{"paper_id":"193d2908-2c48-4976-9ae3-bb06d3784dad","body_text":"Determinants of physical activity satisfaction and digital health adoption in UK adults: results from the 7PSC Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of physical activity satisfaction and digital health adoption in UK adults: results from the 7PSC Study Austen El-Osta, Mohammed A Adam, Sami Altalib, Aos Alaa, David Skinner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8049011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Physical activity (PA) is a cornerstone of self-care and a major determinant of population health. Despite well-established benefits, most adults do not achieve recommended activity levels. Understanding behavioural patterns, barriers and the role of digital technologies is essential to inform public health interventions. This study presents findings on physical activity within the Seven Pillars of Self-Care (7PSC) framework, focusing on behaviours, satisfaction and predictors of digital health adoption. Methods A cross-sectional online survey of 1,532 UK adults was conducted in 2025 as part of the 7PSC Study. The survey captured sociodemographic data, PA behaviours, knowledge of World Health Organization (WHO) guidelines, motivations, barriers and digital technology use. Logistic and ordinal logistic regression models examined predictors of PA satisfaction and digital tool adoption. Results Participants’ mean age was 43 years (SD=12.9), with balanced gender representation. Nearly half (43%) reported daily walking, yet 35% undertook no vigorous and 36% no moderate activity in the past week. Sitting time averaged 6 hours/day. Barriers were widespread, with lack of time (56%) and motivation (55%) most common. Confidence was strongly associated with satisfaction: those “very confident” in staying active had threefold greater odds of PA satisfaction (OR=3.3, 95% CI: 2.24–4.86, p<0.001) compared to those \"confident.\".Each additional barrier was associated with 26% lower odds of satisfaction (OR=0.74, 95% CI: 0.63–0.86, p<0.001). Digital tool use was reported by 53% of participants, predominantly smartphone apps and wearables. Adoption was associated with younger age, female gender, higher readiness and confidence, but not education or barrier burden. Conclusions Findings highlight the central role of confidence and the detrimental impact of barriers on Physical Activity satisfaction, while readiness amplifies sensitivity to barriers rather than buffering them. Interventions should prioritise barrier reduction and confidence-building strategies to enhance self-care through physical activity. Longitudinal studies are needed to establish temporal relationships and causal mechanisms. Physical activity Self-care Seven Pillars of Self-Care (7PSC) Barriers and motivations Confidence and readiness Digital health technologies Wearables and apps United Kingdom Public health Behavioural determinants Figures Figure 1 Figure 2 Background Physical activity (PA) is one of the most powerful determinants of health and a critical component of the self-care paradigm. Regular PA reduces risks of cardiovascular disease, type 2 diabetes, cancers, musculoskeletal disorders and premature mortality, while also promoting mental health, social wellbeing and quality of life (1). Yet, despite decades of public health campaigns and robust evidence on the benefits of PA, global surveillance indicates that more than one in four adults fail to achieve recommended activity levels, with little progress observed over time (2). This persistent inactivity contributes substantially to the global burden of disease and challenges efforts to achieve Sustainable Development Goals related to health, wellbeing and health equity. The World Health Organization (WHO) guidelines recommend that adults engage in at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity activity per week, alongside muscle-strengthening activities on two or more days (3). However, surveillance suggests that knowledge of these recommendations is inconsistent and adherence remains suboptimal. In the United Kingdom (UK), survey data indicate that activity patterns vary across demographic groups, with socioeconomic and health inequalities strongly shaping opportunities for participation (4). Understanding the interplay of behavioural, psychological and environmental factors is therefore essential to inform effective, equitable interventions. The Seven Pillars of Self-Care (7PSC) framework, developed by the International Self-Care Foundation, provides a holistic model for conceptualising self-care behaviours (5). Physical activity is one of the seven pillars, alongside health literacy, mental wellbeing, healthy eating, risk avoidance, good hygiene and rational use of products and services. The 7PSC framework positions PA as a lifestyle behaviour as well as a self-directed capability requiring knowledge, motivation and supportive environments. By embedding PA within this broader model, researchers can examine how individuals balance multiple self-care practices, the barriers they encounter and the enablers that sustain engagement. Barriers to PA are multifaceted and well-characterised. Time constraints, cost, lack of facilities and environmental safety are frequently cited structural obstacles (6), while poor physical or mental health, lack of motivation and uncertainty about how to exercise represent personal barriers (7). Evidence consistently shows that barriers disproportionately affect individuals with lower socioeconomic status, chronic illness or limited access to supportive environments, thereby reinforcing health inequalities (8). Conversely, motivations to be active often centre on mental wellbeing, weight management, illness prevention and enjoyment. Understanding the relative salience of these drivers and obstacles is essential for tailoring interventions to diverse populations. Psychological constructs such as self-efficacy (confidence in one’s ability to act) and readiness to change are central to behavioural models of PA. Higher self-efficacy has been linked to greater engagement and persistence in physical activity, while readiness reflects individuals’ stage in adopting and maintaining behaviours. However, less is known about how these constructs interact with perceived barriers and whether high readiness can buffer against the negative effects of obstacles (9). Exploring these relationships in population-level data can inform strategies that move beyond awareness-raising to address deeper behavioural determinants. In recent years, digital health technologies, including smartphone apps, wearable trackers, smartwatches and online platforms, have emerged as promising tools to support PA self-care. These technologies offer features such as real-time feedback, goal tracking, reminders and social support. Evidence suggests they can increase awareness and motivation, (10), yet their long-term effectiveness is variable and concerns about sustainability, cost and equity remain (10). Adoption is patterned by age, gender, digital literacy and socioeconomic status, raising questions about whether digital health may inadvertently widen disparities in PA engagement. Identifying who adopts digital tools and how these tools influence behaviours is therefore a critical public health priority (11). Despite extensive research on physical activity, three key gaps remain. First, few studies situate PA within a holistic self-care framework such as the 7PSC, limiting understanding of how it interrelates with other health behaviours. Second, although barriers and motivations have been well-documented, less is known about their relative impact on satisfaction with activity levels and how these effects interact with readiness. Third, while digital technologies are increasingly prominent in public health discourse, large-scale evidence on predictors of adoption and their association with confidence, readiness and barriers is limited in UK populations (12, 13). The present study addresses these gaps by analysing physical activity data from over 1,500 UK adults as part of the 7PSC Study. By integrating descriptive behavioural patterns with regression models of satisfaction and digital adoption, this research offers a comprehensive view of physical activity as a self-care behaviour. The primary aim of this study was to characterise physical activity behaviours, knowledge, motivations and barriers among UK adults. We also sought to examine predictors of satisfaction with physical activity, with a focus on confidence, readiness and barrier burden. Another important objective was to assess the adoption and use of digital health technologies to support PA and to identify demographic and psychological predictors of adoption. Methods Study design and setting This cross-sectional survey was conducted in 2025 as part of the 7PSC Study, a large programme of research investigating knowledge, attitudes and behaviours across all seven self-care domains defined by the International Self-Care Foundation. The present analysis focuses on the PA pillar. The study was designed and reported in line with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Participants and recruitment Participants were adults (aged ≥18 years) residing in the UK at the time of survey completion. Recruitment was undertaken through an established online research panel, ensuring broad demographic coverage. Eligibility criteria were minimal to maximise representativeness: adults with the ability to provide informed consent and to complete the online survey in English were included. Individuals under 18 years or not resident in the UK were excluded. Survey instrument The survey instrument was developed iteratively by the research team to account for the 7PSC framework and validated PA measurement tools. It comprised five domains relevant to PA self-care. A sociodemographic characteristics block recorded information about age, gender, ethnicity, education, employment and self-reported disability or long-term conditions affecting daily life. The PA behaviours block included questions adapted from the International Physical Activity Questionnaire (IPAQ-short form) to assess frequency and duration of vigorous, moderate and walking activities in the preceding seven days, as well as sitting time on weekdays. Participants also reported frequency of strength and flexibility activities, stair use and outdoor activities. The knowledge and perceptions block included Items that assessed awareness of WHO PA guidelines, perceived importance of PA, satisfaction with current activity levels and motivations for being active. The barriers and enablers block consisted of eight binary items assessing barriers, including lack of time, motivation, cost, poor physical or mental health, access to facilities, uncertainty about what to do and safety concerns. A composite barrier count score (0-8) was derived by summing reported barriers. The confidence and readiness block measured self-efficacy through a four-level confidence scale (“not confident,” “somewhat confident,” “confident,” “very confident”). Readiness to change was measured on a 10-point scale (1=lowest, 10=highest). Finally, the digital technology use block collected data to assess the adoption of digital health tools using binary (yes/no) and multiple-choice items covering smartphone apps, wearables, smartwatches, online platforms, AI-based assistants and other devices. Frequency, duration of use, perceived usefulness, valued features and reasons for discontinuation were captured. Survey items were iteratively refined through cognitive testing and pilot administration to ensure clarity and comprehensibility. A copy of the main sutvey is included in Supplementary Table 1. Study outcomes Two primary outcomes were PA satisfaction and digital health technology adoption. The PA satisfaction ordinal outcome was measured using a single item: \"How satisfied are you with your current physical activity levels?\" with response options: (1) \"Not satisfied,\" (2) \"Somewhat satisfied,\" (3) \"Satisfied,\" (4) \"Very satisfied.\" This outcome combines two distinct constructs: contentment with current engagement and perceived adequacy relative to WHO guidelines. The satisfaction scale has not been previously validated in published literature; however, test-retest reliability was assessed in a pilot subsample (n=45) over 2 weeks, showing moderate agreement (κ=0.58). For regression modelling, outcomes were treated as ordinal, with higher scores representing greater satisfaction. The Digital Health Technology Adoption binary (yes/no) outcome indicated whether participants reported using at least one digital health technology category (smartphone apps, wearables, smartwatches, online platforms, AI assistants, or other devices) to support PA. This aggregated measure captures any adoption; frequency and type of use were examined separately in sensitivity analyses. As a secondary outcome, barrier count was measured as a composite score (0–8) derived by summing eight binary barrier items; individual barriers were also examined. Statistical analysis Analyses were conducted in StataMP 17. Descriptive statistics summarised sample characteristics, PA behaviours, barriers, motivations and technology use. Continuous variables are presented as means and standard deviation (SD) and categorical variables as frequencies (n) and percentages (%). For inferential analyses, we employed 2 models. Model 1 assessed predictors of PA satisfaction. An ordinal logistic regression was fitted with satisfaction as the outcome. Independent variables included age, gender, education, perceived importance of PA, confidence, readiness score, barrier count and digital technology use. Odds ratios (OR) with 95% confidence intervals (CI) are reported. The proportional odds assumption was assessed using the Brant test. Due to violations of this assumption for some predictors, a partial proportional odds model was employed, allowing specific predictor coefficients to vary across outcome levels while others were constrained to satisfy the assumption. Both the standard and partial proportional odds models were examined for consistency. Measures of model fit, such as pseudo-R², were computed to evaluate model adequacy.. A second model (Model 2) was used to investigate predictors of digital adoption. Logistic regression was used with technology use (yes/no) as the outcome. Predictors were the same covariates as in Model 1, plus PA satisfaction. Predicted probabilities were estimated for selected values of age and readiness. Missing data were minimal (<5% for most variables); age (0.1%), gender (0.3%), education (0.5%), PA behaviours (2.1%), confidence (1.8%), readiness (2.3%) and digital adoption (2.0%). To assess potential bias from missing data, we conducted Little's Missing Completely At Random (MCAR) test (χ²=42.18, p=0.31), indicating that data were reasonably assumed MCAR. Sensitivity analysis comparing complete case results (n=1,457) with multiple imputation results (n=1,532; 20 imputations using predictive mean matching) showed negligible differences in point estimates and 95% confidence intervals; therefore, complete case results are reported. Statistical significance was defined as p<0.05. Ethical considerations The study was approved by the Imperial College London Research Ethics Committee (ICREC #7642258) prior to data collection. All participants provided informed consent electronically before accessing the survey. Participation was voluntary and respondents could withdraw at any time. Data were collected anonymously, stored securely and analysed in accordance with the UK Data Protection Act and GDPR . Patient and Public Involvement The public was involved in the design of the study and questionnaire through piloting and feedback, using the service and participated in disseminating the preliminary findings. Results Participant characteristics A total of 1,532 adults across the UK completed the survey, of whom 1,457 provided complete responses suitable for analysis. Participant characteristics are illustrated in Table 1 . The mean age of participants was 43.0 years (SD=12.9; range 18–80). Gender distribution was balanced, with 49.3% identifying as female (n=718) and 50.2% as male (n=732), while a small minority identified as another gender or preferred not to say (0.5%, n=7). The sample was predominantly White 1178 (80.9%), with Asian/Asian British 8 (6.0%), Black/Black British 84 (5.8%), Mixed/other 107 (7.4%). Nearly two-thirds (64.4%), held a university degree or higher, while 330 (22.6%) reported A-levels/college and 188 (12.9%) reported secondary school qualifications. Most participants reported no disability or long-term condition affecting daily activities 1003 (68.8%), although 129 (8.9%) indicated severe impact, 202 (13.9%) reported minor impact and 93 (6.4%) reported having a condition without daily impairment. Employment status was diverse: 826 (56.7%) were employed full-time, 190 (13.0%) part-time, 145 (10.0%) self-employed and 94 (6.5%) retired. Smaller proportions were students 45 (3.1%), unemployed 58 (4.0%), unable to work due to illness or disability 44 (3.0%), or homemakers/carers 48 (3.3%). Collectively, these distributions suggest a broadly representative cohort spanning working-age adults, though somewhat skewed toward higher educational attainment. The findings of the full survey are illustrated in Supplementary Table 2. Table 1. Participant characteristics (N=1,532) Characteristic N (%) %/SD Age, mean (SD) 43.0 (12.9) Range 18–80 years Sex Female 718 49.3 Male 732 50.2 Other / Prefer not to say 7 0.5 Ethnicity White 1178 80.9 Asian/Asian British 88 6.0 Black/Black British 84 5.8 Mixed/Other 107 7.4 Education University degree or higher 939 64.4 A-level/College 330 22.6 Secondary school 188 12.9 Health status No disability/long-term condition 1003 68.8 Condition affects a little 202 13.9 Condition affects a lot 129 8.9 Condition without impact 93 6.4 Employment status N (%) Employed full-time 826 (56.7%) Employed part-time 190 (13.0%) Homemaker / unpaid carer 48 (3.3%) Other (please specify) 7 (0.5%) Retired 94 (6.5%) Self-employed 145 (10.0%) Student 45 (3.1%) Unable to work due to long-term illness or disability 44 (3.0%) Unemployed / looking for work 58 (4.0%) Descriptive results Awareness of self-care pillars Awareness of the seven pillars of self-care varied markedly. Mental wellbeing 1439 (93.9%), physical activity 1445 (94.4%), healthy eating 1445 (94.4%) and good hygiene 1323 (86.4%) were widely recognised. In contrast, fewer participants reported awareness of knowledge & health literacy 504 (33.8%), risk avoidance 546 (35.6%), or rational use of products & services 205 (13.4%). Only 22 (1.5%) indicated no awareness of any pillar. These results suggest that self-care awareness is strongly weighted toward lifestyle and hygiene-related behaviours, with limited recognition of more structural or decision-making domains. Further details on awareness are in the supplementary table 1 . Physical activity behaviours Frequency and duration Vigorous physical activity was reported by 966 (65.1%) of respondents at least once in the preceding week. However, 519 (34.9%) reported none. The modal response was two days per week 261 (17.6%). Among those engaging, the mean reported duration was 1.1±1.3SD hours and 17.6±16.2SD minutes per day, with a wide range (5–120 minutes). Moderate physical activity was more common yet still limited: 540 (36.4%) reported no moderate activity in the prior week, while 107 (17.8%) and 195 (13.1%) engaged on two or three days, respectively. Average duration per session was 1.2±1.4 SD hours and 14.8±16.2 SD minutes (approximately 72±84 minutes). Walking was near-universal: 1476 (96.2%) reported walking at least once, with 636 (42.8%) reporting daily walking. Average duration per day was approximately 1±1.4 SD hour and 18 ±15.8 SD minutes (approximately 60±84 minutes). Sedentary behaviour was prevalent. Nearly half 711 (47.9%) reported sitting on all seven weekdays and the mean sitting time was 6.0 ±2.5 SD hours and 6.7 ±12.7SD minutes per day. Further details about physical behaviours frequency and duration are shown in supplementary table 2 . Table 2. Physical activity patterns and behaviours Variable n % / Mean (SD) No vigorous activity (past week) 519 34.9% ≥1 day vigorous activity 966 65.1% Mean duration vigorous (hours) - 1.1 (1.3) No moderate activity (past week) 540 36.4% ≥1 day moderate activity 945 63.6% Mean duration moderate (hours) - 1.2 (1.4) Walked ≥1 day (past week) 1476 96.2% Daily walking (7 days) 636 42.8% Mean walking time (hours) - 1.0 (1.4) Daily sitting time (hours) - 6.0 (2.5) Never strength training 585 39.4% ≥3×/week strength training 255 17.2% Never flexibility exercises 597 40.2% ≥3×/week flexibility exercises 171 11.5% Not satisfied with PA 447 30.1% Somewhat satisfied 535 36.0% Very satisfied 99 6.7% Correctly identified WHO 150min guideline 948 64.0% Muscle-strengthening and flexibility activities Engagement in strength training was low: 585 (39.4%) never undertook such activities and only 255 (17.2%) reported ≥3 times weekly. Flexibility activities showed similar patterns, with 597 (40.2%) never participating and just 171 (11.5%) engaging three or more times weekly; Table 3. Lifestyle-related activity Incidental activity varied. Stair use was common, with 584 (39.3%) reporting regular use and 385 (25.9%) reporting always taking stairs. Outdoor activity was less consistent: nearly half 722 (48.6%) participated occasionally, while 286 (26.0%) were regular participants and 204 (13.7%) reported none. As indicated in Table 3 . Table 3: Shows the muscle strength, flexibility and lifestyle-related activities Q12 How often do you engage in muscle-strengthening activities (e.g., weightlifting, resistance exercises)? N (%) N-Miss 47 1-2 times a week 336 (22.6%) 3 or more times a week 255 (17.2%) Always 36 (2.4%) Less than once a week 273 (18.4%) Never 585 (39.4%) Q13 How often do you engage in flexibility exercises (e.g., yoga, stretching)? N-Miss 47 1-2 times a week 301 (20.3%) 3 or more times a week 171 (11.5%) Always 44 (3.0%) Less than once a week 372 (25.1%) Never 597 (40.2%) Q14 How often do you take the stairs instead of the elevator? N-Miss 47 Always 385 (25.9%) Never 74 (5.0%) Occasionally 442 (29.8%) Regularly 584 (39.3%) Q15 How often do you participate in outdoor activities (e.g., hiking, gardening)? N-Miss 47 Frequently 173 (11.6%) Never 204 (13.7%) Occasionally 722 (48.6%) Regularly 386 (26.0%) Satisfaction and knowledge Most participants were not satisfied 447 (30.1%) or only somewhat satisfied 535 (36.0%) with their current physical activity levels. Only 99 6.7% were very satisfied. Knowledge of WHO recommendations was mixed: 948 (64.0%) correctly identified 150 minutes of weekly activity as the benchmark, while 354 (23.9%) believed 300 minutes was required. Knowledge of strength training guidelines was more accurate, with 948 (46.2%) correctly identifying twice per week. However, misconceptions persisted about the type of activity needed for cardiovascular health, with nearly half 685 (46.2%) incorrectly selecting walking over aerobic activity 595 (40.1%). Motivations and barriers The leading motivations for physical activity were mental wellbeing 1007 (65.7%), weight management 979 (63.9%) and illness prevention 768 (50.1%). Enjoyment 687 (44.8%) and social interaction 207 (13.5%) were less frequently cited. Notably, 161 (10.5%) reported no physical activity; table 4 The most common barriers to physical activity were lack of time 850 (55.5%) and lack of motivation 834 (55.0%). Cost 282 (18.4%), poor physical health 252 (16.5%) and mental health barriers 255 (16.7%) also featured prominently. Lack of access 196 (12.8%) and uncertainty about what to do 230 (15.0%) were less common, while safety concerns were rarely cited 71 (4.6%). Composite scoring showed nearly all participants experienced at least one barrier, with one or two barriers most common 949 (62%). Only 100 (6.5%) reported none. Details on the score. Cumulative percentages are shown in Supplementary Table 3. Table 4. Motivations and barriers to physical activity Variable n % Motivations Mental wellbeing 1007 65.7 Weight management 979 63.9 Reduce illness risk 768 50.1 Enjoyment 687 44.8 Social interaction 207 13.5 Not physically active 161 10.5 Barriers Lack of time 850 55.5 Lack of motivation 834 55.0 Cost/affordability 282 18.4 Poor physical health 252 16.5 Mental health barriers 255 16.7 Lack of access to facilities 196 12.8 Not knowing what to do 230 15.0 Safety concerns 71 4.6 Readiness, confidence and behavioural change Readiness to change was moderate to high: mean score 6.8/10. About 61.3% scored ≥7, while 12.5% scored ≤4, indicating ambivalence or resistance. Confidence in maintaining regular activity was limited: only 241 (16.3%) reported being very confident, while 579 (39.1%) were somewhat confident and 214 (14.5%) were not confident. About 591 (40%) had taken recent steps to improve activity levels, such as using apps, joining groups, or setting goals. More details are shown in Supplementary Table 2 . Types of activity Walking was the most common form 1314 (85.8%), followed by gym-based exercise 381 (24.9%), running/jogging 361 (23.6%), home workouts 518 (33.8%) and fitness classes 213 (13.9%). Recreational sports 204 (13.3%) and swimming 172 (11.2%) were less frequent. Only 62 (4.1%) reported no regular activity. Digital technology adoption Overall, 767 (52.6%) reported using at least one digital health tool to support activity, classified as 'adopters'; 691 (47.4%) reported no use ('non-adopters'). Among adopters, the most common technologies were smartphone apps 416 (27.2%), smartwatches 338 (22.1%) and wearable fitness trackers 294 (19.2%). Online platforms 117 (7.6%), AI-based assistants (68; 4.4%) and smart home equipment (48; 3.1%) were less commonly used. Among adopters, frequency of engagement was high: 401 (52.3%) used tools daily and 244 (31.8%) several times per week, 85 (11.1%) weekly and 37 (4.8%) less frequently. Duration of adoption was sustained: 571 (74.4%) had been using digital tools for over one year, 127 (16.6%) for 6–12 months and 69 (9.0%) for less than 6 months. Notably, 153 participants (19.9% of adopters) had discontinued tool use after initial adoption, most commonly due to loss of motivation (65 individuals; 42.5% of those who discontinued). We tested whether results differed when adoption was defined more stringently as regular use (≥3 times weekly) rather than any use. Among this stricter definition, 645 (42.0%) were classified as adopters. Regression models using this alternative definition yielded similar point estimates and overlapping 95% confidence intervals with the primary binary definition, confirming robustness (see Supplementary Table 5A ). Motivational potential varied: smartphone apps, wearables and smartwatches were rated more motivating than online platforms, coaching, or AI assistants. Core features valued were step counts 1151 (75.2%), heart rate monitoring 671 (44.0%) and goal tracking 566 (37.0%). Social/community challenges and gamification were rarely endorsed. Perceived impact was mixed. About 647 (44.4%) reported being slightly more active and 161 (11.0%) much more active, while 619 (42.5%) reported no change. Walking was the most commonly increased activity 1043 (71.6%), followed by running 297 (19.4%) and strength training 239 (15.6%). Barriers to continued use included loss of motivation 673 (44.0%), cost 414 (27.0%) and technical issues 171 (11.2%). Privacy concerns 122 (8.0%) and health reasons 137 (9.0%) were cited less often. Positive reinforcement came primarily from visible progress 881 (57.5%), routine/habit formation 615 (40.1%) and enjoyment 475 (31.0%). Table 5. Digital health technology use and perceptions Variable n % Use digital tools 767 52.6 No tool use 691 47.4 Daily users (among adopters) 401 52.3 Several times/week 244 31.8 >1 year use 571 74.4 Features valued: step count 1151 75.2 Heart rate monitoring 671 44.0 Goal tracking 566 37.0 Increased walking due to tools 1043 71.6 Reported 'slightly/much more active' 808 55.4 Barriers: loss of motivation 673 44.0 Barriers: cost 414 27.0 Barriers: technical issues 171 11.2 Predictors of physical activity satisfaction Ordinal logistic regression showed that confidence and barrier burden were the strongest predictors ( Table 6 ). Participants reporting no confidence had dramatically lower odds of satisfaction (OR=0.037, p<0.001), while very confident participants had over threefold higher odds (OR=3.296, p<0.001). Each additional barrier reduced the odds of satisfaction by 26% (OR=0.738, p<0.001). Sociodemographic variables and digital tool use were not significant. Multicollinearity: Variance inflation factors (VIF) ranged from 1.04 (age) to 1.38 (readiness score), all well below the threshold of 10, indicating no problematic collinearity. Proportional Odds: As noted in Methods, the Brant test indicated violation for readiness and barrier count; partial proportional odds model results were compared and found qualitatively consistent When barriers were examined individually, using Bonferroni-corrected thresholds (p < 0.00625), lack of motivation (OR=0.320, p<0.001), poor physical health (OR=0.233, p<0.001), mental health barriers (OR=0.535, p=0.001) and uncertainty about what to do (OR=0.545, p=0.001) remained statistically significant obstacles. Time, cost, access and safety did not reach corrected significance levels; full details are in Supplementary Table 3 . Interaction models showed barrier effects intensified with increasing readiness. At readiness 3, each additional barrier reduced satisfaction probability by 9.9%; at readiness 7, by 12.9%; and at readiness 10, by 13.2%. This indicates that readiness amplifies rather than buffers the negative influence of barriers ( Supplementary Table 4) . Table 6. Predictors of physical activity satisfaction (ordinal logistic regression) Variable Adjusted OR 95% CI p-value Not confident vs confident 0.037 0.018–0.078 <0.001 Somewhat confident vs confident 0.102 0.072–0.143 <0.001 Very confident vs confident 3.296 2.237–4.855 <0.001 Barrier score (per unit) 0.738 0.635–0.858 <0.001 Readiness score (per unit) 0.926 0.851–1.006 0.069 Somewhat important vs extremely important PA is 0.431 0.247–0.754 0.003 Age (per year) 0.997 0.986–1.009 0.730 Male vs female 0.922 0.694–1.223 0.575 Digital technology use vs non-use 0.938 0.700–1.251 0.656 Note: Brant test for proportional odds assumption: χ² = 24.67, p = 0.003. Partial proportional odds model fitted for readiness and barrier count. Pseudo-R² (McFadden) = 0.182. Reference categories: 'confident' (confidence scale), female (gender), university degree or higher (education), extremely important (PA importance). Odds ratios >1 indicate increased odds of higher satisfaction; <1 indicate decreased odds. 95% CIs not including 1.00 indicate statistical significance. Analysis used complete case analysis for the primary models, retaining 1,457 of 1,532 respondents (95.1%). Logistic regression identified younger age (OR=0.989 per year, p=0.019), female gender (OR=0.780 for males vs females, p=0.028) and higher readiness (OR=1.166 per unit, p<0.001) as significant predictors of digital adoption ( table 7) . Confidence also mattered: those reporting no confidence had 59% lower odds of use (OR=0.412, p<0.001). In contrast, education, barrier count and satisfaction with activity were not significant predictors. Importantly, lower perceived importance of physical activity was associated with lower odds of digital use, even when adjusting for readiness and confidence. Predicted probabilities illustrated the gradient: likelihood of digital use declined from 57% at age 25 to 48% at age 65; Supplementary Table 5 . Conversely, readiness strongly increased adoption probability, from 32% at readiness 1 to 64% at readiness 10; Supplementary Table 6 . VIF ranged from 1.06 (age) to 1.41 (readiness), indicating no problematic correlation. Table 7. Predictors of digital health technology adoption (logistic regression) Variable Adjusted OR 95% CI p-value Age (per year) 0.989 0.980–0.998 0.019 Male vs female 0.780 0.626–0.974 0.028 Not confident vs confident 0.412 0.270–0.629 <0.001 Readiness score (per unit) 1.166 1.096–1.241 <0.001 Quite important vs extremely important 0.741 0.573–0.958 0.023 Somewhat important vs extremely important 0.603 0.422–0.862 0.006 Education (University vs A-level) 1.147 0.879–1.497 0.310 Barrier score (per unit) 0.974 0.870–1.090 0.651 Discussion Summary of principal findings This study, conducted as part of the 7PSCStudy, provides one of the largest UK datasets to date examining physical activity (PA) within a self-care framework. Three principal findings emerge. First, although walking was widely reported, a large minority of participants undertook no vigorous (35%) or moderate (36%) PA in the previous week and daily sitting time averaged six hours, indicating significant behavioural gaps relative to WHO recommendations. Second, barriers to PA were ubiquitous, with lack of time and motivation most frequently reported. Confidence in one’s ability to remain active emerged as the strongest predictor of satisfaction, while readiness amplified the negative effect of barriers rather than offsetting them. Third, digital health technologies were used by more than half of respondents, but adoption was unevenly distributed, favouring younger, female, more confident and more ready participants. Education, barrier load and satisfaction did not significantly predict uptake. Comparison with prior literature Our findings confirm and extend established evidence on low compliance with PA guidelines in the UK. The Health Survey for England and other population-based studies report similar prevalence estimates of inactivity, with marked disparities by age, socioeconomic position and health status (14-16). The present study adds granularity by situating these patterns within a broader self-care framework, underscoring the interdependence of physical, psychological and behavioural determinants. Barriers reported here align with international literature. Time constraints and motivational deficits consistently appear as primary obstacles across diverse populations (17-19). However, the present analysis indicates that barriers linked to personal health (poor physical and mental health, or uncertainty about what to do) had stronger associations with satisfaction than structural factors such as cost or access. This pattern suggests that interventions focusing solely on environmental provision may be insufficient if psychological and capability-related barriers are not simultaneously addressed. Confidence emerged as a pivotal determinant, echoing the central role of self-efficacy in behavioural theories such as Social Cognitive Theory and the Health Belief Model (20, 21). While readiness to change has been emphasised in transtheoretical approaches, our data show that higher readiness did not buffer against the detrimental influence of barriers. Instead, the magnitude of the negative effect of barriers increased with readiness. This finding challenges assumptions that readiness is a simple precursor to behaviour and instead suggests that readiness without barrier reduction may exacerbate frustration, leading to lower satisfaction (22, 23). Digital technology use was widespread, consistent with rapid growth in consumer health technologies. Our results align with previous work showing adoption is higher among younger and female participants. The lack of association with education and barrier burden is notable, suggesting that access to or willingness to use digital tools may be less influenced by structural barriers than expected. Instead, psychological constructs, confidence and readiness appear to drive adoption (24-27). This reinforces the view that digital tools are more likely to be adopted by those already inclined or motivated to be active, raising concerns about digital exclusion and widening inequalities. Interpretation of findings Three key insights merit emphasis. First, confidence is central. The threefold increase in satisfaction among those highly confident in their ability to be active highlights the need for interventions that explicitly build self-efficacy. Confidence is modifiable: structured behavioural support, feedback, small achievable goals and reinforcement strategies can increase individuals’ belief in their ability to maintain PA. Without attention to confidence, public health campaigns risk widening the gap between intention and action. Second, barriers are universal and cumulative. Very few participants reported no barriers and most experienced one or two. Each additional barrier reduced satisfaction by over one quarter. Critically, barrier impact intensified with readiness, meaning that even those most willing to change remained highly vulnerable to obstacles. This suggests a need for comprehensive barrier reduction strategies. Addressing time pressures, providing flexible options, tackling motivational challenges and supporting people with health-related barriers should be prioritised. Third, digital adoption is selective. While digital tools have the potential to support self-care, current patterns of use suggest they reinforce engagement among already motivated groups rather than reaching the most inactive. The fact that neither education nor barrier count predicted use implies that adoption is not simply a function of socioeconomic advantage but is linked to readiness and confidence. For public health, this raises an equity concern: digital health may disproportionately serve the “already active,” leaving behind those with the greatest need. Strengths and limitations This study has several strengths. The survey was embedded within the validated Seven Pillars of Self-Care framework, allowing PA to be studied in context with other self-care behaviours. Detailed measures of behaviours, motivations, barriers, readiness, confidence and digital use enabled both descriptive and inferential analyses. The use of multivariable regression models provides robust evidence on predictors of satisfaction and digital adoption. However, generalisability is tempered by sample composition: participants were significantly more educated than the UK population (64.4% vs. ~33% with university degrees nationally, based on 2021 Census data). Comparison with Health Survey for England (HSE) data on PA prevalence reveals our walking prevalence (96.2% ≥1 day/week) was higher than HSE estimates (~75%), while vigorous inactivity (35%) was similar. These discrepancies may reflect online recruitment bias toward more engaged individuals or true changes in PA patterns since the last HSE wave (2019). The sample was reasonably representative on gender and age distribution but skewed toward White ethnicity (80.9% vs. 81.6% nationally) and higher socioeconomic status as proxied by education. Findings should be interpreted with these limitations in mind and replication in representative population samples is warranted. Limitations must also be acknowledged. The cross-sectional design precludes causal inference; all associations should be interpreted as correlational rather than causal. Importantly, reverse causality cannot be excluded: for example, PA satisfaction may influence confidence or readiness rather than vice versa. The unexpected finding that readiness amplifies barrier sensitivity may reflect this temporal ambiguity—individuals experiencing low satisfaction may retrospectively report high barriers and lower readiness. Future longitudinal designs are essential to establish temporal precedence and directionality. Second, all measures were self-reported and thus susceptible to recall bias (particularly for 7-day PA duration), social desirability bias (overestimation of PA) and information bias. Third, while the sample was diverse in age, gender and ethnicity, participants skewed significantly toward higher educational attainment (64.4% university degree vs. ~33% nationally), potentially limiting generalisability to less-educated populations and raising concern for selection bias. Fourth, recruitment through an online panel introduces digital literacy bias, potentially oversampling those comfortable with digital technology, which may inflate adoption estimates. Fifth, the binary coding of digital adoption obscures nuances in intensity, type and sustained engagement; sensitivity analyses demonstrated robustness but qualitative work would enrich understanding. Finally, digital technology categories evolve rapidly and results may not reflect the latest innovations in AI-enabled or immersive platforms. Public health and policy implications Public health strategies should place greater emphasis on interventions that enhance self-efficacy, including motivational interviewing, skills training and personalised feedback. Confidence is not merely an adjunct but a central determinant of satisfaction and engagement. Pertinently, as time and motivation are consistently cited barriers, interventions should also address health-related obstacles and uncertainty about what to do. Providing tailored, accessible programmes that accommodate varying health needs is critical. The finding that barriers undermine satisfaction even among highly ready individuals highlights the futility of focusing solely on motivational readiness without parallel barrier removal. While digital tools can support PA, they are not a panacea. Policies must address the risk of digital exclusion and ensure that technologies are accessible, affordable and relevant to those most in need. Integration of digital solutions into primary care, community programmes and workplace health initiatives may broaden reach beyond already motivated populations. Situating PA within the Seven Pillars of Self-Care framework offers a holistic lens that recognises the interplay of knowledge, behaviours and environmental supports. Policymakers should integrate self-care frameworks into health promotion strategies, recognising that PA does not occur in isolation but alongside other pillars of wellbeing. Future research priorities include longitudinal designs to explore causal pathways between confidence, readiness, barriers and digital engagement. Mixed-methods approaches could illuminate why readiness amplifies barrier effects and identify strategies to mitigate this paradox. Evaluation of digital interventions should consider not only effectiveness but also equity of adoption and sustained engagement. Conclusion This study provides new insights into physical activity as a pillar of self-care among UK adults. While walking is common, inactivity remains widespread and sitting time high. Barriers are nearly universal, confidence is the strongest predictor of satisfaction and digital tools are selectively adopted by more confident and ready individuals. Crucially, readiness does not mitigate the negative effects of barriers; instead, it intensifies them. For public health, this means that interventions must move beyond motivation to address barriers directly and build confidence. Only through such approaches can self-care through physical activity be enhanced, digital opportunities equitably harnessed and health inequalities reduced. Declarations Ethics approval and consent to participate Approved by Imperial College London Research Ethics Committee (ICREC #7642258). All participants provided informed consent electronically. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This research received no funding. Austen El-Osta is grateful for support from the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Northwest London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. Authors' contributions A.E-O. designed the study protocol, oversaw data collection, performed, and wrote the main manuscript. M.A. and S.A. contributed to study design, statistical analysis and data interpretation. A.A. assisted with data management and preliminary analysis. D.S. provided intellectual input and manuscript review. All authors approved the final version. Acknowledgments We thank the participants and the public contributors involved in piloting the survey and disseminating findings. References Belvederi Murri M, Folesani F, Zerbinati L, Nanni MG, Ounalli H, Caruso R, Grassi L. Physical Activity Promotes Health and Reduces Cardiovascular Mortality in Depressed Populations: A Literature Overview. Int J Environ Res Public Health. 2020;17(15). Strain T, Flaxman S, Guthold R, Semenova E, Cowan M, Riley LM, et al. National, regional and global trends in insufficient physical activity among adults from 2000 to 2022: a pooled analysis of 507 population-based surveys with 5·7 million participants. Lancet Glob Health. 2024;12(8):e1232-e43. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-62. Ominyi J, Clifton A, Nwedu A. Understanding physical activity participation among underserved women: a mixed-methods cross sectional study using an ecological framework. BMC Public Health. 2025;25(1):2178. El-Osta A, Webber D, Gnani S, Banarsee R, Mummery D, Majeed A, Smith P. The self-care matrix: a unifying framework for self-Care.-Selfcare Journal. SelfCare Journal. 2019. Ferreira Silva RM, Mendonça CR, Azevedo VD, Raoof Memon A, Noll P, Noll M. Barriers to high school and university students' physical activity: A systematic review. PLoS One. 2022;17(4):e0265913. Garcia L, Mendonça G, Benedetti TRB, Borges LJ, Streit IA, Christofoletti M, et al. Barriers and facilitators of domain-specific physical activity: a systematic review of reviews. BMC Public Health. 2022;22(1):1964. Maltagliati S, Saoudi I, Sarrazin P, Cullati S, Sieber S, Chalabaev A, Cheval B. Women carry the weight of deprivation on physical inactivity: Moderated mediation analyses in a European sample of adults over 50 Years of age. SSM Popul Health. 2022;20:101272. Ghayour Baghbani SM, Arabshahi M, Saatchian V. The impact of exercise interventions on perceived self-efficacy and other psychological outcomes in adults: A systematic review and meta-analysis. European Journal of Integrative Medicine. 2023;62:102281. Singh B, Ahmed M, Staiano AE, Gough C, Petersen J, Vandelanotte C, et al. A systematic umbrella review and meta-meta-analysis of eHealth and mHealth interventions for improving lifestyle behaviours. NPJ Digit Med. 2024;7(1):179. Meyerowitz-Katz G, Ravi S, Arnolda L, Feng X, Maberly G, Astell-Burt T. Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis. J Med Internet Res. 2020;22(9):e20283. Alzghaibi H. Adoption barriers and facilitators of wearable health devices with AI integration: a patient-centred perspective. Front Med (Lausanne). 2025;12:1557054. Mclaughlin M, Delaney T, Hall A, Byaruhanga J, Mackie P, Grady A, et al. Associations between digital health intervention engagement, physical activity and sedentary behavior: systematic review and meta-analysis. Journal of medical Internet research. 2021;23(2):e23180. Scholes S, Bridges S, Ng Fat L, Mindell JS. Comparison of the Physical Activity and Sedentary Behaviour Assessment Questionnaire and the Short-Form International Physical Activity Questionnaire: An Analysis of Health Survey for England Data. PLOS ONE. 2016;11(3):e0151647. Malkowski OS, Townsend NP, Kelson MJ, Foster CEM, Western MJ. Socioeconomic inequalities in physical activity among older adults before and during the COVID-19 pandemic: evidence from the English Longitudinal Study of Ageing. BMJ Public Health. 2023;1(1):e000100. Understanding and addressing inequalities in physical activity. 2021. Sampaio BOA, de Alencar Santos AR, Nascimento-Ferreira MV, De Moraes ACF. Global prevalence of barriers and facilitators to physical activity in children and adolescents: A systematic review with meta-analysis. Preventive Medicine Reports. 2025;58:103230. Pedersen MRL, Hansen AF, Elmose-Østerlund K. Motives and Barriers Related to Physical Activity and Sport across Social Backgrounds: Implications for Health Promotion. Int J Environ Res Public Health. 2021;18(11). Malkowski OS, Harvey J, Townsend NP, Kelson MJ, Foster CEM, Western MJ. Enablers and barriers to physical activity among older adults of low socio-economic status: a systematic review of qualitative literature. Int J Behav Nutr Phys Act. 2025;22(1):82. Bandura A, editor Social Foundations of Thought and Action1986. Rosenstock IM. The Health Belief Model and Preventive Health Behavior. Health Education Monographs. 1974;2(4):354-86. Livet M, Blanchard C, Richard C. Readiness as a precursor of early implementation outcomes: an exploratory study in specialty clinics. Implementation Science Communications. 2022;3(1):94. Olson JM. Psychological Barriers to Behavior Change: How to indentify the barriers that inhibit change. Can Fam Physician. 1992;38:309-19. Cecconi C, Adams R, Cardone A, Declaye J, Silva M, Vanlerberghe T, et al. Generational differences in healthcare: the role of technology in the path forward. Front Public Health. 2025;13:1546317. 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 Digital Medicine. 2023;6(1). Raunaq FF, Islam S, Anam MZ, Bari ABMM. Assessing the challenges to digital technology adoption in the healthcare sector: Implications for sustainability in emerging economies. Informatics and Health. 2025;2(2):194-209. Ruyobeza B, Grobbelaar SS, Botha A. Forecasting the adoption of digital health technologies: The intention-expectation gap. Evaluation and Program Planning. 2025;112:102670. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8049011\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":546449331,\"identity\":\"7be457c5-815c-4dab-a66b-b47ee335c329\",\"order_by\":0,\"name\":\"Austen 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adoption\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8049011/v1/da6bf5873f631118e43b7508.png\"},{\"id\":96400679,\"identity\":\"5349a561-f3c6-4763-80c6-5adecc6dbd8a\",\"added_by\":\"auto\",\"created_at\":\"2025-11-20 16:07:00\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":38326,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLogistic regression assessing the predictors for digital technology adoption\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8049011/v1/bf82436b8ded11a4bb802c3f.png\"},{\"id\":96456818,\"identity\":\"806c11a6-699e-4c24-a3c1-11eb968aaeb8\",\"added_by\":\"auto\",\"created_at\":\"2025-11-21 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results from the 7PSC Study\",\"fulltext\":[{\"header\":\"Background \",\"content\":\"\\u003cp\\u003ePhysical activity (PA) is one of the most powerful determinants of health and a critical component of the self-care paradigm. Regular PA reduces risks of cardiovascular disease, type 2 diabetes, cancers, musculoskeletal disorders and premature mortality, while also promoting mental health, social wellbeing and quality of life (1). Yet, despite decades of public health campaigns and robust evidence on the benefits of PA, global surveillance indicates that more than one in four adults fail to achieve recommended activity levels, with little progress observed over time (2). This persistent inactivity contributes substantially to the global burden of disease and challenges efforts to achieve Sustainable Development Goals related to health, wellbeing and health equity.\\u003c/p\\u003e\\n\\u003cp\\u003eThe World Health Organization (WHO) guidelines recommend that adults engage in at least 150 minutes of moderate-intensity or 75 minutes of vigorous-intensity activity per week, alongside muscle-strengthening activities on two or more days (3). However, surveillance suggests that knowledge of these recommendations is inconsistent and adherence remains suboptimal. In the United Kingdom (UK), survey data indicate that activity patterns vary across demographic groups, with socioeconomic and health inequalities strongly shaping opportunities for participation (4). Understanding the interplay of behavioural, psychological and environmental factors is therefore essential to inform effective, equitable interventions.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Seven Pillars of Self-Care (7PSC) framework, developed by the International Self-Care Foundation, provides a holistic model for conceptualising self-care behaviours (5). Physical activity is one of the seven pillars, alongside health literacy, mental wellbeing, healthy eating, risk avoidance, good hygiene and rational use of products and services. The 7PSC framework positions PA as a lifestyle behaviour as well as a self-directed capability requiring knowledge, motivation and supportive environments. By embedding PA within this broader model, researchers can examine how individuals balance multiple self-care practices, the barriers they encounter and the enablers that sustain engagement.\\u003c/p\\u003e\\n\\u003cp\\u003eBarriers to PA are multifaceted and well-characterised. Time constraints, cost, lack of facilities and environmental safety are frequently cited structural obstacles (6), while poor physical or mental health, lack of motivation and uncertainty about how to exercise represent personal barriers (7). Evidence consistently shows that barriers disproportionately affect individuals with lower socioeconomic status, chronic illness or limited access to supportive environments, thereby reinforcing health inequalities (8). Conversely, motivations to be active often centre on mental wellbeing, weight management, illness prevention and enjoyment. Understanding the relative salience of these drivers and obstacles is essential for tailoring interventions to diverse populations.\\u003c/p\\u003e\\n\\u003cp\\u003ePsychological constructs such as self-efficacy (confidence in one’s ability to act) and readiness to change are central to behavioural models of PA. Higher self-efficacy has been linked to greater engagement and persistence in physical activity, while readiness reflects individuals’ stage in adopting and maintaining behaviours. However, less is known about how these constructs interact with perceived barriers and whether high readiness can buffer against the negative effects of obstacles (9). Exploring these relationships in population-level data can inform strategies that move beyond awareness-raising to address deeper behavioural determinants.\\u003c/p\\u003e\\n\\u003cp\\u003eIn recent years, digital health technologies, including smartphone apps, wearable trackers, smartwatches and online platforms, have emerged as promising tools to support PA self-care. These technologies offer features such as real-time feedback, goal tracking, reminders and social support. Evidence suggests they can increase awareness and motivation, (10), yet their long-term effectiveness is variable and concerns about sustainability, cost and equity remain (10). Adoption is patterned by age, gender, digital literacy and socioeconomic status, raising questions about whether digital health may inadvertently widen disparities in PA engagement. Identifying who adopts digital tools and how these tools influence behaviours is therefore a critical public health priority (11).\\u003c/p\\u003e\\n\\u003cp\\u003eDespite extensive research on physical activity, three key gaps remain. First, few studies situate PA within a holistic self-care framework such as the 7PSC, limiting understanding of how it interrelates with other health behaviours. Second, although barriers and motivations have been well-documented, less is known about their relative impact on satisfaction with activity levels and how these effects interact with readiness. Third, while digital technologies are increasingly prominent in public health discourse, large-scale evidence on predictors of adoption and their association with confidence, readiness and barriers is limited in UK populations (12, 13). The present study addresses these gaps by analysing physical activity data from over 1,500 UK adults as part of the 7PSC Study. By integrating descriptive behavioural patterns with regression models of satisfaction and digital adoption, this research offers a comprehensive view of physical activity as a self-care behaviour.\\u003c/p\\u003e\\n\\u003cp\\u003eThe primary aim of this study was to characterise physical activity behaviours, knowledge, motivations and barriers among UK adults. We also sought to examine predictors of satisfaction with physical activity, with a focus on confidence, readiness and barrier burden. Another important objective was to assess the adoption and use of digital health technologies to support PA and to identify demographic and psychological predictors of adoption.\\u003c/p\\u003e\"},{\"header\":\"Methods \",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eStudy design and setting\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis cross-sectional survey was conducted in 2025 as part of the 7PSC Study, a large programme of research investigating knowledge, attitudes and behaviours across all seven self-care domains defined by the International Self-Care Foundation. The present analysis focuses on the PA pillar. The study was designed and reported in line with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eParticipants and recruitment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants were adults (aged \\u0026ge;18 years) residing in the UK at the time of survey completion. Recruitment was undertaken through an established online research panel, ensuring broad demographic coverage. Eligibility criteria were minimal to maximise representativeness: adults with the ability to provide informed consent and to complete the online survey in English were included. Individuals under 18 years or not resident in the UK were excluded.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSurvey instrument\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe survey instrument was developed iteratively by the research team to account for the 7PSC framework and validated PA measurement tools. It comprised five domains relevant to PA self-care. A sociodemographic characteristics block recorded information about age, gender, ethnicity, education, employment and self-reported disability or long-term conditions affecting daily life. The PA behaviours block included questions adapted from the International Physical Activity Questionnaire (IPAQ-short form) to assess frequency and duration of vigorous, moderate and walking activities in the preceding seven days, as well as sitting time on weekdays. Participants also reported frequency of strength and flexibility activities, stair use and outdoor activities. The knowledge and perceptions block included Items that assessed awareness of WHO PA guidelines, perceived importance of PA, satisfaction with current activity levels and motivations for being active. The barriers and enablers block consisted of eight binary items assessing barriers, including lack of time, motivation, cost, poor physical or mental health, access to facilities, uncertainty about what to do and safety concerns. A composite barrier count score (0-8) was derived by summing reported barriers. The confidence and readiness block measured self-efficacy through a four-level confidence scale (\\u0026ldquo;not confident,\\u0026rdquo; \\u0026ldquo;somewhat confident,\\u0026rdquo; \\u0026ldquo;confident,\\u0026rdquo; \\u0026ldquo;very confident\\u0026rdquo;). Readiness to change was measured on a 10-point scale (1=lowest, 10=highest). Finally, the digital technology use block collected data to assess the adoption of digital health tools using binary (yes/no) and multiple-choice items covering smartphone apps, wearables, smartwatches, online platforms, AI-based assistants and other devices. Frequency, duration of use, perceived usefulness, valued features and reasons for discontinuation were captured. Survey items were iteratively refined through cognitive testing and pilot administration to ensure clarity and comprehensibility. A copy of the main sutvey is included in\\u0026nbsp;\\u003cstrong\\u003eSupplementary Table\\u003c/strong\\u003e 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStudy outcomes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTwo primary outcomes were PA satisfaction and digital health technology adoption. The\\u0026nbsp;PA satisfaction ordinal outcome was measured using a single item: \\u0026quot;How satisfied are you with your current physical activity levels?\\u0026quot; with response options: (1) \\u0026quot;Not satisfied,\\u0026quot; (2) \\u0026quot;Somewhat satisfied,\\u0026quot; (3) \\u0026quot;Satisfied,\\u0026quot; (4) \\u0026quot;Very satisfied.\\u0026quot; This outcome combines two distinct constructs: contentment with current engagement and perceived adequacy relative to WHO guidelines. The satisfaction scale has not been previously validated in published literature; however, test-retest reliability was assessed in a pilot subsample (n=45) over 2 weeks, showing moderate agreement (\\u0026kappa;=0.58). For regression modelling, outcomes were treated as ordinal, with higher scores representing greater satisfaction.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Digital Health Technology Adoption binary (yes/no) outcome indicated whether participants reported using at least one digital health technology category (smartphone apps, wearables, smartwatches, online platforms, AI assistants, or other devices) to support PA. This aggregated measure captures any adoption; frequency and type of use were examined separately in sensitivity analyses.\\u003c/p\\u003e\\n\\u003cp\\u003eAs a secondary outcome, barrier count was measured as a composite score (0\\u0026ndash;8) derived by summing eight binary barrier items; individual barriers were also examined.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAnalyses were conducted in StataMP 17. Descriptive statistics summarised sample characteristics, PA behaviours, barriers, motivations and technology use. Continuous variables are presented as means and standard deviation (SD) and categorical variables as frequencies (n) and percentages (%).\\u003c/p\\u003e\\n\\u003cp\\u003eFor inferential analyses, we employed 2 models. Model 1 assessed predictors of PA satisfaction. An ordinal logistic regression was fitted with satisfaction as the outcome. Independent variables included age, gender, education, perceived importance of PA, confidence, readiness score, barrier count and digital technology use. Odds ratios (OR) with 95% confidence intervals (CI) are reported.\\u0026nbsp;\\u0026nbsp;The proportional odds assumption was assessed using the Brant test. Due to violations of this assumption for some predictors, a partial proportional odds model was employed, allowing specific predictor coefficients to vary across outcome levels while others were constrained to satisfy the assumption. Both the standard and partial proportional odds models were examined for consistency. Measures of model fit, such as pseudo-R\\u0026sup2;, were computed to evaluate model adequacy..\\u003c/p\\u003e\\n\\u003cp\\u003eA second model (Model 2) was used to investigate predictors of digital adoption. Logistic regression was used with technology use (yes/no) as the outcome. Predictors were the same covariates as in Model 1, plus PA satisfaction. Predicted probabilities were estimated for selected values of age and readiness.\\u003c/p\\u003e\\n\\u003cp\\u003eMissing data were minimal (\\u0026lt;5% for most variables);\\u0026nbsp;age (0.1%), gender (0.3%), education (0.5%), PA behaviours (2.1%), confidence (1.8%), readiness (2.3%) and digital adoption (2.0%). To assess potential bias from missing data, we conducted Little\\u0026apos;s Missing Completely At Random (MCAR) test (\\u0026chi;\\u0026sup2;=42.18, p=0.31), indicating that data were reasonably assumed MCAR. Sensitivity analysis comparing complete case results (n=1,457) with multiple imputation results (n=1,532; 20 imputations using predictive mean matching) showed negligible differences in point estimates and 95% confidence intervals; therefore, complete case results are reported. Statistical significance was defined as p\\u0026lt;0.05.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical considerations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was approved by the Imperial College London Research Ethics Committee (ICREC #7642258) prior to data collection. All participants provided informed consent electronically before accessing the survey. Participation was voluntary and respondents could withdraw at any time. Data were collected anonymously, stored securely and analysed in accordance with the UK Data Protection Act and GDPR .\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePatient and Public Involvement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe public was involved in the design of the study and questionnaire through piloting and feedback, using the service and participated in disseminating the preliminary findings.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eParticipant characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 1,532 adults across the UK completed the survey, of whom 1,457 provided complete responses suitable for analysis. Participant characteristics are illustrated in \\u003cstrong\\u003eTable 1\\u003c/strong\\u003e. The mean age of participants was 43.0 years (SD=12.9; range 18\\u0026ndash;80). Gender distribution was balanced, with 49.3% identifying as female (n=718) and 50.2% as male (n=732), while a small minority identified as another gender or preferred not to say (0.5%, n=7). The sample was predominantly White 1178 (80.9%), with Asian/Asian British 8 (6.0%), Black/Black British 84 (5.8%), Mixed/other 107 (7.4%). Nearly two-thirds \\u0026nbsp;(64.4%), held a university degree or higher, while 330 (22.6%) reported A-levels/college and 188 (12.9%) reported secondary school qualifications. Most participants reported no disability or long-term condition affecting daily activities 1003 (68.8%), although 129 (8.9%) indicated severe impact, 202 (13.9%) reported minor impact and 93 (6.4%) reported having a condition without daily impairment.\\u003c/p\\u003e\\n\\u003cp\\u003eEmployment status was diverse: 826 (56.7%) were employed full-time, 190 (13.0%) part-time, 145 (10.0%) self-employed and 94 (6.5%) retired. Smaller proportions were students 45 (3.1%), unemployed 58 (4.0%), unable to work due to illness or disability 44 (3.0%), or homemakers/carers 48 (3.3%). Collectively, these distributions suggest a broadly representative cohort spanning working-age adults, though somewhat skewed toward higher educational attainment. The findings of the full survey are illustrated in\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003eSupplementary Table 2.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1. Participant characteristics (N=1,532)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"642\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCharacteristic\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN (%)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e%/SD\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eAge, mean (SD)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e43.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(12.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eRange\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e18\\u0026ndash;80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003eyears\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSex\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e718\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e49.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e732\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e50.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eOther / Prefer not to say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEthnicity\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eWhite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1178\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e80.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eAsian/Asian British\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e6.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eBlack/Black British\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e84\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e5.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eMixed/Other\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e107\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e7.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEducation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eUniversity degree or higher\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e939\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e64.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eA-level/College\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e330\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e22.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eSecondary school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e188\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e12.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHealth status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eNo disability/long-term condition\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e1003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e68.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eCondition affects a little\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e202\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e13.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eCondition affects a lot\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e129\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e8.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003eCondition without impact\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e93\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e6.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEmployment status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(%)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Employed full-time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e826\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(56.7%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Employed part-time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e190\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(13.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Homemaker / unpaid carer\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e48\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(3.3%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Other (please specify)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e7\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(0.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Retired\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e94\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(6.5%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Self-employed\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e145\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(10.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Student\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e45\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(3.1%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Unable to work due to long-term illness or disability\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e44\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(3.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 425px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Unemployed / looking for work\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e58\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e(4.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDescriptive results\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAwareness of self-care pillars\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAwareness of the seven pillars of self-care varied markedly. Mental wellbeing 1439 (93.9%), physical activity 1445 (94.4%), healthy eating 1445 (94.4%) and good hygiene 1323 (86.4%) were widely recognised. In contrast, fewer participants reported awareness of knowledge \\u0026amp; health literacy 504 (33.8%), risk avoidance 546 (35.6%), or rational use of products \\u0026amp; services 205 (13.4%). Only 22 (1.5%) indicated no awareness of any pillar. These results suggest that self-care awareness is strongly weighted toward lifestyle and hygiene-related behaviours, with limited recognition of more structural or decision-making domains. Further details on awareness are in the supplementary \\u003cstrong\\u003etable 1\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePhysical activity behaviours\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eFrequency and duration\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eVigorous physical activity was reported by 966 (65.1%) of respondents at least once in the preceding week. However, 519 (34.9%) reported none. The modal response was two days per week 261 (17.6%). Among those engaging, the mean reported duration was 1.1\\u0026plusmn;1.3SD hours and 17.6\\u0026plusmn;16.2SD minutes per day, with a wide range (5\\u0026ndash;120 minutes). Moderate physical activity was more common yet still limited: 540 (36.4%) reported no moderate activity in the prior week, while 107 (17.8%) and 195 (13.1%) engaged on two or three days, respectively. Average duration per session was 1.2\\u0026plusmn;1.4 SD hours and 14.8\\u0026plusmn;16.2 SD minutes (approximately 72\\u0026plusmn;84 minutes).\\u003c/p\\u003e\\n\\u003cp\\u003eWalking was near-universal: 1476 (96.2%) reported walking at least once, with 636 (42.8%) reporting daily walking. Average duration per day was approximately 1\\u0026plusmn;1.4 SD hour and 18 \\u0026plusmn;15.8 SD minutes (approximately 60\\u0026plusmn;84 minutes).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eSedentary behaviour was prevalent. Nearly half 711 (47.9%) reported sitting on all seven weekdays and the mean sitting time was 6.0 \\u0026plusmn;2.5 SD hours and 6.7 \\u0026plusmn;12.7SD minutes per day. Further details about physical behaviours frequency and duration are shown in \\u003cstrong\\u003esupplementary table 2\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2. Physical activity patterns and behaviours\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"647\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003en\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e% / Mean (SD)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eNo vigorous activity (past week)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e519\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e34.9%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;1 day vigorous activity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e966\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e65.1%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eMean duration vigorous (hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e1.1 (1.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eNo moderate activity (past week)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e540\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e36.4%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;1 day moderate activity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e945\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e63.6%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eMean duration moderate (hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e1.2 (1.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eWalked \\u0026ge;1 day (past week)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e1476\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e96.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eDaily walking (7 days)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e636\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e42.8%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eMean walking time (hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e1.0 (1.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eDaily sitting time (hours)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e-\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e6.0 (2.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eNever strength training\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e585\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e39.4%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;3\\u0026times;/week strength training\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e255\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e17.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eNever flexibility exercises\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e597\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e40.2%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;3\\u0026times;/week flexibility exercises\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e171\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e11.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eNot satisfied with PA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e447\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e30.1%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eSomewhat satisfied\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e535\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e36.0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eVery satisfied\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e99\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e6.7%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eCorrectly identified WHO 150min guideline\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 142px;\\\"\\u003e\\n \\u003cp\\u003e948\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 165px;\\\"\\u003e\\n \\u003cp\\u003e64.0%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eMuscle-strengthening and flexibility activities\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eEngagement in strength training was low: 585 (39.4%) never undertook such activities and only 255 (17.2%) reported \\u0026ge;3 times weekly. Flexibility activities showed similar patterns, with 597 (40.2%) never participating and just 171 (11.5%) engaging three or more times weekly; Table\\u003cstrong\\u003e\\u0026nbsp;3.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLifestyle-related activity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIncidental activity varied. Stair use was common, with 584 (39.3%) reporting regular use and 385 (25.9%) reporting always taking stairs. Outdoor activity was less consistent: nearly half 722 (48.6%) participated occasionally, while 286 (26.0%) were regular participants and 204 (13.7%) reported none. As indicated in \\u003cstrong\\u003eTable 3\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 3: Shows the muscle strength, flexibility and lifestyle-related activities\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ12 How often do you engage in muscle-strengthening activities (e.g., weightlifting, resistance exercises)?\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003eN (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;N-Miss\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e47\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;1-2 times a week\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e336 (22.6%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;3 or more times a week\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e255 (17.2%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Always\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e36 (2.4%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Less than once a week\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e273 (18.4%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Never\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e585 (39.4%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ13 How often do you engage in flexibility exercises (e.g., yoga, stretching)?\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;N-Miss\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e47\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;1-2 times a week\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e301 (20.3%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;3 or more times a week\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e171 (11.5%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Always\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e44 (3.0%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Less than once a week\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e372 (25.1%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Never\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e597 (40.2%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ14 How often do you take the stairs instead of the elevator?\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;N-Miss\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e47\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Always\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e385 (25.9%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Never\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e74 (5.0%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Occasionally\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e442 (29.8%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Regularly\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e584 (39.3%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eQ15 How often do you participate in outdoor activities (e.g., hiking, gardening)?\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;N-Miss\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e47\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Frequently\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e173 (11.6%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Never\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e204 (13.7%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Occasionally\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e722 (48.6%)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 525px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp; \\u0026nbsp;Regularly\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 108px;\\\"\\u003e\\n \\u003cp\\u003e386 (26.0%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSatisfaction and knowledge\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMost participants were not satisfied 447 (30.1%) or only somewhat satisfied 535 (36.0%) with their current physical activity levels. Only 99 6.7% were very satisfied. Knowledge of WHO recommendations was mixed: 948 (64.0%) correctly identified 150 minutes of weekly activity as the benchmark, while 354 (23.9%) believed 300 minutes was required. Knowledge of strength training guidelines was more accurate, with 948 (46.2%) correctly identifying twice per week. However, misconceptions persisted about the type of activity needed for cardiovascular health, with nearly half 685 (46.2%) incorrectly selecting walking over aerobic activity 595 (40.1%).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMotivations and barriers\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe leading motivations for physical activity were mental wellbeing 1007 (65.7%), weight management 979 (63.9%) and illness prevention 768 (50.1%). Enjoyment 687 (44.8%) and social interaction 207 (13.5%) were less frequently cited. Notably, 161 (10.5%) reported no physical activity; \\u003cstrong\\u003etable 4\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe most common barriers to physical activity were lack of time 850 (55.5%) and lack of motivation 834 (55.0%). Cost 282 (18.4%), poor physical health 252 (16.5%) and mental health barriers 255 (16.7%) also featured prominently. Lack of access 196 (12.8%) and uncertainty about what to do 230 (15.0%) were less common, while safety concerns were rarely cited 71 (4.6%). Composite scoring showed nearly all participants experienced at least one barrier, with one or two barriers most common 949 (62%). Only 100 (6.5%) reported none. Details on the score. Cumulative percentages are shown in \\u003cstrong\\u003eSupplementary Table 3.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4. Motivations and barriers to physical activity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003en\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e%\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMotivations\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eMental wellbeing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e65.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eWeight management\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e979\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e63.9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eReduce illness risk\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e768\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e50.1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eEnjoyment\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e687\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e44.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eSocial interaction\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e207\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e13.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eNot physically active\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e161\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e10.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBarriers\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eLack of time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e850\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e55.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eLack of motivation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e834\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e55.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eCost/affordability\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e282\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e18.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003ePoor physical health\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e252\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e16.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eMental health barriers\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e255\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e16.7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eLack of access to facilities\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e196\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e12.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eNot knowing what to do\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e230\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e15.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 482px;\\\"\\u003e\\n \\u003cp\\u003eSafety concerns\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e71\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e4.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eReadiness, confidence and behavioural change\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eReadiness to change was moderate to high: mean score 6.8/10. About 61.3% scored \\u0026ge;7, while 12.5% scored \\u0026le;4, indicating ambivalence or resistance. Confidence in maintaining regular activity was limited: only 241 (16.3%) reported being very confident, while 579 (39.1%) were somewhat confident and 214 (14.5%) were not confident. About 591 (40%) had taken recent steps to improve activity levels, such as using apps, joining groups, or setting goals. More details are shown in \\u003cstrong\\u003eSupplementary Table 2\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTypes of activity\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWalking was the most common form 1314 (85.8%), followed by gym-based exercise 381 (24.9%), running/jogging 361 (23.6%), home workouts 518 (33.8%) and fitness classes 213 (13.9%). Recreational sports 204 (13.3%) and swimming 172 (11.2%) were less frequent. Only 62 (4.1%) reported no regular activity.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDigital technology adoption\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOverall, 767 (52.6%) reported using at least one digital health tool to support activity, classified as \\u0026apos;adopters\\u0026apos;; 691 (47.4%) reported no use (\\u0026apos;non-adopters\\u0026apos;). Among adopters, the most common technologies were smartphone apps 416 (27.2%), smartwatches 338 (22.1%) and wearable fitness trackers 294 (19.2%). Online platforms 117 (7.6%), \\u0026nbsp;AI-based assistants (68; 4.4%) and smart home equipment (48; 3.1%) were less commonly used.\\u003c/p\\u003e\\n\\u003cp\\u003eAmong adopters, frequency of engagement was high: 401 (52.3%) used tools daily and 244 (31.8%) several times per week, 85 (11.1%) weekly and 37 (4.8%) less frequently. Duration of adoption was sustained: 571 (74.4%) had been using digital tools for over one year, 127 (16.6%) for 6\\u0026ndash;12 months and 69 (9.0%) for less than 6 months. Notably, 153 participants (19.9% of adopters) had discontinued tool use after initial adoption, most commonly due to loss of motivation (65 individuals; 42.5% of those who discontinued).\\u003c/p\\u003e\\n\\u003cp\\u003eWe tested whether results differed when adoption was defined more stringently as regular use (\\u0026ge;3 times weekly) rather than any use. Among this stricter definition, 645 (42.0%) were classified as adopters. Regression models using this alternative definition yielded similar point estimates and overlapping 95% confidence intervals with the primary binary definition, confirming robustness (see \\u003cstrong\\u003eSupplementary Table 5A\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003eMotivational potential varied: smartphone apps, wearables and smartwatches were rated more motivating than online platforms, coaching, or AI assistants. Core features valued were step counts 1151 (75.2%), heart rate monitoring 671 (44.0%) and goal tracking 566 (37.0%). Social/community challenges and gamification were rarely endorsed.\\u003c/p\\u003e\\n\\u003cp\\u003ePerceived impact was mixed. About 647 (44.4%) reported being slightly more active and 161 (11.0%) much more active, while 619 (42.5%) reported no change. Walking was the most commonly increased activity 1043 (71.6%), followed by running 297 (19.4%) and strength training 239 (15.6%).\\u003c/p\\u003e\\n\\u003cp\\u003eBarriers to continued use included loss of motivation 673 (44.0%), cost 414 (27.0%) and technical issues 171 (11.2%). Privacy concerns 122 (8.0%) and health reasons 137 (9.0%) were cited less often. Positive reinforcement came primarily from visible progress 881 (57.5%), routine/habit formation 615 (40.1%) and enjoyment 475 (31.0%).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 5. Digital health technology use and perceptions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003en\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e%\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eUse digital tools\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e767\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e52.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eNo tool use\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e691\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e47.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eDaily users (among adopters)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e401\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e52.3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eSeveral times/week\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e244\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e31.8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;1 year use\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e571\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e74.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eFeatures valued: step count\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1151\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e75.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eHeart rate monitoring\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e671\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e44.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eGoal tracking\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e566\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e37.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eIncreased walking due to tools\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e1043\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e71.6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eReported \\u0026apos;slightly/much more active\\u0026apos;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e808\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e55.4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eBarriers: loss of motivation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e673\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e44.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eBarriers: cost\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e414\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e27.0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 453px;\\\"\\u003e\\n \\u003cp\\u003eBarriers: technical issues\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e171\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e11.2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePredictors of physical activity satisfaction\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOrdinal logistic regression showed that confidence and barrier burden were the strongest predictors (\\u003cstrong\\u003eTable 6\\u003c/strong\\u003e). Participants reporting no confidence had dramatically lower odds of satisfaction (OR=0.037, p\\u0026lt;0.001), while very confident participants had over threefold higher odds (OR=3.296, p\\u0026lt;0.001). Each additional barrier reduced the odds of satisfaction by 26% (OR=0.738, p\\u0026lt;0.001). Sociodemographic variables and digital tool use were not significant. Multicollinearity: Variance inflation factors (VIF) ranged from 1.04 (age) to 1.38 (readiness score), all well below the threshold of 10, indicating no problematic collinearity. Proportional Odds: As noted in Methods, the Brant test indicated violation for readiness and barrier count; partial proportional odds model results were compared and found qualitatively consistent\\u003c/p\\u003e\\n\\u003cp\\u003eWhen barriers were examined individually, using Bonferroni-corrected thresholds (p \\u0026lt; 0.00625), lack of motivation (OR=0.320, p\\u0026lt;0.001), poor physical health (OR=0.233, p\\u0026lt;0.001), mental health barriers (OR=0.535, p=0.001) and uncertainty about what to do (OR=0.545, p=0.001) remained statistically significant obstacles. Time, cost, access and safety did not reach corrected significance levels; full details are in \\u003cstrong\\u003eSupplementary Table 3\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eInteraction models showed barrier effects intensified with increasing readiness. At readiness 3, each additional barrier reduced satisfaction probability by 9.9%; at readiness 7, by 12.9%; and at readiness 10, by 13.2%. This indicates that readiness amplifies rather than buffers the negative influence of barriers (\\u003cstrong\\u003eSupplementary Table 4)\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 6. Predictors of physical activity satisfaction (ordinal logistic regression)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"642\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAdjusted OR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eNot confident vs confident\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.037\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.018\\u0026ndash;0.078\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eSomewhat confident vs confident\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.072\\u0026ndash;0.143\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eVery confident vs confident\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e3.296\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e2.237\\u0026ndash;4.855\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eBarrier score (per unit)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.738\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.635\\u0026ndash;0.858\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eReadiness score (per unit)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.926\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.851\\u0026ndash;1.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.069\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eSomewhat important vs extremely important PA is\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.431\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.247\\u0026ndash;0.754\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eAge (per year)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.997\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.986\\u0026ndash;1.009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.730\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eMale vs female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.922\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.694\\u0026ndash;1.223\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.575\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eDigital technology use vs non-use\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 113px;\\\"\\u003e\\n \\u003cp\\u003e0.938\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.700\\u0026ndash;1.251\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e0.656\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"top\\\" style=\\\"width: 642px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eNote: Brant test for proportional odds assumption: \\u0026chi;\\u0026sup2; = 24.67, p = 0.003. Partial proportional odds model fitted for readiness and barrier count. Pseudo-R\\u0026sup2; (McFadden) = 0.182. Reference categories: \\u0026apos;confident\\u0026apos; (confidence scale), female (gender), university degree or higher (education), extremely important (PA importance). Odds ratios \\u0026gt;1 indicate increased odds of higher satisfaction; \\u0026lt;1 indicate decreased odds. 95% CIs not including 1.00 indicate statistical significance.\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eAnalysis used complete case analysis for the primary models, retaining 1,457 of 1,532 respondents (95.1%).\\u0026nbsp;Logistic regression identified younger age (OR=0.989 per year, p=0.019), female gender (OR=0.780 for males vs females, p=0.028) and higher readiness (OR=1.166 per unit, p\\u0026lt;0.001) as significant predictors of digital adoption (\\u003cstrong\\u003etable 7)\\u003c/strong\\u003e. Confidence also mattered: those reporting no confidence had 59% lower odds of use (OR=0.412, p\\u0026lt;0.001). In contrast, education, barrier count and satisfaction with activity were not significant predictors. Importantly, lower perceived importance of physical activity was associated with lower odds of digital use, even when adjusting for readiness and confidence. Predicted probabilities illustrated the gradient: likelihood of digital use declined from 57% at age 25 to 48% at age 65; \\u003cstrong\\u003eSupplementary Table 5\\u003c/strong\\u003e. Conversely, readiness strongly increased adoption probability, from 32% at readiness 1 to 64% at readiness 10; \\u003cstrong\\u003eSupplementary Table 6\\u003c/strong\\u003e\\u003cstrong\\u003e.\\u0026nbsp;\\u003c/strong\\u003eVIF ranged from 1.06 (age) to 1.41 (readiness), indicating no problematic correlation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 7. Predictors of digital health technology adoption (logistic regression)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"642\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAdjusted OR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e95% CI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ep-value\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eAge (per year)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.989\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.980\\u0026ndash;0.998\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.019\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eMale vs female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.780\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.626\\u0026ndash;0.974\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eNot confident vs confident\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.412\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.270\\u0026ndash;0.629\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eReadiness score (per unit)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1.166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1.096\\u0026ndash;1.241\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eQuite important vs extremely important\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.741\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.573\\u0026ndash;0.958\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.023\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eSomewhat important vs extremely important\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.603\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.422\\u0026ndash;0.862\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eEducation (University vs A-level)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e1.147\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.879\\u0026ndash;1.497\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.310\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 340px;\\\"\\u003e\\n \\u003cp\\u003eBarrier score (per unit)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.974\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 104px;\\\"\\u003e\\n \\u003cp\\u003e0.870\\u0026ndash;1.090\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e0.651\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSummary of principal findings\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study, conducted as part of the 7PSCStudy, provides one of the largest UK datasets to date examining physical activity (PA) within a self-care framework. Three principal findings emerge. First, although walking was widely reported, a large minority of participants undertook no vigorous (35%) or moderate (36%) PA in the previous week and daily sitting time averaged six hours, indicating significant behavioural gaps relative to WHO recommendations. Second, barriers to PA were ubiquitous, with lack of time and motivation most frequently reported. Confidence in one’s ability to remain active emerged as the strongest predictor of satisfaction, while readiness amplified the negative effect of barriers rather than offsetting them. Third, digital health technologies were used by more than half of respondents, but adoption was unevenly distributed, favouring younger, female, more confident and more ready participants. Education, barrier load and satisfaction did not significantly predict uptake.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eComparison with prior literature\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eOur findings confirm and extend established evidence on low compliance with PA guidelines in the UK. The Health Survey for England and other population-based studies report similar prevalence estimates of inactivity, with marked disparities by age, socioeconomic position and health status (14-16). The present study adds granularity by situating these patterns within a broader self-care framework, underscoring the interdependence of physical, psychological and behavioural determinants.\\u003c/p\\u003e\\n\\u003cp\\u003eBarriers reported here align with international literature. Time constraints and motivational deficits consistently appear as primary obstacles across diverse populations (17-19). However, the present analysis indicates that barriers linked to personal health (poor physical and mental health, or uncertainty about what to do) had stronger associations with satisfaction than structural factors such as cost or access. This pattern suggests that interventions focusing solely on environmental provision may be insufficient if psychological and capability-related barriers are not simultaneously addressed.\\u003c/p\\u003e\\n\\u003cp\\u003eConfidence emerged as a pivotal determinant, echoing the central role of self-efficacy in behavioural theories such as Social Cognitive Theory and the Health Belief Model (20, 21). While readiness to change has been emphasised in transtheoretical approaches, our data show that higher readiness did not buffer against the detrimental influence of barriers. Instead, the magnitude of the negative effect of barriers increased with readiness. This finding challenges assumptions that readiness is a simple precursor to behaviour and instead suggests that readiness without barrier reduction may exacerbate frustration, leading to lower satisfaction (22, 23).\\u003c/p\\u003e\\n\\u003cp\\u003eDigital technology use was widespread, consistent with rapid growth in consumer health technologies. Our results align with previous work showing adoption is higher among younger and female participants. The lack of association with education and barrier burden is notable, suggesting that access to or willingness to use digital tools may be less influenced by structural barriers than expected. Instead, psychological constructs, confidence and readiness appear to drive adoption (24-27). This reinforces the view that digital tools are more likely to be adopted by those already inclined or motivated to be active, raising concerns about digital exclusion and widening inequalities.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInterpretation of findings\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThree key insights merit emphasis. First, confidence is central. The threefold increase in satisfaction among those highly confident in their ability to be active highlights the need for interventions that explicitly build self-efficacy. Confidence is modifiable: structured behavioural support, feedback, small achievable goals and reinforcement strategies can increase individuals’ belief in their ability to maintain PA. Without attention to confidence, public health campaigns risk widening the gap between intention and action.\\u003c/p\\u003e\\n\\u003cp\\u003eSecond, barriers are universal and cumulative. Very few participants reported no barriers and most experienced one or two. Each additional barrier reduced satisfaction by over one quarter. Critically, barrier impact intensified with readiness, meaning that even those most willing to change remained highly vulnerable to obstacles. This suggests a need for comprehensive barrier reduction strategies. Addressing time pressures, providing flexible options, tackling motivational challenges and supporting people with health-related barriers should be prioritised.\\u003c/p\\u003e\\n\\u003cp\\u003eThird, digital adoption is selective. While digital tools have the potential to support self-care, current patterns of use suggest they reinforce engagement among already motivated groups rather than reaching the most inactive. The fact that neither education nor barrier count predicted use implies that adoption is not simply a function of socioeconomic advantage but is linked to readiness and confidence. For public health, this raises an equity concern: digital health may disproportionately serve the “already active,” leaving behind those with the greatest need.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStrengths and limitations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study has several strengths. The survey was embedded within the validated Seven Pillars of Self-Care framework, allowing PA to be studied in context with other self-care behaviours. Detailed measures of behaviours, motivations, barriers, readiness, confidence and digital use enabled both descriptive and inferential analyses. The use of multivariable regression models provides robust evidence on predictors of satisfaction and digital adoption.\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, generalisability is tempered by sample composition: participants were significantly more educated than the UK population (64.4% vs. ~33% with university degrees nationally, based on 2021 Census data). Comparison with Health Survey for England (HSE) data on PA prevalence reveals our walking prevalence (96.2% ≥1 day/week) was higher than HSE estimates (~75%), while vigorous inactivity (35%) was similar. These discrepancies may reflect online recruitment bias toward more engaged individuals or true changes in PA patterns since the last HSE wave (2019). The sample was reasonably representative on gender and age distribution but skewed toward White ethnicity (80.9% vs. 81.6% nationally) and higher socioeconomic status as proxied by education. Findings should be interpreted with these limitations in mind and replication in representative population samples is warranted.\\u003c/p\\u003e\\n\\u003cp\\u003eLimitations must also be acknowledged. The cross-sectional design precludes causal inference;\\u0026nbsp;all associations should be interpreted as correlational rather than causal. Importantly, reverse causality cannot be excluded: for example, PA satisfaction may influence confidence or readiness rather than vice versa. The unexpected finding that readiness amplifies barrier sensitivity may reflect this temporal ambiguity—individuals experiencing low satisfaction may retrospectively report high barriers and lower readiness. Future longitudinal designs are essential to establish temporal precedence and directionality. Second, all measures were self-reported and thus susceptible to recall bias (particularly for 7-day PA duration), social desirability bias (overestimation of PA) and information bias. Third, while the sample was diverse in age, gender and ethnicity, participants skewed significantly toward higher educational attainment (64.4% university degree vs. ~33% nationally), potentially limiting generalisability to less-educated populations and raising concern for selection bias. Fourth, recruitment through an online panel introduces digital literacy bias, potentially oversampling those comfortable with digital technology, which may inflate adoption estimates. Fifth, the binary coding of digital adoption obscures nuances in intensity, type and sustained engagement; sensitivity analyses demonstrated robustness but qualitative work would enrich understanding. Finally, digital technology categories evolve rapidly and results may not reflect the latest innovations in AI-enabled or immersive platforms.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePublic health and policy implications\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePublic health strategies should place greater emphasis on interventions that enhance self-efficacy, including motivational interviewing, skills training and personalised feedback. Confidence is not merely an adjunct but a central determinant of satisfaction and engagement. Pertinently, as time and motivation are consistently cited barriers, interventions should also address health-related obstacles and uncertainty about what to do. Providing tailored, accessible programmes that accommodate varying health needs is critical. The finding that barriers undermine satisfaction even among highly ready individuals highlights the futility of focusing solely on motivational readiness without parallel barrier removal.\\u003c/p\\u003e\\n\\u003cp\\u003eWhile digital tools can support PA, they are not a panacea. Policies must address the risk of digital exclusion and ensure that technologies are accessible, affordable and relevant to those most in need. Integration of digital solutions into primary care, community programmes and workplace health initiatives may broaden reach beyond already motivated populations.\\u003c/p\\u003e\\n\\u003cp\\u003eSituating PA within the Seven Pillars of Self-Care framework offers a holistic lens that recognises the interplay of knowledge, behaviours and environmental supports. Policymakers should integrate self-care frameworks into health promotion strategies, recognising that PA does not occur in isolation but alongside other pillars of wellbeing.\\u003c/p\\u003e\\n\\u003cp\\u003eFuture research priorities include longitudinal designs to explore causal pathways between confidence, readiness, barriers and digital engagement. Mixed-methods approaches could illuminate why readiness amplifies barrier effects and identify strategies to mitigate this paradox. Evaluation of digital interventions should consider not only effectiveness but also equity of adoption and sustained engagement.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis study provides new insights into physical activity as a pillar of self-care among UK adults. While walking is common, inactivity remains widespread and sitting time high. Barriers are nearly universal, confidence is the strongest predictor of satisfaction and digital tools are selectively adopted by more confident and ready individuals. Crucially, readiness does not mitigate the negative effects of barriers; instead, it intensifies them. For public health, this means that interventions must move beyond motivation to address barriers directly and build confidence. Only through such approaches can self-care through physical activity be enhanced, digital opportunities equitably harnessed and health inequalities reduced.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eApproved by Imperial College London Research Ethics Committee (ICREC #7642258). All participants provided informed consent electronically.\\u003c/p\\u003e\\n\\u003cp id=\\\"_Toc191301257\\\"\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research received no funding. Austen El-Osta is grateful for support from the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Northwest London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors' contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA.E-O. designed the study protocol, oversaw data collection, performed, and wrote the main manuscript. M.A. and S.A. contributed to study design, statistical analysis and data interpretation. A.A. assisted with data management and preliminary analysis. D.S. provided intellectual input and manuscript review. All authors approved the final version.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank the participants and the public contributors involved in piloting the survey and disseminating findings.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBelvederi Murri M, Folesani F, Zerbinati L, Nanni MG, Ounalli H, Caruso R, Grassi L. Physical Activity Promotes Health and Reduces Cardiovascular Mortality in Depressed Populations: A Literature Overview. Int J Environ Res Public Health. 2020;17(15).\\u003c/li\\u003e\\n\\u003cli\\u003eStrain T, Flaxman S, Guthold R, Semenova E, Cowan M, Riley LM, et al. National, regional and global trends in insufficient physical activity among adults from 2000 to 2022: a pooled analysis of 507 population-based surveys with 5\\u0026middot;7 million participants. Lancet Glob Health. 2024;12(8):e1232-e43.\\u003c/li\\u003e\\n\\u003cli\\u003eBull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54(24):1451-62.\\u003c/li\\u003e\\n\\u003cli\\u003eOminyi J, Clifton A, Nwedu A. Understanding physical activity participation among underserved women: a mixed-methods cross sectional study using an ecological framework. BMC Public Health. 2025;25(1):2178.\\u003c/li\\u003e\\n\\u003cli\\u003eEl-Osta A, Webber D, Gnani S, Banarsee R, Mummery D, Majeed A, Smith P. The self-care matrix: a unifying framework for self-Care.-Selfcare Journal. SelfCare Journal. 2019.\\u003c/li\\u003e\\n\\u003cli\\u003eFerreira Silva RM, Mendon\\u0026ccedil;a CR, Azevedo VD, Raoof Memon A, Noll P, Noll M. Barriers to high school and university students\\u0026apos; physical activity: A systematic review. PLoS One. 2022;17(4):e0265913.\\u003c/li\\u003e\\n\\u003cli\\u003eGarcia L, Mendon\\u0026ccedil;a G, Benedetti TRB, Borges LJ, Streit IA, Christofoletti M, et al. Barriers and facilitators of domain-specific physical activity: a systematic review of reviews. BMC Public Health. 2022;22(1):1964.\\u003c/li\\u003e\\n\\u003cli\\u003eMaltagliati S, Saoudi I, Sarrazin P, Cullati S, Sieber S, Chalabaev A, Cheval B. Women carry the weight of deprivation on physical inactivity: Moderated mediation analyses in a European sample of adults over 50 Years of age. SSM Popul Health. 2022;20:101272.\\u003c/li\\u003e\\n\\u003cli\\u003eGhayour Baghbani SM, Arabshahi M, Saatchian V. The impact of exercise interventions on perceived self-efficacy and other psychological outcomes in adults: A systematic review and meta-analysis. European Journal of Integrative Medicine. 2023;62:102281.\\u003c/li\\u003e\\n\\u003cli\\u003eSingh B, Ahmed M, Staiano AE, Gough C, Petersen J, Vandelanotte C, et al. A systematic umbrella review and meta-meta-analysis of eHealth and mHealth interventions for improving lifestyle behaviours. NPJ Digit Med. 2024;7(1):179.\\u003c/li\\u003e\\n\\u003cli\\u003eMeyerowitz-Katz G, Ravi S, Arnolda L, Feng X, Maberly G, Astell-Burt T. Rates of Attrition and Dropout in App-Based Interventions for Chronic Disease: Systematic Review and Meta-Analysis. J Med Internet Res. 2020;22(9):e20283.\\u003c/li\\u003e\\n\\u003cli\\u003eAlzghaibi H. Adoption barriers and facilitators of wearable health devices with AI integration: a patient-centred perspective. Front Med (Lausanne). 2025;12:1557054.\\u003c/li\\u003e\\n\\u003cli\\u003eMclaughlin M, Delaney T, Hall A, Byaruhanga J, Mackie P, Grady A, et al. Associations between digital health intervention engagement, physical activity and sedentary behavior: systematic review and meta-analysis. Journal of medical Internet research. 2021;23(2):e23180.\\u003c/li\\u003e\\n\\u003cli\\u003eScholes S, Bridges S, Ng Fat L, Mindell JS. Comparison of the Physical Activity and Sedentary Behaviour Assessment Questionnaire and the Short-Form International Physical Activity Questionnaire: An Analysis of Health Survey for England Data. PLOS ONE. 2016;11(3):e0151647.\\u003c/li\\u003e\\n\\u003cli\\u003eMalkowski OS, Townsend NP, Kelson MJ, Foster CEM, Western MJ. Socioeconomic inequalities in physical activity among older adults before and during the COVID-19 pandemic: evidence from the English Longitudinal Study of Ageing. BMJ Public Health. 2023;1(1):e000100.\\u003c/li\\u003e\\n\\u003cli\\u003eUnderstanding and addressing inequalities in physical activity. 2021.\\u003c/li\\u003e\\n\\u003cli\\u003eSampaio BOA, de Alencar Santos AR, Nascimento-Ferreira MV, De Moraes ACF. Global prevalence of barriers and facilitators to physical activity in children and adolescents: A systematic review with meta-analysis. Preventive Medicine Reports. 2025;58:103230.\\u003c/li\\u003e\\n\\u003cli\\u003ePedersen MRL, Hansen AF, Elmose-\\u0026Oslash;sterlund K. Motives and Barriers Related to Physical Activity and Sport across Social Backgrounds: Implications for Health Promotion. Int J Environ Res Public Health. 2021;18(11).\\u003c/li\\u003e\\n\\u003cli\\u003eMalkowski OS, Harvey J, Townsend NP, Kelson MJ, Foster CEM, Western MJ. Enablers and barriers to physical activity among older adults of low socio-economic status: a systematic review of qualitative literature. Int J Behav Nutr Phys Act. 2025;22(1):82.\\u003c/li\\u003e\\n\\u003cli\\u003eBandura A, editor Social Foundations of Thought and Action1986.\\u003c/li\\u003e\\n\\u003cli\\u003eRosenstock IM. The Health Belief Model and Preventive Health Behavior. Health Education Monographs. 1974;2(4):354-86.\\u003c/li\\u003e\\n\\u003cli\\u003eLivet M, Blanchard C, Richard C. Readiness as a precursor of early implementation outcomes: an exploratory study in specialty clinics. Implementation Science Communications. 2022;3(1):94.\\u003c/li\\u003e\\n\\u003cli\\u003eOlson JM. Psychological Barriers to Behavior Change: How to indentify the barriers that inhibit change. Can Fam Physician. 1992;38:309-19.\\u003c/li\\u003e\\n\\u003cli\\u003eCecconi C, Adams R, Cardone A, Declaye J, Silva M, Vanlerberghe T, et al. Generational differences in healthcare: the role of technology in the path forward. Front Public Health. 2025;13:1546317.\\u003c/li\\u003e\\n\\u003cli\\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 Digital Medicine. 2023;6(1).\\u003c/li\\u003e\\n\\u003cli\\u003eRaunaq FF, Islam S, Anam MZ, Bari ABMM. Assessing the challenges to digital technology adoption in the healthcare sector: Implications for sustainability in emerging economies. Informatics and Health. 2025;2(2):194-209.\\u003c/li\\u003e\\n\\u003cli\\u003eRuyobeza B, Grobbelaar SS, Botha A. Forecasting the adoption of digital health technologies: The intention-expectation gap. Evaluation and Program Planning. 2025;112:102670.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Physical activity, Self-care, Seven Pillars of Self-Care (7PSC), Barriers and motivations, Confidence and readiness, Digital health technologies, Wearables and apps, United Kingdom, Public health, Behavioural determinants \",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8049011/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8049011/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e\\u003cbr\\u003e\\nPhysical activity (PA) is a cornerstone of self-care and a major determinant of population health. Despite well-established benefits, most adults do not achieve recommended activity levels. Understanding behavioural patterns, barriers and the role of digital technologies is essential to inform public health interventions. This study presents findings on physical activity within the Seven Pillars of Self-Care (7PSC) framework, focusing on behaviours, satisfaction and predictors of digital health adoption.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003cbr\\u003e\\n A cross-sectional online survey of 1,532 UK adults was conducted in 2025 as part of the 7PSC Study. The survey captured sociodemographic data, PA behaviours, knowledge of World Health Organization (WHO) guidelines, motivations, barriers and digital technology use. Logistic and ordinal logistic regression models examined predictors of PA satisfaction and digital tool adoption.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003cbr\\u003e\\n Participants’ mean age was 43 years (SD=12.9), with balanced gender representation. Nearly half (43%) reported daily walking, yet 35% undertook no vigorous and 36% no moderate activity in the past week. Sitting time averaged 6 hours/day. Barriers were widespread, with lack of time (56%) and motivation (55%) most common. Confidence was strongly associated with satisfaction: those “very confident” in staying active had threefold greater odds of PA satisfaction (OR=3.3, 95% CI: 2.24–4.86, p\\u0026lt;0.001) compared to those \\\"confident.\\\".Each additional barrier was associated with 26% lower odds of satisfaction (OR=0.74, 95% CI: 0.63–0.86, p\\u0026lt;0.001). Digital tool use was reported by 53% of participants, predominantly smartphone apps and wearables. Adoption was associated with younger age, female gender, higher readiness and confidence, but not education or barrier burden.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003cbr\\u003e\\n Findings highlight the central role of confidence and the detrimental impact of barriers on Physical Activity satisfaction, while readiness amplifies sensitivity to barriers rather than buffering them. Interventions should prioritise barrier reduction and confidence-building strategies to enhance self-care through physical activity. Longitudinal studies are needed to establish temporal relationships and causal mechanisms.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Determinants of physical activity satisfaction and digital health adoption in UK adults: results from the 7PSC Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-20 16:06:56\",\"doi\":\"10.21203/rs.3.rs-8049011/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c21953ee-b873-41ab-99d8-e037407d5342\",\"owner\":[],\"postedDate\":\"November 20th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-11-20T16:06:56+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-20 16:06:56\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8049011\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8049011\",\"identity\":\"rs-8049011\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}