21 blood pressure 22 ) found combining self-monitoring with tailored support (e.g., education/professional support) to be more effective in improving health outcomes than self-monitoring alone. Thus, there is a need to establish how self-monitoring can be strengthened to maximise action. A solution may be to have NHS ‘approved’ self-monitoring apps to increase trust in, and validity of, the results. What this study adds Incorporating self-monitoring results into NHS records presents an opportunity to identify health concerns early and aligns with government initiatives for preventative action to promote population health. 5 , 23 Self-monitoring supports proactive management of health remotely however, challenges may arise if results are unreliable or misunderstood by users and could potentially lead to workload/capacity concerns if users seek follow-up professional support. A systematic review found healthcare providers perceived self-monitoring data to be valuable for supporting patients, healthcare providers, and patient-provider relationships but found limited engagement with this data in clinical practice. 24 Thus, there is uncertainty in what types of self-monitoring data might be practical for healthcare providers to use in clinical workflows. Respondents were willing to self-monitor, if recommended by the NHS, more so for health conditions than personal wellness. While the current study found high levels of trust in the results among those who self-monitor, there is a need to explore healthcare provider perspectives. The preferred action by respondents if self-monitoring identified a problem was to initiate contact with their GP, rather than waiting to be contacted. This approach could potentially add pressure on GP availability although this could be alleviated through initiatives outlined in the 10 Year Health plan for England such as an NHS App to allow for instant advice and access to support. 5 While there are potential benefits of self-monitoring, there is also the potential for inequalities to be exacerbated due to differential access/use across socioeconomic characteristics, differences in digital literacy, and variations in outcomes. 25 The lack of standardization and validation of wearable devices (i.e., not equivalent to clinical-grade medical devices) makes it difficult for GPs to interpret the data. This study identified socioeconomic differences in willingness to self-monitor. As such, approaches that promote inclusive use of self-monitoring such as educational programmes, provider assistance and accessible support present opportunities for wider access and use of self-monitoring of health. 26 Examples of unanswered questions include: How can engagement in self-monitoring of health be enhanced? Why is trust in results for some health aspects lower than others? Why do some individuals not act when a problem is identified? Are there individuals for whom self-monitoring will lead to negative psychological outcomes and who are they? Limitations of this study The survey contained just nine questions, and it did not gather data on the frequency of monitoring, whether self-monitoring resulted in any health improvements, or the motivations for self-monitoring. It is possible that including ‘condition’ in the phrasing of some of the questions might have conflated medical monitoring with personal wellness monitoring: individuals may opt to monitor health for medical reasons (e.g., if they have a specific condition such as diabetes, or risk factor(s) for a disease) that may require ongoing engagement with healthcare providers, or for personal wellness reasons (e.g., monitoring behaviour such as fitness/exercise) independent of the healthcare service. While the survey included examples of self-monitoring for both medical and wellness purposes, only a small number of illustrative health aspects were included and other aspects of health (e.g., sleep, bowel, urine etc.) present opportunities for self-monitoring. Respondents had the option to indicate ‘Other’ health aspects they had self-monitored though few did so. A limitation of the study is that the online survey may have been completed by individuals with high digital literacy. To keep the survey to a reasonable length, limited contextual information was provided on what would be involved in self-monitoring and we did not ask respondents how they would expect to self-monitor each of the health aspects. Therefore, it is possible that respondents, particularly those with lower health literacy, did not have sufficient information to provide informed responses. Further details on the type of monitoring, and whether it is likely to involve pain and/or cost for the user may have led to different findings. Online surveys of health are common e.g., Health Insight Survey conducted by the office for National Statistics and funded by NHS England. 12 However, a concern about online health surveys is acquiescence bias i.e., the tendency for respondents to agree with statements, often driven by a desire to be polite. 27 In the present study, this could result in an inflated willingness to self-monitor. While acquiescence bias cannot be ruled out entirely, this is not supported by the reduced willingness to self-monitor personal wellness compared to health conditions, or the list of perceived problems in the open-ended question. Nevertheless, online surveys are quick to administer to a large and geographically diverse sample. Strengths included gathering information from a broadly representative sample population, examination of sociodemographic influences on willingness to self-monitor health, and incorporation of feedback from members of the public to ensure the survey was understandable and of reasonable length. Conclusions Self-monitoring has the potential to transform health. The findings of this study highlight the UK public’s high level of use and trust in self-monitor. There was a willingness to self-monitor, if recommended by the GP and have their results to be sent directly to their GP, particularly for medical health conditions. These results align with ongoing government initiatives to transform NHS care through preventative, community-based, action. However, the findings also highlight socioeconomic differences in self-monitoring which has the potential to exacerbate inequalities. Future research is needed to address how engagement of self-monitoring can be enhanced equitably and used effectively alongside clinical care to promote better health. Abbreviations CI: confidence interval IMD: index of multiple deprivation NHS: National health Service OR: odds ratio UK: United Kingdom of Great Britain and Northern Ireland Declarations Ethical approval and consent to participate Ethics approval was waived by the University of Manchester, with no requirement for informed consent, because the survey fell within the category of de-identified and non-sensitive research (ref: 2025-24154-43107). In other regards, the study was conducted in accordance with the Declaration of Helsinki. Consent for publication Not applicable Availability of data and materials Data is available from the corresponding author on reasonable request. Competing interests Support from the NIHR Manchester Biomedical Research Centre (BRC) for the submitted work. Funding This research was funded by the National Institute of Health and Care Research (NIHR) Senior Investigator award to KJM and supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care. Authors’ contributions The project was conceptualised by KJM, AMG and GS. KJM obtained funding and was responsible for overseeing the study. KJM, AMG, SAR, GHS and GS designed the study and its methodology. Formal analysis was conducted by SAR, LF and GHS. KJM and AMG were responsible for drafting the manuscript. All authors reviewed and finalised the manuscript. KJM and AMG are co-first authors. KJM acts as guarantor. The corresponding author attests that all of those authors meet authorship criteria and that no others meeting the criteria have been omitted. Acknowledgements The authors gratefully acknowledge the 12 members of the public who provided feedback on an earlier draft of the survey. References Chen T, Hertog E, Mahdi A, Vanderslott S. A systematic review on patient and public attitudes toward health monitoring technologies across countries. NPJ Digit Med. 2025;8(1):1–11. https://doi.org/10.1038/s41746-025-01762-4 . Vegesna A, Tran M, Angelaccio M, Arcona S. Remote Patient Monitoring via Non-Invasive Digital Technologies: A Systematic Review. Telemedicine e-Health. 2017;23(1):3–17. 10.1089/tmj.2016.0051 . PubMed PMID: 27116181. Tan SY, Sumner J, Wang Y, Wenjun Yip A. A systematic review of the impacts of remote patient monitoring (RPM) interventions on safety, adherence, quality-of-life and cost-related outcomes. NPJ Digit Med. 2024;7(1):1–16. 10.1038/S41746-024-01182-W;SUBJMETA . Taylor ML, Thomas EE, Snoswell CL, Smith AC, Caffery LJ. Does remote patient monitoring reduce acute care use? A systematic review. BMJ Open. 2021;11(3):e040232. 10.1136. /BMJOPEN-2020-040232 PubMed PMID: 33653740. UK Government. FIT FOR THE FUTURE 10 Year Health Plan for England [Internet]. 2025 Jul [cited 2025 Oct 8]. Report. Available from: https://assets.publishing.service.gov.uk/media/6888a0b1a11f859994409147/fit-for-the-future-10-year-health-plan-for-england.pdf YouGov. From sex to exercise, here are the things that Brits are most likely to track using apps and tech [Internet]. 2024 [cited 2025 Oct 10]. Available from: https://yougov.com/en-gb/articles/48998-what-brits-are-tracking-health-apps-2024 Davenport C, Richter A, Hillier B, Scandrett K, Agarwal R, Baldwin SW, et al. Direct-to-consumer self-tests sold in the UK in 2023: cross sectional review of information on intended use, instructions for use, and post-test decision making. BMJ. 2025;390:e085546. 10.1136. /BMJ-2025-085546 PubMed PMID: 40701637. Hillier B, Deeks JJ, Alderman J, Kale AU, Macdonald T, Baldwin SW, et al. Direct-to-consumer self-tests sold in the UK in 2023: cross sectional review of regulation and evidence of performance. BMJ. 2025;390:e085547. 10.1136/BMJ-2025-085547 . Fletcher BR, Hinton L, Hobbs FDR, McManus RJ, Bray EP, Hayen A, et al. Self-monitoring blood pressure in patients with hypertension: An internet-based survey of UK GPs. Br J Gen Pract. 2016;66(652):e831–7. 10.3399/bjgp16X . 687037 PubMed PMID: 27578811. Borges do Nascimento IJ, Abdulazeem H, Vasanthan LT, Martinez EZ, Zucoloto ML, Østengaard L, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digit Med Nat Res. 2023. 10.1038/s41746-023-00899-4 . PubMed PMID: 37723240. NHS Confederation. Patient empowerment: what is the role of technology in transforming care? [Internet]. 2023 Jun [cited 2025 Oct 28]. Report. Available from: https://www.nhsconfed.org/publications/patient-empowerment-what-role-technology-transforming-care Office for National Statistics. https://www.ons.gov.uk/surveys/informationforhouseholdsandindividuals/householdandindividualsurveys/healthinsightsurvey [Internet]. [cited 2026 Feb 25]. Health Insight Survey. Available from: https://www.ons.gov.uk/surveys/informationforhouseholdsandindividuals/householdandindividualsurveys/healthinsightsurvey Feng S, Mäntymäki M, Dhir A, Salmela H. How self-tracking and the quantified self promote health and well-being: Systematic review. Journal of Medical Internet Research. JMIR Publications Inc.; 2021. doi:10.2196/25171 PubMed PMID: 34546176. Kyngäs H. Inductive Content Analysis. In: The Application of Content Analysis in Nursing Science Research [Internet]. Springer, Cham; 2020 [cited 2025 Oct 12]. pp. 13–21. Available from: https://doi.org/10.1007/978-3-030-30199-6_2 doi:10.1007/978-3-030-30199-6_2. QSR International Pty Ltd. NVivo (Version 14). 2025. Estrela M, Semedo G, Roque F, Ferreira PL, Herdeiro MT. Sociodemographic determinants of digital health literacy: A systematic review and meta-analysis. Int J Med Inf. 2023;177:105124. 10.1016/J. .IJMEDINF.2023.105124 PubMed PMID: 37329766. Morrissey EC, Glynn LG, Casey M, Walsh JC, Molloy GJ. New self-management technologies for the treatment of hypertension: General practitioners’ perspectives. Fam Pract. 2018;35(3):318–22. 10.1093/fampra/cmx100 . PubMed PMID: 29088438. Armitage CJ, Munro KJ, Mandavia R, M AG M, Schilder AG. What health policy makers need to know about mismatches between public perceptions of disease risk, prevalence and severity: a national survey. Int J Audiol. 2021;60(12):979–84. 10.1080/14992027.2021.1881175 . Levesque JF, Harris MF, Russell G. Patient-centred access to health care: Conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12(1). 10.1186/1475-9276-12-18 . PubMed PMID: 23496984. Hacker K. The Burden of Chronic Disease. Mayo Clin Proc Innov Qual Outcomes. 2024;8(1):112–9. 10.1016/J.MAYOCPIQO.2023.08.005 . Berry R, Kassavou A, Sutton S. Does self-monitoring diet and physical activity behaviors using digital technology support adults with obesity or overweight to lose weight? A systematic literature review with meta-analysis. Obes Rev. 2021;22(10):e13306. 10. 1111/OBR.13306 PubMed PMID: 34192411. Tucker KL, Sheppard JP, Stevens R, Bosworth HB, Bove A, Bray EP, et al. Self-monitoring of blood pressure in hypertension: A systematic review and individual patient data meta-analysis. PLoS Med. 2017;14(9). 10.1371/journal.pmed.1002389 . PubMed PMID: 28926573. Scottish Government. Care in the Digital Age: Delivery Plan 2025-26 [Internet]. 2025 Aug [cited 2025 Oct 10]. Report. Available from: https://www.gov.scot/publications/care-digital-age-delivery-plan-2025-2026/documents/ Guardado S, Karampela M, Isomursu M, Grundstrom C. Use of Patient-Generated Health Data From Consumer-Grade Mobile Devices by Health Care Professionals in the Clinic: Systematic Review. J Med Internet Res. 2024;26:e49320. 10.2196/39389 . Western MJ, Smit ES, Gültzow T, Neter E, Sniehotta FF, Malkowski OS, et al. Bridging the digital health divide: a narrative review of the causes, implications, and solutions for digital health inequalities. Health Psychol Behav Med. 2025;13(1). 10.1080/21642850.2025.2493139 . Badr J, Motulsky A, Denis JL. Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations. Health Policy. Elsevier Ireland Ltd; 2024. 10.1016/j.healthpol.2024.105122 . PubMed PMID: 38986333. Dunsch F, Evans DK, Macis M, Wang Q. Bias in patient satisfaction surveys: A threat to measuring healthcare quality. BMJ Glob Health. 2018;3(2). 10.1136/bmjgh-2017-000694 . Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 May, 2026 Reviewers invited by journal 07 May, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 17 Mar, 2026 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. 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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-9012710","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639490276,"identity":"a9ad16aa-8a4a-455a-b93f-cd8a6d9692a5","order_by":0,"name":"Kevin J 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Manchester","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"","lastName":"Ferrie","suffix":""},{"id":639490278,"identity":"993e2221-e25c-42f4-86ed-1fb35489b0e5","order_by":2,"name":"Sarah A Rhodes","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"A","lastName":"Rhodes","suffix":""},{"id":639490279,"identity":"4323303d-06e8-445a-b69b-ba222377174c","order_by":3,"name":"Gabrielle H Saunders","email":"","orcid":"","institution":"University of Manchester","correspondingAuthor":false,"prefix":"","firstName":"Gabrielle","middleName":"H","lastName":"Saunders","suffix":""},{"id":639490280,"identity":"c544c8ec-4010-4f70-acf2-bbf9bebb6b69","order_by":4,"name":"Gareth Smith","email":"","orcid":"","institution":"Mid and South Essex Hospital NHS Foundation Trust","correspondingAuthor":false,"prefix":"","firstName":"Gareth","middleName":"","lastName":"Smith","suffix":""},{"id":639490281,"identity":"f5cd8cd1-4dcb-4cb6-91eb-b5a2d835d2fa","order_by":5,"name":"Adele M Goman","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Adele","middleName":"M","lastName":"Goman","suffix":""}],"badges":[],"createdAt":"2026-03-02 17:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9012710/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9012710/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109473060,"identity":"4868881c-0c45-4cea-aefa-eaa3c91f89ee","added_by":"auto","created_at":"2026-05-18 13:32:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":240517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of respondents who (A) had ever self-monitored and (B) were willing to self-monitor each health aspect by age.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9012710/v1/dab5f891be0379b8c51b0921.jpeg"},{"id":109759725,"identity":"cee48ca2-8b06-4f0c-bab8-c252150ecbfe","added_by":"auto","created_at":"2026-05-22 07:27:36","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122862,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePercentage of respondents who had (A) self-monitored each health aspect following a request by a health professional, (B) who trusted the results of self-monitoring by health aspect.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9012710/v1/15bec016d8fb2540830d74a7.jpeg"},{"id":109760271,"identity":"f78af28a-0a81-4531-bee9-756ed4e5a1e2","added_by":"auto","created_at":"2026-05-22 07:28:24","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds ratios and 95% confidence intervals for (left panel) ever self-monitoring and (right panel) willing to self-monitor health by sociodemographic characteristics and health aspect.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9012710/v1/273b7c16e5300b8cf56d31c5.jpeg"},{"id":109763838,"identity":"f7d20ba7-87e1-4e47-b606-bc4a5144baf2","added_by":"auto","created_at":"2026-05-22 07:36:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":731635,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9012710/v1/a88e0306-b49e-4e6a-88a0-85e781b0cd3c.pdf"},{"id":109473059,"identity":"b52c2ec7-eaf6-4d09-87e6-e07685e9ce5b","added_by":"auto","created_at":"2026-05-18 13:32:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":97707,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9012710/v1/9ce146f860412532c60a41fd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research Use, trustworthiness and willingness to self-monitor health and personal wellness: a UK population survey","fulltext":[{"header":"Contributions to the literature","content":"\u003cul\u003e\n \u003cli\u003eThe UK Government has outlined aspirations for health monitoring technologies to be part of routine care to support proactive health monitoring and preventative action.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePublic opinion about self-monitoring is important because this has the potential to change how the public will engage with future NHS initiatives on self-monitoring of health.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOur study suggests the UK public use, trust, and are willing to self-monitor their health and personal wellness, and have results sent to their GP.\u003c/li\u003e\n \u003cli\u003eSocioeconomic differences in willingness to self-monitor were found highlighting the need to address how engagement in self-monitoring can be enhanced equitably and used effectively alongside clinical care.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Background","content":"\u003cp\u003eThe revolution in interest in new technologies and adoption of personal health self-monitoring has the potential to transform health care.\u003csup\u003e1\u003c/sup\u003e Health can be tracked using tools such as wearable technologies, biosensors, smart medical devices, apps and over-the-counter diagnostic kits.\u003csup\u003e2\u003c/sup\u003e The motivation for self-monitoring of health may be for medical reasons (e.g., blood glucose monitoring for managing diabetes), or for wellness purposes (e.g., monitoring exercise for fitness). Reported benefits to public health include:\u003c/p\u003e\n\u003col style=\"list-style-type: lower-roman;\"\u003e\n \u003cli\u003eIncreased self-awareness and promotion of preventative behaviours,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eearly detection of health issues,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eenhanced self-efficacy and management of chronic conditions,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003elifestyle changes,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eimproved treatment adherence,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ereduced pressure on healthcare systems,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ebetter communication with healthcare providers, and\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eimproved outcomes.\u003csup\u003e3,4\u003c/sup\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe UK government has expressed considerable interest in self-monitoring of health\u003csup\u003e5\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eand there is an appetite amongst the public to use technology to take greater responsibility for their health.\u003csup\u003e6\u003c/sup\u003e While users generally show positive attitudes toward health monitoring technologies, concerns have been expressed over accuracy, cost, and\u003c/p\u003e\n\u003cp\u003eburden (to the public and healthcare system).\u003csup\u003e1\u003c/sup\u003e There are also concerns about the current market and regulatory oversight for some direct-to-consumer tests.\u003csup\u003e7,8\u003c/sup\u003e Healthcare providers have recognised the potential benefits of self-monitoring for patient health\u003csup\u003e9\u003c/sup\u003e but also concerns (e.g. workload).\u003csup\u003e10\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePublic opinion about self-monitoring of health and wellness is important because, by nature, self-monitoring requires voluntary active engagement\u0026nbsp;if future NHS initiatives including preventative approaches are to be successful. Previous UK surveys\u003csup\u003e6,11\u003c/sup\u003e have examined use of self-monitoring technology, but not whether the public trusts the results or what actions are taken (or preferred) following self-monitoring. We undertook a cross-sectional survey of UK adults that investigated attitudes and experiences towards self-monitoring of health and wellness over a broad range of health aspects. The survey included questions related to willingness to pay and share data, within the context of the NHS.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eRespondents \u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRespondents were adults living in the UK selected by YouGov PLC UK (an internet-based research service) from members signed up to the survey platform. A target sample size of 4000 allowed us to estimate key prevalences to a precision of \u0026plusmn; 1.5%.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOnline survey\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNine survey questions (Supplementary Table S1) were embedded into a YouGov survey completed between 31\u003csup\u003est\u003c/sup\u003e July and August 13\u003csup\u003eth\u003c/sup\u003e, 2025. Questions addressed:\u0026nbsp;\u003c/p\u003e\n\u003col style=\"list-style-type: lower-alpha;\"\u003e\n \u003cli\u003eSelf-monitoring experience, trust in results, actions taken and whether monitoring was recommended by a health professional.\u003c/li\u003e\n \u003cli\u003eWillingness to self-monitor, and, of those willing, the proportion willing to have results automatically sent to their GP or other health professional, willingness to pay for self-monitoring (assuming a similar cost to an NHS prescription) and preferred next steps if a problem were identified. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eMain benefits of, and problems with, self-monitoring.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFor (a) and (b), participants indicated their experience and willingness to self-monitor a range of medical health and wellness health aspects: blood pressure, blood sugar, body composition, cholesterol, diet, eyesight, fitness/exercise, hearing, menstrual cycles/periods, mental and cognitive health, pulse/heart rate, and skin conditions. An \u0026lsquo;Other\u0026rsquo; option was available for respondents to indicate if they had monitored any other health aspect not listed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatient and Public involvement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwelve members of the public (age range: 20-69 years) provided feedback on the draft survey. While none thought it was burdensome or time consuming, they recommended that we provide a definition of \u0026lsquo;self-monitoring\u0026rsquo;, examples of what this might entail, and to add additional health conditions to our list (e.g., skin conditions and blood glucose).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProcedure\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eYouGov invited individuals signed up to their platform to complete the survey via a link in an email. YouGov set quotas and monitored completion rates by key demographics (age, gender, ethnicity, geographical region, and social grade/socio-economic group) using information held on their panel member database. Once the desired number of respondents was reached, the survey link became inactive. Post-weighting was then applied by YouGov to ensure the final dataset was representative of the UK population according to the key demographics. Highest level of education, gender, age, and index of multiple deprivation (IMD) data were provided by YouGov from data held on their panel member database.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were pre-registered on Open Science Framework (https://osf.io/96mzt/overview). YouGov provided summary weighted and unweighted (raw) data for 4341 respondents. There were no major differences between the weighted and unweighted data, so unweighted data were used for all analyses. Two authors independently reviewed open-ended responses and noted any seemingly generated by AI (e.g., ChatGPT). All data from these respondents were removed from analyses (n=71, 1.64%). This resulted in an analytic sample of 4270 respondents.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using Stata 19.0. Categorial responses were summarised in number and percentages. To explore potential differences between self-monitoring experiences and willingness to monitor for medical and personal wellness purposes the research team selected (post-hoc) two health aspects that commonly relate to physiologic monitoring for medical purposes (blood pressure and blood sugar) and two behavioural health aspects that relate more to personal wellness (fitness/exercise and diet) for comparison. Whilst the reasons for monitoring were not gathered in the present study, similar distinctions in use have been considered previously\u003csup\u003e13\u003c/sup\u003e. Logistic regression analyses examined the relationship between key outcomes: (i) Has self-monitored, (ii) Would be willing to monitor, and (iii) Would be willing to pay for an app or equipment) and explanatory variables (preregistered as likely covariates: health condition, highest level of education, gender, age, and IMD). Multilevel logistic regression with a random effect term for \u0026lsquo;person\u0026rsquo; was used for outcomes (i) and (ii). Any respondent answering \u0026lsquo;\u003cem\u003eI don\u0026rsquo;t know\u003c/em\u003e\u0026rsquo; for outcome (iii) was excluded from analyses as were two participants with missing IMD data. For monitoring of \u0026lsquo;menstrual cycle/periods\u0026rsquo; only data from respondents identifying as female were included.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusions remained unchanged after sensitivity analyses that (a) removed IMD and education from analyses in turn due to multicollinearity concerns; (b) reinstated suspicious cases; (c) replaced \u0026lsquo;I don\u0026rsquo;t know\u0026rsquo; responses with a negative response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used 95% confidence intervals (CI) throughout. Exact confidence intervals were included for key prevalence statistics.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOpen-ended question responses were analysed using inductive content analysis\u003csup\u003e14\u003c/sup\u003e achieved in a two-step process using NVivo 14.\u003csup\u003e15\u003c/sup\u003e First, the data were reduced by assigning codes to each response. Where multiple concepts were described within a response, multiple codes were assigned. Second, similar codes were grouped together to form categories. Codes within each category were then further grouped into sub-categories, as considered appropriate.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eRespondents were broadly representative of the UK population in age structure and gender (e.g., slightly more females than males, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eSummary of respondent characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,270\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,023 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,247 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e443 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e722 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e701 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e778 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e652 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e974 (22.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndex of Multiple Deprivation (IMD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecile 1 to 3 (most deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,102 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecile 4 to 7 (middle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,767 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecile 8 to 10 (least deprived)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,399 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHighest education level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e815 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFurther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,593 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,862 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSelf-monitoring experience\u003c/h3\u003e\n\u003cp\u003eAmong the 4270 respondents in the analytic sample, 3354 (79%, 95% CI: 77, 80) indicated they had monitored at least one of the 12 listed aspects of health, 2540 (59%) had monitored at least two, 1757 (41%) had monitored at least three, and 1101 (26%) had monitored at least four. 1858 (44%) had monitored at least one of blood pressure and blood sugar compared to 2009 (47%) who had monitored at least one of fitness/exercise and diet. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows blood pressure was the health aspect with the highest proportion of respondents (41%) who had self-monitored, followed by fitness/exercise (37%). Self-monitoring of blood pressure was more common among older individuals, whereas the converse was true for monitoring of fitness/exercise. The least monitored health aspects were cholesterol (3%) and sensory health (eye/hearing, 5%). Although provided with the opportunity, few respondents (\u0026lt;\u0026thinsp;2%) indicated they had self-monitored any other health aspect.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost of the self-monitoring was self-motivated; blood pressure and blood sugar monitoring were most often recommended by a health professional (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For all health aspects, most respondents indicated they trusted (either a little or a lot) the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Trust was highest for blood pressure (94%) and blood sugar (92%), and lowest for skin conditions (70%) and eyesight (70%). Trust was largely similar when self-monitoring had been recommended by a health professional such as a GP than when it hadn\u0026rsquo;t, with biggest discrepancies for eyes (82% vs 70%) hearing (85% vs 72%) and skin conditions (79% vs 66%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong respondents who had self-monitored, between 15% and 56% (skin conditions and pulse/heart rate, respectively) indicated that monitoring did not show a problem (Supplementary Table S2). Respondents were more likely to indicate that monitoring did not show a problem when this was self-initiated than when it had been recommended by a health professional. The highest discrepancies in monitoring not showing a problem between health professional recommended and self-initiated monitoring were for menstrual (17% vs 60%), heart (24% vs 60%) and hearing (5% vs 40%). Discussing the findings with a GP was the most preferred action if a problem were identified for blood pressure, blood sugar, cholesterol, hearing, mental/cognitive health, menstrual cycle, and skin conditions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Between 14 and 38% of respondents reported taking no action following identification of a problem, with the highest rates of inaction being for pulse/heart rate (38%) and hearing (34%). When monitoring had been recommended by a health professional, there was a lower proportion of respondents saying that \u0026lsquo;no action\u0026rsquo; was taken than when it hadn\u0026rsquo;t been recommended by a health professional. Largest differences in inactivity between monitoring recommended by a health professional versus not were seen for blood pressure (9% vs 30%), menstrual (12% vs 32%) and hearing (11% vs 37%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActions taken by respondents after self-monitoring indicated a problem or concern among respondents who had self-monitored the health aspect. Table excludes those who indicated that monitoring did not show a problem. N(%).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlood pressure (n\u0026thinsp;=\u0026thinsp;1011)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscussed with GP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiscussed with another HP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSought advice online\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChanged my lifestyle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDid not take action\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDon't know\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrefer not to say\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e616 (61%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (12%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (7%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136 (13%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152 (15%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (1%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15 (1%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood sugar (n\u0026thinsp;=\u0026thinsp;285)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8 (3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody composition (n\u0026thinsp;=\u0026thinsp;849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e467 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e195 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol (n\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet (n\u0026thinsp;=\u0026thinsp;791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e487 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20 (3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEyesight (n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFitness/ exercise (n\u0026thinsp;=\u0026thinsp;771)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e413 (54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204 (26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22 (3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing (n\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental/ cognitive health (n\u0026thinsp;=\u0026thinsp;582)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e246 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e134 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19 (3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual cycle (n\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12 (4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulse/heart rate (n\u0026thinsp;=\u0026thinsp;601)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e230 (38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin conditions (n\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eGP\u0026thinsp;=\u0026thinsp;General practitioner; HP\u0026thinsp;=\u0026thinsp;health professional GP\u0026thinsp;=\u0026thinsp;General practitioner; HP\u0026thinsp;=\u0026thinsp;health professional\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that those with greater levels of education were more likely to have self-monitored compared to those with secondary education (further education OR: 1.30, 95% CI: 1.15, 1.48; higher OR: 1.82; 95% CI: 1.60, 2.07), and compared to respondents aged 55\u0026ndash;64, respondents aged\u0026thinsp;\u0026le;\u0026thinsp;44 were more likely to have self-monitored (18\u0026ndash;24 OR: 1.21, 95% CI: 1.01, 1.44; 25\u0026ndash;34 OR: 1.36, 95% CI: 1.16, 1.59; 35\u0026ndash;44 OR: 1.26, 95% CI: 1.08, 1.47). Confidence intervals included 1 for gender (OR: 1.09, 95% CI: 1.00, 1.19).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSelf-monitoring benefits and problems\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the main reported benefits and problems. Benefits fell into five broad categories: enhanced awareness of health, greater control over health and symptoms, changed engagement with healthcare providers, easy access to health information, and psychologically beneficial. Main problems identified were psychological (e.g., anxiety, obsessive behaviours); behavioural (e.g., around adherence, inaction); potential health outcomes being impacted (e.g., by a lack of guidance and professional support); and technical concerns (e.g., accuracy/need of equipment).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCategories and subcategories for main benefits of, and problems with, self-monitoring of health.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubcategories\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBenefits\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eEnhanced awareness of health status, symptoms and changes in health status and/or symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarlier identification of a problem\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarlier diagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlert to take action sooner\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn understanding of whether health was changing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA way to observe trends in health over time\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbility to track the progress of interventions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsights into the impact of lifestyle on health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTools for mapping symptoms to behaviours\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eGreater control over health and symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllows for regular testing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdentifying and reducing risks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreventing problems\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProviding a record of health status over time\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbility to maintain better health\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eChanged engagement with healthcare providers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSharing of data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvoiding an unnecessary medical appointment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowing when it is and is not necessary to seek medical care\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaving NHS resources\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEasy access to health information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHome-based\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConvenient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes not require an appointment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmediate results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ePsychologically beneficial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProviding reassurance and peace of mind\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA motivation to make change and meet goals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmpowering of self-care\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEncouraging of self-discipline\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproving self-confidence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFostering self-accountability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess anxiety provoking than seeing a medical professional\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProblems\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003ePsychological impacts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCause of anxiety, stress, and hypochondriasis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe individual becoming overly reliant on and obsessed with self-monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-monitoring being demotivating if goals are not met\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndesirable results and implications are disregarded\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResults are not trusted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividuals lack self-confidence when using equipment and interpreting results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eBehavioural factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdherence to monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonitoring incorrectly and/or inconsistently\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinding it annoying\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinding time to do it\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInaction following monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinding it physically uncomfortable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003ePotential health outcomes being impacted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLack of guidance and professional support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMisinterpretation of results\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing something important\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTaking incorrect actions following monitoring\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncorrectly recording data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCreating a delay in help seeking or diagnosis of a condition\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctors don\u0026rsquo;t take the data seriously\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTechnical concerns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy of the equipment and the data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquipment failures\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData privacy/security\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeed for, and cost of, monitoring equipment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWillingness to self-monitor\u003c/h2\u003e \u003cp\u003eIf recommended by the NHS, 3678 (86%, 95% CI: 85, 87) were willing to monitor at least one of the 12 health aspects. 3045 (71%) were willing to monitor at least one of blood pressure and blood sugar compared to 2488 (58%) who were willing to monitor at least one of fitness/exercise and diet. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that blood pressure had the highest proportion of respondents (66%) willing to self-monitor, followed by blood sugar and pulse/heart rate (both 53%). Health aspects with the lowest proportion of respondents willing to self-monitor were skin conditions (34%), hearing (37%) and eyesight (38%). Among females, willingness to self-monitor menstrual cycles was high across pre-menopausal ages.\u003c/p\u003e \u003cp\u003eFemales were more likely than males to be willing to self-monitor (OR: 1.25, 95% CI: 1.08, 1.45, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Compared to respondents with secondary education, respondents with further and higher education were more likely to be willing to self-monitor (further education OR: 1.44, 95% CI: 1.17, 1.78; higher OR: 2.43, 95% CI: 1.96, 3.01). The youngest (18\u0026ndash;24) and oldest (65+) respondents were less likely to be willing to self-monitor compared to respondents aged 55\u0026ndash;64 (18\u0026ndash;24 OR: 0.72, 95% CI: 0.54, 0.97; 65\u0026thinsp;+\u0026thinsp;OR:0.54, 95% CI: 0.42, 0.69). Respondents in the most deprived neighbourhoods were less likely to be willing, and respondents in the least deprived areas were more likely to be willing to self-monitor compared to those in areas with middle levels of deprivation (most deprived OR: 0.79, 95% CI: 0.66, 0.95; least deprived OR: 1.27, 95% CI: 1.07, 1.51).\u003c/p\u003e \u003cp\u003eThe majority (78%) of respondents were willing for results to be automatically sent to a GP, whereas only one third were willing to pay for an app or other equipment if required (Supplementary Fig.\u0026nbsp;1). Compared to males, females were less likely to be willing to pay (OR: 0.82, 95% CI: 0.72, 0.95, Supplementary Fig.\u0026nbsp;2) and younger adults were less likely to be willing to pay compared to older adults (18\u0026ndash;24 OR: 0.66, 95% CI: 0.50, 0.87; 25\u0026ndash;34 OR: 0.75, 95% CI: 0.58, 0.96). Respondents with further and higher levels of education were more likely to be willing to pay compared to those with secondary education (further OR: 1.25, 95% CI: 1.02, 1.53; higher OR: 1.57, 95% CI: 1.29, 1.93).\u003c/p\u003e \u003cp\u003eIf self-monitoring identified a problem, the most preferred action for all age groups was contacting one\u0026rsquo;s GP followed by making changes to one\u0026rsquo;s lifestyle (Supplementary Table S3). The proportion of respondents preferring to contact one\u0026rsquo;s GP increased with age and the proportion preferring to seek online advice decreased with age.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMain finding of this study\u003c/h2\u003e \u003cp\u003eThis study examined the experiences and opinions of a broadly representative sample of 4270 UK adults concerning self-monitoring of health and personal wellness. The majority of respondents had self-monitored and/or would be willing to do so if recommended by the NHS and were willing for results to be sent to their GP automatically. The proportion of respondents with experience of self-monitoring was generally similar for health as it was for personal wellness; however, the proportion who would be willing to self-monitor if recommended by the NHS was higher for medical than personal wellness. Main benefits included enhanced awareness and control of health; easier access to health information; more balanced engagement with healthcare providers; and psychological support (e.g., reassurance, motivation). Main problems included psychological factors (e.g., anxiety, obsessive behaviours); behavioural factors (e.g., non-adherence, inaction); health outcomes being impacted by a lack of guidance and professional support; and technical concerns around accuracy/needing equipment). Despite a willingness to self-monitor, only one third of respondents were willing to pay for equipment to do so. There were sociodemographic differences in willingness to self-monitor with males, older adults, those living in areas of high deprivation, and those with lower education less likely to be willing to monitor their health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eWhat is already known on this topic\u003c/h2\u003e \u003cp\u003eConsistent with previous research,\u003csup\u003e11\u003c/sup\u003e the current findings demonstrate: (i) a high interest among the general population to monitor their health if recommended by the NHS, and (ii) the public\u0026rsquo;s openness to have results automatically sent to their GP. The observed sociodemographic differences may be due to differential access to technologies limiting opportunities to engage with self-monitoring and differences in digital health literacy restricting the ability to successfully use self-monitoring tools; indeed, digital health literacy is greater for those with higher education and lower for older adults.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSelf-monitoring was perceived to be beneficial not only because of the information it provided, but also because it motivated behaviour change, and empowered individuals to maintain healthy behaviours, which aligns with benefits previously identified by GPs.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The importance of these aspects must not be underestimated when it comes to health promotion and disease prevention. Many open-ended responses reported the benefit of having \u0026lsquo;evidence\u0026rsquo; to take to the doctor; however, a sizeable proportion of respondents indicated they did not act when a problem was identified. It is possible that some members of the public do not consider problems identified to be of sufficient severity to warrant action.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Additionally, challenges in accessing health services post-monitoring \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and limited opportunities for lifestyle changes (e.g., associated with social determinants of health)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e could hinder action being taken. This study found trust in results was similar for healthcare provider recommended and self-initiated monitoring, but proportionally more respondents took action when monitoring was recommended by a health professional. Reasons for this difference are unknown, but it is possible that self-monitoring recommended by a health professional included follow-up contact and additional tailored advice to support action being taken. Previous systematic reviews (e.g., for diet,\u003csup\u003e21\u003c/sup\u003e blood pressure\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e) found combining self-monitoring with tailored support (e.g., education/professional support) to be more effective in improving health outcomes than self-monitoring alone. Thus, there is a need to establish how self-monitoring can be strengthened to maximise action. A solution may be to have NHS \u0026lsquo;approved\u0026rsquo; self-monitoring apps to increase trust in, and validity of, the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eWhat this study adds\u003c/h2\u003e \u003cp\u003eIncorporating self-monitoring results into NHS records presents an opportunity to identify health concerns early and aligns with government initiatives for preventative action to promote population health.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Self-monitoring supports proactive management of health remotely however, challenges may arise if results are unreliable or misunderstood by users and could potentially lead to workload/capacity concerns if users seek follow-up professional support. A systematic review found healthcare providers perceived self-monitoring data to be valuable for supporting patients, healthcare providers, and patient-provider relationships but found limited engagement with this data in clinical practice.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Thus, there is uncertainty in what types of self-monitoring data might be practical for healthcare providers to use in clinical workflows. Respondents were willing to self-monitor, if recommended by the NHS, more so for health conditions than personal wellness. While the current study found high levels of trust in the results among those who self-monitor, there is a need to explore healthcare provider perspectives. The preferred action by respondents if self-monitoring identified a problem was to initiate contact with their GP, rather than waiting to be contacted. This approach could potentially add pressure on GP availability although this could be alleviated through initiatives outlined in the 10 Year Health plan for England such as an NHS App to allow for instant advice and access to support.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e While there are potential benefits of self-monitoring, there is also the potential for inequalities to be exacerbated due to differential access/use across socioeconomic characteristics, differences in digital literacy, and variations in outcomes.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e The lack of standardization and validation of wearable devices (i.e., not equivalent to clinical-grade medical devices) makes it difficult for GPs to interpret the data. This study identified socioeconomic differences in willingness to self-monitor. As such, approaches that promote inclusive use of self-monitoring such as educational programmes, provider assistance and accessible support present opportunities for wider access and use of self-monitoring of health.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eExamples of unanswered questions include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHow can engagement in self-monitoring of health be enhanced?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhy is trust in results for some health aspects lower than others?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhy do some individuals not act when a problem is identified?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAre there individuals for whom self-monitoring will lead to negative psychological outcomes and who are they?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of this study\u003c/h2\u003e \u003cp\u003eThe survey contained just nine questions, and it did not gather data on the frequency of monitoring, whether self-monitoring resulted in any health improvements, or the motivations for self-monitoring. It is possible that including \u0026lsquo;condition\u0026rsquo; in the phrasing of some of the questions might have conflated medical monitoring with personal wellness monitoring: individuals may opt to monitor health for medical reasons (e.g., if they have a specific condition such as diabetes, or risk factor(s) for a disease) that may require ongoing engagement with healthcare providers, or for personal wellness reasons (e.g., monitoring behaviour such as fitness/exercise) independent of the healthcare service. While the survey included examples of self-monitoring for both medical and wellness purposes, only a small number of illustrative health aspects were included and other aspects of health (e.g., sleep, bowel, urine etc.) present opportunities for self-monitoring. Respondents had the option to indicate \u0026lsquo;Other\u0026rsquo; health aspects they had self-monitored though few did so.\u003c/p\u003e \u003cp\u003eA limitation of the study is that the online survey may have been completed by individuals with high digital literacy. To keep the survey to a reasonable length, limited contextual information was provided on what would be involved in self-monitoring and we did not ask respondents how they would expect to self-monitor each of the health aspects. Therefore, it is possible that respondents, particularly those with lower health literacy, did not have sufficient information to provide informed responses. Further details on the type of monitoring, and whether it is likely to involve pain and/or cost for the user may have led to different findings.\u003c/p\u003e \u003cp\u003eOnline surveys of health are common e.g., Health Insight Survey conducted by the office for National Statistics and funded by NHS England.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e However, a concern about online health surveys is acquiescence bias i.e., the tendency for respondents to agree with statements, often driven by a desire to be polite.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e In the present study, this could result in an inflated willingness to self-monitor. While acquiescence bias cannot be ruled out entirely, this is not supported by the reduced willingness to self-monitor personal wellness compared to health conditions, or the list of perceived problems in the open-ended question.\u003c/p\u003e \u003cp\u003eNevertheless, online surveys are quick to administer to a large and geographically diverse sample. Strengths included gathering information from a broadly representative sample population, examination of sociodemographic influences on willingness to self-monitor health, and incorporation of feedback from members of the public to ensure the survey was understandable and of reasonable length.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSelf-monitoring has the potential to transform health. The findings of this study highlight the UK public\u0026rsquo;s high level of use and trust in self-monitor. There was a willingness to self-monitor, if recommended by the GP and have their results to be sent directly to their GP, particularly for medical health conditions. These results align with ongoing government initiatives to transform NHS care through preventative, community-based, action. However, the findings also highlight socioeconomic differences in self-monitoring which has the potential to exacerbate inequalities. Future research is needed to address how engagement of self-monitoring can be enhanced equitably and used effectively alongside clinical care to promote better health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCI: confidence interval\u003c/p\u003e\n\u003cp\u003eIMD: index of multiple deprivation\u003c/p\u003e\n\u003cp\u003eNHS: National health Service\u003c/p\u003e\n\u003cp\u003eOR: odds ratio\u003c/p\u003e\n\u003cp\u003eUK: United Kingdom of Great Britain and Northern Ireland\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthical approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was waived by the University of Manchester, with no requirement for informed consent, because the survey fell within the category of de-identified and non-sensitive research (ref: 2025-24154-43107). In other regards, the study was conducted in accordance with the Declaration of Helsinki.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSupport from the NIHR Manchester Biomedical Research Centre (BRC) for the submitted work. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Institute of Health and Care Research (NIHR) Senior Investigator award to KJM and supported by the NIHR Manchester Biomedical Research Centre (NIHR203308). The views expressed are those of the author and not necessarily those of the NIHR or the Department of Health and Social Care. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe project was conceptualised by KJM, AMG and GS. KJM obtained funding and was responsible for overseeing the study. KJM, AMG, SAR, GHS and GS designed the study and its methodology. Formal analysis was conducted by SAR, LF and GHS. KJM and AMG were responsible for drafting the manuscript. All authors reviewed and finalised the manuscript. KJM and AMG are co-first authors. KJM acts as guarantor. The corresponding author attests that all of those authors meet authorship criteria and that no others meeting the criteria have been omitted.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the 12 members of the public who provided feedback on an earlier draft of the survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen T, Hertog E, Mahdi A, Vanderslott S. A systematic review on patient and public attitudes toward health monitoring technologies across countries. NPJ Digit Med. 2025;8(1):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41746-025-01762-4\u003c/span\u003e\u003cspan address=\"10.1038/s41746-025-01762-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVegesna A, Tran M, Angelaccio M, Arcona S. Remote Patient Monitoring via Non-Invasive Digital Technologies: A Systematic Review. Telemedicine e-Health. 2017;23(1):3\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/tmj.2016.0051\u003c/span\u003e\u003cspan address=\"10.1089/tmj.2016.0051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 27116181.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan SY, Sumner J, Wang Y, Wenjun Yip A. A systematic review of the impacts of remote patient monitoring (RPM) interventions on safety, adherence, quality-of-life and cost-related outcomes. NPJ Digit Med. 2024;7(1):1\u0026ndash;16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/S41746-024-01182-W;SUBJMETA\u003c/span\u003e\u003cspan address=\"10.1038/S41746-024-01182-W;SUBJMETA\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor ML, Thomas EE, Snoswell CL, Smith AC, Caffery LJ. Does remote patient monitoring reduce acute care use? A systematic review. BMJ Open. 2021;11(3):e040232. 10.1136. /BMJOPEN-2020-040232 PubMed PMID: 33653740.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUK Government. FIT FOR THE FUTURE 10 Year Health Plan for England [Internet]. 2025 Jul [cited 2025 Oct 8]. Report. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://assets.publishing.service.gov.uk/media/6888a0b1a11f859994409147/fit-for-the-future-10-year-health-plan-for-england.pdf\u003c/span\u003e\u003cspan address=\"https://assets.publishing.service.gov.uk/media/6888a0b1a11f859994409147/fit-for-the-future-10-year-health-plan-for-england.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYouGov. From sex to exercise, here are the things that Brits are most likely to track using apps and tech [Internet]. 2024 [cited 2025 Oct 10]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yougov.com/en-gb/articles/48998-what-brits-are-tracking-health-apps-2024\u003c/span\u003e\u003cspan address=\"https://yougov.com/en-gb/articles/48998-what-brits-are-tracking-health-apps-2024\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavenport C, Richter A, Hillier B, Scandrett K, Agarwal R, Baldwin SW, et al. Direct-to-consumer self-tests sold in the UK in 2023: cross sectional review of information on intended use, instructions for use, and post-test decision making. BMJ. 2025;390:e085546. 10.1136. /BMJ-2025-085546 PubMed PMID: 40701637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHillier B, Deeks JJ, Alderman J, Kale AU, Macdonald T, Baldwin SW, et al. Direct-to-consumer self-tests sold in the UK in 2023: cross sectional review of regulation and evidence of performance. BMJ. 2025;390:e085547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/BMJ-2025-085547\u003c/span\u003e\u003cspan address=\"10.1136/BMJ-2025-085547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFletcher BR, Hinton L, Hobbs FDR, McManus RJ, Bray EP, Hayen A, et al. Self-monitoring blood pressure in patients with hypertension: An internet-based survey of UK GPs. Br J Gen Pract. 2016;66(652):e831\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3399/bjgp16X\u003c/span\u003e\u003cspan address=\"10.3399/bjgp16X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 687037 PubMed PMID: 27578811.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges do Nascimento IJ, Abdulazeem H, Vasanthan LT, Martinez EZ, Zucoloto ML, \u0026Oslash;stengaard L, et al. Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digit Med Nat Res. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41746-023-00899-4\u003c/span\u003e\u003cspan address=\"10.1038/s41746-023-00899-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 37723240.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNHS Confederation. Patient empowerment: what is the role of technology in transforming care? [Internet]. 2023 Jun [cited 2025 Oct 28]. Report. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nhsconfed.org/publications/patient-empowerment-what-role-technology-transforming-care\u003c/span\u003e\u003cspan address=\"https://www.nhsconfed.org/publications/patient-empowerment-what-role-technology-transforming-care\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOffice for National Statistics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ons.gov.uk/surveys/informationforhouseholdsandindividuals/householdandindividualsurveys/healthinsightsurvey\u003c/span\u003e\u003cspan address=\"https://www.ons.gov.uk/surveys/informationforhouseholdsandindividuals/householdandindividualsurveys/healthinsightsurvey\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Internet]. [cited 2026 Feb 25]. Health Insight Survey. Available from: https://www.ons.gov.uk/surveys/informationforhouseholdsandindividuals/householdandindividualsurveys/healthinsightsurvey\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng S, M\u0026auml;ntym\u0026auml;ki M, Dhir A, Salmela H. How self-tracking and the quantified self promote health and well-being: Systematic review. Journal of Medical Internet Research. JMIR Publications Inc.; 2021. doi:10.2196/25171 PubMed PMID: 34546176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyng\u0026auml;s H. Inductive Content Analysis. In: The Application of Content Analysis in Nursing Science Research [Internet]. Springer, Cham; 2020 [cited 2025 Oct 12]. pp. 13\u0026ndash;21. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-030-30199-6_2\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-30199-6_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e doi:10.1007/978-3-030-30199-6_2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQSR International Pty Ltd. NVivo (Version 14). 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstrela M, Semedo G, Roque F, Ferreira PL, Herdeiro MT. Sociodemographic determinants of digital health literacy: A systematic review and meta-analysis. Int J Med Inf. 2023;177:105124. 10.1016/J. .IJMEDINF.2023.105124 PubMed PMID: 37329766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorrissey EC, Glynn LG, Casey M, Walsh JC, Molloy GJ. New self-management technologies for the treatment of hypertension: General practitioners\u0026rsquo; perspectives. Fam Pract. 2018;35(3):318\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/fampra/cmx100\u003c/span\u003e\u003cspan address=\"10.1093/fampra/cmx100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 29088438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArmitage CJ, Munro KJ, Mandavia R, M AG M, Schilder AG. What health policy makers need to know about mismatches between public perceptions of disease risk, prevalence and severity: a national survey. Int J Audiol. 2021;60(12):979\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/14992027.2021.1881175\u003c/span\u003e\u003cspan address=\"10.1080/14992027.2021.1881175\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevesque JF, Harris MF, Russell G. Patient-centred access to health care: Conceptualising access at the interface of health systems and populations. Int J Equity Health. 2013;12(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1475-9276-12-18\u003c/span\u003e\u003cspan address=\"10.1186/1475-9276-12-18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 23496984.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHacker K. The Burden of Chronic Disease. Mayo Clin Proc Innov Qual Outcomes. 2024;8(1):112\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/J.MAYOCPIQO.2023.08.005\u003c/span\u003e\u003cspan address=\"10.1016/J.MAYOCPIQO.2023.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerry R, Kassavou A, Sutton S. Does self-monitoring diet and physical activity behaviors using digital technology support adults with obesity or overweight to lose weight? A systematic literature review with meta-analysis. Obes Rev. 2021;22(10):e13306. 10. 1111/OBR.13306 PubMed PMID: 34192411.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTucker KL, Sheppard JP, Stevens R, Bosworth HB, Bove A, Bray EP, et al. Self-monitoring of blood pressure in hypertension: A systematic review and individual patient data meta-analysis. PLoS Med. 2017;14(9). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pmed.1002389\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1002389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 28926573.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScottish Government. Care in the Digital Age: Delivery Plan 2025-26 [Internet]. 2025 Aug [cited 2025 Oct 10]. Report. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.scot/publications/care-digital-age-delivery-plan-2025-2026/documents/\u003c/span\u003e\u003cspan address=\"https://www.gov.scot/publications/care-digital-age-delivery-plan-2025-2026/documents/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuardado S, Karampela M, Isomursu M, Grundstrom C. Use of Patient-Generated Health Data From Consumer-Grade Mobile Devices by Health Care Professionals in the Clinic: Systematic Review. J Med Internet Res. 2024;26:e49320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/39389\u003c/span\u003e\u003cspan address=\"10.2196/39389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWestern MJ, Smit ES, G\u0026uuml;ltzow T, Neter E, Sniehotta FF, Malkowski OS, et al. Bridging the digital health divide: a narrative review of the causes, implications, and solutions for digital health inequalities. Health Psychol Behav Med. 2025;13(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/21642850.2025.2493139\u003c/span\u003e\u003cspan address=\"10.1080/21642850.2025.2493139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBadr J, Motulsky A, Denis JL. Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations. Health Policy. Elsevier Ireland Ltd; 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.healthpol.2024.105122\u003c/span\u003e\u003cspan address=\"10.1016/j.healthpol.2024.105122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. PubMed PMID: 38986333.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunsch F, Evans DK, Macis M, Wang Q. Bias in patient satisfaction surveys: A threat to measuring healthcare quality. BMJ Glob Health. 2018;3(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjgh-2017-000694\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2017-000694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aoph","sideBox":"Learn more about [Archives of Public Health](http://archpublichealth.biomedcentral.com/)","snPcode":"13690","submissionUrl":"https://submission.nature.com/new-submission/13690/3","title":"Archives of Public Health","twitterHandle":"@Archpubhealth","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Self-monitoring, public attitudes, online survey, experiences, willingness, trust, health and personal wellness","lastPublishedDoi":"10.21203/rs.3.rs-9012710/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9012710/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePublic opinion about self-monitoring of health and personal wellness is important but largely unknown. This study investigated public attitudes towards self-monitoring of health and personal wellness.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e4270 UK adults selected to produce a nationally representative sample (by age, gender, ethnicity, social grade/socioeconomic group, geographic region) completed an online survey. Self-monitoring questions addressed experiences (use, motivation, trust, actions taken), benefits, problems, willingness (to use, to have results sent to GP) and the preferred action if a problem is identified. Questions concerned 12 physiologic and behavioural health aspects (blood pressure, blood sugar, body composition, cholesterol, diet, eyesight, fitness/exercise, hearing, menstrual cycles/periods, mental and cognitive health, pulse/heart rate, and skin conditions).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMost respondents had self-monitored (79%) and were willing, if recommended by the NHS, to self-monitor (86%) at least one health aspect. The proportion of respondents who had self-monitored was similar for health and personal wellness; however, the proportion willing to self-monitor was higher for health than personal wellness. Self-monitoring was higher among younger adults and those with more years of education. Most (70\u0026ndash;94%) respondents trusted the results; 78% were willing to have results sent automatically to their GP. If a problem were identified, discussing the results with a GP was a common and preferred action, but 14\u0026ndash;38% of respondents (depending on health aspect) took no action. Males, older adults, those living in areas of high deprivation, and those with lower education were less likely to be willing to monitor their health. Main benefits were enhanced awareness of health, greater control, easier access to information, more balanced engagement with providers, and psychological support. Main problems included anxiety, obsessive testing, low self-efficacy for monitoring, poor motivation, post-monitoring inaction, misinterpretation of results, and technical concerns.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA high proportion of the UK public use/are willing to use and trust self-monitoring of health and wellness. It has the potential to transform care through proactive remote health management and preventative action. However, the findings also highlight socioeconomic differences in self-monitoring. Future research is needed to address how engagement of self-monitoring can be enhanced equitably and used effectively alongside clinical care to promote better health.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003e \u003cspan class=\"ExternalRef\"\u003e \u003cspan class=\"RefSource\"\u003ehttps://osf.io/96mzt\u003c/span\u003e \u003cspan address=\"https://osf.io/96mzt\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e","manuscriptTitle":"Research Use, trustworthiness and willingness to self-monitor health and personal wellness: a UK population survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 13:32:27","doi":"10.21203/rs.3.rs-9012710/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"274361568212244504602157694636519115865","date":"2026-05-21T13:31:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-08T00:51:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T11:28:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T15:23:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Public Health","date":"2026-03-17T08:11:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aoph","sideBox":"Learn more about [Archives of Public Health](http://archpublichealth.biomedcentral.com/)","snPcode":"13690","submissionUrl":"https://submission.nature.com/new-submission/13690/3","title":"Archives of Public Health","twitterHandle":"@Archpubhealth","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62f4a9da-6b08-4ea3-9cc6-930c076d5040","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"274361568212244504602157694636519115865","date":"2026-05-21T13:31:03+00:00","index":66,"fulltext":""},{"type":"reviewersInvited","content":"40","date":"2026-05-08T00:51:14+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T13:32:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 13:32:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9012710","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9012710","identity":"rs-9012710","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}