The Level of Smartphone Addiction and Its Relationship to High Blood Pressure Among Middle-Aged and Elderly People in Rural Areas of China.

The Level of Smartphone Addiction and Its Relationship to High Blood Pressure Among Middle-Aged and Elderly People in Rural Areas of China - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Nat Sci Sleep . 2026 May 25;18:560764. doi: 10.2147/NSS.S560764 The Level of Smartphone Addiction and Its Relationship to High Blood Pressure Among Middle-Aged and Elderly People in Rural Areas of China Liandi Cheng 1, 2, * , Yunhao Zheng 2, * , Xiaoguang Chen 3, 4 , Jiazhi Wu 3, 5 , Yixuan Pan 2 , Xin Xiong 2, ✉ , Zheng Ye 1, ✉ Author information Article notes Copyright and License information 1 West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China 2 State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, People’s Republic of China 3 Guangze County Grid-Based National Health Service Leading Group, Guangze, People’s Republic of China 4 Guangze County General Hospital, Guangze, People’s Republic of China 5 Basic Public Health Division, Guangze County Health Bureau, Guangze, People’s Republic of China ✉ Correspondence: Zheng Ye, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China, Email zhengye_radiology@foxmail.com ✉ Xin Xiong, State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China, Email drxiongxin@scu.edu.cn * These authors contributed equally to this work Received 2025 Nov 5; Accepted 2026 May 8; Collection date 2026. © 2026 Cheng et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v4.0) License ( http://creativecommons.org/licenses/by-nc/4.0/ ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php ). PMC Copyright notice PMCID: PMC13221434  PMID: 42220474 Abstract Objective There is a lack of research on the association between smartphone addiction and hypertension (HTN) in middle-aged and elderly populations, and their associations with employment status and sleep quality are still unclear. Methods This study was conducted in 85 villages in southeastern China. A total of 975 middle-aged and elderly participants (mean age: 60.70 years; 483 males and 492 females) were included. Demographic data were collected. Smartphone addiction was assessed using the Smartphone Addiction Scale–Short Version (SAS-SV), and sleep quality was measured using the Brief version of the Pittsburgh Sleep Quality Index (B-PSQI). Multiple regression and moderated mediation analyses were used to explore the potential moderating role of employment status (employed vs. unemployed/retired) and the mediating role of sleep quality in the association between smartphone addiction and HTN. Results A total of 415 (42.56%) individuals were suffering from HTN. Smartphone addiction was significantly and positively associated with sleep quality ( p < 0.001). And sleep quality was also significantly positively associated with HTN ( p < 0.05). Distinct patterns were observed across employment status groups, with a significant association between smartphone addiction and HTN in unemployed/retired participants (1.511, 95% CI: 1.140–2.002, p = 0.004), whereas this association was mediated by sleep quality among employed participants. The direct association was 0.940 (95% CI: 0.787–1.122) and the indirect association was 1.034 (95% CI: 1.002–1.071). Conclusion Smartphone addiction is associated with HTN among middle-aged and elderly individuals in rural China. Among unemployed or retired individuals, smartphone addiction may be directly associated with HTN, whereas among employed individuals, this association may be mediated by sleep quality. Reducing excessive smartphone use in rural middle-aged and elderly residents and improving sleep quality (especially in employed individuals) may help mitigate HTN risk. Future studies are warranted to explore underlying mechanisms using longitudinal designs. Keywords: smartphone addiction, sleep quality, hypertension, middle-aged and elderly, rural China Introduction Aging has emerged as a global challenge nowadays, and China has the world’s largest aging population. 1 , 2 The Internet penetration rate in China’s rural areas has jumped to 55.9%, 3 and more and more middle-aged and elderly people in rural areas are using smartphones due to the low entry barriers and affordable prices. 4 Smartphones are beneficial for facilitating communication, access to information, and time-saving, and they can also be used for work, socialising and recreation. 5–7 However, excessive use of smartphones has brought about the problem of addiction. 8 Smartphone addiction is defined as poor autonomy and overuse of smartphones, which takes precedence over other daily activities in one’s personal life and has negative physical, emotional, and social impacts. 9 Smartphone addiction is significantly correlated with an increase in negative emotions and a decrease in self-control ability. 10 The South Korean government has classified the older people as its high-risk and potential risk group. 11 And in a participatory observation study in rural China, 17 out of 29 elderly people interviewed were found to be using smartphones for more than 10 hours a day. 3 Smartphone addiction among older people damages their health and disrupts their daily lives. 9 It is associated with negative impacts such as poor sleep quality, depression, anxiety, and stress. 12 The elderly’s peak smartphone usage is likely influenced by their daily activity engagement, social interaction requirements, and sleep disorders such as insomnia during nighttime hours. 3 , 13 More frequent nighttime checking of one’s smartphone was associated with lower sleep quality. 14 In rural China, village aging is evident due to the out-migration of young people, with a countless number of left-behind and empty nest elderly. 15 People experiencing loneliness or social isolation may be more susceptible to addiction, as using smartphone allows one to connect with others and provides immediate relief from loneliness. 12 , 16 The retirement due to advanced age and absence of their offspring might damage their mental health. 1 The middle-aged and elderly people have started using smartphones out of boredom, and some of the older ones have even lost the company of their spouses, which exacerbates these feelings. 3 Meanwhile, middle-aged and older people are at a stage of major transition in their employment status, physical functioning and social identity. 1 Retired middle-aged and older adults have more leisure time to explore new areas of life. 3 However, the majority of Chinese rural residents do not enjoy the same high level of pension and healthcare as urban workers. Some of them thus have to continue working, the mental impact of which is still controversial. 1 Smartphone addiction has been found to be associated with higher blood pressure in younger age groups. 17 , 18 As a major risk factor for cardiovascular disease, hypertension (HTN) is a chronic underlying condition with high prevalence and high health impact. 19 Its well-established risk factors are family genetic history, 20 alcohol consumption 19 and a high-sodium, low-potassium diet. 21 The prevalence of HTN typically increases with age, with a notable surge after age 45, resulting in higher rates among middle-aged and elderly individuals. 22 The prevalence of HTN is higher in rural areas than in urban areas in China, 21 which may be due to higher salt intake in rural areas and lack of health education and health care facilities. Moreover, despite the declining health status of rural older people, their healthcare expenditure is decreasing with age. 23 Meanwhile, rural elderly people face greater challenges in smartphone addiction than urban older people due to generally lower levels of education, poorer economic status, lack of adequate social security and empty nesters. 3 The high rate of smartphone usage among middle-aged and elderly people may be not suitable to be directly compared to that of young people. There is a lack of research on the association between smartphone addiction and HTN in middle-aged and elderly populations, and their associations with employment status and sleep quality are still unclear. Mediation analysis is a valuable statistical tool that can help elucidate potential pathways linking an exposure to an outcome and is increasingly being used in observational studies. 24 , 25 The aim of this study was to investigate the association between smartphone addiction and HTN in a rural middle-aged and elderly population and to assess whether employment status and sleep quality moderate or mediate this relationship. Subjects and Methods Study Design This study was a stratified random sampling, cross-sectional observational study conducted in all 85 villages of Guangze County, Fujian Province, located in southeastern China. This research was conducted from March 2024 to February 2025. The study adhered to the Declaration of Helsinki and was approved by the Guangze County General Hospital Institutional Review Board (Approval Number: GZXYY-202400001). Informed consent was obtained from all individual participants included in the study. Subjects were randomly selected in each village through the primary public health system and recruited into the study by the village doctors. The sampling frame was derived from the resident health records maintained by the primary public health system under the National Basic Public Health Service program. Eligible residents were stratified by village and age group. Within each stratum, participants were randomly selected using a computerized random number procedure. Village doctors then contacted the selected individuals and invited them to participate in the study through home visits or scheduled appointments. Small non-monetary tokens of appreciation (eg., daily necessities) were provided after participation and were not contingent on survey completion. The research team designed the study, developed the questionnaire, and provided standardized training to village physicians on data collection procedures. It was responsible for overseeing data collection, performing quality control, and analyzing the collected data. The inclusion criteria for this study were: (1) age 45 years or older, (2) residence in their village for more than 6 months (only individuals who had resided in the study area for at least six months were included to ensure stable exposure patterns and reliable health records), and (3) willingness to participate in the study and provide informed consent. The exclusion criteria were: (1) absence from the village during the study period, (2) refusal to have blood pressure measured, (3) inability to complete the questionnaire even with the assistance of a village doctor, and (4) cognitive impairment. The minimum sample size required for this study was calculated to be 929 based on Cohen’s f 2 = 0.02 (small effect size), α = 0.05, and power = 0.80, assuming approximately 10 predictors in the multiple regression model. Measures The subjects’ age and sex were recorded based on their ID card information. In this study, middle-aged adults refers to participants aged 45–59 years, and elderly adults refers to participants aged 60 years and older. Subjects were instructed by village doctors to complete questionnaires covering education, income, marital status, employment status, smoking, alcohol consumption, the Smartphone Addiction Scale–Short Version (SAS-SV) 17 and Brief version of the Pittsburgh Sleep Quality Index (B-PSQI). 26 Additionally, subjects were asked to wear light clothing and slippers for height and weight measurements. Body mass index (BMI) equals weight divided by height squared. Smartphone addiction was assessed using the Chinese version of the SAS-SV questionnaire. 17 Minor wording modifications were made to improve applicability for middle-aged and elderly adults. Specifically, school-related contextual description (eg., references to class attendance or assignments) that are relevant to adolescents were removed, while the original item meanings, scale structure, and response options were retained. For example, the original item “Due to smartphone use, I have difficulty concentrating in class” was adapted by removing the phrase “in class”, while retaining the core meaning related to attentional difficulty. The SAS-SV is a 10-item, 6-point Likert scale, with scores ranging from 1 (strongly disagree) to 6 (strongly agree), yielding a total score ranging from 10 to 60 points. Higher scores represent more severe smartphone addiction. The scale focuses on behavioral dependence and impaired control rather than usage frequency only, allowing differentiation between work-related smartphone use and addictive use. Previous studies have demonstrated that the SAS-SV possesses good reliability and validity among Chinese adults, including middle-aged and older adults. 17 Sleep quality was assessed using the B-PSQI. 26 It is a simplified version of the PSQI, consisting of 6 questions and 5 scoring items derived from these questions. Each scoring item ranges from 0 to 3 points, with a total score of up to 15 points; higher scores indicate poorer sleep quality. The Chinese version of the B-PSQI has been used in several previous studies. 27 HTN was assessed by trained and certified village doctors using a validated electronic sphygmomanometer. Blood pressure was measured in a seated position after the participant had rested quietly for at least 5 minutes. 28 Subjects were asked to avoid alcohol, smoking, coffee/tea, and exercise for at least 30 minutes prior to blood pressure measurement. During each visit, blood pressure was measured twice at 1-minute intervals, and the average of the two readings was recorded. If the two readings of systolic blood pressure (SBP) or diastolic blood pressure (DBP) differed by more than 5 mmHg, a third measurement was taken, and the average of the three readings was recorded. For participants without a prior history of HTN, blood pressure measurements were repeated on three separate visits conducted on different days. HTN was diagnosed if SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg was observed consistently across all three visits, based on the averaged readings obtained at each visit. For participants with a documented prior diagnosis of HTN or current use of antihypertensive medication, HTN status was determined based on medical records from the primary public health system. Statistical Analysis The normality of the variables was tested using the Shapiro–Wilk test. For comparing differences between two groups, the Mann-Whiney U -test was employed for non-normally distributed data, while the t -test was used for normally distributed data. Spearman correlation analysis adjusted with the Benjamini-Hochberg method was used for non-normal variables. Mediation analysis with 95% bootstrap CI (10000 simulations) was used to explore the direct association between SAS-SV scores and HTN as well as the indirect association via employment status and B-PSQI scores. All statistical analyses were conducted in R (version 4.2.2, The R Foundation for Statistical Computing). The R packages ‘lavaan’26 (version 0.6.15) and ‘bruceR’27 (version 0.8.10) were used for the mediation analysis, and the regression model reports were generated by the R package ‘modelsummary’28 (version 1.2.0). The significance level α was set to 0.05. Results Basic Characteristics of the Study Subjects A total of 1275 subjects were initially sampled via the primary public health system. Of these, 300 (23.5%) were excluded on site: 155 refused blood-pressure measurement, 112 returned incomplete questionnaires or had missing key variables, 31 were unable to complete the questionnaire even with assistance because of illiteracy, hearing or vision problems, and 2 were suspected of cognitive impairment. Consequently, 975 participants (483 males and 492 females) constituted the final analytical sample. There were 415 HTN subjects while 560 non-HTN subjects. The basic characteristics of the study population are shown in Table 1 . The mean age of the entire study population was 60.70 years old and the median age was 58.84 years old. Table 1. Baseline Characteristics of the Study Population Variables Total Non-HTN HTN p N 975 (100) 560 (57.44) 415 (42.56) Age <0.001 Mean (SD) 60.70 (9.75) 58.63 (9.46) 63.49 (9.44) Median (IQR) 58.84 (14.77) 56.48 (12.92) 61.87 (14.71) Sex (%) 0.370 Male 483 (49.54) 270 (48.21) 213 (51.33) Female 492 (50.46) 290 (51.79) 202 (48.67) Education (%) <0.001 No Formal Education 106 (10.87) 45 (8.04) 61 (14.70) Elementary School 394 (40.41) 212 (37.86) 182 (43.86) Middle School 362 (37.13) 233 (41.61) 129 (31.08) High School 53 (5.44) 31 (5.54) 22 (5.30) Technical School 30 (3.08) 21 (3.75) 9 (2.17) Junior College 27 (2.77) 15 (2.68) 12 (2.89) College/University 3 (0.31) 3 (0.54) 0 (0.00) Income (%) <0.001 ≤1000 RMB 215 (22.05) 100 (17.86) 115 (27.71) 1001–2000 RMB 246 (25.23) 146 (26.07) 100 (24.10) 2001–3000 RMB 254 (26.05) 138 (24.64) 116 (27.95) 3001–4000 RMB 139 (14.26) 97 (17.32) 42 (10.12) 4001–5000 RMB 60 (6.15) 38 (6.79) 22 (5.30) 5001–6000 RMB 28 (2.87) 20 (3.57) 8 (1.93) 6001–9000 RMB 15 (1.54) 7 (1.25) 8 (1.93) 9001–12000 RMB 5 (0.51) 5 (0.89) 0 (0.00) 12,001–15,000 RMB 5 (0.51) 3 (0.54) 2 (0.48) ≥15001 RMB 8 (0.82) 6 (1.07) 2 (0.48) Marriage (%) 0.380 Unmarried 12 (1.23) 8 (1.43) 4 (0.96) Divorce 13 (1.33) 9 (1.61) 4 (0.96) Widowed 69 (7.08) 34 (6.07) 35 (8.43) Married 881 (90.36) 509 (90.89) 372 (89.64) Employment Status 0.010 Employed 633 (64.92) 383 (68.39) 250 (60.24) Unemployed/Retired 342 (35.08) 177 (31.61) 165 (39.76) BMI <0.001 Mean (SD) 23.53 (3.15) 22.94 (2.90) 24.32 (3.29) Median (IQR) 23.38 (4.00) 22.83 (3.45) 24.22 (4.38) Smoking (%) No 679 (69.64) 405 (72.32) 274 (66.02) 0.002 Abstinence 43 (4.41) 14 (2.50) 29 (6.99) Yes 253 (25.95) 141 (25.18) 112 (26.99) Alcohol Drinking (%) No 590 (60.51) 366 (65.36) 224 (53.98) 0.001 Abstinence 142 (14.56) 67 (11.96) 75 (18.07) Yes 243 (24.92) 127 (22.68) 116 (27.95) B-PSQI Score <0.001 Mean (SD) 4.67 (2.82) 4.34 (2.67) 5.12 (2.96) Median (IQR) 4.00 (3.00) 4.00 (4.00) 5.00 (4.00) SAS-SV Score 0.254 Mean (SD) 20.15 (12.45) 20.28 (11.94) 19.96 (13.12) Median (IQR) 14.00 (19.00) 16.00 (19.00) 12.00 (18.00) Open in a new tab Abbreviations : HTN, hypertension; SD, standard deviation; IQR, interquartile range; BMI, body mass index; B-PSQI, brief version of the Pittsburgh sleep quality index; SAS-SV, smartphone addiction scale-short version. Compared to the non-HTN group, subjects of the HTN group were significantly older ( p < 0.001), had an overall lower educational level ( p < 0.001) and had lower income ( p < 0.001). The median BMI of HTN group was 24.22, while the median BMI of non-HTN group was 22.83. The BMI was significantly higher in the subjects of HTN group than in the non-HTN group ( p < 0.001). The median B-PSQI score for the total 975 subjects was 4.00 points. Median B-PSQI score was significantly lower in the non-HTN group than in the HTN group ( p < 0.001). In addition, the median SAS-SV score for our study population was 14.00 points. Median SAS-SV score in the HTN group was overall lower but not statistically significant than that in the non-HTN group ( p = 0.254). Multiple Regression Models The multiple logistic regression models presented in Table 2 showed that the SAS-SV score did not exhibit a significant association with HTN ( p > 0.05). Additionally, the employment status was not significantly associated with HTN in both model 2 and model 3 as well ( p > 0.05). The interaction term between unemployed/retired status and SAS-SV score was significantly and positively associated with HTN after adjusting for age, gender, educational status, income, marital status, BMI, smoking and drinking consumption (OR: 1.572, 95% CI: 1.146–2.175, p < 0.01). Table 2. Multiple Logistic Regression Models for HTN Variable Model 1 Model 2 Model 3 SAS-SV Score 0.974 (0.857, 1.106) 1.101 (0.948, 1.278) 0.971 (0.815, 1.155) Employment Status Employed – Reference Reference Unemployed/Retired – 0.975 (0.700, 1.353) 1.011 (0.724, 1.409) Unemployed/Retired × SAS-SV – – 1.572** (1.146, 2.175) Open in a new tab Notes : OR (95% CI). ** p < 0.01. Bold values indicate significance at p < 0.05. Model 1: No adjustment. Model 2 and 3: Adjusted for age, sex, education, marriage, income, BMI, smoking, and alcohol drinking. Abbreviations : HTN, hypertension; SAS-SV, smartphone addiction scale-short version; OR, odds ratio; CI, confidence interval. Table 3 presents the results of multiple regression models for B-PSQI and HTN. SAS-SV score was significantly and positively associated with B-SPQI score (β: 0.200, 95% CI: 0.126–0.275, p < 0.001). The interaction term between unemployed/retired status and SAS-SV score was negatively and not significantly associated with B-PSQI score (β: −0.096, 95% CI: −0.230–0.037, p > 0.05), but it was significantly positively associated with HTN (OR: 1.607, 95% CI: 1.168–2.231, p < 0.01). In addition, B-PSQI score was also significantly positively associated with HTN (OR: 1.181, 95% CI: 1.020–1.371, p < 0.05). Table 3. Multiple Regression Models for B-PSQI and HTN Variable B-PSQI Score HTN SAS-SV Score 0.200*** (0.126, 0.275) 0.940 (0.787, 1.122) Employment Status Employed Reference Reference Unemployed/Retired −0.133 (−0.276, 0.010) 1.039 (0.742, 1.451) Unemployed/Retired × SAS-SV −0.096 (−0.230, 0.037) 1.607** (1.168, 2.231) B-PSQI Score – 1.181* (1.020, 1.371) Open in a new tab Notes : β (95% CI) for the B-PSQI model score and OR (95% CI) for the HTN model. Adjusted for age, sex, education, marriage, income, BMI, smoking, and alcohol drinking. * p < 0.05, ** p < 0.01, *** p < 0.001. Bold values indicate significance at p < 0.05. Abbreviations : B-PSQI, brief version of the Pittsburgh sleep quality index; HTN, hypertension; SAS-SV, smartphone addiction scale-short version; OR, odds ratio; CI, confidence interval. We further tested age group (middle-aged versus elderly) as a potential effect modifier by including interaction terms between age group and smartphone addiction, employment status, and sleep quality. None of the interaction terms reached statistical significance (all p > 0.05), indicating that age group did not significantly modify the associations under investigation. Therefore, middle-aged and elderly participants were analyzed together in subsequent analyses. Moderated Mediation Analysis The results of moderated mediation analysis have been shown in Table 4 . In the unemployed/retired population, there was a significant association between SAS-SV score and HTN (OR: 1.511, 95% CI: 1.140–2.002, p = 0.004). However, the same significant association was not observed in the employed population (OR: 0.940, 95% CI: 0.787–1.122, p > 0.05). Sleep quality mediated the pathway of smartphone addiction associated with HTN among the employed people ( Figure 1 ). The direct association was 0.940 (95% CI: 0.787–1.122, p > 0.05), and the indirect association was 1.034 (95% CI: 1.002–1.071, p < 0.05). Table 4. Moderating and Mediating Association Path Estimate 95% CI p SAS-SV - HTN a Employed 0.940 (0.787, 1.122) 0.494 Unemployed/Retired 1.511 (1.140, 2.002) 0.004 SAS-SV - B-PSQI b Employed 0.200 (0.126, 0.275) <0.001 Unemployed/Retired 0.104 (−0.011, 0.218) 0.075 SAS-SV - B-PSQI - HTN a Employed 1.034 (1.002, 1.071) 0.048 Unemployed/Retired 0.995 (0.954, 1.031) 0.182 Open in a new tab Notes : a: OR, b: β. Bold values indicate significance at p < 0.05. Abbreviations : SAS-SV, smartphone addiction scale-short version; HTN, hypertension; B-PSQI, brief version of the Pittsburgh sleep quality index; OR, odds ratio; CI, confidence interval. Figure 1. Open in a new tab The direct association of hypertension with smartphone addiction and the indirect association via sleep quality among people with different employment status. SAS-SV: the total score of Smartphone Addiction Scale-Short Version. B-PSQI: the total score of Brief version of the Pittsburgh Sleep Quality Index. OR: odds ratio; CI: confidence interval. The 95% CIs were generated by 10000 bootstrap simulations. * p <0.05, ** p <0.01, *** p <0.001. Bold values indicate significance at p < 0.05. Discussion In this study, we found that smartphone addiction was associated with HTN among middle-aged and elderly people in rural China, with the association varying across subgroups defined by employment status and sleep quality. As smartphone permeates every social sector, public services are increasingly digitized. 11 For example, people are increasingly opting to pay with smartphones over cash. The usage of smartphones confers a multitude of advantages to rural residents, including facilitating their lives through e-commerce platforms and diversifying their diets. 29–31 In recent years, short-video platforms have rapidly attracted the middle-aged and elderly population. 32 The enjoyment of usage, the urge for self-expression and the desire to connect with others all contribute to the digital practices. 33 , 34 It not only enhances their self-perception and self-worth but also inadvertently exacerbates smartphone addiction. 3 This trend is particularly evident as it reflects the fact that the middle-aged and the elderly are seeking new ways to define themselves and engage with post-retirement life. 4 There has been a lot of concern about the identity change of them after retirement. The retirement age in China has a wide range depending on the circumstances, 35 and it is roughly in line with the situation of the middle-aged and elderly groups covered in our study. In order to provide pension security for rural residents, China launched a national programme called the New Rural Pension Scheme (NRPS) in 2009. Rural residents who have reached the age of 60 can receive a basic old-age pension if they meet certain criteria. 36 The scheme has relatively succeeded in covering target population. However, it fails to meet the basic needs of the elderly or the middle-aged in rural areas, and pension benefits are still very low in some areas. 37 With an inadequate pension, they are more likely to continue working and participate in manual labour that has a negative health impact in order to earn a living. 38 In rural China, 54.2% of the elderly population are in the over-age labour force, indicating a prevalent trend of overage employment. 1 Besides, China’s urbanization process and the large-scale rural-urban migration of young adults have resulted in a widespread phenomenon of elderly people being left behind in rural areas. 15 In China, the middle-aged and elderly population generally has a strong sense of family values. 39 A portion of them may consider replacing the monetary support of their children with their own income, thereby reducing the financial burden on adult children. 40 In general, it is common for middle-aged and older people in rural areas to work beyond their retirement age due to the insufficiency of pension income and their desire to reduce the burden on their families. Therefore the high work rate found in this study is representative. In the present study, smartphone addiction is significantly associated with HTN among middle-aged and elderly adults who are unemployed or retired. They have more free time and may use smartphones more frequently. 3 One plausible explanation is that excessive smartphone use may be associated with reduced physical activity and activities of daily living, 41 which in turn has been linked to a higher prevalence of HTN. 42 Physical activity is defined as any bodily movement resulting from skeletal muscle contraction that results in energy expenditure above the resting level. 43 Numerous guidelines have recommended it as a means of preventing HTN. 44–46 Studies from different countries have confirmed that it is negatively associated with the development of HTN. 19 Smartphone addiction may also lead to sedentary behaviour. A study of Spanish university suggested that it is associated with a higher risk of developing HTN. 42 We also found that smartphone addiction was significantly associated with HTN through sleep quality among employed older ones. Previous studies have found an effect of sleep quality on blood pressure. Poor sleep quality is related to elevated blood pressure variability, which might have some adverse effects on multiple organ systems, 47 mainly in the form of sleep disruption and obstructive sleep apnoea (OSA). 48 , 49 Individuals suffering from OSA tend to have a surge in blood pressure in the mornings. 50 A more recent study suggested a cumulative effect of poor sleep quality and lack of physical activity on blood pressure. 51 In our study, the employed older population tends to engage in manual labour, and the work itself entails a certain amount of physical activity. 43 However, since they might can only play with their smartphones after work, excessive use of smartphones at night may have a greater impact on sleep quality. 12 There are some limitations of our study. These findings pertain specifically to rural Chinese populations; whether similar associations hold in urban populations awaits further investigation. Moreover, we only studied the middle-aged and elderly population, and future studies might be conducted on multiple age groups. Finally, it is a cross-sectional study, and further longitudinal studies are needed to determine the moderating and mediating mechanisms through which smartphone addiction associates with HTN among people with different employment status. The findings of this study have several public health implications. Given the high prevalence of both smartphone use and HTN among middle-aged and elderly populations in rural areas, smartphone addiction should be recognized as a potential behavioral risk factor that may contribute to cardiovascular health disparities. From a preventive perspective, tailored strategies should be developed according to employment status. For unemployed or retired individuals, interventions should focus on reducing sedentary behavior and promoting alternative daily activities. For employed individuals, particular attention should be paid to nighttime smartphone use and its impact on sleep quality. Interventions such as sleep hygiene education, limiting screen exposure before bedtime, and promoting awareness of the health consequences of poor sleep may be beneficial. At the community and policy levels, integrating smartphone addiction screening into primary healthcare services and routine chronic disease management programs may help identify high-risk individuals. Moreover, it is also necessary to be mindful of the psychological situation of middle-aged and elderly people in rural areas and to strengthen their old-age security. For future research, longitudinal studies are needed, and objective measurements (such as digital device usage tracking and wearable sleep monitoring) should be incorporated to reduce potential bias associated with self-reported data. Further investigation into the biological and behavioral mechanisms underlying the associations is warranted. Finally, expanding the study population to include urban residents and different cultural contexts would enhance the generalizability of the findings. Conclusions Smartphone addiction is associated with HTN among middle-aged and elderly people in rural China, with employment status serving as a potential moderator and sleep quality as a possible mediator of this relationship. Limitations include the cross-sectional design, which precludes causal inference, and the rural-only sample, limiting generalizability to urban settings. Addressing smartphone addiction in rural middle-aged and elderly residents and improving sleep quality in employed individuals are recommended. Future researches could consider extending the lens to urban settings and embracing longitudinal perspectives. Acknowledgments We gratefully acknowledge the institutional and programmatic support provided to this project by the “4+N” Grid-based National Health Service Funding Committee. We also wish to thank Mr Liangcai Li (Guangze County Grid-based National Health Service Leading Group, Guangze, China) for his valuable assistance during this work. Funding Statement No funding was obtained in the conduct of this study. Data Sharing Statement Some of the data that support the findings of this study can be seen in the article. The remaining data are available from the corresponding author upon reasonable request. Ethics Approval and Informed Consent The study was approved by the Guangze County General Hospital Institutional Review Board (Approval Number: GZXYY-202400001). Informed consent was obtained from all participants involved in the study. Author Contributions Liandi Cheng: Formal analysis, methodology, writing – original draft. Yunhao Zheng: Conceptualization, data curation, formal analysis, methodology, visualization, writing – review and editing. Xiaoguang Chen: Data curation, funding acquisition, investigation, project administration, writing – review and editing. Jiazhi Wu: Investigation, supervision, methodology, writing – review and editing. Yixuan Pan: Formal analysis, visualization, writing – review and editing. Xin Xiong: Conceptualization, methodology, writing – review and editing. Zheng Ye: Conceptualization, methodology, writing – review and editing. All authors gave final approval of the version to be published, approved the submission of the article to the journal, and agreed to be accountable for all aspects of the work. Disclosure The authors report no conflicts of interest in this work. References 1. Chen Y, Wang K, Zhao J, Zhang Z, Wang J, He L. Overage labor, intergenerational financial support, and depression among older rural residents: evidence from China. 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